Original Article
Application of multi-objective optimization techniques to improve the aerodynamic performance of a tunnel ventilation jet fan
Proc IMechE Part C: J Mechanical Engineering Science 2015, Vol. 229(1) 91–105 ! IMechE 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0954406214531566 pic.sagepub.com
Joon-Hyung Kim1,2, Jin-Hyuk Kim2, Joon-Yong Yoon1, Young-Seok Choi2 and Sang-Ho Yang3
Abstract This paper describes the design optimization of a tunnel ventilation jet fan through multi-objective optimization techniques. Four design variables were selected for design optimization. To analyze the performance of the fan, numerical analyses were conducted, and three-dimensional Reynolds-averaged Navier–Stokes equations with a shear stress transport turbulence model were solved. Two objective functions, the total efficiency of the forward direction and the ratio of the reverse direction outlet velocity to the forward direction outlet velocity, were employed, and multi-objective optimization was carried out to improve the aerodynamic performance. A response surface approximation surrogate model was constructed for each objective function based on numerical solutions obtained at specified design points. The non-dominated sorting genetic algorithm with a local search procedure was used for multi-objective optimization. The tradeoff between the two objectives was determined and described with respect to the Pareto-optimal solutions. Based on the analysis of the optimization results, we propose an optimization model to satisfy the objective function. Finally, to verify the performance, experiments with the base model and the optimization model were carried out. Keywords Jet fan, tunnel ventilation, multi objective optimization, computational fluid dynamics, experiment Date received: 3 September 2013; accepted: 20 March 2014
Introduction Tunnel ventilation is a very important design element in the construction of tunnels. Ventilation is necessary to maintain a pleasant and safe driving environment by discharging pollutants, such as carbon monoxide, emitted by automobiles from the tunnel. In addition, in cases of emergencies in tunnels, such as fires, the ventilation plays a role in smoke control, saving lives, and restoring the tunnel to normal functioning. Hence, an appropriate ventilation system is necessary when planning and constructing tunnels. Tunnel ventilation methods are divided into natural ventilation and mechanical ventilation. Natural ventilation is available in tunnels of 500 m or less due to the piston effect of running automobiles. Mechanical ventilation is unavoidable in tunnels constructed recently because most are long tunnels of 500 m or more.1 As shown in Figure 1, methods of mechanical ventilation are divided into transverse, semi transverse, and longitudinal. Transverse and semi transverse ventilation methods require a space to install a duct for
ventilation. The need to excavate a large cross section to house the duct results in high construction costs and difficult maintenance. Longitudinal ventilation does not require a space to install a duct because fresh air is dispersed from the entrance of the tunnel in the longitudinal direction using an axial fan, such as a jet fan.2 Therefore, this type of ventilation is superior in terms of economic feasibility and construction and maintenance. The application of longitudinal ventilation has rapidly increased since the 1970s due to developments in axial fans, including 1 Department of Mechanical Engineering, Hanyang University, Seoul, Republic of Korea 2 Thermal & Fluid System R&D Group, Korea Institute of Industrial Technology, Cheonan-si, Republic of Korea 3 Technology Research Center, Samwon E&B, Kyunggi-Do, Republic of Korea
Corresponding author: Young-Seok Choi, Thermal & Fluid System R&D Group, Korea Institute of Industrial Technology, 89 Yangdaegiro-gil, Ipjang-myeon, Seobuk-gu, Cheonan-si, Chungcheongnam-do 331-822, Republic of Korea. Email:
[email protected]
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Figure 1. Type of tunnel ventilation: (a) transverse, (b) semi-transverse, (c) longitudinal.
Figure 2. Depiction of a general jet fan.
jet fans. In particular, mountainous countries, such as Japan and Korea, and countries with a high demand for tunnels, such as European countries, have employed longitudinal ventilation due to its costeffectiveness. Various studies have been conducted aimed at improving the performance of the jet fans used in these tunnels in Europe and Japan.3–7 In this work, a multi-objective optimization scheme was adopted and computational fluid dynamics (CFD) were performed to improve a tunnel ventilation jet fan. A base model and design variables appropriate to the design specification were selected. On the basis of the design variables, the sets for design of experiment (DOE) were selected. The performance of each selected set was evaluated with CFD. Two objective functions, the total efficiency of the forward direction and the ratio of the reverse direction outlet velocity to the forward direction outlet velocity, were employed, and multi objective optimization was
carried out to improve the performance. Finally, the results of the numerical analysis were validated through an experiment.
Features of the jet fan Axial jet fans used for longitudinal ventilation provide excellent ventilation with a high-speed rotor. The volume of air ventilation can be controlled by varying the angle of the blade. Several small fans can be used to reduce the size of the jet fan and divide the ventilation system. Due to the many advantages of axial fans, most recent longitudinal ventilation systems employ these types of fans. A jet fan comprises a symmetric rotor, a motor as a power source, and a stator that fixes the motor to the casing. Silencers are installed at both sides to reduce noise, as shown in Figure 2. As the jet fan module is installed on the roof of the tunnel, accidental falls or
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Figure 3. Operation of a jet fan: (a) normal operation (forward direction), (b) emergency operation (reverse direction).
breakdowns present a high risk to automobiles or drivers. Therefore, a jet fan must be safe and durable enough to continue operating for a long time without any mishaps. As shown in Figure 3, a jet fan is mostly operated in the forward direction inside a tunnel. However, it is operated in the reverse direction in cases of emergencies, such as fires. In a fire, this changes the direction of the airflow in the tunnel to discharge smoke and secure an escape route for people. The reversibility of the jet fan has been used as a key performance indicator.8 As the importance of energy efficiency has come to the fore recently, many studies have been conducted to increase the efficiency of jet fans to reduce maintenance costs.9 As the rotor of a jet fan has a symmetric shape to meet the need for reversibility, modification of the design of the rotor to increase the total efficiency of the forward direction has been limited. Although reversibility is necessary in emergency operations, the number of emergency situations is marginal, with tunnels operating normally most of the time. For research purposes, the regulations with respect to reversibility can be relaxed with the aim of maximizing the total efficiency of forward direction. In this study, we optimized a jet fan rotor using a multi objective function to satisfy the reversibility performance required in emergency situations and to maximize the total efficiency of the forward direction.
Numerical analysis method The performance of all the models generated for this study was evaluated by numerical analysis. The numerical analysis methods used for the performance evaluation are detailed below.
Domain of numerical analysis Figure 4(a) and (b) shows the numerical analysis domains in the forward and reverse directions, respectively. As mentioned above, reversibility is a key performance indicator for a jet fan. The flow can be changed during the operation of the fan in the reverse direction by altering the position of the rotor and the stator. The performance of the fan was verified in the forward and the reverse directions in this study. The rotor in the current study has six blades, and the stator has nine blades. Numerical analysis of the flow passage of one rotor and two stators was conducted using periodic conditions, taking the analysis time into consideration.10 Periodic conditions were also given to the inlet and outlet flow domains to reduce the analyzed domain to 1/12.
Grid system As shown in Figure 4, the grid system of the rotor and stator was constructed using ANSYS Turbo-Grid Version 13, which has hexahedral grids. The flow domains of the inlet and the outlet were constructed uses ANSYS-Mesh Version 13 on tetrahedral grids. A grid-dependency test was conducted to verify the reliability for the grid system.11 Considering the difference of the dependency between the turbulence models, the each grid-dependency test was implemented. Figure 5 shows the result of the griddependency test. As shown in Figure 5, the performance of the effective outlet velocity (Veff) is predicted uniformly under 0.1% change range regardless of a turbulence model when the number of the total nodes is over the approximate 290,000. However, the performance of the total efficiency of the forward
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Figure 4. Computational domain and grid system of the jet fan: (a) numerical analysis for forward direction, (b) numerical analysis for reverse direction.
direction (%for_t) was stabilized with about 300,000 nodes on k-" model, and with about 320,000 nodes on shear stress transport (SST) model. According to the result, the final grid system for this study was selected
with 320,000 nodes. The number of generated grid nodes was 140,000 in the rotor domain, 70,000 in the stator domain, 65,000 in the inlet domain, and 45,000 in the outlet domain, with a total of
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Figure 5. Result of the grid dependency test. SST: shear stress transport.
320,000 nodes. O-type, H-type, and prism grid generation methods were applied to the walls of the individual flow domains to make the grids dense, so that the yþ value was 20 or less.
Boundary condition As a boundary condition, the atmospheric pressure was given to the inlet area with opening pressure and to the outlet area with static pressure. For the numerical analysis of the jet fan, the atmospheric pressure conditions at the inlet and outlet should be applied because the jet fan is not connected with other systems. The stage-averaged condition was applied to the interface of the inlet-rotor, rotor-stator, and stator-outlet domains. The rotation speed of the rotor was 1780 r/min, and the working fluid was 25 C air.
Numerical analysis scheme The numerical analysis was carried out using ANSYS CFX Version 13, a three-dimensional fluid analysis program. To examine the characteristics of the internal flow fields, Reynolds-averaged Navier– Stokes (RANS) equations were solved using the SST turbulence closure model. The governing equations with the SST model used in the numerical analysis were discretized by a finite volume method using a high-resolution second-order scheme. The SST model, having the advantages of both k-o and k-", employs the k-o model at the near-wall and the k-" model in the bulk-flow regions. This blending function ensures a smooth transition between the two models.12 In general, the fluid machinery such as the jet fan having the rotational body with a high rotation speed has the complicated feature at the
near-wall. Thus, the characteristic of the near-wall cannot be exactly predicted in the case of the k-" model because this model calculates the near-wall with the modeling function. In the system, the performance of the average flow velocity and the pressure relatively has a few prediction errors as the influence of the near-wall is hardly, while the torque for calculating the efficiency has the considerable errors due to being immediately calculated through the near-wall value. Figure 5 shows the result of the error tendency depending on the turbulence model. As a result, the performance of the effective outlet velocity had a few differences between the two models, and was similar to the result for the experiment. On the other hand, the performance of the total efficiency of the forward direction had the remarkable differences between the turbulence models. The result of the SST model was more similar to the result for the experiment than that of the k-" model. Therefore, the SST model was used for the numerical analysis of this work. The root mean-squared residual values of the momentum and mass were set to fall below 1.0E-06. The converged solutions were obtained after approximately 500 iterations. The computations were performed with a PC with an Intel Xeon (R) CPU clock speed of 2.67 GHz. The computational time was about 3 hours, depending on the rate of convergence.
Design optimization Design optimization to enhance the aerodynamic performance of the tunnel ventilation jet fan was carried out in this work. The overall multi objective optimization procedure is described in the flow chart shown in Figure 6.
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Objective function The effective outlet velocity and the total efficiency are key indices of the performance of jet fans, whereas the ventilation flow rate and pressure are used as performance indices for general fans. However, with regard to jet fans used for tunnel ventilation, the ventilation strength is considered important, as well as the ventilation flow rate. Accordingly, the effective outlet velocity, which allows for convenient investigation of the ventilation capacity and the ventilation strength, was used as a key performance indicator in the current study. The effective outlet velocity (Veff) is expressed in the following equation Veff ¼
Q Aeff
ð1Þ
where Aeff indicates the effective outlet area. These effective outlet areas are defined in ISO 13350, as shown in Figure 7.13 The set-up shown in Figure 7(c) was applied because it has a similar shape to the jet fan used in this study.
Figure 6. Multi-objective optimization procedure.
Figure 7. Depiction of the effective outlet area.13
The total efficiency of the forward direction (%for_t) was calculated as the fluid power to the consumed axial power, as in the case of a general fan, and expressed in the following equation for t ¼
ðPt,out,forward Pt,in,forward Þ Q 100 !
ð2Þ
The subscripts in and out refer to the inlet and the outlet, respectively. Pt, Q, , and ! indicate the total pressure, volumetric flow rate, torque, and angular velocity, respectively. As reversibility is very important in a jet fan, the ratio of the reverse direction outlet velocity to the forward direction outlet velocity (%rev_Veff), representing the reversibility performance, was calculated using the following equation %rev Veff ¼
Veff,reverse 100 Veff,forward
ð3Þ
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where the subscripts forward and reverse refer to the forward direction and the reverse direction, respectively. As mentioned in section ‘‘Features of the jet fan’’, the purpose of this study was to maximize the total efficiency of the forward direction of the jet fan, while maintaining as much reversibility as possible. Therefore, the total efficiency of the forward direction and the ratio of the outlet velocity of the reverse direction to those of the forward direction were chosen as the objective functions for optimization.
Selection of the base model Table 1 shows the design specifications of this study. To design an optimum model that satisfied the design specifications of the optimization process, a base model suitable for the design specifications was selected from conventional models. Table 2 lists the detailed specifications of the base model.
shown in Figure 8(a). As shown in Figure 8(b), the thickness profile of the jet fan rotor can be expressed by a Bezier curve with four control points. When control point 3, except for the fixed control points, is moved in the (-) direction horizontally, the maximum thickness point moves forward, and the shape of the blade changes to an airfoil type. Each relative position of control point 3 at the hub and shroud (Hub_th, Shr_th) was included in the model. In addition to the selected design variables, the radius of the rotor and the installation angle also has a significant influence on the performance of a jet fan. The operating point of the jet fan is greatly dependent on these variables. As the operating point of the jet fan was fixed in this study, the radius of the rotor and the installation angle were fixed in all the analyses.
Design variables To perform the optimization, four design variables that affect the performance of jet fans were selected (Table 3). The selected meridional lengths of the hub and the shroud (Hub_m, Shr_m) of the jet fan are
Table 1. Design specifications of the study. N (r/min) Veff (m/s) %for_t (%) %rev_Veff (%)
1780 40–43 Maximize More than 97
Table 2. Design specifications of the base model. Number of rotor blades (EA) Number of stator blades (EA) Rotor tip clearance (mm) Blade angle of hub (degree) Blade angle of shroud (degree) Meridional length of hub (mm) Meridional length of shroud (mm)
6 9 5 48 23 180 100
Table 3. Ranges of design variables.
Hub_m (mm) Shr_m (mm) Hub_th (%) Shr_th (%)
Lower
Base
Upper
160 80 10 10
180 100 55 55
200 120 100 100
Figure 8. Definition of the design variables: (a) meridional length, (b) thickness profile.
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Design of experiment sets
Results of the optimization
This study randomly generated 35 design points within the design space for four design variables with the help of Latin-hypercube sampling (LHS).14 The LHS is an m n matrix, where m is the number of levels (sampling points) examined, and n is the number of design variables. Each of the n columns of the matrix containing the levels 1, 2, . . . , m is randomly paired to form the LHS. Thus, the LHS generates random sample points, ensuring the representation of all portions of the design space. These points generated for the optimization analysis were assessed through the numerical analysis.
In the multi-objective optimization to enhance the aerodynamic performance of the tunnel ventilation jet fan, RSA models for both objective functions were obtained by using the numerical results obtained for the 35 design points sampled randomly by LHS. To construct the RSA model, an analysis of variance (ANOVA) and a regression analysis with t-statistics17 were carried out to measure the uncertainty in the set of coefficients in the polynomial. The values of R2 and R2adj for second-order curve fitting and the root mean square error (RMSE) for the RSA model are listed in Table 4. Here, R2 and R2adj denote the correlation coefficient in the least squares surface fitting and the adjusted correlation coefficient, respectively. The values of R2adj for the total efficiency of the forward direction and the ratio of the reverse direction outlet velocity to the forward direction outlet velocity were 0.950 and 0.970, respectively. These values are reliable according to the 0.9 < R2adj < 1.0 range for producing the accurate RSA model.18 Leave-one-out cross validation (CV) errors19 were also estimated for the RSA models and are listed in Table 4. Figure 9 shows the global POSs generated by the hybrid MOEA, the arbitrary selected optimum design, the evaluations at the design points, and the base model. Each extreme end of the global POSs represents a pair of the highest value of one objective function and the lowest value of the other objective
Optimization techniques This study employed the response surface approximation (RSA) surrogate model to predict the response surface for performing the design optimization of a tunnel ventilation jet fan. The response surface of the second-order polynomial RSA can be expressed as follows f ðxÞ ¼ 0 þ
N X j¼1
j xj þ
N X j¼1
jj x2j þ
N XX
ij xi xj ,
i6¼j
ð4Þ where , N, and x represent the regression analysis coefficients, the number of design variables, and the set of design variables, respectively, and the number of regression analysis coefficients (0, i, etc.) is (N þ 1) (N þ 2)/2. Global Pareto-optimal solutions (POSs) were obtained with the RSA model using a hybrid multi objective evolution algorithm (MOEA)15 based on the real-coded non-dominated sorting genetic algorithm (NSGA-II) developed by Deb for two objective functions. Here, ‘‘real-coded’’ indicates that the crossover and mutations are conducted in real space to obtain a response from the NSGA-II. These POSs were then refined by searching for a local optimal solution for each objective function over all the NSGA-II-obtained optimal solutions using sequential quadratic programming with NSGA-II solutions as initial guesses. One objective function was optimized by treating the others as equality constraints, and the local search was repeated for the second objective function by treating the first as an equality constraint.16 This process produced two new sets of optimal solutions, which were merged with the NSGA-II solutions. From these solutions, the first dominated solution was discarded, and subsequent duplicate solutions were removed to obtain the global POSs. Subsequently, a local search process was performed to improve the quality of the POSs.
Table 4. Results of ANOVA and regression analysis. Objective functions
R2
R2adj
RMSE
CV errors
%for_t %rev_Veff
0.973 0.886
0.950 0.970
5.12 102 6.88 102
8.06 102 1.04 101
RMSE: root mean square error; CV: cross validation.
Figure 9. Definition of the design variables. PSOs: Pareto-optimal solutions.
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function. Any improvement in one objective function leads to a deterioration in the others. A trade-off analysis showed an obvious correlation between the total efficiency of the forward direction and the ratio of the reverse direction outlet velocity to the forward direction outlet velocity. Namely, higher total efficiency of the forward direction was obtained at a lower reverse direction compared to the forward direction, and vice versa. In this study, the arbitrary selected optimum design with the maximized total efficiency of the forward direction was selected. Table 5 compares the design variables of the selected optimization model with those of the base model. The results showed that the hub and the shroud of the optimization model were longer than the hub and the shroud of the base model by 10%. In addition, the operating point 3 of the optimization model controlling the thickness of the blade was shifted toward the leading edge when compared with that of the base model. This indicates that the maximum thickness point was shifted toward the leading edge. For a clearer comparison, the difference in the shape of the hub and the shroud between the models is shown in Figure 10. Table 6 shows the results of the performance evaluation by the numerical analysis of the generated optimization model. The total efficiency of the forward direction at the selected operating conditions for the base model was 71.1%. The total efficiency of the forward direction of the optimization model was
estimated to be 74% using the present optimization and calculated to be 74.1% using numerical analysis. The optimization predicted within a relative error of 0.1%. Consequently, the optimization used here enhanced the total efficiency of the forward direction by 3% compared with the base model. The ratio of the reverse direction outlet velocity to the forward direction outlet velocity was 100.2% in the base model and 97.9% in the optimization model, indicating that the performance of the optimization was lower than that of the base model. However, the performance satisfied the design specifications of this study. As mentioned in section ‘‘Features of the jet fan’’, a jet fan is mostly operated in the forward direction. Thus, this study focused more on improving the performance of the jet fan in the forward direction than on the reversibility performance of the fan. The optimization model showed that the performance satisfied the research purpose.
Comparison of the internal flow fields in the base and optimization model To investigate the causes of the difference in the performance of the base model and the optimization
Table 6. Performance evaluation results (prediction and numerical analysis). %for t (%)
Table 5. Design variables in the design optimization.
Base model Optimization model
Hub m (mm)
Shr m (mm)
Hub th (%)
Shr th (%)
180 200
100 110
100 48
100 54
Figure 10. Shape of the hub and shroud: (a) shroud, (b) hub.
%rev Veff (%)
Numerical Numerical Prediction analysis Prediction analysis Base model – Optimization 74 model
71.1 74.1
– 97.9
100.2 97.9
100 model, the internal flow fields were analyzed on the basis of the numerical analysis results. Figure 11 shows the pressure distribution on the meridional plane of the rotor of the jet fan in the base model and the optimization model during the operation of the fan in the forward direction. A low-pressure region was found in the shroud on the leading edge in the base model and the optimization model, but it was decreased in the latter. The tendency was also found in the streamline distribution, as shown in Figure 12. In the base model, a flow separation regime was observed in the shroud region on the leading edge. In contrast, this was suppressed in the optimization model. Such decreased instability may increase the performance of the optimization model.
Proc IMechE Part C: J Mechanical Engineering Science 229(1) Figure 13 shows the streamline distribution of the individual model during the operation of the fan in each direction. Figure 13(a) and (b) shows the streamline distribution during the operation of the fan in the forward direction the base model and the optimization model. Figure 13(c) and (d) illustrates the streamline distribution during the operation of the fan in the reverse direction in the two models. The figures show that a vortex zone is generated at the trailing edge in the base model during the operation of the fan in the forward direction, whereas this is suppressed in the optimization model. The maximum thickness point is in the central region of the blade in the base model, whereas it is at the leading edge in the optimization model. As a result, the thickness profile
Figure 11. Comparison of the pressure distributions of the rotor in the meridional plane (operating in the forward direction): (a) base model, (b) optimization model.
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Figure 12. Comparison of limiting the streamlines on the rotor blade (operating in the forward direction): (a) base model, (b) optimization model.
Figure 13. Comparison of limiting streamlines on the rotor hub: (a) base model (operating forward direction), (b) optimization model (operating forward direction), (c) base model (operating reverse direction), (d) optimization model (operating reverse direction).
102 continuing to the trailing edge is softer in the optimization model than in the base model. Therefore, the non-uniform outlet flow element in the base model is suppressed in the optimization model. This improvement in the outlet flow also increases the efficiency of the jet fan. During the operation of the fan in the forward and the reverse directions, a vortex zone was generated in the base model. As the base model has a symmetric structure, the flow pattern during the operation of the fan in the forward direction was similar to that during the operation in the reverse direction. However, in the optimization model, a vortex zone wider than that of the base model was generated during the reverse direction but not during the forward direction. As noted above, the maximum thickness point was positioned on the side of the leading edge in the optimization model, and the thickness profile to the leading edge changed more drastically in the optimization model than in the base model. During the operation of the fan in the reverse direction, the flow was toward the leading edge, with a large change in the thickness profile. Thus, very large non uniform flow elements were generated. These are mainly responsible for the lower performance during the operation in the reverse direction in the optimization model than in the base model.
Experiment for performance verification To test aerodynamic the performance, prototypes of the base model and the optimization model were prepared. As the two models were the same (same type of bell mouse, silencer, and stator), except for the rotors, one module was prepared for each model. As shown in Figure 14, the shape of the part that fixes the rotor to the casing was the same. The blades for the base model and the optimization model were prepared so that they could be substituted with each other.
Figure 14. Prototype rotor used in the study.
Proc IMechE Part C: J Mechanical Engineering Science 229(1) The performance test was carried out according to the jet fan test standard (ISO 13350) as shown in Figure 15.13 Figure 16 shows the configuration of the equipment used in the test. As depicted in Figure 15, a venturi nozzle-type test duct with four holes at the side was prepared according to the test standard. The test duct and the jet fan were fixed on the support bed. The test was carried out after mounting the test duct at the inlet of the jet fan. The measurement began 30 min after preheating of the motor. All the data were collected at 0.1-s intervals and saved in a personal computer through a data acquisition device. Table 7 shows the performance evaluation results of each model obtained by the test and by the numerical analysis. To determine the efficiency at the operating point, the test results were corrected by considering the motor efficiency is presented to compare the result with that of the numerical analysis. The efficiency value of the motor used for the correction was that listed by the manufacturer of the motor. The total efficiency of the forward direction at the operation point measured by the test was 69.1% in the base model and 72.4% in the optimization model, indicating that it was higher in the optimization model than in the base model by about 3.3%. The increase was very similar to that predicted by the numerical analysis. The ratio of the reverse direction flow velocity to the forward direction velocity was lower in the optimization model than in the base model, as predicted by the numerical analysis. However, the decrease was 2%, which is very marginal and about 1.1% higher than the design specification used in this study. These results verified that the optimization was successfully carried out.
Effect for optimization of a jet fan The efficiency enlargement of the tunnel ventilation jet fan has the energy-saving effect. To show the effect in
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Figure 15. Experiment specification for the jet fan (ISO 1335013).
Table 7. Performance evaluation results (experiment and numerical analysis). %for t (%)
%rev Veff (%)
Numerical Numerical Experiment analysis Experiment analysis Base 69.1 model Optimization 72.4 model
Figure 16. Configuration of the experimental system.
71.1
100.1
100.2
74.1
98.1
97.9
detail, the energy-saving effect was represented on Table 8 with the product report of the jet fan on this study.20 As shown in Table 8, this jet fan, 45 kW class, decreased approximately 1.8 kW for the required power per one due to the efficiency increase by the optimization. In the case of 6 h operation per a day, approximate 11 kWh power decrease. A jet fan keeps operating all the times of the year, unlike the general fluid machinery occasionally operating, only needed. According to these operation features, the amount of the power of this jet fan per one reduces approximately 4012 kWh a year. In addition, the decreasing amount of the total power is expected to reach 12,035 MWh a year because the demand of this jet fan is approximately 3000 (EA). This decreasing amount of the total power also translates into approximate US$1.2 million. As aforementioned, the optimization on this study has the great energy-saving effect. Argumentatively this optimization scheme is expected to be effectively used when additional optimization designs for the efficiency increase are conducted.
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Proc IMechE Part C: J Mechanical Engineering Science 229(1) Table 8. Energy-saving effect according to optimization of a jet fan on this study. Base model Power (per a jet fan), kW Efficiency, % Required power (per a jet fan), kW Decreasing amount of the required power (per a jet fan), kW Operating time per day, hour Average reduction of the power per a day (per a jet fan), kWh Operating day per year, day Average reduction of the power per a year (per a jet fan), kWh Demand of this jet fan (based on 2013), EA Average reduction of the power per a year (all products), MWh Reduction cost per a year (energy charge: 1$/ 10 kWh), $
Conclusion We conducted the numerical design optimization combined with three-dimensional RANS analysis to improve the performance of a tunnel ventilation jet fan. As reversibility is used as a key performance indicator for tunnel ventilation jet fans, the total efficiency of the forward direction and the ratio of the reverse direction outlet velocity to the forward direction outlet velocity were used as objective functions in the optimization. A performance evaluation of the base model and the optimization model was carried out through a numerical analysis and a test, and the reliability was verified by the comparison of the results. The performance test results showed that the total efficiency during the operation of the fan in the forward direction was 69.1% in the base model and 72.4% in the optimization model, indicating an efficiency increase in the optimization model of about 3.3%. Considering that jet fans are generally operated in the forward direction for long periods of time, this increase may yield substantial energy savings. The ratio of the reverse flow velocity to the forward flow velocity was 100.1% in the base model and 98.1% in the optimization model, indicating that the performance of the optimization model was lower. However, taking into account the improved performance of the fan in the forward direction, the decrease in the performance during the reverse direction may not be a major problem. In addition, the performance of the fan in the reverse direction in the optimization model satisfied design specifications required for emergency operations. Therefore, the optimization model produced in this study may be suitable for the development of axial ventilation jet fans for use in tunnels. Conflict of interest None declared.
45 69.1 65.1
Optimization model 45 71.1 63.3 1.8 6 11 365 4012 3000 12,035 1204
Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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Appendix Notation Aeff Hub_m Hub_th N Pt Q Shr_m Shr_th Veff ! %for_t %rev_Veff
effective outlet area meridional length of hub relative position of the control point 3 at hub rotational speed total pressure volumetric flow rate meridional length of shroud relative position of the control point 3 at shroud effective outlet velocity angular velocity total efficiency of the forward direction ratio of the reverse direction outlet velocity to the forward direction outlet velocity torque
