Modelling and Simulations (IREMOS)

eISSN 2533-1701 Vol. 11 N. 3 June 2018

International Review on

Modelling and Simulations (IREMOS) Contents Cascaded Three Level Inverter Based Shunt Active Power Filter with Modified Three Level Hysteresis Current Control by Jignesh Pandya, Rajendrasinh Jadeja, Tapankumar Trivedi

125

Low Order Harmonic Analysis of 3-Phase SPWM and SV-PWM Inverter Systems Driving an Unbalanced 3-Phase Induction Motor Load by Qamil Kabashi, Myzafere Limani, Nebi Caka, Milaim Zabeli

134

Maximum Power Point Tracking of Multi-Input Inverter for Connected Hybrid PV/Wind Power System Considering Voltage Limitation in Grid by Feby Agung Pamuji, Hajime Miyauchi

143

Design and Comparison of High-Speed Induction Machine and High-Speed Interior Permanent Magnet Machine by Hossein Dehnavifard, Ghadir Radman, Mohamedreza Kalyan

151

Comparative Study of MIMO-OFDM Channel Estimation in Wireless Systems by Obinna Okoyeigbo, Kennedy Okokpujie, Etinosa Noma-Osaghae, Charles U. Ndujiuba, Olamilekan Shobayo, Abolade Jeremiah

158

Unified Deterministic Model of Parallel and Distributed Computers by P. Hanuliak, M. Hanuliak, I. Zelinka

166

The Influence of Distributing the Conveyor Suspensions with Suspended Belt and Distributed Drive on Its Main Technical Characteristics by Alexander V. Lagerev, Evgeniy N. Tolkachev, Igor A. Lagerev

176

A Modelling Study to Analyse the Compression Ratio Effects on Combustion and Knock Phenomena in a High-Performance Spark-Ignition GDI Engine by F. Bozza, L. Teodosio, V. De Bellis, D. Cacciatore, F. Minarelli, A. Aliperti

187

Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved

International Review on Modelling and Simulations (IREMOS) Editor-In-Chief: Santolo Meo Department of Electrical Engineering and Information Technology (DIETI) FEDERICO II University 21 Claudio - I80125 Naples, Italy

Editorial Board: Marios Angelides M. El Hachemi Benbouzid Debes Bhattacharyya Stjepan Bogdan Cecati Carlo Ibrahim Dincer Giuseppe Gentile Wilhelm Hasselbring Ivan Ivanov Jiin-Yuh Jang Heuy-Dong Kim Marta Kurutz Baoding Liu Pascal Lorenz Josua P. Meyer Bijan Mohammadi Pradipta Kumar Panigrahi Adrian Traian Pleşca Ľubomír Šooš Lazarus Tenek Lixin Tian Yoshihiro Tomita George Tsatsaronis Ahmed F. Zobaa

(U.K.) (France) (New Zealand) (Croatia) (Italy) (Canada) (Italy) (Germany) (Bulgaria) (Taiwan) (Korea) (Hungary) (China) (France) (South Africa) (France) (India) (Romania) (Slovak Republic) (Greece) (China) (Japan) (Germany) (U.K.)

Brunel University Univ. of Western Brittany- Electrical Engineering Department Univ. of Auckland – Department of Mechanical Engineering Univ. of Zagreb - Faculty of Electrical Engineering and Computing Univ. of L'Aquila - Department of Electrical and Information Engineering Univ. of Ontario Institute of Technology FEDERICO II Univ., Naples - Dept. of Electrical Engineering Univ. of Kiel Technical Univ. of Sofia - Electrical Power Department National Cheng-Kung Univ. - Department of Mechanical Engineering Andong National Univ. - School of Mechanical Engineering Technical Univ. of Budapest Tsinghua Univ. - Department of Mathematical Sciences Univ. de Haute Alsace IUT de Colmar Univ. of Pretoria - Dept.of Mechanical & Aeronautical Engineering Institut de Mathématiques et de Modélisation de Montpellier Indian Institute of Technology, Kanpur - Mechanical Engineering "Gh. Asachi" Technical University of Iasi Slovak Univ. of Technology - Faculty of Mechanical Engineering Aristotle Univ. of Thessaloniki Jiangsu Univ. - Department of Mathematics Kobe Univ. - Division of Mechanical Engineering Technische Univ. Berlin - Institute for Energy Engineering Brunel University - School of Engineering and Design

The International Review on Modelling and Simulations (IREMOS) is a publication of the Praise Worthy Prize S.r.l.. The Review is published bimonthly, appearing on the last day of February, April, June, August, October, December. Published and Printed in Italy by Praise Worthy Prize S.r.l., Naples, June 30, 2018. Copyright © 2018 Praise Worthy Prize S.r.l. – All rights reserved. This journal and the individual contributions contained in it are protected under copyright by Praise Worthy Prize S.r.l. and the following terms and conditions apply to their use: Single photocopies of single articles may be made for personal use as allowed by national copyright laws. Permission of the Publisher and payment of a fee is required for all other photocopying, including multiple or systematic copying, copying for advertising or promotional purposes, resale and all forms of document delivery. Permission may be sought directly from Praise Worthy Prize S.r.l. at the e-mail address: [email protected] Permission of the Publisher is required to store or use electronically any material contained in this journal, including any article or part of an article. Except as outlined above, no part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior written permission of the Publisher. E-mail address permission request: [email protected] Responsibility for the contents rests upon the authors and not upon the Praise Worthy Prize S.r.l.. Statement and opinions expressed in the articles and communications are those of the individual contributors and not the statements and opinions of Praise Worthy Prize S.r.l.. Praise Worthy Prize S.r.l. assumes no responsibility or liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained herein. Praise Worthy Prize S.r.l. expressly disclaims any implied warranties of merchantability or fitness for a particular purpose. If expert assistance is required, the service of a competent professional person should be sought.

International Review on Modelling and Simulations (I.RE.MO.S.), Vol. 11, N. 3 ISSN 1974-9821 June 2018

Cascaded Three Level Inverter Based Shunt Active Power Filter with Modified Three Level Hysteresis Current Control Jignesh Pandya, Rajendrasinh Jadeja, Tapankumar Trivedi Abstract – A Cascaded three level inverter is controlled by using a three level HCC technique. The method has certain advantages compared to a two level HCC, such as a reduced switching frequency and a variation in switching frequency. However, this method suffers from tracking error in current due to dead zones provided between bands. This work aims at the development of modified three level hysteresis current controller (HCC) for a cascaded three level inverter-based shunt active power filter (SAPF) to mitigate the harmonics in a three-phase three-wire systems. Such method significantly reduces the current tracking error, the maximum switching frequency of the inverter as well as the variation in switching frequency. The simulation of the cascaded three level inverter-based shunt active power filter with a modified three level hysteresis current control is carried out in a PSIM environment for 2.5 kVA system. To validate the effect of degree of overlapping between the bands, two cases of a modified three level HCC are proposed. Both the cases are compared with conventional two-level HCC and three level HCC techniques. The simulation results show the effectiveness of the proposed scheme. Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: Hysteresis Current Controller (HCC), Shunt Active Power Filter (SAPF), Three Level Hysteresis Current Controller

is,abc h Vg,fmin

Nomenclature ic ∆ic RL , LL δ ∆t ∆f Lc , Rc vg Vdc fmin fmax vc TON TOFF In I1 Ls σf Sa1,Sa2, Sa3,Sa4 fsw u(t) ic,abc iL,abc

Compensating Current of SAPF Compensating Current Error DC side Load Resistance and Inductance Dead zone of hysteresis band in three level hysteresis controllers Difference in time between two instances Difference in maximum and minimum inverter switching frequency Inductance and Resistance of Coupling inductor of SAPF Instantaneous Value of Phase Voltage at PCC Magnitude of DC link voltage Minimum switching frequency of the inverter Maximum switching frequency of the inverter Output voltage of Inverter ON time of the switch in one switching period OFF time of the switch in one switching period RMS value of current for nth harmonic RMS value of current for fundamental component Source Inductance Standard Deviation in Frequency of inverter Switches of H-bridge unit of Phase A

Three Phase Source Current Upper limit of hysteresis band Value of vg for minimum switching frequency

I.

Introduction

With the development of power electronics technology, the usage of semiconductor switches-based equipments like UPS, SMPS, Adjustable Speed Drives has increased. Moreover, various equipment such as induction cookers, industrial lighting, as well as furnaces necessitate the use of power electronic circuits for appropriate functioning. The use of Power electronicsbased load has been increased in the industry and it has found more popularity due to the improved utilization of power and its control ability at the consumer end [1][25]. However, the major drawback of power electronicsbased load is the injection of harmonics in the distribution systems. A two-level voltage source inverterbased shunt active power filter (SAPF) is a popular solution for the mitigation of current harmonics at the point of common coupling [21]-[25]. The configuration of shunt active power filter is shown in Fig. 1, where harmonics in the load side are compensated by a shunt active power filter while restoring the sinusoidal behavior of the source current. Unlike the passive filters, which can target specific pre-defined frequencies to which they are tuned, SAPF can eliminate selective harmonics or the range of harmonic frequencies.

Switching Frequency of Inverter Switching Function of Three Level Inverter Three Phase Compensating Current of SAPF Three Phase Load Current


https://doi.org/10.15866/iremos.v11i3.14513

125

Jignesh Pandya, R. Jadeja, Tapankumar Trivedi

Ls Mains

Is,abc

modified three level HCC is discussed in context of SAPF application and it is implemented for a cascaded three level inverter-based shunt active power filter. A detailed investigation on the proportion of the increase in the bands over-lapping is carried out. Moreover, its effect on the magnitude of maximum switching frequency, on the variation of the switching frequency, on the deviation of the switching frequency and on the current tracking error are issues discussed in this article and the results of this effect are presented here. The simulation results and comparison of different methods show the effectiveness of the proposed controller. The technique presented in the paper has the following advantages over conventional approach three level HCC:  The magnitude and the variation of switching frequency are reduced.  The deviation of switching frequency from average frequency is reduced.  There is a reduction in current tracking error. The paper is organized into the following sections: the conventional three level Hysteresis Current Control technique for Cascaded H Bridge Inverter is explained in section II. The concept of the modified three level HCC is illustrated with two different cases in section III. Simulation Results obtained for the proposed method are presented and they are compared in section IV, whereas section V concludes the paper with some remarks on this method.

Vg

IL,abc Nonlinear Load

Ic,abc Lc

Vdc Shunt Active Power Filter Fig. 1. Block Diagram of Shunt Active Power Filter

The effective synthesis of compensating current depends upon the current control technique applied to the control of the inverter. Both linear Current Controller and Non-linear Current Controller are popular approaches for the control of Pulse Width Modulated Voltage Source Inverters (VSI). A linear current controller characterizes a constant switching frequency but it has a limited performance due to the delay associated with the process of error calculation and in the generation of PWM signals. On the other hand, non-linear current controllers use non-linear functions to discriminate among absolute values and to decide the quantized output. These quantized outputs can select the switching combinations suitable for the inverter. While selecting such switching combinations, the scheme should ensure to minimize the variations in the state of switching signals. In the hysteresis current control (HCC) method, a reference current determined by the control algorithm is compared with every instant of the measured current. When current error crosses pre-determined band values, HCC controller selects the appropriate switching state of the switches of VSI. The approach results in dramatic variations in the switching actions, even under ideal operating environments of converters. Nevertheless, Non-linear current controllers offer a very fast response, they are simpler in analog/digital implementation, they have automatic peak current limitation, a fast dynamic response and they are invulnerable to load parameter variation [4]-[7]. A three level hysteresis current controller method is well utilized in three level inverter fed drives as reported in [8]. In two-level voltage source inverter with a conventional two-level HCC, the maximum switching Vdc Vdc frequency , is reduced to with the use 4 Lc ic 16 Lc ic of three level HCC in three level inverter [8]-[12] where Vdc is DC link voltage, ∆ic current error hysteresis band and Lc is coupling inductor. The reported method is still restricted to the inverter fed drives application and its performance with the Shunt Active Power Filter application is yet to be investigated. In the paper, a

II.

Basic Three Level Hysteresis Current Controller

A three-level Cascaded H-Bridge Inverter based SAPF with a three level hysteresis current controller for the mitigation of current harmonics produced by a nonlinear load in three-phase three-wire system is shown in Fig. 2. The Three Level Cascaded H-Bridge Inverter output is given by:

vc (t )  u (t ) 

Vdc 2

(1)

where, vc(t) is the three level CHB inverter output, u(t) is the switching function of three level hysteresis current controller which can take any value of u(t)= {1,0, -1}. Hence, the output of the three-level inverter according V   V to the value is given by vc (t )   dc , 0,  dc  . 2 2   Assuming the sample time of the system to be sufficiently small, the relationship among the inverter voltage of any phase, the phase voltage of PCC and the line current for two level inverter is given by:

vc  Lc

ic  vg t

(2)

The turn ON time for the switch is given by:


International Review on Modelling and Simulations, Vol. 11, N. 3

126


TON 

Lc ic Vdc  vg 2

f  f max  f min 

(3)

 Lc ic Vdc  vg 2

f sw

2 dc

 4vg2



(5)

4Vdc Lc ic

On the other hand, the switching frequency of three level inverter is:

f sw 



vg Vdc  2vg



2Vdc Lc ic



(7)

2Vdc Lc  h   

where h is the upper limit of the band and δ is the lower limit of the band. Therefore, h-δ becomes width of hysteresis band in three level inverter. The switching transition is reduced; thus, the switching frequency is reduced in basic three level HCC as compared to two level inverter. Here, the small dead zone (δ) is provided for avoiding switching as a two-level HCC [13], [14]. But this dead zone (δ) is responsible for an increase in current tracking error. This current tracking error must be minimized by selecting the minimum value of the dead zone. The logic of basic three level hysteresis current controller is also represented in the Fig. 3. In basic three level HCC, the actual current tracks the reference current, either in upper band or lower band. When the current error (Δic) is in upper band, the inverter switches between +Vdc/2 or 0, as the current error crosses limits h and δ respectively. Similarly, when the current error (Δic) is in the lower band, the inverter switches between -Vdc/2 or 0, as the current error crosses limits -h and -δ respectively. In this scheme, only one of the band i.e. upper band or lower band is occupied at a time. The current error transition from the upper band to lower band (or lower band to the upper band) occurs only when the zero-voltage level is not sufficient to reverse the current error direction.

(4)

resulting in switching frequency for two level inverter:

V 



Vg , f min Vdc  2Vg , f min

whereas, the turn OFF time for the switch is given by:

TOFF 

Vdc  16 Lc  h   

(6)

As seen in Eq. (5), the switching frequency largely depends on the instantaneous phase voltage of PCC (Vg), on the phase voltage of Inverter Vdc/2 and on the predetermined value of current error band for given SAPF system. The maximum switching frequency is reduced in Eq. (6) compared to Eq. (5); the maximum switching frequency is obtained in the former case when Vg=0, while in the latter case it is obtained at Vg=Vdc/4. Based on Eq. (6), the maximum variation of switching frequency is given by:

Fig. 2. Three Level Cascaded H-Bridge Inverter based SAPF



127


Two cases are studied in this paper with different levels of over-lapping. In Case I, the level of overlapping of the hysteresis band is less resulting into two distinct bands with significant margins between the upper band and the lower band as shown in Fig. 4(a). The method has clear advantages in terms of reduction in tracking error and easiness of selection of switching state. It is worth noting here that the lesser the overlapping between bands, the easier to distinguish between switching states of three level inverter. The method still has the demerit of current tracking error. On the other hand, switching variation is still significant. These drawbacks can be overcome using case II in which the maximum overlapping is provided between upper and lower bands as shown in Fig. 4(b). The minimum difference between outer limit of upper band (h) and inner limit of lower band (δ) is decided based on: the sample time of the system, DC link voltage, coupling inductor and dead time of the system [8], [15]-[20].

Fig. 3. Basic Three Level HCC

The basic three level HCC offers several advantages over to the two-level HCC, in terms of the reduction of maximum switching frequency and variations in the switching frequency. Nevertheless, both the upper band and lower band are not used simultaneously, which results in an increased current tracking error. To overcome this drawback with the same width of hysteresis band, a modified three level hysteresis current controller is proposed which improves the performance in terms of reduction in the magnitude as well as in the variation in switching frequency as compared to basic three level HCC while it retains all the advantages of two level HCC.

IV.

In this paper, the simulation results of modified three level HCC are presented and the results of both methods are compared with a basic three level HCC as well as a two level HCC. In the modified three level HCC, the width of hysteresis band is considered the same as the basic three level HCC whereas the dead zone (δ) is modified from 0.05 to -0.05 (Case I) and -0.25 (case-II). The simulation of three level cascaded H-Bridge inverter based SAPF, as shown in Fig. 2, is carried out in the PSIM simulator using the parameters mentioned in Table I. A modified three level hysteresis current controller is developed by using comparators as well as sequential circuits. The CHB inverter, used as a SAPF, is connected parallel to the system at a point of common coupling (PCC) via interface inductor (Lc and Rc). The three-phase AC supply is sinusoidal in shape and its simulation result is shown in Fig. 5(a). The current drawn by the nonlinear load (Diode bridge rectifier) is not sinusoidal in nature and its waveform for one phase is shown in Fig. 5(b): the nonlinear load current has a significant number of harmonic components fed into the supply system as well as the distribution system.

III. Modified Three Level Hysteresis Current Controller In contrast to the basic three level HCC scheme, the modified three level HCC introduces an overlapping in the band. The overlapping is such that the upper limit of the current error is set to h, whereas the lower limit is set to  and vice versa. There is an overlapping of the dead zones into opposite bands as shown in Figs. 4(a) and (b). In a basic three level HCC, the dead zone is nearer to the reference, while in the case of the modified three level HCC, it is near to the hysteresis boundary. In the modified three level HCC, the upper dead zone is extended into the lower band and the lower dead zone is shifted to the upper band. In the basic HCC, the width of hysteresis band is h to δ (or –h to -δ) whereas in the modified HCC the width of hysteresis band is h to –δ (or –h to δ).

(a)

Simulation Results

TABLE I SIMULATION PARAMETERS Parameters Rating values Supply Voltage (Vs ) 380 V Line to Line Supply Frequency (fs) 50 Hz Interface Inductor (Lc,Rc) 10 mH, 0.1 Ω DC link Capacitor (Cdc) 3300 μF Ref. DC Voltage (Vdc) 400 V Basic three level HCC h = 0.65, δ=0.05 Hysteresis Band (+h) Modified three level HCC & Dead Zone (+δ) Case I: h = 0.55, δ= -0.05 Case II: h = 0.35, δ= -0.25 Load Resistance (RL) 60 Ω Load Inductance (LL) 100 mH

(b)

Figs. 4. (a) Modified Three level HCC (Case I), (b) Modified Three level HCC (Case II)



128


(a)

(b)

(c)

(d)

(e)

(f)

Figs. 5. (a) Supply Voltage, (b) Load Current, (c) Compensating Current, (d) Source Current, (e) Supply Voltage and Source Current, (f) Dynamic Performance of the system with step change in load at t=2.5 s

The compensating currents supplied by the three level CHB inverter is shown in Fig. 5(c). The compensating current is determined by the instantaneous pq theory. In the cascaded three level inverter based SAPF, the current is synthesized using the proposed modified three level HCC as well as the basic three level HCC technique. As a result, harmonics of the load current are mitigated and the source current is converted to sinusoidal as shown in Fig. 5(d). The power factor of 0.999 (nearly unity) is obtained with the modified three level HCC as shown in Fig. 5(e). The dynamic performance of the system is checked with the step change in load. At the time of t=2.5 s (Fig. 5(f)), there is a step change in load, exhibiting the characteristic of the proposed method during dynamic change. In the proposed method, the current error remains in the specified boundaries during the dynamic change in load. Thus, the dynamic change does not force the controller

to radically change the state of inverter as in the cases of the conventional two-level inverter and the basic three level inverter. In the two level HCC, the actual current tracks the reference current among the hysteresis bands. Based on the current error value, the inverter phase voltage V V switches to either  dc or  dc . 2 2 This results in larger rate of change of current since the applied voltage change is effectively Vdc in magnitude. In the simulation, the hysteresis band limits is h= ±0.3 as shown in Fig. 6(a). The simulation result of the basic three level HCC with hysteresis band h=0.65 and with dead zone δ=0.05 is shown in Fig. 6(b). It is clearly seen from the simulation result that the actual current tracks the reference current in either upper band or lower band and hence at every instant one of the band occupied and the other one is empty and vice versa.



129


(b)

(a)

(c)

(d)

Figs. 6. Actual Current tracks Reference Current in Hysteresis Band: (a) Two level HCC (h=+0.3), (b) Basic Three Level HCC (h=0.65, δ=0.05), (c) Modified Three Level HCC(Case I: h=0.55, δ= -0.05), (d) Modified Three Level HCC(Case II: h=0.35, δ= -0.25)

Here, one of the bands is empty and therefore the current tracking error is increased. This error is minimized with the proposed modification made in the basic three level HCC for Cascaded H Bridge Inverter based SAPF. In Case I, there is a slight over-lapping between the hysteresis bands that makes the value of the dead zone (δ) smaller, whereas in Case II over-lapping is increased, resulting in a larger dead zone (δ) value. The value of dead zone(δ) should be slightly less than the width of the hysteresis band (δ < h) as discussed earlier. In the modified three level HCC, considering Case I, the dead zone value is changed and extended to another direction and so δ= -0.05 and h=0.55, thus maintaining the width of band to be 0.6. This results in slight overlapping of two bands unlike basic three level HCC. The simulation result of the modified three level HCC with Case I is shown in Fig. 6(c). It is clearly seen from the result that now the actual current tracks the reference current simultaneously in both the bands, no band is empty and so the current tracking error is reduced compared to basic three level HCC. In Case II, the dead zone is extended to the other quadrant and it is placed nearer to the other hysteresis boundary value  h . According to this, the modified hysteresis bands have the values of h = 0.35 and  = -0.25 for one band and the values of h = -0.35 and  = 0.25 for the other band. The simulation result is obtained for Case II and it is shown in Fig. 6(d). The simulation result clearly shows that the actual current closely follows the reference current in both the bands and both bands are utilized uniformly during the

entire operation so that the current tracking error is further reduced. The analysis of the load current reveals the THD value of 25.81% for nonlinear load. The simulation results also show that the THD of the source current with the basic three level HCC is 3.77%, while with the modified three level HCC it is reduced to 3.33% and 2.92% in Case I and Case II respectively. THD of the source current is least in Case II since the overlapping proportion is increased in both the bands. In the basic three level HCC, THD of the source current is higher as the compensating current deviates from its reference value in terms of offset of current error which introduces unwanted harmonics in the source current. In all the three cases, THD of the source current is less than 5%, which is in accordance with the IEEE-519 and IEC-61000 standards. The magnitude and the variation in switching frequency are reduced to modified three level HCC in Case II as compared to Case I and basic three level HCC. The comparison among the two-level HCC, basic three level HCC and both the case of modified three level HCC is shown in Table II. The source current in each phase shown in Fig. 7(a) is sinusoidal in nature and it validates the effective compensation ability of the proposed controller. Each bridge of CHB has a separate DC link which should be maintained at the reference value to ensure the operation of the CHB inverter as harmonic and reactive power compensator. A discrete PI controller outputs the required active power value to supply the power losses in inverter as well as coupling the inductor and maintaining constant DC link voltage.



130


(a)

(b)

(c)

(d)

(e)

(f)

Figs. 7. (a) Three Phase Source Current, (b) DC Link Voltage of Each Bridge of CHB, (c) FFT Analysis of Load Current, (d) FFT Analysis of Source Current, (e) Inverter Output Voltage, (f) Line Voltage

Fig. 7(b) shows the behavior of DC voltages of each bridge in CHB in dynamic condition. The FFT analysis of load current and source current with modified three level HCC of Case II is shown in Fig. 7(c) and Fig. 7(d). The inverter output voltage of CHB inverter and the line voltage is shown in Fig. 7(e) and Fig. 7(f). The value of the switching frequency and the variation of the switching frequency in two level HCC, basic three level HCC and modified three level HCC are measured in the simulation and the graph between the time vs frequency for fundamental cycle is plotted in Figs. 8. In two level HCC, the value of maximum switching frequency and the variation of switching frequency are very high as shown in Fig. 8(a) whereas it is significantly reduced in case of basic three level HCC as shown in Fig. 8(b). The magnitude and the variation in switching frequency are further reduced in the proposed modified three level HCC, where the maximum switching frequency is reduced to 9.748 kHz (25.102 kHz as in two level HCC) and the variation in switching frequency is

6.906 kHz (24.291 kHz in two level HCC) in modified three level HCC (Case II) is shown in Figs. 8(c) and 8(d). The standard deviation is calculated in each case suggesting the variation in switching frequency from the average switching frequency. It is worth noting that the standard deviation is reduced in modified three level HCC. The comparison of two level HCC, basic three level HCC and modified three level HCC of both the cases is given in Table II. The comparison is based on %THD, maximum switching frequency, minimum switching frequency, variation in switching frequency and standard deviation. The size of hysteresis band is kept the same for two level HCC, basic three level HCC and modified three level HCC (Case I and II). The comparison proves that an increased proportion of overlapping in the hysteresis band of three level HCC provides better results in comparison to the basic three level HCC and the modified HCC conventionally in the literature.



131


time (s)

time (s)

(a)

(b)

time (s)

time (s)

(c)

(d)

Figs. 8. Variation of switching frequency in (a) two level HCC, (b) Basic Three level HCC, (c) Modified Three level HCC Case I , (d) Modified Three level HCC Case II TABLE II COMPARISON OF VARIOUS METHODS FOR HYSTERESIS CURRENT CONTROL Two Level Basic Three Modified Three Level HCC Parameter for Comparison HCC Level HCC Case I Case II h=±0.3

h=0.65 δ=0.05

h= 0.55 δ= -0.05

h= 0.35 δ= -0.25

4.22

3.77

3.33

2.92

Max. Switching Frequency fmax (kHz)

25.102

10.906

10.325

9.748

Min. Switching Frequency fmin (kHz)

0.811

2.057

2.718

2.842

Variation in Switching Frequency (fmax - fmin )

24.291

8.849

7.607

6.906

6.477

1.78

1.68

1.66

Hysteresis Band (h) and Dead Zone (δ) 

2  In

% THD in Source Current =

n2

I1

Standard Deviation of Frequency in kHz

f 

1 N    f  f sw  N i1  sw 

2

i

V.

problems are reduced with the proposed method. Due to the reduction in current tracking error, the source current THD of the system is also reduced. The method still has two major limitations: a. Due to the nature of the reference compensating current and its rate of change, the method does not always select the optimal vector. This increases the switching losses of the inverter. b. The applicability of the method to higher order filters

Conclusion

A modified three level hysteresis current controller for three level Cascaded H Bridge inverter-based Shunt Active Power Filter is proposed and developed in the paper. The conventional three level HCC available in the literature suffers from an increased current tracking error, a larger variation in switching frequency as well as the high value of maximum switching frequency. These



132


in SAPF application still requires attention. However, the standard deviation of the switching frequency from the average switching frequency is reduced in the proposed method compared to other methods in the literature. The effectiveness of the method is validated using 2.5 kVA SAPF developed in PSIM environment.

[19]

[20]

References [1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10] [11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

Akagi, H., New trends in active filters for power conditioning. IEEE transactions on industry applications, 1996. 32(6): p. 13121322. Grady, W. M., M. J. Samotyj, and A. H. Noyola, Survey of active power line conditioning methodologies, IEEE Transactions on Power Delivery, 1990. 5(3): p. 1536-1542. Watanabe, E. H., M. Aredes, and H. Akagi, The pq theory for active filter control: some problems and solutions. Sba: Controle & Automação Sociedade Brasileira de Automatica, 2004. 15(1): p. 78-84. Kazmierkowski, M. and M. A. Dzieniakowski. Review of current regulation techniques for three-phase PWM inverters. in Industrial Electronics, Control and Instrumentation, IECON'94, 20th International Conference on. 1994, IEEE. Kazmierkowski, M. P. and L. Malesani, Current control techniques for three-phase voltage-source PWM converters: A survey. IEEE Transactions on industrial electronics, 1998. 45(5): p. 691-703. Petit Suárez, J., H. Amarís, and G. Robles, Current control schemes for three-phase fourwire shunt active power filters: a comparative study. Revista Facultad de Ingeniería Universidad de Antioquia, 2010(52): p. 206-214. Trivedi, T., R. Jadeja, and P. Bhatt, Improved Direct Power Control of Shunt Active Power Filter with Minimum Reactive Power Variation and Minimum Apparent Power Variation Approaches, Journal of Electrical Engineering & Technology, 2017. 12(3): p. 1124-1136. Bode, G. and D. Holmes. Implementation of three level hysteresis current control for a single phase voltage source inverter. in Power Electronics Specialists Conference, 2000. PESC 00. 2000 IEEE 31st Annual. 2000. IEEE. Corzine, K. A., A hysteresis current-regulated control for multilevel drives. IEEE Transactions on Energy Conversion, 2000. 15(2): p. 169-175. Holmes, D. G. and T. A. Lipo, Pulse width modulation for power converters: principles and practice. John Wiley & Sons. Shukla, A., A. Ghosh, and A. Joshi, Hysteresis modulation of multilevel inverters. IEEE Transactions on power Electronics, 2011. 26(5): p. 1396-1409. Zare, F. and G. Ledwich, A hysteresis current control for singlephase multilevel voltage source inverters: PLD implementation. IEEE Transactions on power electronics, 2002. 17(5): p. 731-738. Gupta, R., A. Ghosh, and A. Joshi, Multiband Hysteresis Modulation and Switching Characterization for Sliding-ModeControlled Cascaded Multilevel Inverter. IEEE Transactions on Industrial Electronics, 2010. 57(7): p. 2344-2353. Gupta, R., A. Ghosh, and A. Joshi. Cascaded multilevel control of DSTATCOM using multiband hysteresis modulation. in 2006 IEEE Power Engineering Society General Meeting. 2006. Lafoz, M., et al. A novel double hysteresis-band current control for a three-level voltage source inverter. in Power Electronics Specialists Conference, 2000. PESC 00. 2000 IEEE 31st Annual. 2000. IEEE. Loh, P., G. Bode, and P.-C. Tan, Modular hysteresis current control of hybrid multilevel inverters. IEE Proceedings-Electric Power Applications, 2005. 152(1): p. 1-8. Shukla, A., A. Ghosh, and A. Joshi, Improved multilevel hysteresis current regulation and capacitor voltage balancing schemes for flying capacitor multilevel inverter. IEEE Transactions on Power Electronics, 2008. 23(2): p. 518-529. Vodyakho, O., T. Kim, and S. Kwak. Three-level inverter based

[21]

[22]

[23]

[24]

[25]

active power filter for the three-phase, four-wire system. in Power Electronics Specialists Conference, 2008. PESC 2008. IEEE. 2008. IEEE. Vodyakho, O. and C. C. Mi, Three-level inverter-based shunt active power filter in three-phase three-wire and four-wire systems. IEEE transactions on power electronics, 2009. 24(5): p. 1350-1363. Chauhan, S. K., M. C. Shah, P. Tekwani, Investigations on current error space phasor based self adaptive hysteresis controller employed for shunt active power filter with different techniques of reference compensating current generation, (2012) International Review on Modelling and Simulations (IREMOS), 5 (2), pp. 803-817. Georges, S., Kanaan, H., Hayek, A., Al-Haddad, K., Modeling, Simulation and Control Design of a PWM Three-Phase FourWire Shunt Active Power Filter for a 60 kW Industrial Load, (2017) International Journal on Energy Conversion (IRECON), 5 (2), pp. 51-59. Abdul Rahman, N., Mohd Radzi, M., Che Soh, A., Mariun, N., Abd Rahim, N., Dual Function of Unified Adaptive Linear Neurons Based Fundamental Component Extraction Algorithm for Shunt Active Power Filter Operation, (2015) International Review of Electrical Engineering (IREE), 10 (4), pp. 544-552. Santiprapan, P., Areerak, K., Areerak, K., The Enhanced – DQF Algorithm and Optimal Controller Design for Shunt Active Power Filter, (2015) International Review of Electrical Engineering (IREE), 10 (5), pp. 578-590. Jayachandran, J., Malathi, S., Neural Network Based ILST Control Strategy for a DSTATCOM with Solar Photovoltaic System for Power Quality Improvement, (2017) International Review of Automatic Control (IREACO), 10 (5), pp. 399-409. Kouadria, M., Allaoui, T., Denaï, M., High Performance Shunt Active Power Filter Design Based on Fuzzy Interval Type-2 Control Strategies, (2015) International Review of Automatic Control (IREACO), 8 (5), pp. 322-330.

Authors’ information Jignesh Pandya is Lecturer at R C Technical Institute, Ahmedabad. He completed M. Tech. from Nirma University, Ahmedabad. He is a Ph.D. scholar at School of Engineering, RK University, Rajkot. His areas of Interest include power electronics converters and Pulse Width Modulated power electronics converter.

Rajendrasinh Jadeja is Professor and Dean, Faculty of Engineering at Marwadi University, Gujarat, India. He is a Senior Member of IEEE. He is also a Chair of IEEE Gujarat Section. His area of interest and research include control of power electronic converters, AC drives, Pulse Width Modulated power electronic converters and Power Quality Issues and its mitigation techniques. Tapankumar Trivedi is Asstt. Proessor at Marwadi Education Foundation’s Group of Institutions. He completed M. Tech. from Indian Institute of Technology (IIT), Roorkee. He is currently pursuing Ph.D. at Charotar University of Science and Technology. His areas of interest include Power Quality Improvement devices, control of power electronic converters, and Multilevel Inverter-fed IM Drives.



133


Low Order Harmonic Analysis of 3-Phase SPWM and SV-PWM Inverter Systems Driving an Unbalanced 3-Phase Induction Motor Load Qamil Kabashi, Myzafere Limani, Nebi Caka, Milaim Zabeli Abstract – The Variable Frequency Drives (VFD) are a common type of controlling the speed of 3-phase induction motors which are based on two control modulation techniques of Voltage Source Inverter: Sinusoidal Pulse Width Modulation (SPWM) and Space Vector Pulse Width Modulation (SV-PWM). The SPWM control technique is known as an easier implementation, but the SV-PWM control technique can be distinguished by an easier digital realization and a better utilization of DC bus. All 3-phase PWM inverters produce a total harmonic distortion (THD) on its output, which causes electromechanical and thermal stresses. The aim of this paper is comparing the low order current harmonics while driving an unbalanced 3-phase induction motor (the case of deterioration of the insulation as a result of inter-turn short circuits between few turns of the same phase) using SPWM and SV-PWM two-level 3-phase Voltage Source Inverters (VSI). For this type of unbalanced 3-phase induction motor, the SVPWM and SV-PWM schemes are modeled and simulated using Matlab/Simulink software. A detailed comparative analysis of THD-phase currents is made for both modulation strategies at different switching frequencies while keeping the constant amplitude modulation index. Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: Sinusoidal Pulse Width Modulation (SPWM), Space Vector Pulse Width Modulation (SV-PWM), Total Harmonic Distortion (THD), Turn-To-Turn Short Circuit, Unbalance Induction Motor (IM)

I.

Usually in variable speed drive applications, an IM is supplied by a 3-phase voltage source inverter and a rotating magnetic field that rotates the rotor of machine, is produced. Therefore, 3-phase induction motor can be considered as a self-starting motor with voltagefrequency dependent motor, since its speed is a function of the input voltage and frequency [3]. Generally, the life span of a rotating machine depends on the imperfections of production technology, or on unfavorable operating conditions (electrical, mechanical or environmental) [4], [5]. These may be the reasons why sometimes the phase windings of an induction motor exhibits an unbalanced structure. The loophole may be recognized through some characteristic signatures. These are the current, the vibration, the flux, the partial discharge, the gas analysis, the power, the air gap torque and the temperature signatures [6]. Unbalanced operation may occur during faulty working conditions, a damage caused by contaminants, abrasion, vibration or voltage surge, from deterioration of winding’s insulation by ageing or excessive heating, when few turns belonging to the same phase of 3-phase motor windings shorts turn-to-turn. These short circuits in a particular phase often cause excessive increases in phase currents and rapidly deteriorate winding’s insulations [7], [8], [25], [26].

Introduction

The variable speed drives (VSD) have changed over the last two decades, due to the development of numerous control speed algorithms. Because of this, for many years, DC motors were preferable in industry as a result of the flexibility on smooth speed adjustment. However, DC motors are associated to some disadvantages, such as: higher costs, complicated construction, consumable physical part (brushes) and limitations on polluted and explosive environment applications [1]. Currently, applications of VSD use SPWM and SVPWM voltage source inverters (two or more levels) to drive variable speed induction motors (IM), since they can ensure producing voltage with variable amplitude and frequency. The induction motors (IM) differs from DC motors by some advantages such us: low cost, simplicity of contruction, more efficiency, ruggedness, reliablity; but they require more complex multivariable and nonlinear control techniques, such as speed and torque control [2]. The 3-phase induction motor has three sets of windings (stator), which is made of an insulated copper or aluminum that converts electrical energy into a mechanical energy (via rotor).



134

Qamil Kabashi, Myzafere Limani, Nebi Caka, Milaim Zabeli

Winding shorted turn-to-turn is one of the most common Insulation type failures as shown in Fig. 1.

The motivation for this research is based on the frequent activation of the three-phase power unit protection of variable speed pumping motors, while driving with 3-phase SPWM and SVPM Inverters. Due to the unbalance of the particular of stator phase, when used SPWM control technique protect unit will be activated rarely. However, after some time, as a result of the deterioration of winding’s insulation and the increase in the number of shortly connected turn-turn to the SPWM control technique, the protection unit will not allow the motor to run (except without load), whereas with the SVPWM control technique, the 3-phase will function for some time (about 30 minutes) and then the protection unit will be activated (as confirmed the results of this study). Considering this, the manuscript presents the simulation analysis of a 3-phase SPWM&SV-PWM inverter systems (keeping the constant amplitude modulation index at different switching frequencies) including harmonic assessment of the inverter output phase currents while driving the unbalanced 3-phase induction motors. Thus, in induction motors many additional problems occur: unbalanced stator currents and voltages, oscilation in torque, reduction in efficiency and torque, overheating and excessive vibration [9]. Morever, these failures can increase the magnitude of certain harmonic current components of an unbalanced phase. Principally, the switching losses of IGBT’s are depend on the modulation strategies used to control the inverter, but, in this manuscript, they are not taken into account, because IGBT switches have been considered to be ideal.

Fig. 1. Winding shorted turn-to-turn

Fig. 2 represents a model of the short circuit of two near coils (turn to turn shorted) of the one of the stator phases winding. Thus, the current in this winding phase will be greater than the current on the other two winding phases, because the resistance of this coil will be reduced.

Fig. 2. Turn to turn short circuit

The induction motor will work for a certain time, but with the rapid change of its speed due to transient processes and overloaded conditions, the heating effect in the coils provides a smaller resistance following the melting of the insulation. The circuit of a typical model of a 2-level 3-phase VSI is shown in Fig. 3. The output from this inverter is used to drive a 3-phase unbalanced load. So, when winding in the same phase has short-circuit turn-to-turn, consequently this stator winding has a lower resistance and inductance than the normal and the physical number of turns is different from the other of two stator phases.

II.

Overview of Sinus Pulse Width Modulation (SPWM)

Sinusoidal PWM is a type of "carrier-based" pulse width modulation. Among all Pulse Width Modulation (PWM) schemes, the sine-PWM technique is still a popular and simple method utilized in power inverter and motor control fields. This PWM technique is based on the individual switching of IGBT’s inverter (Fig. 3), where PWM signals are produced by a comparison technique of three sinusoidal modulation signals of low frequency, with the single triangular carrier signal of high frequency. These modulation signals are 120º shifted with each other. PWM generating signals (with an analog method by three comperators or by using microcontrollers) allow upper switches (S1, S3 and S5) to operate in a complementary manner with lower switches (S2, S4 and S6). In Fig. 4, when Vm  sin( t )  vctriang the switch S1 is turned on and S2 is turned off, then VAN = Edc. When Vm  sin(t )  vctriang , S2 is on and S1 is off, it leads to vAN = 0. Considering the symmetry on switching patterns (gate driving PWM signals), three IGBTs can simultaneously be turned on in three different legs of the

Fig. 3. A 3-phase Voltage Source Inverter while driving an unbalanced induction motor



135


inverter, three upper or three lower switches simultaneously, but they can never be turned on the upper and lower IGBTs in an inverter leg, in order to avoid the short circuit of the DC bus (Edc) [10]. As shown in Fig. 4 the magnitude of the carrier signal is greater or equal than the magnitude of the modulation signals. Thus the amplitude modulation index is limited below one (0 < ma < 1). In this case the linear relationship between the input and PWM output voltage is maintained. Amplitude modulation index (ma) is the ratio of the peak value of the sinusoidal modulated signal and the carrier triangle signal [10], [11]. Assuming that Vm  sin(t ) , Vm  sin(t  1200 ) and

The switching frequency of the active switches in the two-level inverter can be found solving fsw = fc = fm × mf [10]- [14]. The frequencies of the current signal and modulation signals are:

frequency of Vtriangle signal  f sw frequency of Vmoulation signal  f1

where, fs = PWM frequency (switching frequency), f1 = fundamental frequency. In all PWM inverters, the effective value of the output voltage depends on the amplitude of modulation index, whereas fsw remains constant [15], [16]. As it will be shown from the simulation model in Fig. 5, the switches used in medium power applications (more than few tens kW) can be switched only at the limited kilohertz frequency and the carrier frequency cannot be arbitrarily too high. As a result of some switching frequencies of respective switch poles, following a reduction of Total Harmonic current Distortion (THDi), but not of the Total Harmonic voltage Distortion, too. THD current of the unbalanced phase will be larger and will consequently generate additional heating, instability of function and short life span for use application generally.

Vm  sin(t  2400 ) are the modulating signals and let the magnitude of triangular carrier signal vary between the peak values of  Vc and  Vc , then: ma 

Vm Vc

(3)

(1)

When the amplitude of the modulating signal is larger than the amplitude of the carrier signal, over modulation occurs (resulting in ma > 1), but the linear relationship between the input and PWM does not continue anymore, due to the introduction of lower frequency harmonics in the output waveform and subsequent distortion of the load current [10], [12].

Fig. 5. Simulink model for SPWM fed unbalanced induction motor

III. Overview of Space Vector Pulse Width Modulation (SV-PWM) SV-PWM avoids unnecessary switching and provides an opportunity to improve SPWM technique based on the availability of digital signal processors in control of electric drives. This modulation control technique is based on a mathematical abstraction with rotating vectors in a complex coordinate system, where the three phase quantities (abc) can be transformed to their equivalent two-phase quantity stationary frame (α-β plane via Clark’s transformation) and to a synchronously rotating frame (d-q plane via Park transformation) [17], [18]. The Transformation between abc, Clarke plane, and Park plane is presented as:

Fig. 4. Conventional SPWM generation technique for three phase voltage source inverter

In SPWM inverter, the frequency modulation ratio (mf) is also important. It is the ratio of the frequency of the triangular carrier signal (fc) to the frequency of the modulation sinusoidal signal (fs). It controls harmonics in the output voltage [13], [14]: mf 

fc fm

(2)



136


a  d  b   T  T  p q  c     c 

 1 Vd  2      Vq  3  0 

(4)

where, Tc and Tp represent Clark and Park Matrix respectively:  1 2  Tc    3  0



1 2 3 2

 cos  Tp     sin 

1 2 3  2 

    

2 3 2 sin 3

cos

    

Va    Vb  Vc 

(8)

SV-PWM technique approximates reference vector Vref by a combination of eight switching patterns (Vo to V7). The eight switching states of the VSI, line to line output voltages and their corresponding space vectors are given in Table I. None of these eight switching vectors move in space and are referred to as stationary vectors [10].

(5)

sin   cos  

2 3 2 sin 3

cos

TABLE I SPACE VECTORS WITH SWITCHING STATES AND LINE TO LINE OUTPUT VOLTAGES OF INVERTER

(6)

Switch. Vectors V1 (000)

These transformations can be applied in VSI both for phase currents and phase voltages, where the inverter output is a rotate voltage vector with frequency equal of the sinusoidal frequency. The magnitude and the angle of the rotating vector (Vref) are shown in Fig. 6.

S1

S3

S5

S6

S4

S1

Vab

Vbc

Vca

On

Off

Off

On

Off

On

+Edc

0

-Edc -Edc

V2 (110)

On

On

Off

Off

Off

On

0

+Edc

V3 (010)

Off

On

Off

On

Off

On

-Edc

+Edc

0

V4 (011)

Off

On

On

On

Off

Off

-Edc

0

+Edc

V5 (001)

Off

Off

On

On

On

Off

0

-Edc

+Edc

V6 (101)

On

Off

On

Off

On

Off

+Edc

-Edc

0

V7 (111)

On

On

On

Off

Off

Off

0

0

0

V0 (000)

Off

Off

Off

On

On

On

0

0

0

The vectors V1 to V6 divide the plane into six sectors (each sector is equal to 600). The reference rotating vector in Fig. 7 (Vref) for a particular angular position (θ = ωt) onto the stationary reference frame α-β is generated by two adjacent nonzero vectors and two zero vectors depending on which sector it is [10], [16], [21], [22]. The hexagon is the maximum boundary of the space vector, and the circle is the trajectory of the regular sinusoidal outputs in linear modulation [23].

Fig. 6. The relationship between abc to the stationary α-β and rotating d-q frame of reference

Space vector representation of three-phase quantities Va(t), Vb(t) and Vc(t) with space distribution of 1200 apart is given by (7) [19], [20]:

V ref  Vd  jVq 

2 Va (t )  aVb (t)  a 2Vc (t ) 3





(7) Fig. 7. Space vector diagram of the two level inverter

where a  e j 2 / 3 and Va(t), Vb(t), Vc(t) are 3-phase sinusoidal components. Based on the transformation (4) the equation (7) can be written in this form:

Based on the volt-second principle, the reference voltage vector can be formed by switching active and zero vectors of each sector.



137


Implementing the conventional SV-PWM using the SVM rules when Vref falls into sector 1 it can be expressed as follows [23], [24]:

V 1T1  V 2T2  V 0T0  V ref Ts

(9)

T  T  T  V ref   1  V 1   2  V 2  0  V 0   Ts   Ts  Ts 

(10)

The dwell time T1, T2 and T0 for the vector V1, V2 and zero vectors (V7 or V0) in Fig. 8 are [24]:

 T1  Ts  ma  sin(   ) 3 T2  Ts  ma  sin 

Fig. 9. Simulink model of three-phase VSI fed unbalanced 3-phase induction motor

(11)

T0  Ts  T1  T2 where 0 ≤ θ ≤ π/3, ma is the amplitude modulation index (0 ≤ ma ≤ 1): ma 

3  Vref Edc

(12)

Fig. 10. Subsystem Simulink model for determining PWM switching time on SV-PWM scheme

An unbalance of 1% is acceptable and it does not affect on the 3-phase windings. An unbalance above 5% will result in capacity reduction by 25%. So, e.g. a current overheat of 130–150 % is admissible during 60 s, and IM stator asymmetry of 10 % results in current overload of separate phases by 130 % [7]. All the results achieved in this paper are obtained after building the SPWM and SV-PWM schemes using the MATLAB-Simulink – Powersim - Library. The simulated models are based on the following data: DC bus voltage Edc = 400 V, 3-phase unbalanced induction motor (the resistance and the inductance of unbalanced phase is 7 Ω respectively 3 mH), whereas resistance and inductance of two other balanced phases are 7.7 Ω respectively 4 mH. The difference between the unbalanced DC phase resistance and two other balanced resistance phases is about 9%. During the continuous operation of the motor (in unbalanced condition), the heating of the unbalanced phase winding will be excessive, hence it will follow a further degradation of insulation, and thus, it will increase the number of turn-to-turn short circuits. Two modulation techniques (SPWM and SV-PWM) are used in simulations, and the obtained results are based on the measurements of: phase current waveforms in the unbalanced and balanced phases and THDi in the unbalanced and balanced phases, considering five different operation conditions in function of switching

Fig. 8. Reference vector (Vref) formed by summation of neighbor space vectors V1, V2 and V7 (or V0)

The switching frequency (fsw = 1/Ts) and the amplitude modulation index (ma) have an impact on total distortion and losses on an unbalanced 3-phase induction motor load. The THD current of the unbalanced phase in this control mode will be smaller compared to the SPWM technique and consequently will generate less additional heating, less instability of function and an unbalanced induction motor will generally have a longer life span. All results in SV-PWM technique are obtained using the MATLAB/Simulink schemes (Figs. 9 and 10).

IV.

Simulation Results and Discussion

During the technological process of producing 3-phase motors, often imperfection could often occur, consequently unbalanced cases appear. Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved


138


frequencies (fsw = 900 Hz, 1050 Hz, 1500 Hz, 3000 Hz, and 7500 Hz) when ma = 0.85. The value ma = 0.85 is related with fundamental line-to-line and phase voltages with respective European 3-phase grid distribution. Condition 1. Analysis in fsw = 900 Hz (ma = 0.85) Three phase currents iA, iB and iC are represented in Fig. 11 and in Fig. 12 using SPWM and SV-PWM technique, respectively. Fig. 14. THDi of unbalanced phase A (SV-PWM)

Fig. 15. THDi of balanced phases B and C (SPWM) Fig. 11. 3- Phase currents on unbalanced induction load (SPWM)

Fig. 16. THDi of balanced phases B and C (SV-PWM)

It can be seen that the low-order harmonic amplitude of the h(16) in the SPWM technique is greater than h(16) in the SV-PWM technique and is one of the determining factors for the creation of additional turn-to-turn short circuit. Below are just presented the results obtained in Condition 5 (fsw = 7500 Hz), whereas obtained complete results in:  Condition 2. Analysis in fsw = 1050 Hz (ma = 0.85)  Condition 3. Analysis in fsw = 1500 Hz (ma = 0.85)  Condition 4. Analysis in fsw = 3000 Hz (ma = 0.85) are summarized in Table II.

Fig. 12. 3-Phase currents in the unbalanced induction load (SV-PWM)

THDi (SPWM and SV-PWM) results of unbalanced (A) are presented in Fig. 13 and in Fig. 14, while those of the two other balanced phases (B and C) are presented in Fig. 15 and in Fig. 16. In these operation conditions THD% (iA) of the unbalanced phase (A) in SPWM and SV-PWM are 18.19% and 16.25% respectively, whereas the highest amplitude of the low order harmonics appeared at h(16). In SPWM and SV-PWM this harmonic amplitude is 11.45% and 7.51% of fundamental, respectively.

TABLE II COMPARISON OF THDI IN VARIOUS SWITCHING FREQUENCIES fsw

THDi-A% (SPWM) THDiA% (SV-PWM) THDiB,C% SPWM THDi B,C% (SV-PWM

900Hz

1050 Hz

1500 Hz

3000 Hz

7500 Hz

18.19%

15.57%

11.48%

8.94%

9.64%

16.25%

14.09%

9.95%

4.99%

2.02%

16.94%

14.81%

11.23%

8.64%

8.98%

15.04%

13%

9.15%

4.59%

1.85%

Condition 5. Analysis in fsw = 900 Hz (ma = 0.85) Three phase currents iA, iB and iC are represented in Figs. 17 and 18 using SPWM respectively SV-PWM technique.

Fig. 13. THDi of unbalanced phase A (SPWM)



139


Fig. 21. THDi of balanced phases B and C (SV-PWM)

Fig. 17. 3- Phase currents on unbalanced induction load (SPWM)

Fig. 22. THDi of balanced phases B and C (SPWM)

In these operation conditions THD% (iA) of unbalanced phase (A) in SPWM and SV-PWM are 9.64% respectively 2.02%. The amplitude of highest low order harmonic in SPWM appeared at h(5)= 5.5% of fundamental, whereas in SV-PWM the amplitude of highest low order harmonic is h(2) = 0.16% of fundamental. Table II results are presented in Figs. 23 and 24.

Fig. 18. 3-Phase currents on unbalanced induction load (SV-PWM)

THDi (SPWM and SV-PWM) of unbalanced phase A is presented in Figs. 19 and 20, whereas THDi (SPWM and SV-PWM) of two other balanced phases B and C are presented in Figs. 21 and 22. Although in Fig. 23 with an increasing switching frequency from 1050 Hz to 3000Hz, THDi will be decreasing in the unbalanced phase (A); in condition 5 (fsw = 7500 Hz, ma = 0.85) an increase of THDi, can be noticed, especially the 5th harmonic, that is the determinant factor which affects the fastest melting of insulation of unbalanced winding phase. This effect does not appear in the SV-PWM technique where the highest low order harmonic is within the allowed limits.

THDi% in unbalanced phase A THDi-A% (SPWM)

THDi-A% (SV-PWM)

20,00% 15,00% 10,00% 5,00% 0,00% 900 Hz

1050 Hz 1500 Hz 3000 Hz 7500 Hz

Fig. 23. THD% in unbalanced phase A

THDi% in balanced phase B and C THDi-B,C% (SPWM)

Fig. 19. THDi of unbalanced phase A (SPWM)

THDi-B,C% (SV-PWM)

20,00% 15,00% 10,00% 5,00% 0,00% 900 Hz

1050 Hz 1500 Hz 3000 Hz 7500 Hz

Fig. 24. THD% in balanced phase B and C Fig. 20. THDi of unbalanced phase A (SV-PWM)



140


The deterioration of winding insulation in unbalanced phase results on an increase of the number of turn-to-turn short circuits, and it also leads to the reduction of the DC resistance to 5 Ω (about 36%). Therefore, the protect unit does not allow to run the motor, because it is down.

V.

[8] [9]

Conclusion

The main research in this paper was focused on the deterioration of the isolation in the particular phase of the induction motor (due to the inter-turn short circuits between few turns of the same phase), when the switching frequency of SPWM and SV-PWM two level 3-phase VSI is between 900 Hz to 7500 Hz. From analyzed switching frequencies in this research, the frequency fsw = 3000 Hz, according to the results, is the lowest frequency showing significant improvement of THD% of current in unbalanced phase winding, at both modulation techniques (SPWM and SV-PWM). Fluctuations resulting from higher THDs in the unbalanced phase winding deterioration cause additional damage to it. Torque and rotating speed, produced by the unbalanced motor becomes fluctuating. These sudden changes in torque cause more vibration and consequently additional degradation of insulation and increase the number of turn-to-turn short circuits. From the comparison of THDi (in unbalanced phases) in the two modulation techniques used as well as the greater amplitude of the low order harmonic it is noticed that in the SPWM technique this degradation of the unbalanced winding insulation becomes faster than with SV-PWM at any switching frequency used. Our major findings are obtained in the 7500 Hz switching frequency, in which the 5th harmonic show high value. Therefore it is not recommended to use the SPWM technique in this switching frequency because of the short life span of unbalanced induction motor, as well as the more frequent activation of the protection unit.

[10]

References

[21]

[1] [2]

[3]

[4]

[5]

[6]

[7]

[11]

[12]

[13]

[14]

[15]

[16]

[17] [18]

[19]

[20]

R. J. Hamilton, "DC Motor Brush Life," IEEE Transactions on Industry Applications , vol. 36, no. 6, pp. 1682-1687, 2000. D. C. Happyanto and A. Wijayanto, "Implementation of Genetic Algorithm for Parameter Tuning of PID Controller in Three Phase Induction Motor Speed Control," IPTEK Journal of Engineering, vol. 1, no. 1, pp. 1-8, 2014. M. Y. Tarnini , "Microcontroller Based Low Power Three Phase Induction Motor Frequency Drive," International Journal of Applied Engineering Research, vol. 8, pp. 6029-634, 2016. S. Karmakar, S. Chattopadhyay, M. Mitra and S. Sengupta, "Induction Motor and Faults," in Induction Motor Fault Diagnosis Singapore, Springer, 2016, pp. 7-28. L. Zarri, M. Mengoni, Y. Gritli, A. Tani, F. Filippetti, G. Serra and D. Casadei, "Behavior of multiphase induction machines with unbalanced stator windings," in 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives, Bologna, 2011. Sen, T., Bansal, H., Dynamic Modelling and Fault Diagnosis of an Induction Motor, (2014) International Review on Modelling and Simulations (IREMOS), 7 (4), pp. 575-583. doi:https://doi.org/10.15866/iremos.v7i4.3293. M. Zagirnyak, A. Kalinov and A. Kostenko, "Induction motor

[22]

[23]

[24]

[25]

[26]


stator windings asymmetry influence on frequency converter autonomous voltage inverter," in 2nd IEEE International Conference on Intelligent Energy and Power Systems (IEPS), Kiev, 2016. B. Amin, Induction Motors: Analysis and Torque Control, Berlin: Springer, 2001. H. Su and K. T. Chong, "Vibration signal analysis for electrical fault detection of induction machine using neural networks," Neural Computing and Applications, vol. 20, no. 2, pp. 183–194, 2011. B. Wu, High power converters and Ac-drives, New Jersey: John Wiley & Sons, 2006. W. Ahmed and S. M. U. Ali, "Comparative study of SVPWM (space vector pulse width modulation) & SPWM (sinusoidal pulse width modulation) based three phase voltage source inverters for variable speed," in 1st International Conference on Sensing for Industry, Control, Communications, & Security Technologies, Karachi, 2013. Rama Prasad Reddy, M., Brahmaiah, B., Brahmananda Reddy, T., Generalized Scalar PWM Algorithm for VSI Fed Induction Motor Drives with Reduced Complexity, (2014) International Review of Electrical Engineering (IREE), 9 (3), pp. 476-485. R. Bhukya and P. Satish Kumar, "Modeling, Analysis and Comparative of Down Sampling," Indonesian Journal of Electrical Engineering and Computer Science, vol. 7, no. 3, pp. 698-707, 2017. H. V. Holagh, T. A. Najafabadi and M. Mahoor, "Improved Selective Harmonic Elimination for Reducing Torque Harmonics of Induction Motors in Wide DC Bus Voltage Variations," in IEEE North American Power Symposium (NAPS), Morgantown, 2017. K. Jain, A. Garg, R. Rajoria and P. Chatuvedi, "Simulation & Performance Analysis of Two Level AC-DC-AC Converter with IM," International Journal of Soft Computing and Engineering vol. 2, no. 6, pp. 450-452, 2013. J. YingYing, W. XuDong and M. LiangLiang, "Application and Simulation of SVPWM in three phase inverter," in IEEE 6th International Forum on Strategic Technology, Harbin, 2011. S. K. Peddapelli, Pulse Width Modulation: Analysis and Performance in Multilevel Inverter, Berlin: De Gruyter, 2017. S. K. Soni and A. Gupta, "Analysis of SVPWM Based Speed Control of Induction Motor Drive with," International Journal of Scientific Engineering and Technology, vol. 2, no. 9, pp. 932-938, 2013. Ö. Türksoy, Ü. Yılmaz, A. Tan and A. Teke, "A Comparison Study of Sinusoidal PWM and Space Vector PWM Techniques for Voltage," NESciences, vol. 2, no. 2, pp. 73-84, 2017. A. B. M. S. Rahman, "Performance analysis of 3φ DC-AC PECs with different switching schemes," in 4th IEEE International Conference on Smart Energy Grid Engineering, Oshawa, 2016. G. K. Nisha, S. Ushakumari and Z. V. Lakaparampil, "Harmonic Elimination of Space Vector Modulated Three Phase Inverter," in International MultiConference of Engineers and Computer Scientists, Hong Kong, 2012. V. T. Somasekhar, S. Srinivas and K. K. Kumar, "Effect of ZeroVector Placement in a Dual-Inverter Fed Open-End Winding Induction-Motor Drive With a Decoupled Space-Vector PWM Strategy," IEEE Transactions on Industrial Electronics, vol. 55, no. 6, pp. 2497-2505, 2008. M. Gaballah and M. El-Bardini, "Low cost digital signal generation for driving space vector PWM inverter," Ain Shams Engineering Journal, vol. 4, no. 4, pp. 763-774, 2013. P. Tripura, Y. S. Kishore Babu and Y. R. Tagore, "Space Vector Pulse Width Modulation Schemes for Two-Level Voltage Source Inverter," ACEEE Int. J. on Control System and Instrumentation, vol. 2, no. 3, pp. 34-38, 2011. Rachek, M., Messaoudi, Y., Oukacine, B., NaitLarbi, S., Accurate Multi-Turn Model of Induction Motors Under Stator Short Circuits and Phases Breakdown Faults, (2016) International Journal on Energy Conversion (IRECON), 4 (1), pp. 17-25. Meo, S., Ometto, A., Rotondale, N., Diagnostic-oriented modelling of induction machines with stator short circuits, (2012) International Review on Modelling and Simulations (IREMOS), 5 (3), pp. 1202-1209.


141


Nebi Caka received his Dipl. Ing. degree in Electrical Engineering from University of Sarajevo, Bosnia and Herzegovina, in 1971 and his Mr. sc. degree in Electrical Engineering from University of Zagreb, Croatia in 1988. He received his Ph.D. degree in Electrical and Computer Engineering from the University of Prishtina, Kosovo in 2001. He also received the M. sc. (1979) and Mr.sc (1986) degrees in Albanian Philology from the University of Prishtina, Faculty of Philology. He is currently involved in the Academy of Arts and Sciences of Kosovo as external expert for Computer language processing. His research interests are in Microelectronics, Optoelectronics and Image laser processing, and in Computer linguistics.

Authors’ information Faculty of Electrical and Computer Engineering, University of Prishtina, Prishtina, Republic of Kosovo. E-mail: [email protected] Qamil Kabashi received the Dipl.Ing. (1996), Mr.sc. (2007) and Ph.D (2012) degrees in Electrical Engineering from the University of Prishtina, Faculty of Electrical and Computer Engineering. He currently holds the position of Associate Professor at the Faculty of Mechanical and Computer Engineering, University of Mitrovica. His research interests are in the Power Electronics, focused on harmonic effects by PWM Invereters.

Milaim Zabeli (corresponding author) received the Dipl.Ing. (1994), Mr.sc. (2006) and Ph.D (2012) degrees in Electrical Engineering from the University of Prishtina, Faculty of Electrical and Computer Engineering. He currently holds the position of Associate Professor at the Faculty of Mechanical and Computer Engineering, University of Mitrovica. His research interests are in the Digital Electronics, focused on VLSI design.

Myzafere Limani received her Dipl.Ing. degree in Electrical Engineering from University of Prishtina, Kosova, in 1978 and her Mr.sc. degree in Electrical Engineering from University of Belgrade, Serbia in 1988. She received her Ph.D. degree in Electrical and Computer Engineering from the University of Prishtina in 2000. She is currently a Full Professor in the department of Electronics at the University of Prishtina, Faculty of Electrical and Computer Engineering and a Correspondent Member of the Academy of Arts and Sciences of Kosovo. Her current research interests include electronics and audio signal processing.



142


Maximum Power Point Tracking of Multi-Input Inverter for Connected Hybrid PV/Wind Power System Considering Voltage Limitation in Grid Feby Agung Pamuji1,2, Hajime Miyauchi1 Abstract – In this paper, a new control method is proposed for a multi-input inverter to track the maximum power point of hybrid photovoltaic (PV) and wind turbine generator (WTG) systems connected to a 380 V grid. This new control scheme has two main functions, namely, shifting the voltage of PV and WTG to the optimum condition and maintaining stability of the grid system. The proposed maximum power point method shifts the voltage of PV and WTG step-by-step until the delta voltage is close to zero, which indicates that the grid power is adequate. The delta voltage is the point at which the controller must stop shifting the voltage to maintain grid stability. From the simulation results, the power of a hybrid PV/WTG system using the proposed maximum power point tracking controller is the maximum, while the voltage and frequency of the grid system are kept constant, compared to that possible with the perturbation & observation method. Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: Photovoltaic, Wind Turbine, Maximum Power Point Tracking

community. Types of renewable energy, such as photovoltaic system (PV) and wind turbine generator (WTG), are fluctuating in nature. Thus it becomes difficult to maintain voltage and frequency at constant levels. PV is highly dependent on sunlight. WTG relies on wind. To raise the reliability of renewable energy, these two types of renewable energy systems can be combined to create a hybrid system. Nasif Mahmud et al. proposed that integration of renewable energy sources into traditional power systems is one of the most viable technologies for meeting the ever-increasing energy demand efficiently. However, in doing so, many challenges arise, and they must be overcome to ensure smooth network operation. Voltage regulation is the most significant technical challenge that tends to limit the amount of penetration of renewable distribution generators into the conventional distribution network [3]. Y.-M. Chen et al. proposed a novel multi-input inverter for grid-connected hybrid PV and WTG systems to simplify the power system and reduce cost. The proposed multi-input inverter consists of a buck/buckboost fused multi-input dc–dc converter and a full-bridge dc–ac inverter. The output power characteristics of the PV array and the WTG are introduced. The perturbation and observation (P&O) control method is used to implement the maximum power point tracking (MPPT) algorithm for the input sources [1]. The P&O control method cannot ensure stability of grid system because the hybrid system always transfers the maximum power to the grid system without knowledge about the grid power

Nomenclature Output voltage of PV Output current of PV Series resistance of cell Shunt resistance of cell Radius of wind turbine Air density Wind speed Tip speed ratio (TSR) Electron charge (1.6 C) Short circuit current of cell Reverse saturation current Dimensionless junction material factor Boltzmann constant (1.3 J/K) Temperature (K) Number of cells connected in parallel Number of cells connected in series Output power of WTG Generated turbine mechanical power Vane angle Angular velocity of the turbine Power coefficient Efficiency of wind turbine

I.

Introduction

In this century, electricity is essential to human life, but not all areas on the planet are electrified, for example, rural areas in Indonesia. In such areas, renewable energy is essential to fulfill the electricity demands of the



143

Feby Agung Pamuji, Hajime Miyauchi

requirement. This leads to a condition of over voltage if the grid system does not need power. In the present study, a control method is developed using a multi-input inverter and a fuzzy logic controller (FLC) for a 380-V grid-connected PV and WTG hybrid system to obtain the maximum power point. The inputs of this control scheme are delta voltage, which is the difference between actual voltage and optimum voltage, and weather conditions (irradiance and wind velocity). Delta voltage is used to understand the power of the hybrid system and the energy requirement of grid system. If the hybrid system transfers the maximum energy to the grid when the grid does not need that much energy, such a transfer causes over voltage in the grid system. The proposed method controls the hybrid system to produce the maximum power without disturbing grid stability. The proposed maximum power point method shifts the voltage of the PV and the WTG systems stepby-step by using the delta voltage. The delta voltage is used to detect when the controller must stop shifting the voltage from the viewpoint of maintaining grid stability.

II.

The outputs of FLC PV (d1) and FLC WTG (d2) provide the proper trigger to the multi-input dc/dc converter to shift the voltage of the hybrid PV/WTG system from the actual voltage to the optimum voltage. II.1.

Maximum Power Point Controller

In the conventional MPPT method, that is, the P&O method, the voltage of the PV and the WTG systems are always shifted to the optimum voltage by comparing subsequent power and voltage outputs with previous power and voltage outputs, the shifting voltage of P &O can occur the grid system in unstable, because the voltage of PV and WTG are always shifted to the optimum condition, but the optimum voltage does not always match the minimum requirement of the inverter at 537 V (√2 ). In the proposed MPPT controller method, the controller shifts the voltage of PV and WTG step-by-step until the delta voltage is close to zero, at which point the controller stops shifting the PV and WTG voltages because the power transferred by the PV and the WTG systems to the grid is adequate. To detect when the controller should stop shifting the voltage of the PV and the WTG systems, the delta voltage parameter is used, which is the difference voltage between the voltages of the inverter and the grid. The controller stops shifting the voltages of the PV and the WTG systems when the delta voltage is close to zero. From Fig. 2, the proposed MPPT method shifts the voltages of the PV and the WTG systems step-by-step until P2, at which the power of the grid is adequate and the controller stops shifting the voltage. However, if this is not sufficient, the controller will shift the voltage until the power output reaches P1 or Pmax by using this method. Thus, the maximum power of the hybrid system can be obtained and ensured that the grid voltage remains stable.

Hybrid System

A hybrid system comprises two or more types of renewable energy in combination for improving reliability. In this paper, a hybrid PV/WTG system is proposed to connect to a 380-V grid. Figure 1 shows the proposed hybrid PV/WTG system. Power from the PV and the WTG systems is regulated by the multi input dc/dc converter and transferred to the 380-V grid. The multi input dc/dc converter is controlled using FLCs to shift the voltage of the hybrid PV/WTG system and obtain the maximum power. For coordinating the two controllers, the delta voltage parameter is used, which is the difference in voltage between the inverter and the grid. The delta voltage of PV is influenced the delta voltage of WTG and vice versa. The delta voltage parameter is usedto coordinatethe two controllers (FLC PV and FLC WTG).

Fig. 1. Hybrid PV/WT system



144


power is determined by the efficiency of wind turbines, as expressed using Eq. (3): =

Photovoltaic System

The current–voltage (I–V) characteristic of a solar cell is given by Eq. (1): =

−

exp (

−

(

)

+

+

=

−1

)

(3)

Based on the Bezt limit, the maximum efficiency of wind turbine is 0.57. This efficiency value is determined by the coefficient of power and tip speed ratio (TSR). Power coefficient is the ratio of the mechanical power of a turbine to the wind power captured by the turbine blades. TSR is the ratio of the speed of a propeller turbine to wind speed. They are expressed in Eqs. (4) and (5), respectively:

Fig. 2. Proposed maximum power point method

II.2.

1 2

(4)

=

(1)

(5)

where, is the power coefficient, is TSR, and is the angular velocity of the turbine. The correlation among the mechanical power, power coefficient and TSR is expressed in Eq. (6):

where and represent the output voltage and current of PV, respectively; and are the series and shunt resistances of the cell; is the electron charge (1.6 C); is the short circuit current of the cell; is the reverse saturation current; is a dimensionless junction material factor; is the Boltzmann constant (1.3 J/K); is the temperature (K); and and are the number of cells connected in parallel and in series. To design the controller, the PV characteristics shown in Fig. 3 are required.

=

1 2

( , )

(6)

where is the vane angle along the direction of the wind turbine. Mechanical power is the power to be transferred to the generator. To design the controller, it is necessary to consider the wind turbine characteristics, as shown in Fig. 4.

Fig. 3. Characteristic of Photovoltaic

The curves in Fig. 3 show the maximum points of power. The voltages at these points are called the optimum voltages. A converter is needed to shift from an actual voltage to the optimum voltage to achieve the maximum power. II.3.

Fig. 4. Characteristic of Wind Turbine

The curves in Fig. 4 show the maximum point of power. The voltage at this point is called the optimum voltage. A converter is needed to shift from an actual voltage to the optimum voltage to produce the maximum power.

Wind Turbine

The output power of the WTG can be expressed by Eq. (2): =

1 2

III. Fuzzy Logic Controller III.1. Fuzzy Logic Controller for PV System

(2)

The Hybrid system consists of PV and WTG with a multi-input dc/dc converter and an inverter. The multiinput dc/dc converter shifts the voltage and connects to

where is the radius of the wind turbine, is air density, and is wind speed. The generated turbine mechanical Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved


145


the 380-V AC grid. The advantage of this proposed MPPT method is that the hybrid system can transfer power to the grid in the optimum condition without disturbing grid stability. The parameters of FLC must be designed. Because the buck-boost/buck fused multi-input dc–dc converter is used, delta voltage is one of the parameters required to know the input and output conditions of the converter. Cell temperature and irradiance should also be considered as controller parameters because irradiance influences the energy produced by the PV system, and cell temperature influences the efficiency of the PV system. The FLC of the PV system is shown in Fig. 5.

Delta voltage is the difference between the optimum voltage and an actual voltage. As the range of membership function is decided using the PV characteristics, the range of delta voltage is 260 V in this study. For shifting the PV voltage, two membership functions are used. Membership function small is used from 0 V to 10 V, which indicates the PV voltage approaches the optimum voltage. Membership function large is used between 5 V and 260 V, which indicates the PV voltage is far from the optimum voltage. Then, the controller must use the high duty cycle to shift the voltage to the optimum voltage. Cell temperature affects the PV power efficiency. The warm membership function covers the range 15°C–41°C. The cold membership function covers the range 0°C– 20°C. The hot membership function covers the range 35°C–70°C. In addition to irradiance, cell temperature is always influenced by air temperature. On sunny days, irradiance is between 0.45 and 1 kW/m2, which indicates that PV can produce a considerable amount of power. On cloudy days, irradiance is between 0.025 and 0.5 kW/m2. On dark days, irradiance is between 0 and 0.05 kW/m2. In Fig. 9, the duty cycle is on the horizontal axis. Each membership function assigns the duty cycle of the converter to shift an actual voltage to the optimum voltage and to keep the minimum input voltage at 537 V DC (√2 ) so that the inverter can produce 380 V AC. The membership function is divided into two regions-low and high. The low membership function ranges between 0 and 0.7, indicating that the PV voltage approaches the optimum voltage. The high membership function ranges between 0.6 and 1, indicating that the PV voltage is far from the optimum voltage.

Fig. 5. Fuzzy Logic Controller of Photovoltaic

The FLC outputs the duty cycle that is suitable for the multi-input dc/dc converter from the viewpoint of shifting an actual PV voltage to the optimum PV voltage. The associated membership functions are shown in Figs. 6–8.

Fig. 9. Membership Function of Duty Cycle for Photovoltaic system

Fig. 6. Membership Function of Delta Voltage

The fuzzy logic consists of the membership functions of the inputs and the output. There are three inputs, namely, irradiance of sunlight, cell temperature, delta voltage, and one output, namely, duty cycle. The delta voltage indicates the difference between the optimum voltage and an actual voltage, and it indicates that the grid system needs power. If the grid system needs power, delta voltage exists. Then, the controller of PV detectes on membership function as large membership function and to shift the voltage of PV, the controller provides the output on membership function as high membership function. The converter will shift the voltage until the delta voltage on membership function as small membership function which indicates the grid system does not need power. Then, the controller provides

Fig. 7. Membership Function of Cell Temperature

Fig. 8. Membership Function of Sun Light Irradiance



146


output at low membership function, and the converter stops shifting the voltage. III.2. Fuzzy Logic Controller of Wind Turbine To design the FLC of the MPPT controller for WTG, the FLC parameters must be decided. Wind velocity is one parameter of the controller because it influences the energy produced by WTG. The FLC for the maximum power point of WTG is shown in Fig. 10.

Fig. 12. Membership Functions of Delta Voltage

Fig. 13. Membership Functions of Duty Cycle for Wind Turbine Fig. 10. Fuzzy Logic Controller for Wind Turbine

The fuzzy logic consists of the membership functions of the inputs and the membership functions of the output. There are two inputs, namely, wind velocity and delta voltage, and one output, namely, duty cycle. The delta voltage indicates the difference between the optimum voltage and an actual voltage, and it indicates that the grid system needs power. If the grid system needs power, delta voltage is present. Then, the membership function is set to large to shift the voltage of WTG, and the controller provides an output under the high membership function. The converter then shifts the voltage until the delta voltage corresponds to the small membership function, which indicates the grid system does not need power. Thereafter, the controller provides output corresponding to the low membership function and stops shifting the voltage.

The FLC is used to provide a suitable duty cycle for a multi-input dc/dc voltage converter to shift an actual voltage to the optimum voltage. From Fig. 11, there are two membership functions for wind velocity.

Fig. 11. Membership Function of Wind Velocity

The range of membership function is decided based on the characteristics of WTG. The widest range of delta voltage based on the characteristics of WTG is 300 V. For shifting the voltage of WTG, two membership functions can be used. The small membership function is used between 0 and 10 V, which indicates that the WTG approaches the optimum voltage. The large membership function can be used over 5 V, indicating the WTG is far from the optimum voltage and the controller must employ the high duty cycle to shift the WTG voltage to the optimum voltage. From Fig. 13, it can be seen that each membership function is the value of the duty cycle of the converter to shift an actual voltage to the optimum voltage and to keep the minimum input voltage at 537 V DC (√2 ) so that the inverter can produce 380 V AC. The membership functionis dividedinto two value ranges: low membership function is between 0 and 0.7, indicating the WTG voltage approaches the optimum voltage. The high membership function is between 0.6 and 1, indicating the WTG voltage is far from the optimum voltage.

III.3. Pulse Width Modulation The converter employs pulse width modulation, as shown in Fig. 14. The controller output is the duty cycle in which the pulse width (pulse active time) is divided by total signal period.

Fig. 14. Pulse Width Modulation

IV.

Simulation Model

The specifications of the PV, and WTG, and grid systems are listed in Tables I, II, and III, respectively.



147


TABLE I SPECIFICATIONS OF PHOTOVOLTAIC SYSTEM Parameters

Rating values

Module type Number per cell per module Power Voltage at maximum condition

Sunpower SPR-305-WHT 96 15 kW 250 – 270 VDC

requirement of the inverter, that is, 537 V (√2 ). Hence, the inverter cannot transfer power to the grid efficiently. The proposed MPPT controller can increase the output of the hybrid system and maintain grid stability.

TABLE II SPECIFICATIONS OF WIND TURBINE GENERATOR Parameters

Rating values

Base wind speed Power Voltage at maximum condition

12 m/s 3.3 kW 330 – 390 VAC Permanent Magnet Synchronous generator

Generator Type

TABLE III SPECIFICATIONS OF GRID SYSTEM Parameters

Rating values

Frequency V Ph-Ph (RMS)

50 Hz 380 Volt

Capacity

74,330 VA

Generator Type

Synchronous generator Fig. 15. Output Power of Hybrid System

As listed in Table I, the voltage range of the PV system is 250–270 VDC. This voltage is the input to the multi-input dc/dc converter. This voltage range is required to produce the minimum output voltage of 537 V DC, using which the inverter can produce three-phases AC at 380 V. As listed in Table II, the voltage range of WTG is between 330 and 390 V AC. Using this as the input, the multi-input dc/dc converter generates at least 537 VDC so that the inverter can produce three-phase AC at 380 V. As listed in Table III, the grid frequency is 50 Hz, and the phase-to-phase voltage is 380 V.

V.

Figure 16 shows that the reactive power output of the hybrid system. It is around zero with the proposed MPPT controller because the controller is set to transfer only active power to the grid.

Result and Discussion

In this section, the simulation results are presented, obtained using the proposed MPPT method with FLC and compared them to the results obtained using the conventional MPPT method as P&O method. Figure 15 shows the output power of the hybrid system when the irradiation and the wind velocity fluctuate. At the start of the simulation, the inverter could not produce the desired 380 V, so the power flowed from the grid to the inverter. After 0.05 second, the inverter could produce 380 V, so the power flowed from the inverter to the grid. By using the proposed MPPT method, the output power of the hybrid system can be maintained constant at almost 8.75 kW under the condition that the grid requires 8.75 kW from the hybrid system. By contrast, the conventional MPPT controller with the P&O method does not shift the output power from the hybrid system by more than 8.75 kW owing to the occurrence of overvoltage in the grid system. The P&O method always maintains the optimum voltage, but the optimum voltage does not always match the minimum

Fig. 16. Reactive Power of Hybrid System

Figure 17 shows the output voltage of the hybrid system. The output voltage of the hybrid system with the proposed MPPT controller is constant at 380 V. However, in case of the conventional MPPT controller with the P&O method, the output voltage of the hybrid system is not constant because the P&O method always perturbs the voltage to check the maximum power point. Figure 18 shows how the voltage waveform expands in 0.02 s. The output voltage of the hybrid system with



148


the proposed MPPT controller is a pure sine wave. By contrast, the output voltage with the P&O method is an impure sine wave because the P&O method can perturb the voltage.

voltage of the hybrid system with the proposed MPPT controller was constant, and its waveform was a pure sine wave. Thus, with the proposed MPPT controller, the hybrid PV/WTG system can produce the maximum power and transfer it to the grid successfully without disturbing grid stability. The output of the hybrid PV/WTG system can therefore fulfill the grid system demand. The limitations of the proposed method are as follows: 1. The controller cannot maintain stability if there is transient disturbance in the grid system. Transient disturbance occurs due to lightning disturbances, load shedding, and other reasons. 2. The controller cannot transfer PV/WTG power to the grid if the grid voltage is higher than the inverter voltage. In the future, a MPPT controller will be designed to inject power into a 20 kV grid without disturbing grid stability.

Fig. 17. Output Voltage of Hybrid System

Acknowledgements The Authors thanks Kumamoto University and Institute Teknologi Sepuluh Nopember for their support to finish this paper.

References [1]

Yaow – Ming chen, Yuan – Chuan Liu, Shih – Chieh Hung, and Chung – Sheng Cheng, Multi input inverter for grid – connected hybrid PV/Wind power system, IEEE Transaction on power electronic,Vol. 22(Issue 3):1070-1077, May 2007. [2] Soedibyo, A., Pamuji, F., Ashari, M., Grid Quality Hybrid Power System Control of Microhydro, Wind Turbine and Fuel Cell Using Fuzzy Logic, (2013) International Review on Modelling and Simulations (IREMOS), 6 (4), pp. 1271-1278. [3] Nasif Mahmud, A. Zahedi, Review of control strategies for voltage regulation of the smart distribution network with high penetration of renewable distributed generation, Renewable and Sustainable Energy Reviews, Elsevier, Vol. 64: 582-595, October 2016. [4] Chih-Ming Hong, Chiung-Hsing Chen,Intelligent control of a grid-connected wind-photovoltaic hybrid power Systems, International Journal of Electrical Power & Energy System, Elsevier, Vol. 55:554-561, February 2014. [5] Aymen Chaouachi, Rashad M. Kamel and Ken Nagasaka, A novel multi – model neuro - fuzzy – based MPPT for three – phase grid – connected photovoltaic system, Solar Energy, Elsevier, Vol. 84(Issue 12): 2219-2229, December 2010. [6] N. E. Mitrakis, J. B. Theocharis and V. Petridis, A multilayered neuro – fuzzy classifier with self – organizing properties, Fuzzy Sets and Systems, Elsevier, Vol. 159(Issue 23):3132-3159, December 2008. [7] Abdelkrim Menadi, Sabrina Abdeddaim, Ahmed Ghamri, and Achour Betka, Implementation of fuzzy – slidding mode based control of a grid connected photovoltaic system, ISA Transaction, Elsevier, Vol. 58:586-594, September 2015. [8] N. F. Guerrero – Rodriguez and Alexis B. Rey – Boue, Adaptive – frequency resonant harmonic – compesator structure for a 3 – phase grid – connected photovoltaic system, EnergyConversion and Management, Elsevier, Vol 87:328-337, November 2014. [9] Hussain Shareef, Ammar Hussein Mutlag and Azah Mohamed, A Novel approach for fuzzy logic PV inverter controller optimization using lightning search algorithm, Neurocomputing, Elsevier, Vol. 168:435-453, November 2015. [10] Nabil A. Ahmed, Masafumi Miyatake, A. K. Al – Othman, Power fluctuations suppression of stand alone hybrid generation

Fig. 18. Voltage of Hybrid System in Period (T=0.02)

Based on the simulation results, the difference in performance between the proposed method and the P&O method is summarized in Table IV. TABLE IV PERFORMANCE OF PROPOSED METHOD AND P & O METHOD Parameters

Grid Voltage Sine Wave of Grid Voltage Transferring Power Current Frequency of Grid Voltage Frequency of Grid

Proposed Method

P & O method

Stable

Unstable

Pure Sine Wave

Unpure Sine Wave

Optimum

Not optimum

50 Hz

50 Hz

50 Hz

50 Hz

VI.

Conclusion

The hybrid PV/WTG system with the MPPT controller based on the FLC method was proposed and successfully connected to a 380-V grid. The output Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved


149


[11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

[20]

[21]

[22]

[23]

[24]

combining solar photovoltaic/ wind turbine and fuel cell systems, Energy Conversion and Management, Elsevier, Vol. 49 (Issue 10):2711-2719, October 2008. Nicu Bizon, Mihai Oproescu and Mircea Raceanu, Efficient energy control strategies for a standalone renewable / fuel cell hybrid power source, Energy Conversion and Management, Elsevier, Vol. 90:93-110, January 2015. David Lara, Gabriel Merino and Lautaro Salazar, Power converter with maximum power point tracking MPPT for small wind – electric pumping systems, Energy Conversion and Management, Elsevier, Vol. 97:53-62, June 2015. M. A. Hasan, S. K. Parida, An overview of solar photovoltaic panel modeling based on analytical and experimental viewpoint, Renewable and Sustainable Energy Reviews, Elsevier, Vol. 60:7583, July 2016. Mokhlis, M., Ferfra, M., Optimization of Photovoltaic Panels Efficiency Using a Backstepping Control Technique Under Partial Shading Conditions, (2017) International Review on Modelling and Simulations (IREMOS), 10 (6), pp. 437-446. doi:https://doi.org/10.15866/iremos.v10i6.12495 Skik, N., Abbou, A., Robust Nonlinear Control of Three Phase Grid Connected PV Generator Through DC/AC Inverter, (2017) International Review on Modelling and Simulations (IREMOS), 10 (4), pp. 213-221. doi:https://doi.org/10.15866/iremos.v10i4.11688 El Azzaoui, M., Mahmoudi, H., Boudaraia, K., Sensorless Fuzzy MPPT Technique of Solar PV and DFIG Based Wind Hybrid System, (2017) International Review on Modelling and Simulations (IREMOS), 10 (3), pp. 152-159. doi:https://doi.org/10.15866/iremos.v10i3.11361 Hamed Athari, Mehdi Niroomand and Mohammad Ataei, Review and Classification of Control System in Grid-tied Inverter, Renewable and Sustainable Energy Reviews, Elsevier, Vol. 72:1167-1176, May, 2016. Ahmad Ale Ahmad, Mohammad Pichan and Adib Abrishamifar, A New Simple Structure PLL for both Single and Three Phase Applications, International Journal of Electrical Power & Energy System, Elsevier, Vol.74:118-125, January, 2016. N. Jaalam, N. A. Rahim, A. H. A Bakar, Chia Kwang Tan, Ahmed M. A. Haidar, A comprehensive review of synchronization method for grid connected converter of renewable energy source, Renewable and Sustainable Energy Reviews, Elsevier, Vol.59:1471-1481, June, 2016. L. S. Barros, C. M. V. Barros, An internal model control for enhanced grid-connection of direct-driven PMSG-based wind generators, Electric Power System Research, Elsevier, Vol.151:440-450, October, 2017. Ricardo Escudero, Julien Noel, Jorge Elizondo, James Kirtley, Microgrid Fault Detection Based on Wavelet Transformation and Park’s Vector Approach, Electric Power System Research, Elsevier, Vol. 152:401-410, November 2017. Mourad Tiar, Achour Betka, Said Drid, Sabrina Abdeddaim, Mohamed Becherif, Abdulkader Tabandjat, Optimal energy control of a PV-fuel cell hybrid system, International Journal of Hydrogen Energy, Elsevier, Vol.42:1456-1465, January 2017. Hassan Fathabadi, Novel Fast and High accuracy Maximum Power Point Tracking method for hybrid photovoltaic/ Fuel Cell energy Conversion System, Renewable Energy, Elsevier, Vol.106:232-242, June 2017. Jian Zhao, Xuesong Zhou, Youjie ma, Wei Liu, A novel Maximum Power Point Tracking strategy based on optimal voltage control for photovoltaic systems under variable environmental conditions, Solar Energy, Elsevier, Vol.122:640649, Desember 2015.

Authors’ information 1

Kumamoto University, Japan.

2

Institut Teknologi Sepuluh Nopember, Indonesia.

Feby Agung Pamuji is born in Tulungagung at 6th february 1987. He had graduated Bachelor and Master in Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia at 2009 and 2012. He worked at Institut Teknologi Sepuluh nopember as a lecturer from 2012, he continued to study doctoral degree in Kumamoto University from 2015. His Major is Electric Engineering special in energy conversion. Hajime Miyauchi graduated from Kyoto University, Kyoto, Japan, in 1981. He earned his MS and PhD degrees from Kyoto University in 1983 and 1991, respectively. He worked at Kyoto University from 1985 to 1993 as an assistant professor, and joined Kumamoto University, Kumamoto, Japan, in 1993 as an associate professor. His specialty is power system engineering and power system economics. He is a member of IEE Japan, IEEE and CIGRE. .



150


Design and Comparison of High-Speed Induction Machine and High-Speed Interior Permanent Magnet Machine Hossein Dehnavifard1, Ghadir Radman2, Mohamedreza Kalyan1 Abstract – Designing high-speed machines is a complicated process that needs thorough investigation due to their various applications in the industry. The choice of selecting between Induction Machines (IMs) and Permanent magnet Machines (PMs) depends on the required robustness, efficiency, and cost. In this paper, an IM and Interior Permanent magnet Machine (IPM) with similar output power (10kW), rotational speed (30,000rpm), and input voltage (380V) with Fractional-Slot Concentrated Winding (FSCW) are designed, optimized, and analyzed. The comparison is based on design challenges, cost, and efficiency. A genetic Algorithm is used to optimize the proposed machines and finite element analysis (Maxwell) is employed to support the result. Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: High-Speed Machines, Machine Design, Induction Machine, Interior Permanent Magnet Machine

Stack length Stator core thickness Stator current density Stator filling factor Stator inner diameter Stator outer diameter Stator reactance Stator resistance Stator slot height Terminal voltage Terminal current Tooth cross section area Tooth flux Tooth flux density Tooth saturation coefficient Tooth width Turns per coil Winding factor

Nomenclature Symbols

Parameters Airgap flux density Angle of Rotation Apparent power Conductor cross section area Core flux density Excitation voltage Frequency Poles Pole pitch Power Magnetization current Magnetization reactance Number of Rotor Slots Number of Stator Slots Rated slip Rated speed Ratio of Excitation Voltage Rotational speed Rotor core height Rotor current density Rotor diameter Rotor filling factor Rotor referred reactance Rotor referred resistance Rotor slot height Saturation flux density Shaft diameter Specific electric loading Specific magnetic loading Stack aspect ratio Stacking factor

I.

Introduction

Significant investigation has been carried out on the development of high-speed machines (HSMs) in the last few decades that has seen a rapid growth in their use [1][31]. Electric machine design involves a complex process which requires several assumptions for each step in order for the process to become more efficient and accurate [4]. Depending on the type of machine, different design policies are available. The design methods can be categorized as follows: 1- Peripheral speed, which is a classic method to design electric machines and is usually used for designing synchronous machines,



151

H. Dehnavifard, G. Radman, M. Kalyan

2- Rotor shear stress, which is used for electric induction motors (usually for squirrel cage), 3- Output power, which is used for induction generators [5]. Designing HSMs is an even more complicated process than desiging electrical machines [2]. Depending on the frequency and number of poles, an electric machine can go up to its synchronous speed which tops 3000 rpm (for 2 poles at 50Hz). To raise the rotational speed above 3000 rpm requires increasing the frequency or using a drive train. Since a gearbox will increase the cost and reduce the reliability of the system, manufacturers prefer to increase the rotational speed by adjusting the frequency by employing converters and drives [6], [7]. The power electronics and control strategies of these machines have also developed over recent years in order to control more complex HSMs. HSMs are commonly utilized in applications with typical operational speeds in excess of 10,000 rpm [8].The demand for high-speed machines has been particularly driven by the requirements for cost reduction, increased robustness, and higher efficiencies. There are various advantages of using HSMs which include: • High power density, smaller size, and increased reliability in comparison with low-speed machines [9]. • The lack of a gearbox increases reliability and mechanical stiffness, reduces costs of maintenance [10], offers increased compactness, lightweight, and maintenance-free operation [11]. The rise of demand for HSMs and high-power machines is highlighted by Fig. 1 [12]. The figure illustrates the widespread use of HSMs in various applications with vastly different speed and power requirements. A trend observed from Fig. 1 is that there exists a trade-off between high-speed and high-power. Applications typically require high-speed, low-power (such as machine tools) or low-speed, high-power (such as compressors). It is rare to find applications that require high-speed and high-power since the design of these machines is very complex and controlling these machines is also complicated. There are three main machine topologies used as HSMs: Induction Machine (IM), Permanent Magnet Machine (PM) and Switched Reluctance Machine (SRM). Majority of electric machines are induction machines. In general, there are two type of IMs, squirrel cage and wound rotor.

High-speed IMs are usually squirrel cage induction machines (SCIMs) which are more robust, cost-effective and more efficient than wound rotor induction machines (WRIMs) [2]. Furthermore, SCIMs have a simple design but there is no access to their rotor unlike in WRIMs. High-speed IMs are commonly used for hightemperature applications such as aircraft generators [13], [14]. Typical speeds achieved by IM range from 10000 to 100000 rpm with power ranging between 10 and 8000 kW[15]. PMs have become popular over the years for use in high-speed applications due to their high efficiency, torque and power density. Various configurations of high-speed PMs have been designed, tested and manufactured [16]. Increasing attention has been given to the use of high-speed PMs in a wide range of applications such as micro gas turbines, dental drilling tools, pumps, and airborne radars in the military industry [17]. PMs are preferred for HS applications largely because they experience less copper loss. However, permanent magnets are rare earth minerals which are very costly, thus PMs in general are more expensive than IMs. Typical speeds achieved by PM range from 2000 to 500000 rpm with power ranging between 0.1 and 1100 kW [15]. Furthermore, there are two PM designs used for HSMs, Surface Permanent magnet Machines (SPMs) and Interior Permanent magnet Machines (IPMs). In SPMs, magnets are closer to the airgap and they are exposed to all kind of harmonics produced by windings, therefore they have more magnetic losses, and dissipating these losses are challenging due to the SPMs’ structural constraint [18], [19]. IPMs have a higher core loss because of the space harmonic generated by the coils. Possible rotor slots can also cause more harmonics which ultimately increases the stator core loss in IPMs [19], [20]. Although SPMs can be used as HSMs, with an additional retaining sleeve to keep the magnet on the rotor at high-speeds, they are limited by maximum current and maximum back EMF for obtaining desired efficiency and power density over 10000 rpm [21]. SRMs are the most robust machines used in HS applications. However, designing SRMs is very much challenging and they require complicated control systems [22]. Most of the SRMs implemented are low power machines such as vacuum cleaners. In this paper, IMs and IPMs are designed and analyzed. In the next section the design algorithm and sizing equations are discussed. Analytical details of the machines are calculated in Section 3 and these details are used in finite element analysis in Section 4.

II.

Electric Machine’s Design

There are numerous variables that must be considered when designing an electrical machine such as the electrical circuit, magnetic circuit, insulation, etc. These considerations are usually integrated into an algorithm which generates a few solutions after a few iterations.

Fig. 1. Demand for HSMs in recent years



152


The solution with the best efficiency, torque density, maximum raising temperature within insulation’s standard thermal restrictions, and lower cost is adapted as the selected design. This section discusses the design algorithm and sizing equations for electric machines. II.1.

II.2.

Sizing Equations

The output power method is typically used in literature to size an electric machine, and thus has been used in this paper. II.2.1. Machine Sizing

Design Algorithm

The apparent power (S) of the machine at the airgap can be written in terms of the main dimensions of the machine as [16], [23]:

The main steps in IM design are shown in Fig. 2. With reference to the figure, the design process may start with (1), the design specifications and assigned values of flux densities and current densities. The next step (2) is to calculate the stator bore diameter Dsi, stack length, stator slots, stator outer diameter Dso, after the stator and rotor currents are found. The rotor slots and back iron height then follows. All dimensions are adjusted in (3) to standardized values (stator outer diameter, stator winding wire gauge, etc.). Then in (4), the actual magnetic and electric loadings (current and flux densities) are verified. If the results of the magnetic saturation coefficient (1 + Kst) of the stator and rotor tooth is not equal to assigned values, the design restarts (1) with adjusted values of tooth flux densities until sufficient convergence is obtained in 1 + Kst. Then, step by step of computation I0 is done as from (5 to 8); where equivalent circuit parameters are calculated in (6), losses, rated slip Sn and efficiency are determined in (7) and then power factor, locked rotor current and torque, breakdown torque, and temperature rise are assessed in (8). In (9) the performance is checked and if found unsatisfactory, the whole process is restarted (1) with new values of flux densities and/or current densities and stack aspect ratio λ=L/τ(where τ is the pole pitch). The decision in (9) may be made based on an optimization method which might result in going back to step (1) or directly to step (3) when the chosen construction and geometrical data are altered according to an optimization method (deterministic or evolutionary).

(1) where is the fundamental winding factor, is the rotor diameter (m), is the stack length (m), is the rotational speed (rev/s), is the specific magnetic loading (T) and is the specific electric loading of the machine (A/m). The output power (P) of the machine can be related to its apparent airgap power by the following expression [16], [23]: (2) where

is the ratio of excitation voltage (

) to

terminal voltage ( ) of the machine, = Angle of rotation, and terminal current ( ). The product determines the machine’s output torque capability and can be determined by: (3) where P is output power (kW), is ratio of excitation voltage to terminal voltage and is power factor. Once the term is determined, the selection of and depends on the practical requirements and application of the machine [24]. The aspect ratio facilitates the selection of these values depending on the application. For IMs, the aspect ratio varies between 1 and 2. IMs obtain good efficiency if the aspect ratio is selected from 1.4 to 1.6 [25]. For PMs, the aspect ratios reported in literature vary from 1 to 3. It has been reported that PMs generate lower cogging torque when the aspect ratio is closer to 2 and they have better efficiency when the aspect ratio is between 2.5 and 2.7 [16]. II.2.2. Sizing Stator and Rotor The stator dimensions are primarily determined by the material selection as this governs the flux density to avoid saturation of the material. The cross-sectional areas of the stator and rotor cores should be adequately designed to avoid saturation of these areas. The yoke height should be designed using the

Fig. 2. Electric machines design algorithm



153


following expression [26]:

similar for both machines in order to compare the efficiency and cost. Fractional-Slot Concentrated Winding (FSCW) is employed due to better filling factor ( ), shorter end-turn and better power density for both machines [28]. They were simulated using M19 29G silicon steel material for the rotor and stator cores. Neodymium magnets (NdFeB) are embedded in the rotor of the IPM which has a magnetic remanence of 1.07 at room temperature, a relative permeability of 1.037 and a maximum operating temperature of 240º C. The mechanical and manufacturing limitations such as peripheral speed, bearing size etc. are considered in their design. IPM’s rotor is designed for 150 percent of the rated speed in order to have good tolerance at rated speed in the bridge between magnets and center-post thickness. Table I indicates the assumption required to the design of both machines. These machines are designed and optimized based on the algorithm and sizing equations in Section II and the results are illustrated in Table II. Table III shows the equivalent circuit parameters for both machines calculated through the design process.

(4) where is the stacking factor, are the heights of the stator and rotor cores, respectively and is the saturation flux density of the steel in the stator and rotor. Saturation of the stator teeth is avoided by providing adequate tooth area which means the number of stator slots need to be selected carefully. There are no definite rules for selecting the number of stator slots, however, leakage reactance, tooth pulsation, overload capacity, cost, mechanical strength, weight of the machine, thermal system and iron losses must be considered in choosing the number of stator slots. These conditions are ensured when the number of stator slots is chosen based on the tooth width: (5) where

is the tooth width,

is the stator slot

TABLE I ASSUMED PARAMETERS TO DESIGN FOR IM AND IPM

pitch and number of stator slots is . The required stator slot height for the specific electric loading, slot-fill factor and current density chosen can be expressed as:

Parameter Voltage (Vrms) [V] Power [kW] Frequency [Hz] Rated Speed [rpm] Poles Slots (Stator/Rotor) Rotor diameter [mm] Shaft diameter [mm]

(6) where is the specific electric loading, is slot-fill factor and is current density. The process of designing the rotor is different for an IM and a PM. In IMs, the rotor is the same as the stator, but has fewer number of slots and is either filled with aluminum bars or copper coils. A 3-phase current passes through these slots generating an alternating magnetic field and torque. To avoid cogging and crawling, the number of rotor slots is selected based on the stator slots. In PMs, the rotor is usually made from silicon steel, is slotless and consists of magnets, instead of copper coils, that produce a direct magnetic field. The placement of these magnets and their thickness have significant impact on PMs’ performance [27]. In HSMs, it is often required to pass the rotor design through additional multi-variable optimizations to assure the power quality at rated efficiency and mechanical concerns such as rotor slots and bearings.

IM

IPM

380 10 1000 28,400 4 24/33 47 20

380 10 1000 30,000 4 36/80 40

IV. Finite Element Analysis (FEA) Results Both the proposed machines were simulated using ANSYS MAXWELL and the results are presented in this section Figs. 3 show the flux density plots for both machines. The designs were optimized to ensure that there is no saturation in the critical areas of the machines. TABLE II GEOMETRICAL DIMENSIONS FOR IM AND PM Parameter Value IM 130 Stator Outer Diameter PM 137 IM 50 Stator Inner Diameter PM 83 IM 31 Stack Length PM 120 IM 1.5 Airgap length PM 1.5 IM 20, 5 Slot height (Stator and Rotor) PM 14,--IM --Magnet Size PM 40 × 3 × 120 IM 10 Turns per coil PM 2

III. Comparing the Proposed Machines This paper will compare a high-speed SCIM and IPM. Both machines have 10 kW input power, 380 V input voltage, and their frequency is 1000 Hz. Their synchronous speed is 30,000 rpm. The airgap length, current density, and airgap flux density are assumed


Units mm mm mm mm mm mm --


154


TABLE III CALCULATED EQUIVALENT CIRCUIT PARAMETERS FOR IM AND IPM Parameters Stator Resistance (Ω) Stator Leakage Reactance (Ω) Rotor Resistance (Ω) Rotor Leakage Reactance (Ω) Magnetization Reactance (Ω) D-axis Inductance (nH) Q-axis Inductance (nH) Saliency Ratio

IM

IPM

0.03 0.67 0.091 0.22 0.87 ---

0.019 ----425378 823808

--

1.937

Figs. 4 show the currents for both machines. As expected, the magnitudes of currents decrease for the IM when speed increases. Torque decreases when speed increases which means there is less current drawn by the machine. and decrease at the same time to control the balance between torque and flux weakening.

(a)

(b) Figs. 4. Predicted current for (a) IM and (b) IPM over the speed range above 2300 r/min under rated-load condition (10 kW)

(a)

(a)

(b) Figs. 3. Flux density plots for (a) IM and (b) IPM

In the IPM, the decreases due to the lower torque required to maintain constant power operation and increases because of flux weakening to keep the voltage within the available limits. The predicted losses in the machines are shown in Figs. 5.

(b) Figs. 5. Predicted losses for (a) IM and (b) IPM over the speed range under rated–load condition (10 kW)



155


From Figs. 4 and 5, it is clear that the copper losses mainly depend on the current, since AC losses have been minimized in the design process for the windings (by considering the strand size and Litz wire construction). The IPM has a lower copper loss than the IM because it only has a stator winding and their resistance is smaller than that of the IM windings. In both machines, core losses rise with speed due to high-frequency and rich space harmonics generated by the windings. The rotor slots can also cause additional harmonics which increases the losses. In the IPM, the magnet loss is very low because it is buried in the core and they are far away from airgap which result in less harmonic loss.

with speed significantly due to decreasing copper losses. IPM’s efficiency rises slightly because it depends on core losses and it is almost steady when speed increases. It illustrates that both machines meet maximum power over a wide range of speed, however, IPM is more efficient at lower speeds. The output torque of both machines is shown in Figs. 7 from start to rated steady-state speed. As shown in Table II, IM’s rotor is smaller and lighter (due to magnets in IPM’s rotor) than IPM rotor. Therefore, IM’s torque increases smoothly while IPM’s torque overshoots, slightly. Furthermore, the torque ripple is 3 percent over 1 ms for IM while it is 17 percent within 0.062 ms. It means IPM carries stronger torque ripple than IM.

V.

Conclusion

A high-speed IM and IPM have been designed, analyzed and compared in this paper. FSCW was used for winding to reduce copper losses in both machines at the expense of slightly increased harmonic losses. As expected, the IM uses less and cheaper materials than the IPM, resulting a lower cost. In addition, the cost of IPM can increase due to the complex manufacturing process and required labor. Lastly, the results of this paper are based on FEA without considering mechanical losses. So, it is possible that the mechanical losses are different for two machines because of their mechanical designs and also the drive trains of two dynamometers. To eliminate this source of discrepancy, the two machines are compared in terms of their predicted electrical efficiencies. Therefore, IPM is more efficient than IM while IM has better power quality due to less torque ripple.

Fig. 6. Predicted efficiency for IM and IPM over the speed range under rated-load condition (10 kW)

References [1] (a) [2]

[3]

[4]

[5]

[6] (b) [7] Figs. 7. Output torque for (a) IM and (b) IPM at rated-speed [8]

In Fig. 6, the efficiency of both machines at ratedpower (10 kW) is shown. The IM’s efficiency increases Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved

O. Bottesi and L. Alberti, “Comparison of small-size generator for high-efficiency hydroelectric energy production,” in 2017 IEEE International Electric Machines and Drives Conference (IEMDC), 2017, pp. 1–8. S. Li, Y. Li, W. Choi, and B. Sarlioglu, “High-Speed Electric Machines: Challenges and Design Considerations,” IEEE Trans. Transp. Electrif., vol. 2, no. 1, pp. 2–13, Mar. 2016. W. L. Soong, G. B. Kliman, R. N. Johnson, R. A. White, and J. E. Miller, “Novel High-Speed Induction Motor for a Commercial Centrifugal Compressor,” IEEE Trans. Ind. Appl., vol. 36, no. 3, pp. 706–713, 2000. T. A. Lipo, Introduction to AC Machine Design, Third. Madison, USA: Wisconsin Power Electronics Research Center, University of Wisconsin, 2011. H. Dehnavifard, M. A. Khan, and P. S. Barendse, “Development of a 5-kW Scaled Prototype of a 2.5 MW Doubly-Fed Induction Generator,” IEEE Trans. Ind. Appl., vol. 52, no. 6, pp. 4688– 4698, Nov. 2016. J. N. Stander, G. Venter, and M. J. Kamper, “Review of DirectDrive Radial Flux Wind Turbine Generator Mechanical Design,” Wind Energ., vol. 15, no. 3, pp. 459–472, 2011. B. M. Wilamowski and J. D. Irwin, Power Electronics and Motor Drives. CRC Press, 2011. R. D. van Millingen and J. D. V. M. R. D. Van Millingen, C. Eng, “Phase Shift Torquemeters for Gas Turbine Development and Monitoring,” in International Gas Turbine and Aeroengine


156


[9]

[10]

[11]

[12]

[13]

[14]

[15]

[16] [17]

[18]

[19]

[20]

[21]

[22]

[23]

[24] [25] [26]

[27] [28]

[29]

Congress and Exposition, 1991, pp. 1–10. Z. Kolondzovski, “Thermal and Mechanical Analysis of HighSpeed Permanent-Magnet Electrical Machines,” Aalto University, 2010. A. Borisavljevic, Limits, Modeling and Design of High-Speed Permanent Magnet Machines. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. M. A. Rahman, A. Chiba, and T. Fukao, “Super high speed electrical machines - summary,” in IEEE Power Engineering Society General Meeting, 2004., vol. 2, pp. 1272–1275. R. R. Moghaddam, “High speed operation of electrical machines, a review on technology, benefits and challenges,” in 2014 IEEE Energy Conversion Congress and Exposition (ECCE), 2014, pp. 5539–5546. M. Centner and U. Schafer, “Optimized Design of High-Speed Induction Motors in Respect of the Electrical Steel Grade,” IEEE Trans. Ind. Electron., vol. 57, no. 1, pp. 288–295, 2010. A. J. Mitcham and N. Grum, “An Integrated LP Shaft Generator for the more Electric Aircraft,” in IEE Coloquium on All Electric Aircraft, 1998, p. 8/1--8/9. D. Gerada, A. Mebarki, N. L. Brown, C. Gerada, A. Cavagnino, and A. Boglietti, “High-Speed Electrical Machines: Technologies, Trends, and Developments,” IEEE Trans. Ind. Electron., vol. 61, no. 6, pp. 2946–2959, Jun. 2014. J. Gieras, Permanent Magnet Motor Technology, vol. 20096073. CRC Press, 2009. E. Carraro, M. Degano, M. Morandin, and N. Bianchi, “PM synchronous machine comparison for light electric vehicles,” in 2014 IEEE International Electric Vehicle Conference (IEVC), 2014, pp. 1–8. M. Vetuschi et al., “Limits, Modeling and Design of High-Speed Permanent Magnet Machines,” Proc. - 2014 Int. Conf. Electr. Mach. ICEM 2014, vol. 3, no. 1, pp. 150–155, Sep. 2014. P. B. Reddy, A. M. El-Refaie, K.-K. Huh, J. K. Tangudu, and T. M. Jahns, “Comparison of Interior and Surface PM Machines Equipped With Fractional-Slot Concentrated Windings for Hybrid Traction Applications,” IEEE Trans. Energy Convers., vol. 27, no. 3, pp. 593–602, Sep. 2012. J. Singh, “Harmonic Analysis and Loss Comparison of Microcomputer-Based PWM Strategies for Induction Motor Drive,” Electr. Mach. Power Syst., vol. 27, no. 10, pp. 1129– 1139, 1999. W. L. Soong, P. B. Reddy, A. M. El-Refaie, T. M. Jahns, and N. Ertugrul, “Surface PM Machine Parameter Selection for Wide Field-Weakening Applications,” in 2007 IEEE Industry Applications Annual Meeting, 2007, pp. 882–889. K. Ohyama, M. N. F. Nashed, K. Aso, H. Fujii, and H. Uehara, “Design using Finite Element Analysis of Switched Reluctance Motor for Electric Vehicle,” in 2006 2nd International Conference on Information & Communication Technologies, vol. 1, pp. 727–732. H. Dehnavifard, “Development of a Scaled Doubly-Fed Induction Generator for Assessment of Wind Power Integration Issues,” University of Cape Town, 2016. J. R. Hendershot and T. J. E. Miller, Design of Brushless Permanent- Magnet Motors. Ohio: Magna Physics, 1994. I. Boldea and S. A. Nasar, The Induction Machines Design Handbook. CRC Press/Taylor & Francis, 2010. S. Huang, J. Luo, S. Member, F. Leonardi, and A. Member, “A General Approach to Sizing and Power Density Equations for Comparison of Electrical Machines,”IEEE Transactions on Industry Applications, vol. 34, no. 1, pp. 92–97, 1998. J. R. Hendershot, “MotorSolve analysis of the 2010 Toyota Prius Traction Motor,” 2015, pp. 1–64. A. M. EL-Refaie, M. R. Shah, J. P. Alexander, S. Galioto, K.-K. Huh, and W. D. Gerstler, “Rotor End Losses in Multiphase Fractional-Slot Concentrated-Winding Permanent Magnet Synchronous Machines,” IEEE Trans. Ind. Appl., vol. 47, no. 5, pp. 2066–2074, Sep. 2011. Ismagilov, F., Vavilov, V., Bekuzin, V., Ayguzina, V., Topology Evaluation of a Slotless High-Speed Electrical Machine with Stator Core Made of an Amorphous Alloy for the Aerospace Industry, (2017) International Review of Aerospace Engineering (IREASE), 10 (3), pp. 131-139.

doi:https://doi.org/10.15866/irease.v10i3.12615 [30] Barta, J., Uzhegov, N., Ondrusek, C., Pyrhönen, J., High-Speed Electrical Machine Topology Selection for the 6kW, 120 000 rpm Helium Turbo-Circulator, (2016) International Review of Electrical Engineering (IREE), 11 (1), pp. 36-44. doi:https://doi.org/10.15866/iree.v11i1.7687 [31] Ismagilov, F., Uzhegov, N., Vavilov, V., Gusakov, D., Design Aspects of a High-Speed High-Voltage PMSM for Aerospace Application, (2017) International Review of Aerospace Engineering (IREASE), 10 (3), pp. 122-130. doi:https://doi.org/10.15866/irease.v10i3.12487


Center for Energy Systems Research, Tennessee Technological University, USA. 2

Department of Electrical Engineering, University of Cape Town, South Africa. Hossein Dehnavifard was born in Tehran, Iran in 1985. He earned his B.Sc. in Electronic and Electrical Engineering from Semnan University in 2008 and his MSc in the same field from Amirkabir University of Technology in 2011. Dehnavifard then did his doctoral degree in Electrical Engineering with a specialization in Electrical Machine and Drives at the University of Cape Town in 2016. He has designed, prototyped and tested a microDFIG through Ph.D. He is currently working on Electric Machines, Drives and Power Electronics as a postdoctoral scientist at Tennessee Technological University, United State. His research interests are Electric Machines, Drives, Power Electronics and Fuel Cells. Mohamedreza Kalyan was born in Nairobi, Kenya in 1991. He earned his B.Sc. Mechatronics from The University of Cape Town (UCT) in 2010 and is currently completing his MSc in Electrical Engineering at UCT. Kalyan has designed, prototyped and tested an interior permanent magnet machine for a high speed gas turbine application for his MSc degree.



157


Comparative Study of MIMO-OFDM Channel Estimation in Wireless Systems Obinna Okoyeigbo, Kennedy Okokpujie, Etinosa Noma-Osaghae, Charles U. Ndujiuba, Olamilekan Shobayo, Abolade Jeremiah Abstract – Determining the channel characteristics and how it affects transmitted signals is known as channel estimation. MIMO OFDM is a combination of MIMO and OFDM techniques. In this paper, comparative studies of MIMO, MISO and SISO channel estimation, using OFDM was performed. The block type and the comb type pilot arrangements were compared, and also various interpolation techniques were also compared to determine the optimum performance. The Bit Error Rate and MSE were employed as the performance metrics. This research was carried out by modeling and simulating the wireless communication system using MATLAB. It was discovered that the FFT interpolation has the best performance compared to other interpolation techniques. It was also shown that the MMSE outperformance the LS, but this is achieved at the cost of system complexity. An improvement in system performance was also observed with an increase in the number of antennas (i.e SISO, MISO and MIMO respectively), and also the OFDM helps combat interference and improves the bandwidth efficiency. Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: MIMO, MISO, SISO, OFDM, Bit Error Rate, Channel Estimation, Wireless Communication

I.

The Bit Error Rate (BER) to the Signal-to-Noise ratio (SNR) was used as the metric for performance analysis. This is done for the various interpolation techniques. The various interpolation techniques were evaluated to determine the most effective interpolation technique. The Mean Square Error (MSE) for the two types of pilot arrangements which are (the comb and the block types) was got to determine the most effective pilot arrangement to use. BER values of the systems with and without OFDM for estimating channels with SISO were also tried. The BER for MISO (2XI) and SISO channels were compared to determine the best channel estimation technique to be used. Lastly, BER values for MISO LS channel estimation were compared. A threshold was set for the pilot insertion ratio, to determine the most effective channel estimation technique through interpolation.

Introduction

MIMO is the acronym for systems that have multiple inputs and multiple outputs. The input and output pairs are particularly the number of antennas that a communications system has. This smart way of antenna propagation has led to significant improvements in communication technology. MIMO systems have helped to increase communication system capacity linearly [1][25]. This increase is dependent on the amount of antenna used. MIMO systems can increase the frequency spectrum efficiency of the communication system without the need for more bandwidth and power [1]. There is need for the receiver to have prior knowledge of the communication channel for it to be able to detect the signal correctly. This is only possible if the receiver can monitor the changes that occur in the communication channel. This is generally known as channel estimation and it forms an essential part of any communications system [2]. There are two modes of channel estimation. Channel estimation can either be pilot-based or decision-directed [3]. For pilot based systems, the ability to accurately estimate the communication channel is based on a pre-shared symbol known as the pilot symbol. This is shown in Figure 1. In this paper, channel estimation in a wireless communication system was simulated using MATLAB [24], [25]. For the simulation to be effective, a system with a transmitter, receiver and channel was implemented.

II. II.1.

Foundational Concepts

Orthogonal Frequency Division Multiplexing

Orthogonal Frequency Division Multiplex (OFDM) is a modulation technique that is well suited to MIMO channels. The principle of operation of the OFDM is the division of data streams with very high bit rates into several parallel streams with lesser bit rates, with each stream being modulated on distinct subcarriers [4].



158

Obinna Okoyeigbo et al.

that can be used to simulate Orthogonal Frequency Division Multiplexing are the Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (IFFT).

Fig. 1. Block diagram showing channel estimation and equalization at the receiver

OFDM divides the obtainable bandwidth into a number of lesser bands or sub-bands. This allows data to be transmitted without any dependence on the other bands of frequency. The smart way of selecting a channel in this type of communication system with multiple paths is the splitting of these channels into different amounts of flat sub-channels which have orthogonal subcarriers. OFDM has a robust capability of being able to achieve orthogonality in both frequency and time domain. This characteristic makes the OFDM the best multiplexing technique that can provide a satisfactory means of working with fading channels that are frequency selective, multipath and signal interference amongst others with less system sophistication [5], [6]. Coding and interspersing of the frequency components of subcarriers can also be exploited by OFDM to achieve diversity in frequency for channel optimization. The available spectrums of the frequency channels can also be fully utilized by the OFDM because of its unique characteristic of ensuring these separate parallel orthogonal subcarriers overlap. Figure 2 is a pictorial representation of the OFDM system.

Figs. 3. Comparison between (a) Conventional FDM and (b) OFDM

After the signal has been reversed to the analog domain using the IFFT, a cyclic prefix is attached to the signal to effectively show the domain in which the signal is represented. This scenario is reversed at the receiving end. To eliminate ISI, the frequency selective channel must be split into flat fading channels [7], [8]. Delays generated during the dispersion of the spectrum might cause loss of orthogonality. This can lead to errors. However, this issue is eliminated by the cyclic prefixing of the signal after FFT and the orthogonality is upheld. The process of cyclic prefixing is simply repeating the first section of the pilot symbol after the end of the first symbol. Simply put, there is an extension of the OFDM frame (which can be referred to as the delay). This increase in length of the frame helps eliminate interference. The cyclic prefix and the OFDM frame (which gives the total frame length), must be much higher in value than the impulse response of the channel to be able to eradicate the effect of interference (ISI). The cyclic convolution of the OFDM channel can be achieved using cyclic prefixes.

Fig. 2. Block Diagram of an OFDM system [7]

OFDM can be reliably referred to a case of Frequency Division Multiplexing (FDM) only with a restraint of orthogonality in the subcarriers. In realizing this orthogonality, the subcarriers are arranged to generate a spacing of ∆f = k/T. T denotes the duration of the symbol and k represents a positive going number which is seeded the value of one (1) most times. This is the reason OFDM has the unique ability to partially overlap subcarriers without causing any distortion to the signals conveyed by the adjacent subcarriers. As shown in Figures 3, the crest of the individual subcarriers corresponds with a null value of the interfering neighbor subcarrier, which eliminates distortion of the signals. The illustration shown in the first part of Figures 3, depicts a conventional FDM system where there is no form of orthogonality between the subcarriers. In the second part of Figures 3, there is orthogonality between the subcarriers leading to more efficient use of available bandwidth. In the discrete time domain, the best tools

II.2.

Multiple- Input Multiple- Out (MIMO) System

MIMO techniques find its maximum and efficient use in wireless systems. They considerably fix the performance and competency of wireless systems through multiplexing and increased gain (due to the diversity of the channel). In relation to the energy of a transmitted bit, the multiplexing capacity of MIMO systems translate into increased data rate whereas channel diversity culminates into lower error rate. The Binary Error Rate (BER) takes care of the issue of fading of the transmitted signals. The combination of OFDM and MIMO has generated a lot of attention because it promises to provide a lot of ways to manipulate wireless systems to generate desirable results [9], [10], and [11]. There has been significant gain due to low BER and increase in channel capacity for multiple users MIMO than for single user MIMO.



159


II.3.

MIMO-OFDM

signal and the interfering pilots. This leads to poor performance of the system due to interference at the receiver. But it should be noted that if the amount of pilots used is increased, the performance can be made better by increasing the channel’s estimate. However, the channel’s bandwidth will suffer because of the increased amount of pilots used. To achieve a better system, in which the amount of bandwidth occupied by the pilot system is reduced drastically, our attention would be focused on the use of interpolation to get the estimate of the channel’s frequency response between pilots without affecting the system and accuracy of channel estimations. Comb and block pilot arrangements are the two types of pilot systems that are available for channel estimation techniques [15], [16]. The main difference between the comb and the block type arrangement is that the pilot tones are inserted into each OFDM symbol at periodic intervals of the subcarrier frequency in the comb type, while the pilots are inserted within the frequency subcarrier in the block type arrangement. Transmission is done periodically in the time domain to achieve channel estimation. Figures 5 depict the comb-type and the blocktype arrangements of pilots respectively. It can be observed that each of the signal coming from MIMO antennas are decomposed into parallel subcarriers using serial to parallel conversion, before the signal is modulated using the IFFT transformation. The signal is appended with a cyclic prefix before it is then transmitted. At the receiver, the entire process is reversed.

Figure 4 is a depiction of the MIMO-OFDM block. MIMO can be used in conjunction with OFDM to form a compound MIMO-OFDM system. The combination of these techniques has been found to be able to produce a system that is bandwidth efficient, free from the interference caused by ISI, able to provide high channel capacity and improved data rate.

Fig. 4. Channel Estimation Block Diagram for the MIMO-OFDM System [11]

The usually selective fading channel issue in OFDMMIMO systems are usually turned into orthogonal fading channels that are flat with the aid of IFFT and FFT as discussed earlier. The combined MIMO-OFDM has been proven to achieve enhanced system performance than the usual SISO-OFDM. In contrast to SISO systems, the delay spread spectrum achievable in MIMO-OFDM systems delivers a combination of increased sub-band channels and multiplexing gain compared to flat-fading channels [12]. This is obtained by bit-stream demultiplexing to yield lesser sub-streams, and modulating the individual substreams with the help of OFDM before transmitting the signal. From Figure 4 it can be observed that each of the signal coming from MIMO antennas are decomposed into parallel subcarriers using serial to parallel conversion, before the signal is modulated using the IFFT transformation. The signal is then appended with a cyclic prefix before it is then transmitted. At the receiver, the entire process is reversed. II.4.

Figs. 5. Diagram of the Block-type and Comb-type Arrangement [2]

Figure 5(a) depicts the block pilot arrangement. It can be seen from the diagram that the pilot is placed at regular intervals which coincides with the frequency of the coherent time so as to deal with the varying time of the channel which corresponds to Equation (1) [17], where the period of the block is represented by Si :

Pilot Arrangements in Channel Estimation

Si 

There are basically two types of pilot arrangements in channel estimation. Pilots are known symbols, which are transmitted and used to estimate the channel. They could be placed either at the beginning of the entire frame or at the end of the frame. Often, the pilots can also be placed at specific intervals within the frame. The authors in [13], [14] presented an issue of pilot contamination during channel estimation. This stems from disagreement of orthogonality between the pilots of the information

1 f Doppler

(1)

The comb type pilot arrangement is given in Figure 5(b). In this arrangement, the pilots are carefully placed in a repetitive and orderly manner. This arrangement provides a form of bandwidth coherence that makes it possible to keep track of the frequency selective fading channel. This is achieved by making the comb-type pilots duration smaller than the channel’s coherent bandwidth.



160


The inverse of the signal’s maximum delay spread spectrum (σmax) gives a close estimate of the coherence of the channel’s bandwidth. The spacing between the pilots in the comb type, can be found using Equation (2):

Sf 

1

 max

pilot symbols will occupy as a result of being transmitted with the information block, the amount of pilots transmitted need to be reduced. The effect of interpolation on the channel involves the estimation of the channel at the placement point of the pilot symbols which is the point of missing data and can be achieved by comparing the information of estimates of the neighboring channels. Simply put, the estimated points where the subcarriers of the data symbols should be inserted can be derived by the interpolation of adjacent pilot symbols. Interpolation can be achieved by various means and they include, second order polynomial interpolation, piecewise constant interpolation, FFT based interpolation, Spline interpolation and Linear interpolation. Linear interpolation provides a very simple way to determine the pilot data that is somewhere between two known successive pilot subcarriers [22], [23]. Equation (5) gives the impulse response of the estimate of a channel’s subcarrier system, k ,mL  k   m  1 L , using linear interpolation:

(2)

where the separation times or period of the pilots is denoted by S f [18], [19], [20],[ 21]. II.5.

Pilot Symbol Aided Channel Estimation

Here, the received signal is multiplied by the inverse of the transmitted pilots, to generate the channel estimate. This gives an accurate estimate of the channel, and in turn provides better performance of the system. But the placements of these pilot systems have increased the overall bandwidth of the channel. Also, the cost implication of this method cannot be overlooked. To remove this problem, the interpolation technique is utilized. Equation (3) is a Diagonal Matrix that can be used to place training symbols or pilots within K subcarriers, provided that the OFDM subcarriers are orthogonal and do not have inter channel interference [3]:  X  0  0  0   0 X 1    X  (3)     0    0 X  K  1  0





H (k )  H (mL  l ) 



 1 l 



L

H



P ( m)  

l H P (m  1)  L

(5)



 H P (m)  l ( H P (m  1)  H P (m)), 0  l  L L When Spline interpolation is considered, the Spline function is used to implement third order or cubic Spline interpolation at different data points by placing a unique cubic function between pairs of existing data points. FFT interpolation technique works by first transforming the signal which is in form of a vector containing periodic function, increasing the points on the system and finding the IFFT of the increased data point. A sizeable number of pilots are required when transmitting for channel estimation in order to achieve channel frequency spectrum. The channel estimates of the data subcarriers are obtained using interpolation technique. This places a very high emphasis on interpolation.

where k = 0, 1, 2… N-1, X[k] denotes a kth subcarrier’s pilot symbol, which has a mean value of: E{N[k]}=0 and variance of: Var{N[k]}=  x2 :

0  0  Y [0]   X [0]   Y [1]   0  X [1]    Y         0      0 X [ K  1] Y [ K  1]  0 (4)  H [0]   N [0]   H [1]   N [1]               H [ K  1] N [ K  1]    

III. Methodology The simulation parameters and the initialization parameters are shown in Table I.

system

Interpolation or midpoint estimation is the process of determining the value that is somewhere between two known values. Due to the amount of bandwidth that the

TABLE I SYSTEM SIMULATION PARAMETERS AND VALUES System Parameters Value Simulation runs 1000000 Data Length 128 Frame Length 64 for QPSK SNR Values in (db) 0-30 Channel Type Multipath Channel Number of Channel Taps 5 Cyclic Prefix 10 Pilot Data Ratio 1:1, 1:3, 1:7, and 1:15 Modulation Techniques QPSK Antenna Configurations SISO, MISO (2X1)



The received signal vector can be expressed as Y, where, H represents the channel response vector, and N represents the noise vector, which has a mean value of: E{N[k]}=0 and variance of: Var{N[k]}=  x2 [10]. II.5.1.

Channel Interpolation

161


IV.

0

Result and Analysis

10

IV.1. Comparison of the Various Interpolation Techniques

-1

10

In order to find the optimal interpolation technique, comparisons were made between the techniques based on BER and SNR. Figure 6 shows the BER versus SNR for the various interpolation techniques. As shown in the diagram, the FFT technique provides the best interpolation performance. Next to the FFT technique is the Spline technique, then, the piecewise (cubic) technique and lastly, the linear interpolation. The FFT interpolation technique compared to the others also shows the least complexity. The BER curve for the FFT provides a near to perfect estimate of channel when compared to the characteristics of the block type. This shows that FFT gives the optimum channel estimation with efficient energy utilization and reduced complexity.

Mean Square Error

-2

BER

-3

10

15 SNR (dB)

20

25

10

15 SNR (dB)

20

25

30

IV.3. Comparison of the BER with and without OFDM, for SISO Channel Estimation

-4

10

5

Implementing the comb type estimation requires interpolation in the time domain to obtain the estimates at intervals where pilots are not inserted. This affects the accuracy when considering the total number of pilots used. Considering the LS and MMSE channel estimation, when a pilot-data ratio of 1:7 is used (i.e inserting 8 pilots), a poor system performance was observed even when the SNR values were increased, compared to the results obtained with a 16 and 32 pilot (i.e. a pilot-data ratio of 1:3 and 1:1 respectively). When similar pilot ratio is used for the LS estimation, the performance of the system is still in the acceptable limits shown by the “black line” on the graph. In summary, it can be deduced that system performance begins to deteriorate with decrease in number of pilots.

linear interpolation Spline interpolation Piecewise (Cubic) interpolation Pilots Completely Inserted FFT interpolation

5

0

Fig. 7. Block Pilot Type and Comb Pilot Type Channel Estimation

-2

0

CT MMSE (1:3) CT LS (1:7) CT MMSE (1:7) BT & CT LS (1:1),(1:3) CT MMSE (1:3) BT & CT(1:1) MMSE

-6

10

10

10

-4

10

-5

0

-1

-3

10

10

10

10

10

30

This comparison shows the relevance of OFDM technique in channel estimation, to reduce the effect of ISI in the communication channel. This is achieved by comparing the LS and MMSE channel estimation when OFDM technique is applied and when it is not. The result obtained is presented by the graph in Figure 8 which shows that increasing the SNR, decreases the BER, giving a better performance with OFDM, but without OFDM, the performance depreciates with further increase in SNR above 15 dB. This came about when the high-power subcarrier signals interfering with the main channel are not orthogonal to each other causing ISI in the overall system. With all the observations made above, it can be concluded that the OFDM is the most effective and preferred tool in battling the inter symbol interference (ISI) in channel estimation.

Fig. 6. Channel Estimation Techniques and their comparisons

IV.2. Block Pilot Type and Comb Pilot Type Channel Estimation Figure 7 shows that MMSE outperforms the LS giving an SNR gain of 10-15 dB, with same MSE and pilot ratios. The good performance of the MMSE is at the cost of high system complexity and power consumption. The complexity of the system in turn leads to increased mathematical computations than what is obtainable with the LS estimate. The more the mathematical operation performed, the higher the execution cycle when the system is simulated, which also increases the systems power consumption. Figure 7 also shows the BER versus SNR for the block type arrangement. Here, the pilots are placed in all the subcarriers at regular intervals of time. The result shows that the block pilot has an enhanced performance with the basic assumption that the channel is slow fading (i.e. it doesn’t change during the OFDM symbol duration.

IV.4. Comparing Channel Estimation for MISO and SISO Using BER as Performance Metric The comparison in Figure 9 shows how increasing the



162


number of transmit antennas (MISO), can actually improve the system performance. Using a BER threshold of 10-3, a gain of about 13 dB was observed in the MISO channel estimation than the SISO when LS and MMSE are considered. It is also noticed that when the values of SNR begin to go up, the rate at which the MMSE BER system performance improves is greater than that of the LS. Furthermore, the BER performance for the SISO MMSE outperformed the SISO LS by a gain of about 2.5 dB, while a BER performance of 5dB was experienced when the MISO MMSE and the MISO LS were compared.

But when the pilot ratio is reduced, (i.e. 1:7 and 1:15), errors in the channel estimates due to the reduction in the data available for interpolation were experienced, even with increase in SNR. Therefore, using a pilot-data ratio of 1:7 and 1:15 are insufficient to effectively interpolate the LS channel correctly. The same observation was made with the SISO system. 0

10

-1

10

-2

10 0

10

-3

10

-1

10

BER

L.S without OFDM MMSE without OFDM L.S with OFDM MMSE with OFDM

-4

10

-5

BER

10

-6

10

-2

10

MISO LS (Pilot-Data Ratio=1:1) Perfect Channel Knowldge MISO L.S (Pilot-Data Ratio=1:3) MISO LS (Pilot-Data Ratio=1:7) MISO LS (Pilot-Data Ratio=1:15)

-7

10

0

5

10

-3

15 SNR (dB)

20

25

30

10

Fig. 10. MISO Channel Estimation BER Comparison with different Pilot-Data Ratio -4

10

0

5

10

15 SNR (dB)

20

25

30

V.

Fig. 8. SISO Channel Estimation with and without OFDM using BER as a Performance Metric

The block type pilot arrangements, comb type pilot arrangements, the LS and MMSE channel estimation technique using MISO and SISO, with and without OFDM, have been modelled, simulated and compared, and conclusions have been made. The FFT has the best interpolation performance, followed by the spline, piecewise and linear. The MMSE is more resistant to noise, and outperforms the LS, with 10-15dB gain in SNR. The LS requires a high SNR to match the MMSEs performance. The good performance of the MMSE is achieved at the cost of complexity in the system, unlike the LS which is simple and less complex. Comparing the LS and MMSE using different pilot-data ratios, the LS, has an optimum pilot-data ratio of 1:3, while the MMSE has 1:7 because it minimizes the errors. Therefore the MMSE has a better spectral/bandwidth efficiency compared to the LS. Combining the various techniques with the OFDM plays a vital role in utilizing bandwidth and combating interference thereby improving systems performance. Increasing the number of antennas, improves the performance of the system, as observed in the MISO system outperforming the SISO system. These various techniques can be combined in different ways, depending on the requirements of the system.

0

10

MISO 2X1 L.S (1:3) MISO 2X1 MMSE (1:3) SISO L.S (1:3) SISO MMSE (1:3)

-1

10

-2

BER

10

-3

10

-4

10

-5

10

-6

10

0

5

10

15 SNR (dB)

20

25

Conclusion

30

Fig. 9. MISO and SISO Channel Estimation Comparison

IV.5. Comparison for MISO LS Channel Estimation Using BER as Performance Metric Alongside Varying Pilot-Data Ratio Figure 10 is used to describe the comparison of the BER value of the MISO LS channel estimation considering various pilot-data ratio. As observed from the graph, using a pilot-data ratio of 1:1 and 1:3 are used, would suffice for accurate interpolation of the pilot data to give a desirable channel estimate.

Acknowledgements This paper is sponsored by Covenant University, Ota, Ogun State, Nigeria.



163


References [1] [2]

[3]

[4]

[5]

[6]

[7] [8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

A. Sibille, C. Oestges, and A. Zanella, “MIMO from Theory to Implementation,” Academy Press, Burlington, USA, 2011. S. Yatawatta, A. P. Petropulu, and C. J. Graff, “Energy-Efficient Channel Estimation in MIMO Systems,” EURASIP Journal on Wireless Communications and Networking, vol. Article ID 27694, pp. 1–11, 2006. Y. S. Cho et al, “MIMO-OFDM Wireless Communications with MATLAB,” IEEE Press, John Wiley and Sons (Asia) Pte Ltd. 2010. K. Kwak et al, “New OFDM Channel Estimation with Dual-ICI Cancellation in Highly Mobile Channel,” IEEE Trans. Wireless Communications, vol. 9, no. 10, p. 3156, Oct. 2010 . H. Yang, “A road to future broadband wireless access: MIMOOFDM based air interface”, IEEE Commun. Mag., vol. 43, no. 1, pp. 53-60, Jan. 2005. H. Sampath, et al, “A fourth-generation MIMO-OFDM broadband wireless system: design, performance and Field trial results”, IEEE Communications Magazine, No. 9, pp. 143-149, Sep. 2002. K.-L. Du and M. N. S. Swamy, “Wireless Communication Systems”, Cambridge University Press, New York, 2010. A. Ghosh, D. R. Wolter, J. G. Andrews and R. Chen, “Broadband wireless access with WiMax/802.16: current performance bench marks and future potential”, IEEE communications magazine, vol. 43, pp. 129-136, 2005. D. Gesbert, M. Shafi, S. D. Shan, P. J. Smith and A. Naguib, “From theory to practice: an overview of MIMO space-time coded wireless systems”, IEEE Journal on Selected Areas in communications, vol. 21, pp. 281-302, 2003. Ashu Taneja “Review On Channel Estimation for MIMO-OFDM System Payal Arora” International Journal of Future Generation Communication and Networking Vol. 9, No. 5, pp. 189-196, 2016. Z. Wu, “MIMO-OFDM Communication Systems: Channel Estimation and Wireless Location”, PhD Dissertation, Electrical and Computer Engr., Louisiana State University and Agricultural and Mechanical College, May 2006. Lizhong Zheng, and David N. C. Tse, “Diversity and Multiplexing: A Fundamental Tradeoff in Multiple-Antenna Channels,” IEEE Transactions on Information Theory, Vol. 49, No. 5, May 2003. S. Coleri, M. Ergen, A. Puri, and A. Bahai, “Channel Estimation Techniques Based on Pilot Arrangement in OFDM Systems” IEEE Trans. Broadcasting, vol. 48, no. 3, pp 223-229, Sept. 2002. J. Rinne and M. Renfors, “Pilot spacing in orthogonal frequency division multiplexing systems on practical channels,” IEEE Trans. Consumer Electronics, vol. 42, no. 4, Nov. 1996. M. H. Hsieh and C. H. Wei, “Channel Estimation for OFDM Systems Based on Comb-Type Pilot Arrangement in Frequency Selective Fading Channels” IEEE Trans. Consumer Electronics, vol. 44, no. 1, pp. 217-225, Feb. 1998. J Okokpujie, K., Chukwu, E., Noma-Osaghae, E., Okokpujie, I., Novel Active Queue Management Scheme for Routers in Wireless Networks, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (1), pp. 52-61. doi: https://doi.org/10.15866/irecap.v8i1.13408 Etinosa Noma Osaghae, Kennedy Okokpujie, Charles Ndujiuba, Olatunji Okesola, Imhade P. Okokpujie, Epidemic Alert System: A Web-based Grassroots Model. International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018). http://www.iaescore.com/journals/index.php/IJECE/article/view/9 007 Kennedy Okokpujie, Olamilekan Shobayo, Etinosa NomaOsaghae, Okokpujie Imhade, Obinna Okoyeigbo, Realization of MPLS-Based VPN Network for Improved Qos Metrics. TELKOMNIKA (Telecommunication Computing Electronics and Control) 16, no. 5 (2018). http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/73 26. Okokpujie Kennedy O., Okoyeigbo Obinna, Okhaifoh J.E., Omoruyi Osemwegie, Nsikan Nkordeh. “Performance Analysis

[20]

[21]

[22]

[23]

[24]

[25]

and Modeling of MIMO Systems” International Journal of Applied Engineering Research Vol. 11, Number 23, pp. 1153711541, 2016. O. Okoyeigbo, K. Okokpujie, O. Omoruyi, N. Nkordeh “Comparative Analysis of Channel Estimation Techniques in SISO, MISO and MIMO Systems,” International Journal of Electronics and Telecommunications 2017, Vol. 63, NO. 3, PP. 299-304, 2017. Kota, P., Gaikwad, A., Analysis of Detection Methods for Space Frequency Block Code MIMO-OFDM System in Fast Fading Channels, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (7), pp. 635-640. doi:https://doi.org/10.15866/irecap.v7i7.13613 Agboje, O., Idowu-Bismark, O., Ibhaze, A., Comparative Analysis of Fast Fourier Transform and Discrete Wavelet Transform Based MIMO-OFDM, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (2), pp. 168-175. doi:https://doi.org/10.15866/irecap.v7i2.10803 Kota, P., Gaikwad, A., Fireflies Algorithm Based Optimal Scrambling to Reduce PAPR in SFBC Based MIMO-OFDM, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (7), pp. 626-634. doi:https://doi.org/10.15866/irecap.v7i7.13567 Okokpujie, K., Chukwu, E., Noma-Osaghae, E., Okokpujie, I., Novel Active Queue Management Scheme for Routers in Wireless Networks, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (1), pp. 52-61. doi:https://doi.org/10.15866/irecap.v8i1.13408 Amhaimar, L., Ahyoud, S., Asselman, A., An Efficient Combined Scheme of Proposed PAPR Reduction Approach and Digital Predistortion in MIMO-OFDM Systems, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (5), pp. 378-385. doi:https://doi.org/10.15866/irecap.v7i5.11190

Authors’ information Obinna Okoyeigbo has a Bachelors Degree in Electronic Engineering, and an MSc. in Communications Engineering, from The University of Manchester. He is a member of The Nigerian Society of Engineers, and a Registered Engineer with COREN. He has previously worked with Alcatel-Lucent, and is currently involved in research and lecturing at Covenant University. His research interests include; Next Generation Communication Systems 5G & Beyond, IoT, Channel Estimation, MIMO etc. He can be reached by email: [email protected] Kennedy Okokpujie holds a Bachelor of Engineering (B.Eng.) in Electrical and Electronics Engineering, Master of Science (M.Sc.) in Electrical and Electronics Engineering, Master of Engineering (M.Eng.) in Electronics and Telecommunication Engineering and Master of Business Administration (MBA). He is currently on his Ph.D in Information and Communication Engineering/ Lecturing with the Department of Electrical and Information Engineering at Covenant University, Ota, Ogun State, Nigeria. He is a member of the Nigeria Society of Engineers and the Institute of Electrical and Electronic Engineers (IEEE). His research areas of interest include Biometrics, Wireless Communication and Digital signal Processing. Contact him at: [email protected]



164


Dr. C. U. Ndujiuba holds a PhD in Electrical & Electronics Engineering from the University College London (University of London); Mastere Specialise (Masters with Specialisation)in Radio Communications from Ecole Superieure d’Electricite (SUPELEC) France; MSc in Electrical Engineering from the University of Lagos; BSc in Electronics & Communications Engineering from the London Metropolitan University. Dr. Ndujiuba is a Chartered Electronics Engineer (CEng) and a highly skilled wireless professional. He has more than 25 years RF, Microwave, Fixed-line (SDH and PDH), and PMR experience, with considerable international exposure. Dr. Ndujiuba has attended several conferences and published many technical papers in major professional journals. Prior to joining Covenant University Ota Nigeria in 2011, Dr. Ndujiuba was the Technical Director of Globe Trunk Ltd UK. His research interests include: Monolithic Microwave Integrated Circuits (MMIC), Active Filters, Ultra-Low Noise Amplifiers, Active Devices and Circuits, UWB Transmitter, Modelling & Simulation, Dielectric Resonator Antennas and Detection and Collision Avoidance of Unmanned Aerial Vehicles. Etinosa Noma-Osaghae is with the Department of Electrical and Information Engineering at Covenant University, Ota, Ogun State, Nigeria. He has a master’s degree in Electronic and Telecommunication. Information Theory, Digital Signal Processing and Wireless Communication are his research areas of interest. E-mail: [email protected] Shobayo Olamilekan had his first degree in Electrical and Computer Engineering from Federal University of Technology Minna, Nigeria in 2011. He later proceeded for his second degree in Computer and Networks Engineering from Sheffield Hallam University, UK in 2016. He has worked as Network Infrastructure Engineer at the National Institute of Information Technology (NIIT). He has also worked as a Lecturer and researcher in Covenant University Ota Nigeria, we he lectured Computer architecture, Networking and C programming. He his CCNA and CCNP certified and has vast experience in programming with the C language.



165


Unified Deterministic Model of Parallel and Distributed Computers P. Hanuliak, M. Hanuliak, I. Zelinka Abstract – Analytical models (standard, corrected) to the problematic of unified analytical modelling of parallel and distributed computers based on queuing theory [15]has been already published. This paper extends the published results describing non Markov (deterministic) analytical mode. This analytical model considers one M/D/m queuing theory system for every computing node to model its computing activities and another M/D/1 queuing theory system for each node’s communication channel. To illustrate the accuracy of the developed deterministic model, this paper presents, in its experimental part, the results and their comparison with the previous developed analytical models including the results of simulation model to estimate the measure of improvement. All achieved verified results allow to derive mixed analytical models using theoretically all possible combinations of M/M/m or M/D/m queuing theory systems for computing activities and M/M/1 or M/D/1 queuing theory systems for modelling communication channels. The developed non Markov analytical model that has been tested in this paper, using various input parameters, which could influence performance of actually dominant parallel systems (multiprocessor/multicore SMP, NOW, Grid and meta computer), could be interesting from the point of practical use. Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: Parallel Computer, Queuing Theory, Grid, Multiprocessor/ Multicore SMP, Network of Workstation (NOW), Modelling, Optimisation, Queuing Theory System, Probability Data Structures, System of Linear Equations (SLE)

Queuing theory symbols and laws λ Arrival rate at entrance to a queue m Number of identical servers in the queuing system ρ Traffic intensity (dimensionless coefficient of utilisation) ρ ≡ R = λ E(ts) Traffic intensity (0 < ρ < 1) of one service ρ = λ E(ts) / m Traffic intensity of m servicing servers q Random variable for the number of demands in a system at steady state w Random variable for the number of demands in a queue at steady state E(ts) The average service time of a server E(q) The average number of demands in a system at steady state E(w) The average number of demands in a queue at steady state E(tq) The average time spent in system (queue + servicing) at steady state E(tw) The average time spent in the queue at steady state Little laws E(q) = λ E(tq) E(w) = λ E(tw)

Nomenclature DPT GEM Grid FIFO IPC HPC LIFO Meta computer M/D/m, /M/m, M/D/1, M/M/1 Multiprocessor/ multicore SMP NOW – p RT SLE SMP ws

Destination probability tables Gauss elimination method High integration of NOW modules Queue for service on a first-come first-served basis Inter process communication High performance computing. Queue for service on a last-come first-served basis Massive parallel computer (Internet etc.) Queuing theory systems Multiprocessor/multicore symmetrical parallel computer Network of workstations Number of computing elements (size of parallel computer) Routing table System of linear equations Synchronous multiprocessor/ multicore parallel computer Workstation as a high performed computing node of NOW module



166

P. Hanuliak, M. Hanuliak, I. Zelinka

I.

powerful and flexible than the computing nodes of supercomputers [18].

Dominant Parallel Computers

The basic computing element in parallel and distributed computers is a processor or its simplified core version [1]. In actual dominant parallel and distributed computers it is better to use the term computing node. In sequential computes both terms are equal. But in modern multiprocessor/multicore SMP, it would be better to use the term computing node which consists of defined number of computing elements. The actual dominant parallel computers are [1]-[37]: • multiprocessor/multicore SMP computing nodes, • network of workstations (NOW), • high integration (network) of NOW modules named as Grid module. These parallel systems consist of high powerful computing nodes (sequential, multiprocessor/multicore SMP) in which the computing nodes themselves use various parallel principles. The typical implemented parallel principles are super scalar pipeline architecture of computing elements and multiprocessor/multicore SMP [8]. Such high powerful computing nodes (workstation) are used as the basic building components in network of coupled workstations (NOW) or of high integrated network of NOW modules named Grid module [37]. The massive extension of computer networks also caused their applied use for high performance computing (HPC). This important period named downsizing of used supercomputers (Cray, SGI, massive parallel computers) to more universal parallel computers based on connected powerful computing nodes (workstations) named NOW modules. The next period of parallel computers is going deeper into their architecture changes in order to guarantee high computation and memory capacity of suggested modular parallel computers named Grids. Generally, the ideas of the Grid have to be close to the definition of meta computer [21]. The meta computer is defined as an highly integrated network of computer networks where all the cooperated networks allow to share a massive number of embedded resources (processors/cores, memory modules, I/O channels etc.) controlled by one integrated operation system (massive virtual parallel computer).

II.

II.2.

Multiprocessor/multicore symmetrical parallel computer (SMP) consists of a multiple use of equal computing elements (processors, cores) which are embedded on the same motherboard. The typical SMP characteristics are: • each processor or core of SMP system can access at least some part of shared main memory modules [1], • I/O channels/devices are used in a shared way. Some I/O channels/devices may be used only by defined individual computing nodes (local I/O channels/ devices) [32], • integrated operation system controls all the needed cooperation of the shared SMP resources [19]. A typical NOW module is illustrated in Fig. 1. The used computing nodes PCi (workstations) could be powerful single or multiprocessor/multicore SMP computing nodes. One or more PCi could be any supercomputer [34]. Parallel Applications Sequential Applications

Parallel Programming Environments

Cluster Supporting SW (Midlleware) PC/Workstation

PC/Workstation

PC/Workstation

C o m n . D r iv e r s (S W )

C o m n . D r ive r s (S W )

C o m n . D r iv e r s (S W )

N e tw o rk c a rd (H W )



High Speed Network/Switch

Fig. 1. Typical architecture of NOW module

II.3.

Grid Systems

Grid system represents a new form of using, organising and managing of computing node’s resources with emphasis to their deep sharing. Any massive Grid (meta computer) can be described with its typical characteristic: • massive network of integrated networks consisting of shared computing nodes and their shared resources. In order to connect computing nodes, high speed networks are used (Myrinet, Infiniband, Quadrics, 10G Ethernet). All the used resources are controlled by central operation system which creates the real illusion of a virtual powerful supercomputer; • provides functions of meta computing by a shared computing environment, which gives all connected users access to its all shared resources; • offers the services of meta computations based on distributed parallel computing or remote computing from individual user computing node (workstation).

Architectures of Parallel Computers II.1.

Multiprocessor/Multicore Symmetrical Parallel Computer

Network of Workstations

Use of coupled NOW modules has become very effective also in solving large complex problems. Such trends were driven by its cost effectiveness compared to the previous massive multiprocessor computers (supercomputer) with their tightly coupled processors (computing elements) and memory modules. The loosely coupled NOW modules have quickly become widely accepted also in HPC (high performance computing). Each computing node of NOW module was treated similarly as a computing node of any supercomputer. But the computing nodes of NOW module were more Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved


167


Both of them could yield transparent analytic solutions based on variables in form of used technical parameters.

Grid resources (pool)

Users

Managment (administrator)

III.2. Queuing Theory Processor 1

Processor n

Data 1

Data i

Storage 1

Storage j

I/O 1

The basic idea behind queuing theory models for computing node analysis is to represent modelled components as a coupled network of servers and waiting queues. In this network any server represents modelled devices which provide some time consuming activities on the network flow of input demands. Processing unit (processor, core, CPU), I/O device, communication channel, memory module etc. can be considered as a server. Every time consuming activity (latency) can be modelled as one queuing theory system. A waiting queue represents a place where input demands queue for some service of a server. Applied queuing theory model of one or more coupled queuing theory systems. In such queuing theory network demands or communicated data blocks (packets) or anything else that requires the sort of server processing, are entering to the network of queuing theory systems. In this network, input demands arrive at some rate, they queue for service on some service order (FIFO, LIFO etc.), they receive service, output queuing theory system is given. The described form of behaviour, with demands entering and leaving the modelled network of queuing theory systems is called open queuing theory model [33].

I/O k

S haring Mechanisms

Fig. 2. Architecture of Grid module

As an existing example of a meta computer, Internet can be considered as a high integrated massive network of its coupled networks. The virtual pool of shared resources of meta computer is dynamic, because the used resources could be added and withdrawn according to owner’s will, and their workload could be changed. Typical number of resources of a pool could be thousands or even more. HPC parallel environment assumes a pool of computing elements (computing nodes) from which the virtual parallel computer is formed [11], [36]. This pool consists of computing nodes (single, SMP, and supercomputers), provided so that the users have access to all shared resources. Created virtual pool of used computing nodes we consider as static and its size varies from 10 to 100 computing nodes. We can characterise the existed parallel environments as follows: • HPC computing environment provides the maximum of system performance [13], • Grid is optimised to guarantee the maximum of shared resource capacities [24].

III.3. Theory of Complexity Theory of complexity is extensively used in modelling of algorithms (sequential, parallel) [10]. It helps us to derive searched analytical evaluation expressions such as parallel execution time, speed-up, efficiency and isoefficiency.

III. Modelling Methods For modelling of connected computing nodes, we can use developed modelling methods. These modelling methods are: • analytical:  queuing theory [5], [30],  order analysis [2], [9],  Petri nets [4], [17]; • simulation [6], [22]; • experimental:  benchmarks [23], [20],  direct measuring [7], [35].

IV.

Network of Queuing Theory Systems

It is very useful to apply queuing theory results to connected computing nodes. The suitable results are the ones of an open network of queuing theory systems which correspond to communication networks in parallel computers. In [15], queuing theory results have been applied to a network of computing nodes based on M/M/m and M/M/1 (Markov) queuing theory systems. In this article, the non Markov queuing theory systems M/D/m and M/D/1 are used.

III.1. Analytical Modelling Methods Analytical methods can provide exact solutions very quickly, but the accuracy of any developed analytical model has to be verified with other modelling methods (simulation, experimental) [27]. Applied mathematical methods known as queuing theory and theory of complexity belong to the potential analytical methods.

V. V.1.

Non Markov Model M / D / 1 Queue Theory System

In M/D/1 queuing theory system, the parameters of the traffic intensity  and of the average service time are given as follows:



168




 1 

E  ts  

1  constant 

VI.1. Modelling of the NOW Module

(1)

Any communication network of NOW module can be represented by a weighted graph where its computing nodes are represented as graph’s nodes. Data units arrive at random at a source computing node and follow a defined route towards destination computing node. Data lengths of cooperated decomposed parallel processes will be considered as random variable following the deterministic distribution. Communicated data units are sent independently via communication network of coupled computing nodes to a destination computing node. The queue of incoming data units is served to a First-in First-out (FIFO) order. The defined communication network generally represents oriented graph consisted of U-computing nodes according to Fig. 3 where: • 1, 2, ..., U represent total external intensities of incoming data stream to the given i-th computing node; • rij are the relation probabilities from computing node i to the neighbouring j-th computing node for i ≠ j, i=j = 1,...U. The output probability of demands from i-th computing node to other j-th computing nodes is given as: • ui – number of used communication channels at ith computing node • U – number of computing nodes (workstations) • β1, β2, ..., βU are the correspondent whole external outputs of data units from computing nodes.

Using the previous relations, the average number of demands in the queue E(w) and the average number of demands in this queuing theory system E(tw) are: E ( w) 

2  (2   ) E (q )  2 (1   ) 2 (1   )

(2)

The waiting time in the queue for each demand E(tw) and the end-to-end delay (waiting plus servicing) for each demand E(tq) are:

E (tw ) 

V.2.

3  2  E (tq )  2  (1   ) 2  (1   )

(3)

M/D/m Queuing Theory System

In M/D/m queuing theory system parameter of traffic intensity  and the average service time are given as follows:



 1  1 E (ts )   constant  m 

(4)

The average number of demands in a queue will be defined by following the approximate relation (5):

E (tw )   45 m  2 E (tw )  M / D /1   1  (1  i ) (m  1) 16 i m E (tw )  M / M /1   E (t ) M / M / m   w    The further parameters as E(tq), E(tw) and E(q) can be derived from the Little laws.

VI.

NOW Module

NOW module builds a basic modular element of any high integrated parallel system (Grid, meta computer). The computing nodes of NOW module (workstations) are based on multiprocessor/multicore SMP parallel computer and powerful sequential processors. Data exchange in modelled servicing network represents all needed communication as follows: • data exchange among parallel processes named as inter process communication (IPC), • control communication. In principle, every constraint on structure of servicing network (communication network) is assumed. Then, it is possible to model each computing nodes (workstation) with two dominant delays: • computation execution time (computing latency), • communication latency of transmission channels [25], [28].

Fig. 3. Adjusted abstract model of network of U- computing nodes

The suggested analytical model corresponds in queuing theory to group of open servicing network models. In this group, there is the dominant existence of an external input and an external output of demands to and from servicing (communication) network [26], [31]. Demands enter externally the given i-th computing node (from outside of servicing network) with Poisson arrival distribution given by intensity i demands per second.



169


ρi - the computing node utilisation at i-th computing node for all used computing elements (processors, cores) • mi - number of used SMP processors/cores at i-th computing node • E(tw) (M/D/1), E(tw) (M/M/1) and E(tw) (M/M/m) are the average queue delays for the correspondent M/D/1, M/M/1 and M/M/m queuing theory systems. The approximation formulae from the points has been selected: • simple computation, • if the number of used computing node processors equals one the relation gives the exact solution of E(tw) (M/D/1) queuing theory system, • if the number of computing elements mi >1 the chosen approximation produces the error under 1%.

After servicing at i-th computing node, a demand goes to the next j-th computing node with a probability rij in an internal way (within servicing network). The first queue of each computing node is serviced by one or more SMP processors. Being serviced at a given computing node, the serviced demands continue as follows: • demands will be routed to another computing node of the communication network by being placed to the one of the used computing node’s communication channels, • demands are at the destination computing nodes and they output communication network (external outputs).

•

VI.2. Deterministic Analytical Model of NOW The previous developed analytical model, shown in [15], assumes incoming demands as Poisson arrival stream with the exponential inter arrival time distribution among entering arrivals to the communication channels of the given computing node. The used ideas of the previous model are based on the presumption of computing node independence which allows to disassemble any servicing network to its independent computing nodes. On this basis, we have modelled i-th computing node using M/M/m queuing theory system for computing node activities and the M/M1 queuing theory system to model every used communication channel. But both the assumed conditions have not satisfied neither each input stream, nor any architecture of computing nodes, nor the character of computing node’s service time distribution. These facts may contribute to imprecise results. To take into account the mentioned real facts, it has been developed the deterministic analytical model based on: • non Markov M/D/m queuing theory system to model in a more real way the computation activities in every node of NOW module, • M/D/1 queuing theory system to model each used communication channel in i-th computing node. Based on M/D/1 queuing theory system, we are able to improve not real exponential distribution of inputs to j-th communication channels. These changes could contribute to clarify the behaviour analysis of the NOW network for the typical communication activities and for the variable input loads. To find the average queue delay E(tw) for M/D/m queuing theory system, it can be used the approximation formula [30] as follows (6):

VI.3. NOW Model Based on M/D/m and M/D/1 Assumed that U is a number of computing nodes of a whole communication network, for each i-th computing node according to Fig. 4, their parameters are defined as follows: • i represents the average number of demands to the ith computing node (the sum of its external and u

internal inputs i 

 ij  i ), i 1

• • •

ij represents the input flow to the j-th communication channel, βi represents the external output from i-th computing node, U     i represents the sum of the individual external i 1

intensities, • E(tq)i represents the average servicing time in the M/D/m queuing theory system (waiting and servicing) of the i-th computing node • E(tq)ij represents the average servicing time of the j-th M/D/1 queuing theory system (waiting and servicing) which corresponds to the delay of the j-th communication channel. The whole waiting time of demands in the NOW module E(tq)now will be the sum of E(tq)i for the all computing nodes ui (i=1, 2, … U) as follows: U

E (tq ) now 

 E (tq )i

(7)

i 1

The end-to-end delay of NOV module E(tq)now can be defined with the following equation:

E (tw ) ( M / D / mi )   45mi  2 E (tw ) ( M / D /1)   1  (1  i ) (mi  1)  16 i mi E (tw ) ( M / M /1)   E (t )( M / M / m )  w i  

E (tq ) now 

1U    i  E (tq )i    i 1  

The first part in which: Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved



i  E (tq )i 

ui



 ij  E (tq )ij  j 1

(8)



defines for defined


170


deterministic model of NOW module the contribution of the queuing theory system M/D/m of i-th computing

destination computing node. The destination probabilities table (DPT) contains a probability for i, j item, that communicated data, which rise in i-th computing node, are destined for j-th computing node. DPT has U × U elements which are formally defined as DPT (i, j) elements. DPT (i, j) item determinates which portion from external input γi should be transmitted from i-th computing node to j-th computing node. It has been noticed as i DTP(i, j). Also, the path through communication network has been defined with the sequence (x1, x2, ... , xU) in which the conditions are true only if: • physical communication path, which connects xk and xk1 , k  1, 2, ... , m -1 ,

ui

 ij  E (tq )ij node

and

second one

j 1

the delay  contribution of all communication channels ui in i-th computing node to the end-to-end delay E(tq)now of NOW module. We have defined to model the communication channels as M/D/1 queuing theory systems.

•

x j and xk ,  j , k j  k (they are no loops).

Then the authors have defined the path as record ”path (jk, i)” as the ordered sequence of those computing nodes, which are on the connection from the j-th computing node to the k-computing node and they go step by step via i-th computing nodes. Finally, the sum: Fig. 4. The precise mathematical model of i-th computing node

U



External input demands enter the servicing network at i-computing node with the Poisson stream distribution with rate γi. Every computing node has a multiple number of m servers (SMP with m processors, for m = 1 sequential computer). The service times are determined with their constant value 1/i, (i = l, …, U). When a demand leaves i-th computing node it goes to coupled computing node j with probability rij. Now, supposing that i is the average value of all arrivals at computing node i, consisting of inputs from external (outside of servicing network) and inputs from other connected computing nodes (internal), if analysed network is in steady state, i is also the rate of demands leaving i-th computing node including external outputs at given node. Therefore, it is possible to formulate the set of flow balance equations:

has been defined over all k-destination computing nodes for which i-th computing nodes are on the path from the source j-th computing node. Using the previous sum the relation for the input flow to i-th computing node is given as follows: The internal input flow: U

U

  j  DTP( j, k )

(11)

j 1 k 1

The searched total input flow to i-th computing node is: U

U

i   i    i  DTP ( j , k )

U

i   i   i rij for i  j  1, 2, , U

(10)

k path ( j  k ,i )

(12)

j 1 k 1

(9)

i 1

where i  j , k  path ( j  k , i ) . Let xi be the fixed processing time of the computing element (processor, core) of i-th computing node and E(tw)i (M/D/m) the average waiting time in the i-th computing node. Then the mean waiting time in i-th computing node E(tw)i (M/D/mi) is given as:

To compute i the probability data structures can be used: • routing table (RT), • destination probability tables (DPT). VI.4. Probability Data Structures The logical way from i-th computing node j-th destination computing node in communication network has been defined in this paper. The defined logical ways are the elements of the deterministic routing table (RT). Then the item RT (i, j), with indexes i≠j=1, ..., U (for i=j=0), defines the first neighbouring computing node on the route from the i-th computing node to the j-th

E (tw ) ( M / D / mi )  1  (1  i ) (mi  1)      45 mi  2 E (tw ) ( M / D /1)     16 i mi E (tw ) ( M / M /1)    E (tw ) ( M / M / mi ) 


(13)


171


where the used parameter ρi represents the utilisation of i-th computing node as shown below:

i 

i xi mi

This parameter defines which part of all incoming inputs λi (external + internal) to i-th computing node, will be routed to j-th communication channel within this computing node. We are able to determine λij from λi using the defined probability tables RT(i, j). The E(tw)ij (M/D/1) is given as follows:

(14)

The expressions of the mean waiting times of the used queuing theory systems in the approximation formula are:

i xi E (tw )i ( M / D /1)  2(1  i )  x E (tw )i ( M / M /1)  i i 1  i

E (tw )ij (M /D/1) 

(15)

E (tq )ij  E (tw )ij (M /D/1)   xij 

(16)

mi 1

 j 0

m    j  i i j! 

(17)







  x ij ij



 ij  1     xij  j 1 ij   Results

In Fig. 5, it can be seen a summary of the results of the end-to-end delays of the experimental communication network consisting of 5–computing nodes as given below: • the basic model based on M/M/m (for m=1 M/M/1) systems [1], • the corrected model with M/M/1corr [1] , • the non Markov deterministic model on M/D/m (for m=1 M/M/1), • the simulation model. To vary the computer node utilisation, the external flow has been changed in the same way for every used computing node. The summary comparison of the relative errors of all developed models related to simulation results is illustrated in Fig. 6. It can be seen that the best results are given by the developed non Markov model. Otherwise, the standard model based on M/M/m, in which if the computer node utilisation ρi varies from 0,2 to 0,9 (20 – 90%) has given the worst results: the relative error varies from 6 to 24 %. This is due to the influences of communication queue latencies and the not real character exponential input character to communication channels. These errors are very imprecise in the cases of high computing node utilisation (from 60 – to 90%).

(18)

defines the deterministic servicing time for j-th communication channel of i-th computing node, the parameter ρij, which defines the utilisation of the j-th communication channel of i-th computing node, results as:

Sij

 

VII.

The mean waiting time in any i, j communication channel (j-th communication channel of i-th computing node) corresponds to the suggested M/D/1 system. If xij

ij xij

1U  i E (tw )i (M /D/mi )  xi    i 1 ui

By substituting relations for ρi, E(tw)i (M/D/1), E(tw)i (M/M/1) and E(tw)i (M/M/mi) in the relation for E(tw)i (M/D/mi), it is possible to define E(tw)i (M/D/m). The final mean average latency of computation activities in ith computing node is given as the sum of mean waiting time E(tw)i (M/D/m) and the defined value xi (fixed processing time) as follows:

ij 

1  ij 

(21)

 xij

E (tq ) now 

xi  mi  i mi  mi  mi ! (1  i )  1  i  

E (tq )i  E (tw )i (M /D/mi )  xi

ij  xij

If the parameters E(tq)i and E(tq)ij are substituted with the derived expression for E(tq)now, it is possible to achieve the final expressions of the whole mean latency (end-to-end) of NOW model as follows (22):

i



(20)

(1  ij )

The total average delay value E(tq)ij is:

E (tw )i  M / M / mi  

 mi  i m mi ! 1  i 

ij  xij

(19)

where the parameter Sij defines the transmission speed of the j-th communication channel in i-th computing node. Because transmission speed of data units (words, fix block of data words) is constant, it has been assumed, for simplicity, that Sij =1. The input flow to the j-th communication channel of i-th computing node has been defined as parameter λij. Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved


172


VIII. Conclusion and Perspectives The used implementations of various forms of parallel principles have created the stable conditions to apply analytical evaluating methods to dominant parallel computers (multiprocessor/multicore SMP, NOW, Grid), for a long time. Therefore, it is possible to model connected computing nodes based on multiprocessor/multicore SMP and powerful sequential computers (workstations). In applied use of analytical modelling based on queuing theory, there is a chance to model also massive NOW and Grid modules or perspective extreme massive parallel computers as massive Grids or meta computer as well. In summary, all the developed analytical models could be applied to modelling and evaluation of connected computing nodes as follows: • NOW module based on powerful sequential computer and multiprocessors/multicores SMP (workstations) • Grid module (network of NOW modules), • meta computers (massive network of Grid modules). In short, it is possible apply the developed analytical models for all mentioned forms of parallel computers with coupled computing node based from one communication networks to high integrated Grid modules, without raising the computation time. The potential limiting factors of the developed non Markov could be the memory space complexity to implement RT(i, j) and the DPT(i, j) probability tables. These probability tables have complexity space of O(n2) memory cells. Furthermore, this aspect could limit communication network analysis to the numbers of U computing nodes about 200 - 500 for actual considered multiprocessor / multicore SMP as computing nodes; but the architecture of modern parallel computers consists on a hierarchical modular architecture (NOW modules, Grid modules). In relation to this, the mentioned values are adequate enough to model real communication networks of parallel computers on the superposition principle. In future research work we will be looking for other unified analytical models (parallel and distributed computing) to optimise input load balancing [14], IPC communications [16], effective (optimised) communication networks [12], performance prediction in massive parallel architectures (Grid, meta computer), waiting overhead times caused by technically limited shared resources in unified parallel and distributed computing [29]. In relation to it we would like to analyse some of actually existing complex problems in unified parallel and distributing computing as follows: • influence of dominant parallel computer architectures (NOW, Grid, meta computer) to parallel execution time of PA (shared memory, distributed memory, mix of them), • developing analytical models for more precise modelling of technically limited capacities of shared resources (buffers, communication channels, memory

Fig. 5. The final comparison of the developed models

Fig. 6. The summary comparison of relative errors

This indicates very important critical fact because there is a bad need of accurate results in the range of higher utilisation (from 0,6 – to 0,9) for practical use. On the contrary, the best non Markov model based on M/D/m have produced, in every case, a relative error not greater than 7 %. In this paper’s experiments, the same communication speeds in every computing node’s communication channel has been assumed. If the used communication channels do not have the same communication speeds, it is necessary to perform statistical evaluation of average communication speed. In all the considered models (analytical, simulation), the performed experiments have proved that, when the i-th computing node utilisation ρi decreases, the end-to-end delay E(tq)now decreases too; on the contrary, the decrease of node’s transmission channel speed raises communication channel utilisation and this implies that communicated data wait longer in communication channel. This increases the whole i-th computing node latency and the end-to-end-latency in modelled NOW module. The previous developed models based on M/M/m and M/M/1 corrected queuing theory systems [15] are based on solution of system linear equations (SLE) to solve λi. To solve SLE, several parallel algorithms use GEM (Gauss elimination method). But GEM has its computation complexity O(n3) floating points multiplications and a similar number of additions [3]. However, these values are adequate enough to model real communication networks with their high modular structure.



173


• •

[21] Kirk D. B., Hwu W. W., Programming massively parallel processors (Morgan Kaufmann, 2010, Pages 280). [22] Kostin A., Ilushechkina L., Modelling and simulation of distributed systems (Imperial College Press, 2010, Pages 440). [23] Kratky M., Chovanec P. and Kratky P., Processing of multidim. range query using SIMD instructions, Comm in Comp. and Inf. Science, Springer, pp. 223-237, 2011, Germany. [24] Kshemkalyani A. D., Singhal M., Distributed Computing (Cambridge University Press, 2011, Pages 756). [25] Kushilevitz E., Nissan N., Communication Complexity (Cambridge University Press, 2006, Pages 208). [26] Le Boudec Jean-Yves, Performance evaluation of computer and communication systems, (CRC Press, 2011, Pages 300). [27] McCabe J., D., Network analysis, architecture, and design (Morgan Kaufmann, 2010, Pages 496). [28] Meerschaert M., Mathematical modeling (4-th ed.) (Elsevier, 2013, Pages 384). [29] Misra S., Misra Ch. S., Woungang I., Selected topics in communication network and distributed systems (Imperial college press, 2010, Pages 808). [30] Natarajan G., Analysis of Queues: Methods and Applications (CRC Press, 2012, Pages 802) [31] Peterson L. L., Davie B. C., Computer networks – a system approach (Morgan Kaufmann, 2011, Pages 920). [32] Patterson D. A., Hennessy J. L., Computer Organization and Design (4th edition) (Morgan Kaufmann, 2011, Pages 914). [33] Riano l., McGinity T. M., Quantifying the role of complexity in a system’s performance, Evolving Systems, Springer Verlag, 2011, Pages 189 – 198. [34] Resch M. M., Supercomputers in Grids, Int. J. of Grid and HPC, No.1, 2009, Pages 1-9 [35] Tullis Thomas, Albert William, Measuring the User Experience Collecting, Analyzing, and Presenting Usability Metrics (Morgan Kaufmann, 2013, Pages 320). [36] Zhuge H., The Knowledge Grid, (Imperial College Press, 2011, Pages 260). [37] Wang L., Jie Wei., Chen J., Grid Computing: Infrastructure, Service, and Application (CRC Press, 2009).

modules, I/O channels etc.) in unified analytical models (parallel computing, distributed computing), influence of routing (fixed, adaptive) in communication networks of computing nodes, analysing the role of the assumed independence condition, if we are looking for higher moments of overhead latencies in unified analytical models (IPC communication, synchronisation, parallelisation, architecture of parallel computers etc.).

References [1]

[2] [3]

[4]

[5] [6]

[7]

[8]

[9] [10] [11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19] [20]

Abderazek A. B., Multicore systems on-chip – Practical Software/Hardware design (Imperial college press, 2010, Pages 200). Arora S., Barak B., Computational complexity - A modern Approach, (Cambridge University Press, 2009, Pages 578). Bronson R., Costa G. B., Saccoman J. T., Linear Algebra Algorithms, Applications, and Techniques 3rd ed. (Elsevier Science & Technology, 2014, Pages 536). Coulouris G., Dollimore J., Kindberg T., Distributed Systems – Concepts and Design (5-th ed.), (Addison Wesley, 2011, Pages 800). Dattatreya G. R., Performance analysis of queuing and computer network (University of Texas, Dallas, 2008, Pages 472). Dirgová Luptáková, I., Šimon, M., Huraj, L., & Pospíchal, J., Neural Gas Clustering Adapted for Given Size of Clusters, Mathematical Problems in Engineering, Vol. 2016, 2016, Hindawi, USA. Dubhash D. P., Panconesi A., Concentration of measure for the analysis of randomised algorithms (Cambridge University Press, 2009). Dubois M., Annavaram M., Stenstrom P., Parallel Computer Organisation and Design (Cambridge University Press, 2012, Pages 560). Goldreich O., Computational complexity (Cambridge University Press, 2010, Pages 632). Goldreich O., P, NP and NPC (Cambridge University Press, 2010, Pages 214). Hager G., Wellein G., Introduction to High Performance Computing for Scientists and Engineers (CRC Press, 2010, Pages 356) Hanuliak, P., Hanuliak, M., Optimisation of Communication Complexity in Parallel Computing, (2016) International Review on Computers and Software (IRECOS), 11 (2), pp. 109-115. doi:https://doi.org/10.15866/irecos.v11i2.8471 Hanuliak J., Hanuliak I., To performance evaluation of distributed parallel algorithms, Kybernetes, Volume 34, (No. 9/10), 2005, Pages 1633-1650. Hanuliak, P., Hanuliak, M., Modelling of Communication Complexity in Computers, (2016) International Journal on Communications Antenna and Propagation (IRECAP), 6 (2), pp. 68-81. doi:https://doi.org/10.15866/irecap.v6i2.8444 Hanuliak, P., Hanuliak, M., Unique Analytical Model of Parallel Computers, (2016) International Review on Modelling and Simulations (IREMOS), 9 (4), pp. 246-255. doi:https://doi.org/10.15866/iremos.v9i4.9716 Hanuliak J., Modeling of communication complexity in parallel computing, American Journal of Networks and Communication, Science PG, Volume 3, (Special Issue 1), 2014, Pages 29-42. Harchol Balter Mor, Performance modelling and design of computer systems (Cambridge University Press, 2013, Pages 576). Hennessy J. l., Patterson D. A., Computer architecture – a quantitative approach (5-th ed.) (Morgan Kaufmann, 2011, Pages 856). Hwang K. and coll., Distributed and Parallel Computing (Morgan Kaufmann, 2011, Pages 472). John L. K., Eeckhout L., Performance evaluation and benchmarking (CRC Press, 2005).

Authors’ information VSB-Technical University of Ostrava, 17. listopadu 15/2172, 708 33 Ostrava – Poruba, Czech Republic. Peter Hanuliak received PhD in 2007 at University of Zilina, Faculty of Management Science and Informatics in Zilina, Slovakia. Since 2008 he takes part in staff of lectors at The Dubnica Technical Institute in Dubnica nad Vahom, Slovakia. Currently, he is cooperating with the Technical University of Ostrava. He published over 50 papers in reviewed international scientific journals included current articles and five monographs (USA, United Kingdom, Czech Republic and Slovakia). His main research interests are complex (including overhead latencies) performance modelling, optimisation and prediction in parallel algorithms with shared and distributed memory as well. Michal Hanuliak received PhD in 2008 at University of Zilina, Faculty of Management Science and Informatics in Zilina, Slovakia. In 2014 he joined the lector staff at The Dubnica Technical Institute in Dubnica nad Vahom, Slovakia. Currently, he is cooperating with the Technical University of Ostrava. He published over 40 papers in reviewed international scientific journals included current articles and four monographs (USA, United Kingdom, Czech Republic and Slovakia). His main research interests are modelling, optimisation and prediction of parallel computer performance and their applications as well.



174


Ivan Zelinka graduated consecutively from the Technical University in Brno (1995 – MSc.), the UTB in Zlin (2001 – PhD.) and again from the Technical University in Brno (2004 – assoc. prof.) and VSB-TU (2010 - professor). He is the author of numerous journal articles including current journals as well as books in Czech and the English language. Currently, he is the head of the Department of Applied Informatics and throughout his career he has supervised numerous MSc. and Bc. diploma theses in addition to his role of supervising doctoral students, including students from abroad. His main research interests are modelling, optimisation focused on unconventional computing, bioinspired algorithms, soft computing in general and complex systems and the applications of all mentioned scientific fields as well.



175


The Influence of Distributing the Conveyor Suspensions with Suspended Belt and Distributed Drive on Its Main Technical Characteristics Alexander V. Lagerev, Evgeniy N. Tolkachev, Igor A. Lagerev Abstract – This research goal is to determine the rational number of drive and non-drive suspensions on a conveyor track with suspended belt and distributed drive. The article provides a methodology to calculate the conveyor main technical characteristics. Based on the previously developed mathematical model of the conveyor with suspended belt and distributed drive, a series of computations is performed and the results of changes in the conveyor main technical characteristics are shown, varying the number of suspensions types. Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: Belt-Type Conveyor, Dynamics Modeling, Suspension, Suspended Belt, Distributed Drive, Rational Parameters

Nomenclature 0 ξ x

B c Exy

Thickness of the traction frame spacer Damping index Normal stress of tension and compression in the belt longitudinal direction Normal bending stress in the belt longitudinal direction Normal bending stress in the belt transverse direction Normal bending stress in the belt transverse direction measured along the bottom Normal bending stress in the belt transverse direction measured along the edge surface Normal tensile stress in the belt longitudinal direction Angular velocity of the drive suspension drive roller Cross-sectional area of the traction frame in the conveyor belt longitudinal direction Cross-sectional area of the traction frame in the conveyor belt transverse direction Conveyor belt width Elastic element resistance coefficient Belt elasticity modulus in longitudinal direction

E yz

Belt elasticity modulus in transverse direction

Fd

Damping force

Fb x

Force acting in the belt longitudinal direction

b  xy

σ byz

σ bz

 ze

 zs dr Ab xz

L% M dr mdr,n

N ndr

T v xdr

y ymax

The distance between the points of the belt suspension in ratio to its width The driving torque Reduced mass of the drive and non-drive suspension Power capacity The number of non-drive suspensions on the slideway bearing The force applied to the suspension from the side of the belt attachment lug Conveying speed Linear acceleration at a tangent to the slideway bearing Distance from the bending belt fiber to the neutral fiber Width of the traction frame

I.

Introduction

Fe

Elastic force

hp

Pitch along the suspension path

i0

The number of spacers in the traction frame Belt curvature

High productivity, continuous cargo flow and automatized control conditioned the wide use of belt conveyors in mining, metallurgic, building, chemical and in other branches of industry in the Russian Federation However, despite being conveyor transport one of the most economically-efficient means of materials transportation [1]-[4], the critical weaknesses of traditional belt conveyors which are hard to be eliminated [5]-[7] led to a lot of mining, metallurgic and other companies starting to use railway and road transportation means, which are less productive but more reliable and more workable in mining and climatic conditions [8], [9]. The belt conveyors most serious operational shortcomings are conditioned by the interaction between



Ab yz

k

176

Alexander V. Lagerev, Evgeniy N. Tolkachev, Igor A. Lagerev

the moving load-carrying belt and the stationary roller bearings. In order to eliminate the key factor reducing the technical and economic effectiveness of using belt conveyors, a group of Russian scientists worked out, researched and implemented the design of a new type of non-spillable conveyor with suspended belt [9]-[16]. The conveyor with suspended belt is, actually, a hybrid of the traditional belt conveyor and rail transport. Its main feature is that stationary roller bearing units are not present all along the whole conveyor track, and the loadcarrying belt is held in a suspended position by means of suspensions fixed along the edge surface and moving together with the belt along the infinitely closed slideway bearings [8], [14]. In this type of conveyor there are no spillage, no dusting and no cargo crushing along the whole track; in this case, the transportation energy output is reduced up to 1.5 times, the belt service life is 1.5-2 times increased, the productivity is increased by 30% and the belt width is preserved. The above advantages are confirmed by the operational experience in the manufacturing environment. [8]-[11], [17]. Nevertheless, the conveyor with suspended belt and the stationary lumped drive has certain limitations as to the maximum track length. The frictional way of traction transfer from the belt to the pulley requires its considerable tension; however, the presence of rigid slideway bearings does not allow to realize the kinematic scheme with the multi-pulley drive and limits the top speed of the tensionioner [18], [19]. Therefore, in recent years, a group of scientists have been conducting a research and they have been some developing options to construct a conveyor with suspended belt and distributed drive (Fig. 1). Works [18], [20]-[23] feature a more detailed description of this construction. It should be noted that the main distinctive features of the kinematic scheme and the construction of such conveyor is the drive that is implemented according to the distributed circuit by equipping a number of suspensions with an individual drive mechanisms (with gear motor drives in particular), the return drive and tension ends being eliminated from

the construction. This technical solution not only contributes to the reduction of metal consumption and overall dimensions of the conveyor terminal sections, but also has all the possibilities to reduce the transportation energy consumption, increasing the belt service life and the conveyor reliability. The most important and responsible stage in the process of designing modern technical systems largely determining the operability, technical level, quality and efficiency of the designed machine is the one of scientific and technological research based on models. It allows to predict the basic properties of the machine and to determine the rational design and operational parameters. Previously, the authors developed and presented the mathematical models of the conveyor with suspended belt and distributed drive taking into account the parameters of the vertically-closed path, suspensions design features, the mechanic characteristics of the independent drives built into the suspensions, inertial and power characteristics of the structural elements, as well as the longitudinal deformation of the load-carrying belt. These models allow to calculate the most important dynamic characteristics [24]-[26]. The present article is related to the series of computations on the basis of the elaborated mathematical model and dedicated to the issue of finding the rational number of suspensions in the conveyor with suspended belt and distributed drive. This assignment is particularly relevant, since a different number of suspensions, on the one hand, determines the interval of their placement along the track, and consequently, the unit load on the suspensions elements from the load carrying belt, and, on the other hand, the drive and idle suspensions ratio determine the power of suspensions drives. Together, these factors influence the conveyor total capacity and the transportation speed. Besides, the stated features of the conveyor are decisive in matters of increasing the energy effectiveness and the conveyor performance.

Fig. 1. Design of the conveyor with the suspended belt and distributed drive



177


II.

expensive and responsible element. We will also consider the securing of minimal equivalent stress in the dangerous points of the belt section and the amplitude of their changes along the entire conveyor track as a criterion of the conveyor rational work. Under the influence of forces applied by the moving suspensions in the load-carrying belt longitudinal direction there appear normal tensile and compression stresses which are calculated as follows:

Main Technical Characteristics Computation

In the course of numerical integration of the differential equation system describing the dynamics of the conveyor with suspended belt and distributed drive, linear and angular displacements are determined, as well as suspensions velocity, too. The values obtained are used in the process of calculating the conveyor technical characteristics. Thus, one of the most important technical characteristics determining the conveying machinery energy consumption is the power used for the cargo transportation. The conveyor maximum energy efficiency is achieved at the minimum total capacity of the suspensions drive. Therefore one of the criteria for the rational work of the conveyor with suspended belt is to be the minimal total capacity of the suspensions drive. The power of the i-th drive of the drive suspension is defined as:

N dri   M dr dri   dri

x 

ndr

1

n



  n  N dri  j 1 

i 1

(4)

where Fb x stands for the longitudinal (axial) force and

Ab yz stands for the area of conveyor belt tractional frame. The value of the longitudinal force in the belt is made up by the elastic and damper forces:

Fb x  Fe  Fd

(1)

(5)

where:

The conveyor total capacity was calculated as twice the sum of the average values of the drive suspensions power installed on one circuit of the conveyor track: N 2

Fb x Ab yz



Fe  2с  xi  xi 1  h p



Fd  4ξ mdr,n  xi   с  xi  xi 1 

(2)

(6) (7)

j

The cross-section area of the belt tractional frame is: Another important feature of the conveyor with suspended belt is the transportation velocity. On the one hand, the speed increase results in the proportional increase of the conveyor load transportation performance; on the other hand, with fixed capacity, the velocity increase will contribute to the decrease in the unit load and to the usage of a belt of a smaller size type. Consequently, the maximum transportation velocity is also an important criterion of the rational work of the conveyor with suspended belt and distributed drive. In computations the conveyor belt velocity was taken to be equal to the average value of the suspension speed:

v

1 n

Ab yz  B  ymax

(8)

where ymax  i0 0 stands for the width of the tractional frame. At the terminal turning sections of the conveyor track the belt undergoes additional normal stress from bending in the longitudinal direction. According to [27] in such cases normal stress can be determined as: b  xy 

Exy y R

(9)

n

 xdri

(3)

where Exy stands for the elasticity modulus in the

While selecting the conveyor rational parameters, the stress in the conveyor belt is extremely important. The traction transfer between the suspensions, the suspendable version, the absence of conveyor pillars in the carrying and return runs, as well as fluting in the belt cross section are the determining factors in forming the belt stress-strain state. Taking into account the fact that the stress decrease will result in a decreasing of the belt unit load by means of using a smaller number of traction spacers as well as preserving the size type of the loadcarrying belt, it will increase the service life of this rather

longitudinal direction; y  0,5 ymax stands for the distance from the bending fiber to the neutral layer. The load-carrying conveyor belt suspended between the slideway bearings by means of suspensions undergoes the tensile stresses from the gravitation force of the transported material and the belt as well as its bending in the cross section. In their computations the authors assumed that in the А point of the belt cross section found at the bottom (Fig. 2), tensile stresses that occur through the unit load from the load and the belt are equal to zero, and the actual stresses are formed only by means of bending of the

i 1



178


load-carrying conveyor flight.

drive tractional frame between the pair of suspensions with the analogous bearing. Tensile stresses from bending along the belt edge surface are calculated according to the dependences provided above, the difference being that z-coordinate is taken to be equal to zero. The values of the equivalent stresses in the corresponding points of the conveyor belt profile are determined according to Strength Theory IV [30].

III. Numerical Analysis Fig. 2. Conveyor belt cross section

In order to make a series of computations and analyze the influence of the number of suspensions on the main technical characteristics of the conveyor with suspended belt and distributed drive, the authors have chosen the reference variant of the construction with the following technical characteristics (Table I) and the conveyor track configuration featured in Fig. 3. Initially, having made computations of the reference conveyor dynamic characteristics according to the given basic data, the authors obtained the values of tensile stresses in the key points of the conveyor belt cross section and longitudinal direction (Fig. 4). Stresses occurring along the belt bottom in its bending have the maximum values (1.76 МPа) in the area of loadcarrying run in which the belt has the greatest fluting 4-6. Minimal stresses (0.6 МPа) act in the belt in load stations and unloading 2, 9, where the distance between the slideway bearings is the largest and the belt is completely flattened. The values of stresses (0.23 МPа) in the return run area 12-14 are a little higher, because of the slight fluting of the belt in these sections. In this case, tensile stresses on the belt edge have smaller values. The pronounced peaks (0.63 МPа) correspond to the flattened sections at the edges in the load-carrying run 2, 8. In this case, the minimum values of stresses in the load-carrying run are equal to 5-0.33 МPа, while in the turning sections are equal to 0.21 МPа, and in the return run are equal to 13-0.24 МPа. Tensile stresses in the belt longitudinal direction increase in the load-carrying run 1-8 and decrease in the return run 10-16, in the zone of terminal turning sections 9, 17 there is a stress burst by approximately 0.85 МPа, which is conditioned by the load-carrying element bending. Maximum values of tensile stresses in the belt can occur in different currents of the path and cross section which is conditioned by a combination of parameters: unit load, distance between the slideway bearings, elasticity modulus, the terminal turning section radius. Therefore, all the revealed stresses were computated and analyzed taking into account the parameters variation. The number of drive and non-drive suspensions, as well as their position along the conveyor track, is closely interconnected. An increase or a decrease in the number of suspensions within the entire conveyor track results in the corresponding change in the distance between them.

In this case the tensile stresses acting in the belt transverse direction are equal to:

 zb   byz  E yz yk

(10)

where E yz   0, 2...0,5  Exy stands for the elasticity modulus in the transverse direction [28] and k stands for the belt curvature. It is known that the degree of curvature can be calculated according to the following dependence [29]:

k

y " z 

(11)

2 32

1   y '  z  

where y  z  stands for the dependence of the belt profile from the distance between the slideway bearings obtained in [19] (12):

 1730  y  z    1,87  1580   2  e0 ,0338 L% L   % 





z B L%

e

0 ,0338

z B

  2  

While calculating tensile stresses along the belt bottom, z-coordinate adopts the following value:

z  0,5 B  L%

(13)

Another controlling case corresponds to the stress calculation in the belt edge surface in points B’ or B” (Fig. 2). Here the tensile stresses from the unit load of the belt and the cargo are added to bending stresses:

 ze   zs   byz

(14)

Tensile stresses from the unit load of the belt and the cargo: T  zр  (15) Ab xz where Ab xz  h p  ymax is the cross sectional area of the Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved


179


TABLE I REFERENCE CONVEYOR TECHNICAL CHARACTERISTICS Symbol L

Quantity

Unit

30

m

0,625

m

0

deg

Estimated productivity Volumetric density of the transported cargo Cargo friction angle

200

t/h

1,2

t/m3

20

deg

Estimated conveying speed Total number of suspensions on one circuit Number of drive suspensions (discrete sections) on the circuit Number of suspensions in the discrete section on one circuit

1,0

m/s

80

pcs.

16

pcs.

5

pcs.

hp

Suspensions spacing

0,8

m

Fcl

Pressing force of the drive suspension tensioner Output torque rating of the drive suspension Rated revolutions

140

N

10,0

N·m

R

 Q

ρ

 vb

n ndr nds

M dr nr

Length of the conveyor plan view Radius of the terminal turning section Belt angle

Value

148,0

rpm

165,0

rpm

1,0

kg

mdr

Off-load rotational velocity Mass of the clamping device metalwork Mass of the drive roller

2,0

kg

mgm

Mass of the gear motor

4,2

kg

mmcd

Mass of the carrying device metalwork Mass of the non-drive roller Mass of non-drive suspension metalwork

7,0

kg

0,07

kg

5,0

kg

ni mmcld

mn mmn

rdr

Outer radius of the drive roller

0,065

m

rn

Outer radius of the non-drive roller

0,025

m

r0

Inner radius of the roller Drive roller coefficient of rolling friction (rubber-steel) Drive roller coefficient of sliding friction (rubber-steel) Non-Drive roller coefficient of rolling friction (steel-steel) Coefficient of sliding friction in the non-drive roller axis Number of idle rollers in the drive suspension Number of idle rollers in the nondrive suspension Belt width

0,006

m

0,0077

m

0,5

-

0,001

m

0,03

-

6

pcs.

4

pcs.

0,8

m.

3

pcs.

1,0/2,0

mm

ymax

Number of belt spacers Width of the top cover /lower cover Width of the belt tractional frame

3

mm

σ s 

Belt strength

400

N/mm

Ex

380

MPA

190

MPa

k

Belt elastic modulus Belt elastic modulus in the transverse direction Stiffness coefficient

570

kN/m

ξ

Subsidence ratio

0,05

-

f dr

dr fn

nо kdr kn

B i0

1 /  2

Ez

In order to determine the rational number and the combination of different types of suspensions along the conveyor path the following computations were made. Taking into account the limitations in the suspensions maximum spacing for different possible configurations of the discrete sections featured in Fig. 5(а) and in Table II the conveyor total capacity and the load transportation velocity were computed. The provided data of distribution of the total number of suspensions depending on the number of different types of suspensions are congruent with their spacing along the slideway bearings. Thus, as to the reference conveyor track, the increase in the number of suspensions results in the non-linear reduction of the distance between them (Fig. 5(b)). From the numerical results obtained at calculations inference, it should be drawn that the increase in the number of drive as well as non-drive suspensions has a linear effect on the increase of the conveyor total capacity (Fig. 5(c)). Alongside with this, the suspensions average velocity has a different distribution character (Fig. 5(d)) depending on the combination of the suspensions types in the conveyor track. The decrease in the discrete section length by reducing the number of non-drive suspensions results in the linear increase in the motion speed. In these circumstances, the increase in the number of the drive suspensions also makes it possible to attain the speed increase. However, in the latter case the change in speed occurs according to the non-linear dependence. This increase is especially sharp when the number of drive suspensions is small (from 4 to 8); after that, its increase continues according to the dependence close to the linear one. Thus, for instance, when the total number of suspensions in the discrete section is equal to 5 which corresponds to the reference value, the curves of the conveyor total capacity and the belt velocity depending on the number of the drive suspensions look like the shape featured in Figs. 6. By the combination of design parameters, the reduction in the number of discrete sections to the smallest value, equal to 8, is accompanied by the capacity decrease by 32% and by the velocity decrease by 5%. The corresponding total capacity and transportation velocity curves at the suspensions spacing increase by means of adding the discrete sections is featured in Figs. 7. On the assumption of the results obtained, inference should be drawn that the suspensions spacing increase by 1.5 times results in the total capacity decrease by 26.9 % and in the velocity decrease by 2.8 %. When the number of suspensions is decreased and the spacing between them is increased accordingly, the motion non-uniformity increases which results in the slight growth in the longitudinal tension oscillation amplitude σ x (Fig. 8(а)) while moving along the loadcarrying run 1-8, as well as along the return run 10-16 of the conveyor. The maximum stress of around 1,5 МPа occurs at the terminal turning section 9.



180


Fig. 3. The shape of slideway bearings of the conveyor flight: 1-9 – load-carrying run; 11-16 – return run; 2-3 –load station; 3-4, 11-12 –channeling sections; 7-8, 15-16 – flattening sections; 5-6, 13-14 – downward sections; 9-11, 16-1 – terminal turning sections; 9-10 – unloading point

Fig. 4. Stress curve depending on the belt tension while moving along the conveyor path

Figs. 5. Numeric distribution of results depending on the conveyor discrete section configuration: (a) suspensions in the track; (b) suspensions spacing; (c) total capacity; (d) average velocity



181


TABLE II NUMERIC DISTRIBUTION OF SUSPENSIONS ALONG THE CONVEYOR TRACK DEPENDING ON THE DISCRETE SECTION CONFIGURATION Number of suspensions in the discrete section, Number of drive suspensions, [pcs.] [pcs.] 8 7 6 5 4 3 2 20 160 140 120 100 80 60 40 18 144 126 108 90 72 54 36 16 128 112 96 80 64 48 32 14 112 98 84 70 56 42 12 96 84 72 60 48 36 10 80 70 60 50 40 8 64 56 48 40 32 6 48 42 36 4 32 -

(a)

(b) Figs. 6. Dependence of the conveyor technical characteristics on the number and spacing of suspensions along the track: (a), (b) capacity and velocity depending on the number of discrete sections

Figs. 8. Conveyor path stress curves depending on the increase in the number of discrete sections: (a) longitudinal; (b) equivalent along the bottom; (c) equivalent along the edge surface (a)

Along the belt edge surface the maximum stress (1.30 МPа) occurs in turning section 9 (Fig. 8(c)). The dependence of the conveyor total capacity and the transportation velocity on the number of suspensions included in the discrete section when the number of drive suspensions remains constant and is equal to 16 are featured in Figs. 9. In this case, the reduction in the number of suspensions from the reference number of 5 to the minimal number of 2 allows to decrease the conveyor total capacity by 27% and to increase the belt velocity by about 4%. Alongside with that, the change in the suspensions spacing by means of adding or reducing the number of the discrete section suspensions has a non-linear effect on the conveyor total capacity and velocity (Figs. 10). The increase in the suspensions spacing by 1.5 times is accompanied by the decrease in the drive total capacity by 17.8 %, as well as by the increase in the transportation velocity by 2.2 %.

(b) Figs. 7. Dependence of the conveyor technical characteristics on spacing of suspensions along the path: (a), (b) capacity and velocity at the permanent number of the discrete section suspensions equal to 5

Stresses that occur in the cross section along the belt bottom σ zдн and the belt edge surfaces σ zкр , remain constant, therefore the equivalent stresses in the belt cross sections are transformed insignificantly (Figs. 8(b), 8(c)). The maximum stresses (around 1.74 МPа) along the belt bottom (Fig. 8(b)) occur predominantly in the load-carrying run with the belt fluted cross section 4-6, as well as at the terminal turning section 9 (around 1.30 МPа). Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved


182


drive and non-drive suspensions determining the discrete section configuration within the permanent number of suspensions equal to the reference number of 80 pieces, the results of changes in the conveyor total capacity and the transportation velocity were obtained (Figs. 12). (a)

(b) Figs. 9. Dependence of the conveyor technical characteristics on the number and correlation of suspensions along the track: (a), (b) capacity and velocity depending on the number of suspensions in the discrete section

(a)

Figs. 11. Conveyor path stress curves depending on the increase in the number of discrete sections: (a) longitudinal; (b) equivalent along the bottom; (c) equivalent along the edge surface

(b) Figs. 10. Dependence of the conveyor technical characteristics on spacing of suspensions along the path: (a), (b) capacity and velocity at the permanent number of the discrete section suspensions equal to 16 (a)

The decrease in the number of discrete section suspensions and the corresponding increase in the suspensions spacing also affect the character of stress distribution in the belt longitudinal direction σ x . Figs. 11 feature the result of adding non-drive suspensions to the discrete section which is the increase in the stress range. Their values increase up to the end of the loadcarrying run 1-8 and the return run 10-16, as well as at turning sections 9, 17 from 1.1 to 1.5 МPа and from 0.7 to 0.4 МPа correspondingly (Fig. 11(a)). Stresses in the belt cross section along the bottom σ zдн

(b) Figs. 12. Dependence of the conveyor technical characteristics on the number and correlation of suspensions along the track: (a), (b) capacity and velocity depending on the discrete section configuration

and along the belt edge surface σ zкр remain equal to the reference values (Fig. 4). Therefore, the corresponding equivalent stresses σ eдн and σ eкр (Figs. 11(b), (c)) undergo minimal changes along the conveyor track. The greatest value growth (from 1.0 to 1.4 MPа) at the increase in the number of discrete section suspensions occurs at turning section 9 at the end of the return run. Ultimately, by means of varying the number of the

From the curves given inference, it should be drawn that the increase in the number of drive suspensions and the corresponding decrease in the discrete sections length within the permanent total number of suspensions results in the non-linear growth of the conveyor capacity and the belt velocity increase. The usage of the conveyor configuration scheme 8×10 allows to reduce the conveyor total capacity by 14%, as well as to reduce the motion speed by 12%.



183


The distribution of longitudinal stresses (Fig. 13(a)) in the conveyor belt in the discrete section configuration differs from the reference variant by the fact that, with the increase in the number of non-drive suspensions and the corresponding decrease in the number of drive ones, there is an increase in the stress oscillations amplitude, especially in the conveyor load-carrying 1-8 and return 10-16 runs. The values of cross-sectional stresses σ zдн

The spacing value is of dual nature in this case: if its increase occurs due to the decrease in the number of drive suspensions, the velocity decreases, if the distance between suspensions is increased due to the decrease in the number of the discrete section suspensions, the transportation velocity grows. The way of distributing stresses in the conveyor belt, predominantly preserving the character of value changes when the number of suspensions varies, shows that both increase in the number of the discrete section suspensions and suspensions spacing increase result in the gradual stress growth by the path terminal sections. At the same time, there occurs to the increase in the suspensions oscillations amplitude together with the belt which is mainly explained by the increase in the suspensions spacing. Proceeding from the assumption that choosing the number of suspensions along the path starts with the selection of their spacing, inference should be drawn that changes in the proportion of drive and non-drive suspensions occur within their permanent number in such a way that the total number of suspensions is to be multiple of the drive ones. Therefore, the addition of the suspensions of one type leads to the reduction of the other. Consequently, taking into account the way the number of suspensions influences the conveyor technical characteristics, it is recommended as a guideline to assume the largest number of drive suspensions. This is explained by the fact that firstly, a slight increase in the conveyor total capacity as a result of computations is annihilated by its factual change when the demanded value of transportation velocity is provided by means of variations in gear motors mechanical characteristics; secondly, the increase in the number of suspensions leads to the decrease in the outer load on the construction units and allows to use the suspensions of smaller overall dimensions and metal intensity; thirdly, the increase in the number of drive suspensions makes it possible on the one hand to increase the mechanic system robustness by means of the capacity reservation, and on the other hand, to select the suspensions drives more precisely without deviation of the power input from the total capacity. Thus, the conducted research made it possible to analyze the influence of the proportion of suspensions of different types on the basic technical characteristics of the conveyor with suspended belt and distributed drive and determine the rational parameters. However, the exact selection of the necessary number of drive and non-drive suspensions calls for not only objective optimization but also the study of the suspensions stress-strain state [31]-[33]. These issues were chosen as the line of further research.

and σ zкр remain unchanged, therefore the corresponding equivalent stresses σ eдн and σ eкр are transformed insignificantly. The largest values of the equivalent stresses along the belt bottom are in the load-carrying run 4-6 (1.7 МPа), as well as at the peak in the terminal turning section 9 (1.5 МPа). Along the belt edge surface the maximum stresses preserve at the terminal turning section 9 (1.5 МPа).

Figs. 13. Conveyor path stress curves depending on the increase in the number of discrete sections suspensions: (a) longitudinal; (b) equivalent along the bottom; (c) equivalent along the edge surface

IV.

Conclusion

Having analyzed the influence of the number of suspensions of different types along the conveyor path as well as their spacing, the inference should be drawn that with the decrease in the number of suspensions and the increase in the distance between them, the decrease in the conveyor total capacity occurs. Furthermore, the simultaneous decrease in the number of drive and nondrive suspensions leads to the decrease of the intensity of the total capacity value. In the interim, the transportation velocity increases with the increase in the number of drive suspensions and decrease in the discrete section length which, in aggregate, also contributes to its more intensive growth.

Acknowledgements This work was funded by RFBR according to research project No. 16-38-00058 mol_a.



184


[21] E. N. Tolkachev. Analysis of the dynamics of suspensions of discrete section of the conveyor with suspended belt and distributed drive. Nauchno-tekhnicheskiy vestnik Bryanskogo gosudarstvennogo universiteta, No. 1: 55-64, 2015. URL: http://ntv-brgu.ru/wp-content/arhiv/2015-N1/2015-01-10.pdf. [22] A. V. Lagerev, E. N. Tolkachev. The study of the motion of suspensions of discrete section of a conveyor with suspended belt, distributed drive, and the vertically-closed track with the singlemass dinamic model, Vestnik Bryanskogo gosudarstvennogo tekhnicheskogo universiteta, No. 4: 33–40, 2013. [23] E. N. Tolkachev. Analysis of the dynamics of suspensions of discrete section of the conveyor with suspended belt and distributed drive. Nauchno-tekhnicheskiy vestnik Bryanskogo gosudarstvennogo universiteta, No. 1: 55-64, 2015. URL: http://ntv-brgu.ru/wp-content/arhiv/2015-N1/2015-01-10.pdf. [24] Lagerev, A., Tolkachev, E., Lagerev, I., Modelling of a Vertical Loop Conveyor with Suspended Belt and Distributed Drive, (2016) International Review on Modelling and Simulations (IREMOS), 9 (4), pp. 271-279. doi:https://doi.org/10.15866/iremos.v9i4.9808 [25] E. N. Tolkachev. Specifics of determining the forces are applied to the suspensions of conveyor with suspended belt and distributed drive, depending on their spatial configuration on the route. Nauchno-tekhnicheskiy vestnik Bryanskogo gosudarstvennogo universiteta, No. 2: 44-51. 2015. URL: http://ntv-brgu.ru/wp-content/arhiv/2015-N1/2015-02-06.pdf. [26] A. V. Lagerev, E. N. Tolkachev. Mathematical model of a special conveyor with suspended belt and distributed drive, Vestnik Bryanskogo gosudarstvennogo tekhnicheskogo universiteta, No.3: 44 – 52, 2014. [27] А. V. Darkov, G.S. Shapiro. Structural resistance. Moscow.Visshaya Shkola, 1975. 654 p. [28] L. N. Аtakulov. Grounding of the transitional section paramters for the loading unit in the steep inclined conveyor with pressure belt designated for the open-pit mining: thesis synopsis. Moscow, 2007. 24 p. [29] S. М. Nikolsky The Course of Mathematical Analysis. Моscow. Fiziko-matematicheskaya literatura (Physics and Mathematics literature), 2000. – 592 p. [30] А. V. Аleksandrov, V. D. Potapov, B. P. Derzhavin Structural resistance. Moscow.Visshaya Shkola,. 2004. 560 p. [31] I. A. Lagerev. Loadlifting machines calculations by finite element method. Bryansk, BSTU, 2013. 116 p. [32] N. A. Titov. Nonlinear finite element calculations in problems of strength of lifting-transport machines. Nauchno-tekhnicheskiy vestnik Bryanskogo gosudarstvennogo universiteta. No. 2: 51-58, 2016. URL: http://ntv-brgu.ru/wp-content/arhiv/2016-N2/201602-04.pdf. [33] A.V. Vershinckii, I.A. Lagerev, A.N. Shubin, A.V. Lagerev Raschet metallicheskikh konstructsiy podyemno-transportnykh mashin metodom konechnykh elementov [Calculation of metal constructions of lifting-transport machines by finite element method]. Bryansk, Bryanskiy Gosudarstvennyy Universitet, 2015. 210 p.

References [1]

[2]

[3] [4] [5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

[20]

Antonyak Ezhi. Theoretical research and designing the advanced belt conveyors. Gorny informatsionno-tekhnichesky bulleten [Mining informational and analytical bulletin], No. 10: 154-157, 2003. O. E. Sheshko. Ecological and Economic Assessment of the Advanced Rock Machines. Nauchny vestnik MGGU [The Journal of Moscow State Mountain University], Moscow, No. 12: 281– 288, 2013. Y. A. Bakhturin Modern Open-Cut Transport. Reference book «Gornaya Tekhnika 2005». pp. 6-16. O. T. Balabayev Special Course of Study of Conveyor Transport. Karaganda Sate Technical University. Karaganda, 2015. 178 p. R. T. Swinderman, L. J. Goldbeck, A.D. Marti. Foundations-3 The Practical Resource for Total Dust and Material Control. Martin Engineering Neponset, Illinois. USA, 2002. 216 p. Devendra Kumar, R. K. Mandloi. Analysis & Prospects of Modification in Belt Conveyors. International Journal of Engineering Research and Applications (IJERA), Vol. 3 (Issue 1): 581-587, 2013. Raghvendra Singh Gurjar, Arvind Yadav. Failure Analysis of Belt Conveyor system in a Thermal Power Plant. International Journal of Advanced Technology & Engineering Research (IJATER), Vol. 2 (Issue 3): 195-200: 2012. V. I. Averchenkov, S. V. Davydov, V. P. Dunaev, V. N. Ivchenko, S. V. Kurov, M. Yu. Rytov, V. I. Sakalo, Conveyors with suspended belt (Moscow, Mashinostroenie-1, 2004). Y. A. Podoprigora, V. N. Ivchenko, V. F. Schupanovsky. The introduction of the conveyor with suspended belt. Gorny Zhurnal [Mountain Journal], No. 5-6: 72-75, 1997. V. N. Ivchenko, S. V. Kurov, S. V. Davidov, V. Y. Babay. Maintaining the conveyors with suspended belt. Gorny Zhurnal [Mountain Journal], No. 3: 66-70, 2003. V. N. Ivchenko, S. V. Kurov. Non-spilling belt conveyors. Gornaya Promyshlennost [Mining Engineering], No. 4 (62): 3942, 2005. Y. A. Podoprigora, V. N. Ivchenko. The designing and technological aspects of constructing and maintaining the belt conveyors with the suspended belt. Nedelya Gornyaka [The Miner’s Weekly]. Moscow, MGGU. 99-102, 1999. P. V. Boslovyak, E. P. Zueva. Comparative analysis of steel structures stationary conveyors with hanging belt. Nauchnotekhnicheskiy vestnik Bryanskogo gosudarstvennogo universiteta, No. 2: 59-65, 2016. URL: http://ntv-brgu.ru/wpcontent/arhiv/2016-N2/2016-02-05.pdf. P. V. Boslovyak, E. P. Zueva. Universal method for optimal design main structural assemblies of steel structures stationary conveyor with hanging ribbon. Nauchno-tekhnicheskiy vestnik Bryanskogo gosudarstvennogo universiteta, No. 1: 32-42, 2015. URL: http://ntv-brgu.ru/wp-content/arhiv/2015-N1/2015-0107.pdf. P. V. Boslovyak, E. P. Zueva. The effect of stress relaxation on the parameters of machining and inspection tooling. Nauchnotekhnicheskiy vestnik Bryanskogo gosudarstvennogo universiteta, No. 2: 23-30, 2015. URL: http://ntv-brgu.ru/wpcontent/arhiv/2015-N2/2015-02-03.pdf. P. V. Boslovyak, E.P. Zueva. Realization methodology for optimal design of steel structures conveyors with hanging belt. Nauchno-tekhnicheskiy vestnik Bryanskogo gosudarstvennogo universiteta, No. 1: 37-45, 2016. URL: http://ntv-brgu.ru/wpcontent/arhiv/2016-N1/2016-01-04.pdf. K. A. Vasyliev, A. K. Nokolayev, K. G. Sazonov. Transport machines and mining equipment. SPb, Lan’ Publishing House, 2012. 544 p. A. V. Lagerev, V. P. Dunaev. Conveyors with suspension carrying belt - new type of continuous transport machines. Inzhenerniy zhurnal. Spravochnik, No.10: 9-14. 2009. D. E. Lunev. Justification of rational design and operational parameters of conveyor with suspended belt for the mining industry, Cand. Diss. (Engineering), SPb, 2007. 119 p. A. V. Lagerev, E. N. Tolkachev, P. V. Boslovyak. Design and research of the conveyor with hanging ribbon. Bryansk, RIO BGU, 2016. 303 p.

Authors’ information Academician I.G. Petrovskii Bryansk State University, Russia. Alexander Lagerev was born in Bryansk, Russia, in 1959. He received his Ph.D. degree in turbomachines from St. Petersburg State Polytechnical University in 1994. Prof. Lagerev is the Assistant Director of the Research Institute of Fundamental and Applied Research at Academician I.G. Petrovskii Bryansk State University.



185


Evgeniy Tolkachev was born in Bryansk, Russia, in 1990. He received the qualification of engineer majoring in mechanical-handling, building, road machines and equipment from Bryansk State Technical University in 2012 His research is devoted to modeling conveyors with a suspended belt and a distributed drive. Igor Lagerev was born in Yoshkar-Ola, Russia, in 1986. He received his Ph.D. degree in lifting and transport machines from Bryansk State Technical University in 2011. Prof. Lagerev is the Vice Rector for Innovations at Academician I.G. Petrovskii Bryansk State University, Bryansk, Russia.



186


A Modelling Study to Analyse the Compression Ratio Effects on Combustion and Knock Phenomena in a High-Performance Spark-Ignition GDI Engine F. Bozza1, L. Teodosio1, V. De Bellis1, D. Cacciatore2, F. Minarelli2, A. Aliperti1 Abstract – The modern internal combustion engines show complex architectures in order to improve their performance in terms of brake torque and fuel consumption. Among the different solutions, a compression ratio (CR) increase represents a well assessed path to achieve the above result. However, CR has to be limited in order to comply with the mechanical and thermal engine safety and to avoid knocking combustion. In the present work, a 10-cylinder naturally aspirated spark ignition engine is investigated to evaluate the effects of an increased CR on the performance. In a preliminary stage, the engine is experimentally tested under full load operation for a base CR of 12.6. The main performance parameters and the in-cylinder pressure cycles are measured. The engine is schematized in a onedimensional model (GT-Power™), where “user routines” are implemented to simulate the turbulence, combustion, knock and heat transfer phenomena. The 1D model is validated against experimental data at full load, denoting a good accuracy. The model is then used to estimate the engine performance variations passing from the base CR up to an increased CR value of 13.3. The results underline a reduced improvement of the engine performance for the higher CR configuration, mainly deriving from a higher thermodynamic efficiency. The proposed methodology shows the capability to predict the effects of a partial engine re-design on a completely theoretical basis and presents the potential to be very helpful in reducing the related experimental costs and time-to-market. Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: 1D Modelling, Naturally Aspirated SI GDI Engine, Engine Performance, Compression Ratio

TDC VRC VVA VVT WOT AL AT cv

Nomenclature 1D/3D BMEP BSFC CAD CFD CR FTDC GDI ICE IMEP KL LHV MAPO MBT MFB50 PID PMEP RANS SA SI

One / three dimensional Brake mean effective pressure Brake specific fuel consumption Crank angle degree/ Computer Aided Design Computational Fluid Dynamic Compression ratio Firing top dead centre Gasoline direct injection Internal Combustion Engine Indicated Mean Effective Pressure Knock Limited Lower Heating value of the fuel Maximum Amplitude of Pressure Oscillations Maximum Brake Torque 50% of mass fraction burned Proportional integral derivative Pumping mean effective pressure Reynolds Averaged Navier Stokes Spark Advance Spark Ignition

d D D3 k K Lmax Lmin Lt m n nmax pcyl


Top Dead Centre Variable compression ratio Variable valve actuation Variable valve timing Wide Open Throttle Area of the Laminar Flame Front Area of the Turbulent Flame Front Constant volume specific heat of the incylinder gases Dissipation term of turbulent kinetic energy Dissipation term of mean kinetic energy Fractal Dimension Turbulent kinetic energy Mean kinetic energy Length scale of the maximum flame front wrinkling Length scale of the minimum flame front wrinkling Turbulent length scale Mass Engine speed Maximum engine speed In-cylinder pressure


187

F. Bozza, L. Teodosio, V. De Bellis, D. Cacciatore, F. Minarelli, A. Aliperti

P R SL t tprod u’ Uflow V xu,knock xb pknock u 

Production term of turbulence Average gas constant Laminar Flame Speed time Characteristic time scale In-cylinder turbulence intensity In-cylinder mean flow velocity Volume Mass fraction of unburned gas at knock event Mass fraction of burned gas Pressure increase at knock event Unburned gas density Engine crank angle

I.

applications, the naturally aspirated SI engines are currently designed with a CR between 8.0 and 13.4 [8], while for turbocharged ones the maximum CR is limited to 11.5-12 [9], in order to prevent knocking combustions. The above values represent a compromise among the conflicting needs at full loads, where the CR has to be limited, and at part loads, where an increased CR (up to 14-15) would be preferable. To overcome the above conflicting demands, advanced systems with continuous [10] and discrete [11] CR settings are currently under investigation and development. Despite the above discussed concerns, a CR increase appears an effective path to improve the engine performance at both full and part load operation. Some experimental studies regarding the CR effects are available in the current literature. Balki M.K. et al. [12] investigated the effects of different CRs, ranging from 8 up to 9.5, on the combustion and pollutant emissions of a SI engine fueled with unleaded gasoline, pure ethanol and methanol. The tests were performed at a fixed engine speed (2400 rpm) and spark timing, wide open throttle (WOT) and stoichiometric mixture. As expected, a higher BMEP was observed at increasing CR for both ethanol and methanol. In the case of unleaded gasoline, the maximum BMEP is obtained for a CR of about 8.75, lower than the maximum investigated value of 9.5. This was explained by an increased surface-to-volume ratio of the combustion chamber, a slower combustion and enhanced friction losses at higher CRs. Thomas R. et al. [13] tested a single cylinder SI engine with a variable CR fueled with pure gasoline and 20% blend of n-butanol in gasoline, comparing the performance at different engine loads. The results showed that the brake thermal efficiency improves at higher CRs for all investigated load levels, especially at part load. Costa R. C. et al. [14] experimentally analyzed the effect of three CRs on the performance of a SI engine, fueled by hydrous ethanol and a blend of ethanol/gasoline. They performed tests at WOT operation, also under knock limited operation, highlighting higher BMEP and lower fuel consumption by increasing the CR. Fewer numerical studies about CR effects are available in the current literature. Rakopoulos C.D. et al. [15] carried out a combined numerical/experimental study on the performance of a hydrogen-fueled SI engine at various CRs and equivalence ratios. WOT conditions were considered, while the spark timing was set at maximum brake torque (MBT) value and the engine speed was kept constant at 600 rpm. The numerical results confirmed the experimental trend of an improved indicated efficiency with higher CRs. In the light of the above literature overview, during the design phase of a new engine, the CR increase appears a very effective path to improve the engine performance. Of course, the possibility to evaluate and compare different solutions from a numerical point of view would contribute to reduce the costs and time related to the development of a new engine. 3D CFD codes are the most accurate numerical

Introduction

Nowadays the internal combustion engines (ICEs) have to comply with different requirements. For instance, the increasing carbon taxes and fuel costs have made the costumers more sensitive to the vehicle fuel economy. Furthermore, an improved fuel consumption is also required to cope with the increasingly stringent legislation about pollutants and CO2 emissions [1]. At the same time, the car manufacturers focused on the engine performance improvement, in terms of power/torque with the aim to enhance the vehicle drivability and the “funto-drive”. To achieve the discussed aims, the modern engine architectures are becoming more and more complex. Referring to the spark ignition (SI) ICEs, various technical solutions are under investigation, including turbocharging, versatile valve actuation systems, gasoline direct injection (GDI), cylinder deactivation, increased and variable compression ratio. The turbocharging technique is currently employed for the SI engine to reduce the fuel consumption at low- and mid- loads [2], through the reduction of the engine displacement (downsizing) [3]. The modern valve actuation systems (Variable Valve Actuation – VVA and Variable Valve Timing – VVT) allow for a flexible load control with a reduced throttling of the intake pipes, thus improving the pumping losses and consequently the fuel consumption at part load [4]. The GDI engines show fuel consumption benefits compared to the conventional portinjected gasoline engines [5]. The cylinder deactivation reduces the fuel consumption at low load by limiting the number of active cylinders [6]. Referring to the compression ratio (CR) effect on the engine performance, it is well known that, from a theoretical point of view, a CR increase improves the thermodynamic efficiency. However, from a practical point of view, a constraint on the maximum CR level has to be assigned to preserve the engine safety. In fact, on one hand, higher CRs imply more severe mechanical and thermal stresses for the engine, mainly related to excessive in-cylinder pressure and temperature. In addition, higher CRs may promote the onset of knocking combustion. Finally, as theoretically explained in [7], the advantages become less and less relevant at increasing CR. For the above reasons, concerning automotive



188


tools to carry out the above task, but they require a high computational effort. Therefore, very few design solutions and for a reduced number of operating conditions can be investigated. On the other hand, a 1D approach, thanks to lower computational time, appears the more suitable tool to realize the above tasks, provided that it is coupled to refined phenomenological submodels able to describe complex in-cylinder phenomena, such as turbulence, heat transfer, combustion and knock. Only in this way, the effects of the CR variations on the engine performance can be properly evaluated, taking into account the modifications in the in-cylinder pressure and temperature levels, wall heat losses and knock onset. This is the main aim of the proposed work, where the effects of an increased CR on the engine performance are investigated by a 1D model. The study regards a high performance 10-cylinder SI engine, characterized, in the base architecture, by a CR of 12.6. In a preliminary stage, the engine is experimentally tested under full load operation and the main overall performance parameters and in-cylinder pressure cycles are acquired. Then, a 1D model of the entire engine is build-up in GT-Power™ environment and validated against the experimental findings. Starting from the baseline architecture, the CR is “virtually” increased up to 13.3, representing the maximum allowable CR level by reducing the TDC clearance. The engine performance with base and increased CR are compared both at full and part load, denoting the potential advantages of the latter solution.

II.

calibration strategy, for each speed, the spark advance (SA) is selected to realize operation at MBT. In case of knock occurrence, the SA is properly delayed to work at knock borderline. The knock intensity is quantified based on the frequency analysis of the instantaneous in-cylinder pressure traces, providing the MAPO index. The SA is selected to limit the MAPO indicator below a proper threshold level, assigned based on the engine manufacturer experience and know-how [16]. The equivalence ratio is enriched by increasing the engine speed to maintain the inlet temperature at the catalytic converter below a certain allowable level and to mitigate the knock intensity. This occurs due to the heat subtracted to the in-cylinder charge by the fuel evaporation. TABLE I ENGINE CHARACTERISTICS AND PERFORMANCE Characteristic data

Value

Engine Model

V10 cylinders, 40 valves

Displacement

5204 cm3

Stroke/Bore

92.8 mm / 84.5 mm

Connecting rod length

154 mm

Compression ratio

12.6

Max Brake Power

444 kW @ 8100 rpm

Max Brake Torque

574 Nm @ 6500 rpm

III. Models Description

Engine System and Experimental Analysis

The 1D engine schematization is developed within the GT-Power commercial code, based on a one-dimensional description of the flow inside the intake and exhaust pipes and on a zero-dimensional description of incylinder processes. The combustion and turbulence phenomena are computed using proper sub-models introduced into the code through “user routines”. The combustion process is described by the “fractal combustion model” [17], which is a phenomenological model sensing both the combustion system geometry (head and piston shape, spark plug position, etc.) and the operating parameters (spark advance, air to fuel ratio, etc.). The latter is founded on a two-zone schematization, burned and unburned gases, coupled by the burning rate term:

As said before, a high performance naturally aspirated 10-cylinder engine, equipped with both direct and port fuel injectors, is studied. The main data of the considered engine are reported in following Table I. The engine is installed at the test bench and experimentally analyzed at full load and various speeds. The main overall performances are collected, including brake torque, brake specific fuel consumption (BSFC), air flow rate, etc. A single cylinder, located at the external position of an engine bank, is equipped with a dry pressure transducer located between the intake and exhaust valves. As known, a dry sensor may be affected by a certain uncertainty and instability with respect to a wet one, especially during the expansion phase, when the sensor undergoes relevant thermal shocks. However, in the proposed experimental activity, a dry sensor is selected because of its ease of installation, not requiring a cooling circuit. An optical encoder is used to trigger the acquisition of the in-cylinder pressure signal. The measured traces are processed by a charge amplifier and by an analogic/digital converter. The digital output is elaborated by the indicating software for the computation of the main indicated and combustion parameters (pressure-volume diagram, IMEP, maximum in-cylinder pressure, PMEP, mass fraction burned, knock parameters, etc.) in real time. Concerning the engine

dmb  u AT S L dt

(1)

u being the unburned gas density, AT the turbulent flame front area and SL the laminar flame speed. The latter is evaluated by the correlation proposed in [18], as a function of pressure, temperature, fuel type, air-to-fuel ratio and charge dilution. Concerning the turbulent flame front area, based on the fractal geometry theory, an increase with respect to the laminar one, AL, is applied by the following equation (2):



189


AT  Lmax    AL  Lmin 

The turbulent length scale, Lt, is indeed assumed to not change according to the engine operating conditions. A prefixed trend is assigned to get the best possible agreement with 3D results. The turbulence sub-model is tuned with reference to 3D findings. Concerning the knock modeling, a kinetic scheme for the oxidation of a three-component fuel (iso-octane, nepthane and toluene) is solved at each time step in the unburned gas zone [21]-[22]. The kinetic scheme here adopted was developed by Liu et al. [21] and includes 5 elements, 56 species and 168 reactions. The mechanism handles both low- and hightemperature reactions and is built to well reproduce the ignition delay and the temporal evolution of most important species. The knock event is recognized as a sudden jump of the end-gas temperature, due to the auto-ignition occurrence [23]. The knock index, pknock, is computed as the pressure increase occurring in an isochoric combustion of the unburned gas at the knock event [23], xu,knock, according to equation (7):

D3  2

(2)

Lmax and Lmin being the length scale of the maximum and minimum flame front wrinkling, respectively, and D3 representing the fractal dimension [19]. For the estimation of AL, the flame front is assumed as a smooth spherically-shaped surface and its area is derived by an automatic procedure implemented in a CAD software, processing the actual 3D geometry of the combustion chamber. The evaluation of the above quantities, namely Lmax, Lmin and D3, requires the estimation of the turbulence intensity (u’) and of the integral length scale (Lt). The adopted 0D turbulence model belongs to the K-k family and describes the time-evolution of the turbulent and mean kinetic energies, taking into account the main production and destruction terms [20]. The model also characterizes the energy cascade mechanism from the mean flow kinetic energy, K, eq. (3), to the turbulent kinetic energy, k, eq. (4). The production and dissipation of the above energies are computed by equations (5) and (6), respectively:

K

1 mU 2flow 2

(3)

3 mu '2 2

(4)

k

dK  K in  K ex  K tum  P  D dt

(5)

dk   kin  kex  P  d dt

(6)

pknock 

R xu ,knock m f LHV cv Vknock

(7)

R and cv being the average gas constant and the constant volume specific heat of the in-cylinder gases, mf and LHV the mass and the lower heating value of the fuel, and Vknock the in-cylinder volume at the knock event. It is worth to underline that the above formulation of the knock index closely resembles the experimental MAPO definition. The substantial congruence between the experimental and numerical knock indices allows hence to select a coherent knock threshold level between the experiments and the calculations. The in-cylinder heat transfer is described by the modified Woschni correlation (Woschni-GT) [24], included in the GT-Power software. The engine friction losses are computed by the model of Chen-Flynn [25], whose coefficients are identified based on experimental data.

A brief explanation of the above terms is reported below:  K in and kin are the kinetic energy production rates related to the flow entering the cylinder;  K ex and kex are the kinetic energy production rates related to the flow going out from the cylinder;  K tum takes into account the production of mean flow kinetic energy under the ideal hypothesis of isentropic compression of a tumble vortex;  P is the production term of turbulence, which couples equations (5) and (6). It is sum of two contributions: the first is related to mean flow degradation in a characteristic time scale, the second one describes the tumble vortex collapse;  D and d are the dissipation terms; they take into account the energy degradation into heat due to viscous effects. The integration of equations (5) and (6) provides the instantaneous values for K and k and hence Uflow and u’.

IV.

Turbulence Model Tuning

As stated before, in a preliminary stage, 3D CFD analyses are carried out to derive the mass-averaged incylinder mean flow velocity, turbulence intensity and integral length scale. The above variables are evaluated at each crank angle and used to tune the discussed K-k turbulence sub-model. Following the approach described in [20], the 3D analyses are carried out under motored operation and the related time-depending boundary conditions, applied at the 3D computational domain ends, are derived by the 1D calculations. In Fig. 1 and Fig. 2, the outcomes of the 1D turbulence sub-model are compared to the related 3D findings at a medium engine speed, in terms of turbulence intensity, mean flow velocity and integral length scale. The above results, as well as all the other



190


Turbulence Intensity

ones proposed in this work, are normalized or showed without the axis scales for reasons of confidentiality. For instance, the generic engine speed, n, is divided by the maximum level investigated in the experimental campaign, nmax, and expressed in terms of speed fraction, n/nmax. The 1D/3D turbulence intensity comparisons are depicted in Fig. 3 for the minimum and maximum engine speeds. The most important result of the above 1D/3D assessments is the capability of the adopted phenomenological model to properly sense the effects of different engine operating conditions, especially close to the firing TDC, when the combustion takes place. In fact, in accordance with the reference 3D trends, higher turbulence levels around the combustion TDC at increasing engine speeds is predicted by the adopted turbulence model. The turbulence level is satisfactorily described along the whole engine cycle, excepting during the exhaust and the early stage of the intake phase. The above disagreement does not exert any relevant influence on the prediction of the combustion process. The model also provides an estimation of the mean flow velocity, as shown in Fig. 1. The numerical results denote a satisfactory accuracy especially during the intake and compression strokes, while higher errors regard the expansion and exhaust phases.

Fig. 3. 1D vs. 3D turbulence intensity under motored operation at 0.12 and 1.00 n/nmax

The simplified mathematical approach, used to describe the integral length scale evolution, is capable to satisfactorily follow the 3D reference trend, as depicted in Fig. 2. The proposed results are obtained without requiring any case-dependent tuning, hence proving the generality of the adopted methodology. The demonstrated predictive capability of the turbulence sub-model, under various operating conditions, represents a fundamental prerequisite for the combustion model applications discussed in the following sections.

V.

Model Validation at Full Load

In the validation calculations at full load, discussed in this section, the experimentally actuated air-to-fuel ratio and valve strategies are imposed as input data in the simulations, while the throttle valve is set fully opened (WOT). The spark timing is automatically adjusted to match the measured combustion phasing (MFB50). The combustion model tuning parameters are identified in order to resemble as better as possible the measured incylinder pressure traces and the related burn rates. It must be stressed that a single set of tuning constants has been assigned for all the examined operating conditions. Seventeen full load operating points are investigated, and the related results are shown in Fig. 4-Fig. 7. The figures show the numerical/experimental comparisons in terms of volumetric efficiency, brake torque, BSFC and in-cylinder peak pressure. The volumetric efficiency, depicted in Fig. 4, is satisfactorily predicted (average error of 1.43 %), with most of the points within the error band ± 5%, hence denoting the good accuracy in the engine geometry schematization. As shown in Fig. 5, the brake torque is evaluated with a similar accuracy (average error of 1.71 %), proving the reliability of the adopted combustion model. The BSFC results of Fig. 6 present an even reduced dispersion around the null-error line, depending the concurrent effects of both flow, combustion and heat transfer modeling. In this case, the average error is 1.33%. Finally, the in-cylinder peak pressure (Fig. 7), mainly affected by cylinder filling and

Fig. 1. 1D vs. 3D turbulence intensity and mean flow velocity under motored operation at 0.54 n/nmax

Fig. 2. 1D vs. 3D turbulence integral length scale; 3D result under motored operation at 0.54 n/nmax



191


combustion evolution, is predicted with a satisfactory accuracy, with an average error of 1.72%.

The above cycle-averaged results denote the global consistency of the developed 1D engine model. However, a more detailed deepening about the combustion modeling can be carried out with reference to the numerical/experimental comparisons of the pressure cycles, pcyl, and of the related burn rate profiles, dxb/d. The latter are depicted in Fig. 8-Fig. 11 for the normalized speeds of 0.24, 0.48, 0.73 and 0.97 n/nmax.

Model Brake Torque, Nm

Fig. 4. Experimental vs. numerical volumetric efficiency at WOT operation

Fig. 8. In-cylinder pressure cycle and burn rate at 0.24 n/nmax and WOT operation

Model BSFC, g/kWh

Fig. 5. Experimental vs. numerical brake torque at WOT operation

Fig. 9. In-cylinder pressure cycle and burn rate at 0.48 n/nmax and WOT operation Fig. 6. Experimental vs. numerical BSFC at WOT operation


Fig. 7. Experimental vs. numerical in-cylinder peak pressure at WOT operation



192


This behavior does not clearly appear in the experimental trend because of the knock occurrence in some operating conditions, namely for the speeds around 0.42 and 0.73 n/nmax. In fact, in the above conditions, the MFB50 has to be delayed compared to the MBT value to limit the in-cylinder pressure and temperature and to avoid knock onset. However, also the experimental MFB50 trend globally exhibits the convenience of a combustion phasing advance at increasing speed. As shown in Fig. 12, the 1D model results accounting for the knock presence (“Model KL”) show a good agreement with the experimental outcomes, especially for the speeds around 0.73 n/nmax. This proves the capability of the proposed approach in correctly capture the concurrent effects of heat exchange, combustion and knock phenomena. The overall performance parameters of the engine (volumetric efficiency, brake torque, BSFC and incylinder peak pressure) derived in the above calculations are almost superimposed to the ones realized with the experimental MFB50. Hence, the related results are not discussed here for sake of brevity. The above described tuning and validation of the combustion and knock models represents the base for the numerical study regarding the effects of an increased compression ratio, illustrated in the next section.


It can be observed that compression stroke is quite well reproduced by the model, denoting the good estimation of the cylinder filling. Looking at the burn rate profiles, the model shows the capability to satisfactorily describe the combustion process along all its duration. Slight inaccuracies only regard the final stage of the ramp, while the peak level is accurately detected in all cases. The above inaccuracies are probably due to an intrinsic limitation of the proposed approach: realistically, due to the asymmetric combustion chamber geometry and spark plug location, the flame front does not propagate with a spherical shape, contrary to the basic hypothesis of the combustion model. Once assessed the consistency of the proposed modeling approach in terms of engine breathing and combustion description, further calculations are carried out to tune the knock model. Preliminary, 1D simulations are performed at full load, not considering the possible knock onset, with the aim of identifying the combustion phasing corresponding to MBT condition. In a second stage, further analyses are carried out, where, in case of knock detection, the combustion is automatically delayed to realize operation at the knock borderline. The maximum allowed numerical knock intensity is assigned, based on a threshold level coherent with the experimental MAPO index. The results of the above investigations are reported in Fig. 12, showing in red dashed line with squares the experimental MFB50 (“Exp”), in black dashed line with triangles the numerical MFB50 at MBT condition (“Model MBT”) and in black continuous line with circles the numerical MFB50 at knock borderline (“Model KL”). A first outcome of the simulations is that the MFB50 at MBT condition has to be advanced with the engine speed. It is an effect of the in-cylinder heat losses, whose trend is depicted in Fig. 13: at the lower speeds, a certain delay of the combustion phasing is required to limit the in-cylinder peak pressure and temperature and, hence, the heat losses; on the other hand, moving to the higher speeds, this demand is less felt, thanks to the reduced time available for the heat exchange.

Fig. 12. Experimental vs. numerical combustion phasing at WOT operation

Fig. 13. Heat transfer/Total Fuel Energy at WOT operation



193


VI.

To deeply explain the above behavior, two engine speeds, namely 0.24 and 0.73 n/nmax, are selected and the in-cylinder pressure cycles of the examined CRs are compared. Fig. 19 underlines, for the case at 0.24 n/nmax, that the increased CR, as expected, involves a higher pressure during the compression phase. Since the same combustion phasing is set, a higher peak is also reached. Conversely, Fig. 20 shows, for the case at 0.73 n/nmax, that a lower in-cylinder peak pressure is attained with the higher CR, because of the delayed combustion phasing.

Compression Ratio Variation Results

The validated engine model is employed to investigate the effects of an increased compression ratio, both at full and part load operation. The base engine configuration (“Base CR”), having a CR of 12.6, is virtually modified by increasing the CR level up to 13.3 (“High CR”), resulting in a CR percent variation of 5.6%. According to the reference ideal Otto / Beau de Rochas thermodynamic cycle [7], the maximum theoretical increase in the efficiency for the considered CR variation is of about 1.4%. Of course, a reduced efficiency benefit has to be expected because of the “real” effects, such as the heat exchange, not isochoric combustion at TDC, not perfect behavior of the working fluid, etc. The new engine configuration is obtained with the same cylinder displacement, without any modification of the piston/head geometries, but only reducing the piston/head TDC clearance. Probably such a modification is not applicable in a real engine, because of problems regarding tolerances and interference between the head and the piston. However, the results proposed in this section represent a potential starting point for an engine re-design (in particular of the head and of the piston), if significant advantages will be found. To this aim, further numerical analyses at full load are carried out in the “High CR” configuration, assigning the following input data:  experimental air-to-fuel ratio;  experimental valve strategy;  throttle valve fully opened. Similarly to the “Base CR” case, preliminary analyses (not reported here for sake of brevity), without considering the knock presence and aiming at identifying the combustion phasing under MBT condition, showed that the optimal MFB50 negligibly changes with respect to the base case. The comparison of the numerical results for Base CR and High CR cases, taking into account the knock presence, are shown in Fig. 14-Fig. 18. In those analyses, the strategies to identify the MFB50 and the knock threshold level are the same as the ones discussed and applied for the “Base CR” configuration. As expected, a higher CR determines better performance in terms of brake torque (Fig. 14), BSFC (Fig. 15) and indicated efficiency (Fig. 16), for the whole considered engine speed range. The above advantages are lower than the theoretical expectations, summing up to a maximum value of about 1.0%. As expected, with reference to the knock phenomenon, more critical operations are detected for the higher CR, resulting in a delayed MFB50, as shown in Fig. 17. The increased CR does not always correspond to a greater in-cylinder peak pressure, especially for the higher engine speeds, because of a delayed combustion phasing, as shown in Fig. 18.

Fig. 14. Brake torque comparison between “Base CR” and “High CR” at WOT operation

Fig. 15. BSFC comparison between “Base CR” and “High CR” at WOT operation

Fig. 16. Indicated efficiency comparison between “Base CR” and “High CR” at WOT operation



194


To draw a more complete overview, a further comparative numerical study is carried out at part load. Four operating points are considered, for the normalized engine speeds of 0.24, 0.48, 0.73 and 0.97 n/nmax, and for a load level corresponding to the 25 % of maximum value. The above operating points are labelled in Fig. 21 as n/nmax @ BMEP/BMEPmax, BMEPmax representing the maximum load for each engine speed. In the simulations, a stoichiometric air-to-fuel ratio is imposed, while the throttle valve is adjusted by a PID controller to match the prescribed BMEP. Due to the knock absence, the SA is selected to realize the MBT operation. The results are shown in Fig. 21, reporting the comparison between the BSFC for both “Base CR” and “High CR” configurations, and the related percent variation. The above figure underlines that BSFC advantages occur at increasing speed, ranging from 0.42 % at 0.24 n/nmax up to 0.85% at 0.97 n/nmax.

Fig. 17. MFB50 comparison between “Base CR” and “High CR” at WOT operation

Fig. 18. Maximum in-cylinder pressure comparison between “Base CR” and “High CR” at WOT operation

Fig. 21. BSFC comparison between “Base CR” and “High CR” at part load operation

The main reason of these benefits is, once again, a higher indicated efficiency, but some drawbacks, such as an increased pumping work and in-cylinder heat transfer, limit the above advantages. Summarizing the proposed analyses show that the increased CR involves slightly improved performance, with comparable or even reduced thermo-mechanical stress for the engine structure. However, the extent of the above advantages probably does not represent an adequate reason to take in consideration, from an industrial point of view, the redesign of the engine combustion chamber and, in particular, of its head and piston geometry. The existing engine design already reaches the actual maximum performance compatible with the current architecture presenting a CR of 12.6. However, referring to the scientific relevance of the proposed analyses, the discussed outcomes appear consistent with the theoretical expectations, both from a qualitative and quantitative point of view. This is mainly due to the adoption of a physical modeling of complex and concurring phenomena occurring in the cylinders, such as turbulence, combustion, knock and heat transfer.

Fig. 19. In-cylinder pressure cycles comparison between “Base CR” and “High CR” at 0.24 n/nmax and WOT operation

Fig. 20. In-cylinder pressure cycles comparison between “Base CR” and “High CR” at 0.73 n/nmax and WOT operation



195


VII.

Conclusion

References [1]

The paper describes the effects of an increased compression ratio on the behavior of a high performance naturally aspirated 10-cylinder SI engine. The analyses are carried out by a 1D modeling approach, enhanced by phenomenological sub-models for the description of turbulence, combustion, knock and heat transfer phenomena. In a first stage, the considered engine is characterized at the test bench under full load operation and various speeds. The acquired data, both global parameters (brake torque, volumetric efficiency, BSFC) and in-cylinder pressure cycles, are used to validate the 1D engine model. In a second stage, the model is employed to numerically compare, both at full and part load operations, the effects of different compression ratios: the base CR of 12.6 and an increased CR of 13.3. The latter is the maximum CR level compatible with the existing piston/head geometries. For both configurations, an engine calibration of the spark timing at full load is performed with the aim of realizing the MBT condition or, in case of knock onset, operation at the knock borderline. The proposed results at full load show that a higher CR involves an increased brake torque and a reduced BSFC. However, a delayed combustion phasing is obtained at knock limited operation with the increased CR, resulting in a lower maximum in-cylinder pressure and mechanical stress for a wide range of engine speeds (0.60-1.00 n/nmax). The analyses at part load underline that an increased CR involves BSFC benefits, once again because of an improved thermodynamic efficiency. Nevertheless, the extent of the discussed advantages, in most cases below 1%, does not justify, from an industrial point of view, the analyzed CR increase from 12.6 up to 13.3. It is the case to underline that the adopted modeling approach may be affect by some criticisms. The most relevant one regards the capability of the turbulence model in sensing the CR variations. Preliminary 1D calculations, not reported in the paper for brevity, showed that the turbulence model response to CR modifications is in line with the theoretical expectations. This issue will be addressed in the next development of this activity by additional 3D CFD simulations. Similarly, an experimental verification of the engine performance predictions for the increased CR configuration will prove the reliability of the adopted numerical methodology. However, the proposed 1D approach, carried out on a validated model, shows the potentials to physically predict the effects of a CR increase on the engine performance, taking into account the mutual influence of complex phenomena such as turbulence, combustion, knock and heat transfer. Hence, the discussed methodology could represent a useful tool to support and drive the development process of an engine re-design.

[2]

[3]

[4]

[5]

[6]

[7] [8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]


European Environment Agency, Annual European Union greenhouse gas inventory 1990–2012 and inventory report 2014, Report No.: 9/2014. G. Fontana, E. Galloni, Variable valve timing for fuel economy improvement in a small spark-ignition engine, Applied Energy, Vol. 86 (Issue 1): 96–105, January 2009. doi: 10.1016/j.apenergy.2008.04.009 N. Fraser, H. Blaxill, G. Lumsden, and M. Bassett, Challenges for Increased Efficiency through Gasoline Engine Downsizing, SAE Int. J. Engines Vol. 2 (Issue 1): 991-1008, April 2009. doi: 10.4271/2009-01-1053 P. Shayler, L. Alger, Experimental Investigations of Intake and Exhaust Valve Timing Effects on Charge Dilution by Residuals, Fuel Consumption and Emissions at Part Load, SAE Technical Paper 2007-01-0478, SAE 2007 World Congress & Exhibition, Detroit, MI (USA), April 2007. doi: 10.4271/2007-01-0478 G. Cipolla, F. Bozza, Spark Ignition Engines: State of the Art and Current Technologies. Future Trend and Developments, Handbook of clean energy systems, (Wiley Online Library, 2015 pp 1-35). doi: 10.1002/9781118991978.hces078 M. Wilcutts, J. Switkes, M. Shost, and A. Tripathi, Design and Benefits of Dynamic Skip Fire Strategies for Cylinder Deactivated Engines, SAE Int. J. Engines Vol. 6 (Issue 1): 278288, April 2013. doi:10.4271/2013-01-0359 J. Heywood, Internal Combustion Engines Fundamentals, (McGraw Hill, 1988). P. Smith, J. Heywood, W. Cheng, Effects of Compression Ratio on Spark-Ignited Engine Efficiency, SAE Technical Paper 201401-2599, SAE 2014 International Powertrain, Fuels and Lubricants Meeting, Birmingham (UK), October 2014. doi: 10.4271/2014-01-2599 T. Lake, J. Stokes, R. Murphy, R. Osborne, and A. Shamel, Turbocharging Concepts for Downsized DI Gasoline Engines, SAE Technical Paper 2004-01-0036, SAE 2004 World Congress & Exhibition, Detroit, MI (USA), March 2004. doi: 10.4271/2004-01-0036 R. Malpress, D.R. Buttsworth, A Comparison Between TwoPosition Variable Compression Ratio and Continuously Variable Compression Ratio Engines Using Numerical Simulation, Paper No. ICEF2009-14042, pp. 401-411, ASME 2009 Internal Combustion Engine Division Fall Technical Conference, September 2009. doi: 10.1115/ICEF2009-14042 L. Teodosio, V. De Bellis, F. Bozza, and D. Tufano, Numerical Study of the Potential of a Variable Compression Ratio Concept Applied to a Downsized Turbocharged VVA Spark Ignition Engine, SAE Technical paper 2017-24-0015, SAE 13th International Conference on Engines and Vehicles, Capri (Na), Italy, September 2017. doi: 10.4271/2017-24-0015 K .M. Balki, C. Sayin, The effects of Compression ratio on the performance, emissions and combustion of a SI (spark ignition) engine fueled with pure ethanol, methanol and unleaded gasoline, Energy Vol. 71: 194-201, July 2014. doi: 10.1016/j.energy.2014.04.074 R. Thomas, M. Sreesankaran, J. Jaidi, D. M. Paul, P. Manjunath, Experimental Evaluation of the Effect of Variable Compression Ratio on Performance and Emission of SI Engine Fuelled with Petrol and n-Butanol Blend at Different Loads, Perspectives in Science Vol. 8: 743-746, September 2016. doi: 10.1016/j.pisc.2016.06.076 R. C. Costa, J. R. Sodrè, Compression ratio effects on an ethanol/gasoline fuelled engine performance, Applied Thermal Engineering Vol. 31 (Issue 2-3): 278-283, February 2011. doi: 10.1016/j.applthermaleng.2010.09.007 C. D. Rakopoulos, G. M. Kosmadakis, J. Demuynck, M. De Paepe, and S. Verhelst, A combined experimental and numerical study of thermal processes, performance and nitric oxide emissions in a hydrogen-fueled spark-ignition engine, Int. J. of


196


[16]

[17]

[18]

[19]

[20]

[21]

[22]

[23]

[24]

[25]

Hydrogen Energy Vol.36 (Issue 8): 5163-5180, April 2011. doi: 10.1016/j.ijhydene.2011.01.103 V. De Bellis, L. Teodosio, D. Siano, F. Minarelli, and D. Cacciatore, Knock and Cycle by Cycle Analysis of a High Performance V12 Spark Ignition Engine. Part 1: Experimental Data and Correlations Assessment, SAE Int. J. Engines Vol.8 (Issue 5): 1993-2001, September 2015. doi:10.4271/2015-24-2392 V. De Bellis, E. Severi, S. Fontanesi, and F. Bozza, Hierarchical 1D/3D approach for the development of a turbulent combustion model applied to a VVA turbocharged engine. Part II: Combustion model, Energy Procedia Vol. 45: 1027–36, January 2014. doi: 10.1016/j.egypro.2014.01.108 D. Rhodes, J. Keck, Laminar burning speed measurements of indolene-air-diluent mixtures at high pressures and temperatures, SAE Technical paper 850047, SAE International Congress and Exposition, Detroit, MI (USA), February 1985. doi: 10.4271/850047 G.L. North, D.A. Santavicca, The fractal nature of premixed turbulent flames, Combustion Science and Technology Vol. 72 (Issue 4-6) :215-232, August 1990. doi: 10.1080/00102209008951648 V. De Bellis, F. Bozza, S. Fontanesi, E. Severi, F. Berni, Development of a Phenomenological Turbulence Model through a Hierarchical 1D/3D Approach Applied to a VVA Turbocharged Engine, SAE Int. J. Engines Vol. 9 (Issue 1): 506-519, April 2016. doi: 10.4271/2016-01-0545 Y. Liu, M. Jia, M. Xie, B. Pang, Development of a new skeletal chemical kinetic model of toluene reference fuel with application to gasoline surrogate fuels for computational fluid dynamics engine simulation, Energy & Fuels Vol. 27 (Issue 8): 4899–4909, July 2013. doi: 10.1021/ef4009955 J. Andrae, Comprehensive chemical kinetic modeling of toluene reference fuels oxidation, Fuel Vol. 107: 740-748, May 2013. doi: 10.1016/j.fuel.2013.01.070 V. De Bellis, F. Bozza, L. Teodosio, G. Valentino, Experimental and Numerical Study of the Water Injection to Improve the Fuel Economy of a Small Size Turbocharged SI Engine, SAE Int. J. of Engines Vol.10 (Issue 2): 550-561, March 2017. doi: 10.4271/2017-01-0540 G. Woschni, A Universally Applicable Equation for the Instantaneous Heat Transfer Coefficient in the Internal Combustion Engine, SAE Technical Paper 670931, National Fuels and Lubricants, Powerplants, Transportation Meetings, February 1967. doi: 10.4271/670931 S. K. Chen, P. F. Flynn, Development of Single Cylinder Compression Ignition Research Engine, SAE Technical paper 650733, National Powerplant and Transportation Meetings, February 1965. doi: 10.4271/650733

Fabio Bozza is Full Professor of Fluid Machines and Internal Combustion engines at the Industrial Engineering Department of the University of Naples “Federico II” (Italy). Research interests are related to experimental and numerical studies of internal combustion engines, including turbulent combustion, knock, combustion and gasdynamic noise, turbocharging, engine design and virtual calibration, hybrid electric powertrains. Author of more than 120 scientific publications (93 SCOPUS). Scientific Council member of the Industrial Engineering Doctoral School at University of Naples “Federico II”. Member of the Governing Board of SAE-Naples Section. Faculty Advisor for “Formula SAE” Project. ATI, ATA and SAE member. Session organizer for ATI Conference, SAE World Congress, SAE FFL Meeting and ICE Congress. He received the Oral Presentation Award at SAE World Congress more than twice and thus he received the Lloyd L. Withrow Distinguished Speaker Award at SAE World Congress 2016. Luigi Teodosio graduated with honors in Mechanical Engineering at University of Naples “Federico II” (Italy) in 2012, discussing a Master thesis on 3D CFD simulation of combustion process in a Diesel engine. He received the PhD degree in Mechanical Systems Engineering at University of Naples “Federico II” (Italy) in 2016, discussing a thesis concerning the 0D modelling of turbulent combustion and knock process in a Spark-ignition engine. He is currently a Research Fellow at the Department of Industrial Engineering of the University of Naples “Federico II”. His research interests include the experimental and numerical studies of internal combustion engines, with particular reference to the in-cylinder processes, gas-dynamic noise, turbocharging, optimization of engine design and calibration, hybrid powertrains. Author of about 15 scientific publications (8 SCOPUS). Best in oral presentation award at SAE 2014 International Powertrains, Fuels & Lubricants meetings. Best paper award at ATI 2015 Italian Conference in PhD Special Session. Vincenzo De Bellis, graduated with honors in Mechanical Engineering at University of Naples “Federico II” (Italy) in 25/05/2005, discussing a thesis on the combustion characteristics of pyrolysis oils from biomass. He received the PhD degree in Mechanical Systems Engineering at University of Naples “Federico II” (Italy) in 2012, discussing a thesis concerning the 1D simulation of steady and unsteady turbocharger operations for automotive applications. He is currently a Researcher at the Industrial Engineering Department of the University of Naples “Federico II” (Italy). Research interests include experimental and numerical studies of turbochargers, internal combustion engines, and hybrid electric powertrains. Author of more than 40 scientific publications (30 SCOPUS). ATI and SAE member. Diego Cacciatore, graduated in Mechanical Engineering at University of Naples “Federico II” (Italy) in July 97, discussing a thesis on LPG fuel spray characterization. He received the Master in car engineering at University of Modena. He is currently Head of engineering & experimental engine at Automobili Lamborghini S.p.A. Author of about 15 scientific publications. Fabrizio Minarelli, graduated with honors in Mechanical Engineering at University of Bologna (Italy) in 2011, discussing a thesis on the 3D CFD fluid dynamics simulation and optimization of a step exhaust manifold. He received the Master “Race Engine Engineering”. He is currently engine analysis team leader in R&D engine engineering and testing at Automobili Lamborghini S.p.A. Author of 2 scientific publications.


DII- Department of the Industrial Engineering, University of Naples “Federico II”, Via Claudio n.21, 80125 Napoli, Italy. Tel. +39 081-7683274 Fax. +39 081-2394165 E- mails: [email protected] [email protected] [email protected] [email protected]

Antonio Aliperti, graduated with honors in Mechanical Engineering at University of Naples “Federico II” (Italy) in June 2016, discussing a thesis on combustion, cycle-by-cycle variation and knock phenomena models applied to spark-ignition engines. He is currently a PhD student in Mechanical Systems Engineering at University of Naples “Federico II” (Italy). He collaborates with the R&D department of Automobili Lamborghini S.p.A. Research interests include: numerical simulation of internal combustion engines and hybrid electric powertrains.

2

Automobili Lamborghini Spa, Via Modena n.12, 40019 S. Agata Bolognese, Bologna, Italy. E-mails: [email protected] [email protected]



197

International Review on Modelling and Simulations (IREMOS) Aims and scope The International Review on Modelling and Simulations (IREMOS) is a peer-reviewed journal that publishes original theoretical and applied papers concerning Modelling, Numerical studies, Algorithms and Simulations in all the engineering fields. The topics to be covered include but are not limited to: theoretical aspects of modelling and simulation, methods and algorithms for design control and validation of systems, tools for high performance computing simulation. The applied papers can deal with Modelling, Numerical studies, Algorithms and Simulations regarding all the engineering fields; particularly about: the electrical engineering (power system, power electronics, automotive applications, power devices, energy conversion, electrical machines, lighting systems and so on); the mechanical engineering (kinematics and dynamics of rigid bodies, vehicle system dynamics, theory of machines and mechanisms, vibration and balancing of machine parts, stability of mechanical systems, computational mechanics, mechanics of materials and structures, plasticity, hydromechanics, aerodynamics, aeroelasticity, biomechanics, geomechanics, thermodynamics, heat transfer, refrigeration, fluid mechanics, micromechanics, nanomechanics, robotics, mechatronics, combustion theory, turbomachinery, manufacturing processes and so on); the chemical engineering (chemical reaction engineering, environmental chemical engineering, materials synthesis and processing and so on). IREMOS also publishes letters to the Editor and research notes which discuss new research, or research in progress in any of the above thematic areas. Instructions for submitting a paper The journal publishes invited tutorials or critical reviews; original scientific research papers (regular papers), letters to the Editor and research notes which should also be original presenting proposals for a new research, reporting on research in progress or discussing the latest scientific results in advanced fields; short communications and discussions, book reviews, reports from meetings and special issues describing research on all aspects of Modelling, Numerical studies, Algorithms and Simulations in all the engineering fields. All papers will be subjected to a fast editorial process. Any paper will be published within two months from the submitted date, if it has been accepted. Papers must be correctly formatted, in order to be published. An Author guidelines template file can be found at the following web address: www.praiseworthyprize.org/jsm/?journal=iremos Manuscripts should be sent on-line or via e-mail as attachment in .doc and .pdf formats to: [email protected]

bstracting and Indexing Information: Academic Search Complete – EBSCO Information Services Cambridge Scientific Abstracts – CSA/CIG Elsevier Bibliographic Database SCOPUS ProQuest Index Copernicus - IC Journal Master List 2016: ICV 113.15 Autorizzazione del Tribunale di Napoli n. 78 del 1/10/2008

2533-1701(201806)11:3;1-W Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved