Int. J. Computer Applications in Technology, Vol. 30, Nos. 1/2, 2007
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Fan-shape optimisation using CFD and genetic algorithms for increasing the efficiency of electric motors Noel León-Rovira* Center for Innovation in Design and Technology, Tecnológico de Monterrey, Campus Monterrey. Ave. Eugenio Garza Sada # 2501, Colonia Tecnológico, CP 64841 Monterrey, Mexico E-mail:
[email protected] *Corresponding author
Eduardo Uresti Center for Artifitial Intelligence, Tecnológico de Monterrey, Campus Monterrey. Ave. Eugenio Garza Sada #2501, Sucursal de Correos ‘J’, Colonia Tecnológico, CP 64841 Monterrey, Mexico E-mail:
[email protected]
Waldo Arcos Center for Innovation in Design and Technology, Tecnológico de Monterrey, Campus Monterrey. Ave. Eugenio Garza Sada # 2501, Colonia Tecnológico, CP 64841 Monterrey, Mexico E-mail:
[email protected] Abstract: The electric motor efficiency represents the effectiveness with which the motor converts electrical energy into mechanical energy. As the energy losses are converted into heat, which is dissipated by the motor frame aided by internal and external fans, a better cooling system adds up to better efficiency. In recent years, improvements in motor efficiency have been achieved but at higher costs. By using Genetic Algorithms (GAs), changes are introduced to the fan shape looking for a better aerodynamic performance. The evaluation of the achieved fan efficiency with the modified shapes is performed with Computational Fluid Dynamics (CFD) simulation software. Keywords: shape optimisation; genetic algorithms; shape parameterisation; CFD. Reference to this paper should be made as follows: León-Rovira, N., Uresti, E. and Arcos, W. (2007) ‘Fan shape optimisation using CFD and genetic algorithms for increasing the efficiency of electric motors’, Int. J. Computer Applications in Technology, Vol. 30, Nos. 1/2, pp.47–58. Biographical notes: Noel León-Rovira is a Professor at the Center for Innovation in Design and Technology at Mexico’s Tecnológico de Monterrey. He holds a Degree in Mechanical Engineering, as well as a PhD in Mechanical Engineering (Summa cum Laude), both from the Dresden University of Technology, Germany. He also made Postdoctoral studies on design methodology and computer-aided design. He is the Director of the Research Program Creativity, Inventiveness and Innovation in Engineering. He specialises in product design, design methodology and computer-aided engineering. Eduardo Uresti is a Professor at the Center for Intelligent Systems at Mexico’s Tecnológico de Monterrey. He holds a degree in Mathematics, a master degree in Mathematics and a PhD in Computer Science. He has served as professor in the Physics and Mathematics School (ESFM) of the National Politechnical Institute (IPN) and also in the Physics and Mathematics Faculty at UANL Mexico. His current interests include the use of genetic algorithms for multi-modal and multi-objective optimisation (new algorithms and their applications) and the application of Artificial Intelligent strategies in teaching and learning.
Copyright © 2007 Inderscience Enterprises Ltd.
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N. León-Rovira, E. Uresti and W. Arcos Waldo Arcos received the BS Degree in Mechanical with Minor in Electrical Engineering from the Autonomous University of Nuevo León, Nuevo Leon, Mexico, and the Master’s Degree in Manufacturing Systems from Tecnológico de Monterrey, Nuevo Leon, Mexico, in 2002 and 2006, respectively. He is currently responsible for the mechanical and electrical design of electric motors in an electric motors company.
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Introduction
Improving the energy use in electric motors is relevant because improved efficiency leads to slower growth in electricity demand. In the following sections, a way is shown of obtaining a higher efficiency of electric motor with a better heat dissipation that is achieved by a better fan aerodynamic performance. Losses due to electrical resistance in electric motors take the form of heat, which has to be dissipated. Improvements in motor efficiency are achieved by reducing losses. In electric motors, reduction of losses is achieved in various ways, such as higher core-steel grade, higher slot fill, slots redesign and reduced windage design. All these options have been used throughout the years, but even though these methods have achieved increments in electric motor efficiency, the consequence has incremented total motor costs. The objective of achieving higher efficiency through fan-shape optimisation using GAs is obtaining the same benefits but without increasing the costs. GAs have been successfully applied in other shape optimisation cases for optimising aerodynamic shapes (Marco et al., 2004; Obayashi et al., 2000a; 2000b; Olhofer et al., 2001; Kelner et al., 2005). In this case, the application starts from a shape optimisation of a fan, which used at first only straight-line blades’ cross sections. Using CFD software, it is possible to simulate the effect of changing the fan blades’ shapes with GAs. However, for optimisation purpose, the CFD simulation time required is tremendous. In a recent study, Kelner et al. (2005) relate a study where the CFD simulation time was greatly reduced, based on the differentiation and high-order Taylor-series expansion of the discretised Reynolds-Averaged Navier–Stokes (RANS) equations around an independently computed reference flow solution. In the present study, the simulation time was reduced taking advantage of the periodicity of the geometry, simulating only one-eighth of the fan rotor. Moreover, in order to reduce the total computational time, two different commercially available CFD packages were compared and the one which require less computational time was used. The objective is to increase the efficiency of a Total Enclosed Fan-Cooled (TEFC) induction motor, using the same raw materials and manufacturing processes. The electrical design of the motor is not subject of analysis.
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Innovation as concept optimisation
Based on former publications, it may be stated that product innovation may also be implicit as “optimisation
not restricted to parametric variation” but extended to ‘constrained concept variations’. As has been shown in several case studies, an extended parametric optimisation is achieved by adding shape and topology as possible search directions. This extended optimisation has been achieved by allowing shape variation substituting the original shapes, which were based on straight lines and arcs, with spline curves (Leon et al., 2004). The current fan blade profile has two identical parallel straight lines. The new fan blade design proposed in this paper, although starting from the same straight profiles, is represented by splines, shape of which may be modified for finding new profiles beyond the limits than would be possible using only straight-line profiles. New constraints have been added to the GA that restricts the minimum permitted thickness of the fan blade profile to avoid possible flexion due to the air impact over the fan blades.
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Basic thermal consideration
Thermal issues affect the performance of a motor. The coil temperature rise, that is, the temperature difference of stator coil over an ambient temperature, depends on two factors: •
the temperature gradient between stator coil and housing for conductive heat flow, which can be solved analytically
•
the temperature difference between housing and air for heat flow by convection and radiation.
For a TEFC machine, the problem of heat transfer from housing to air for a given temperature gradient is associated with the aerodynamic flow pattern. This part is difficult to solve analytically, and, because of this, an empirical relation has been established, based on data available from test results. Conduction of losses inside the motors is particularly important in areas that include air gaps and voids, for example inside the coil insulation between conductors and cores and between the stator core and housing of totally enclosed motors. A totally enclosed machine is one so enclosed as to prevent the free exchange of air between the inside and the outside of the case, but not sufficiently enclosed to be termed airtight. There are different types of enclosures and its use depends upon the application and the working environment. For this research, a TEFC electric motor enclosure is used. In this type of enclosure, an external fan pulls air in through a fan cover and blows it over the external motor surface. Heat transfer occurs by forced convection.
Fan shape optimisation using CFD and genetic algorithms
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Genetic Algorithms (GAs)
GAs are search algorithms based on the mechanics of natural selection and natural genetics. They combine survival of the fittest among string structures with a structured yet randomised information exchange to form a search algorithm with some of the innovative flair of human search. In every generation, a new set of artificial creatures (strings) or individuals is created using the fittest as parents. While randomised, GAs are no simple random walk. They efficiently exploit historical information to speculate on new search points with expected improved performance (Goldberg, 1989). Normally, when a GA is used for function optimisation, each individual in the population represents a point in the search space of the problem to be solved. The aptitude of an individual is closely related to the value of the function in the point being represented by the individual. Different types of GAs have been proposed, from the simple GA (Goldberg, 1989) to others with special selection schemes as Genitor (Whitley, 1989). In the simple GA, the whole population is replaced by a new set of individuals each generation. The new set of individuals is produced in pairs. In order to produce two new individuals, a pair of parents is selected from the current population. Those individuals with a better aptitude have more chances of being selected. Once a pair of individuals is selected, crossover and mutation are applied. The crossover consists of constructing a pair of new individuals by taking parts of the genetic material from both parents. The expected effect is the combination of the characteristics present in both parents. In the simplest case, the genetic material of an individual consists of the string and the crossover consists of randomly taking a point in which both parents can simultaneously be divided and then joining the first part of the first parent with the second part of the second parent. The second individual can be constructed with the remaining parts of the genetic material of the parents. Mutation consists of making changes in the genetic material in both resulting new individuals. Compared with the simple GA, Genitor has some differences, as it produces one new individual each generation, which replaces the worst individual in the current population. As in the simple GA, two parents are selected, but they are selected according to their ranking in the population: the whole population is ordered according to the value of the function evaluation. In some GA applications, it may be more convenient to use a real vector as genetic material instead of a string of characters (Wright, 1991; Barone et al., 2002; Bleier, 1998; Capello and Mancuso, 2003; Chapman, 2004; Goldberg, 1989; Eshelman and Schaffer, 1993), in such applications, the use of different crossover operators is required.
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AC motor losses
Electric motor efficiency is a measure of the effectiveness with which a motor converts electrical energy to mechanical
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energy. It is defined as the ratio of power output to power input or as the ratio of output to output + losses (http://www.reliance.com/mtr/b7087_5/b7087_5_2.htm.): Motor Efficiency =
Output Output . = Input Output + losses
AC motor losses are classified in components. Electrical losses are converted into heat, which is dissipated by the motor housing aided by internal or external fans. Stator and rotor losses are caused by current flowing through the motor winding and are proportional to the squared current times the winding resistance (I2R). Hysteresis and eddy current losses are confined to the laminated core of the stator and rotor and can be reduced by utilising steels with low core loss characteristics, as found in high-grade silicon steel. Friction and windage losses are due to all sources of friction and air movement in the motor and may be appreciable in large high-speed or TEFC motors. The stay load losses are due mainly to high-frequency flux pulsations, caused by design and manufacturing variations (http://www.reliance.com/mtr/ b7087_5/b7087_5_2.htm.). There are two basic ways for increasing the efficiency of an electric motor based on its fan characteristics. One way is by minimising windage losses while maintaining the airflow rate. In that case, the rest of the losses will not be benefited. The efficiency increase is due only to the reduction of windage losses. Another way to boost electric motor efficiency is by increasing the airflow rate provided by the fan, without increasing the energy required for driving the fan. This way will benefit the rest of the losses mentioned earlier, while the windage losses remain constant. Fan size augmentation is a straightforward way for increasing airflow (Bleier, 1998). However, fan size is constrained by the space available in the fan cover and it increases the energy required for driving the fan. By varying the shape of the fan blades, the fan efficiency can be enhanced in such a way that the airflow is increased and a better cooling effect reduces the stator losses, core losses and rotor losses without increasing the energy absorbed for driving the fan. Therefore, shape variations generated with GAs may be useful in achieving best results considering the size constraints.
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Identifying the influence of the fan performance
For better identifying the effect of increasing the fan efficiency, two preliminary experiments were made: the first was a laboratory test and the second was an analytical calculation. In the first case, a motor was tested in a laboratory, eliminating the energy consumption of the fan by using an external fan cooling system. The internal fan was removed from the motor; therefore, the fan energy consumption was zero (an ideal situation). The efficiency increase measured was 1.09%.
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N. León-Rovira, E. Uresti and W. Arcos The mesh was generated inside of the 3D CAD system using an interface provided by the CFD software. The grid is set up with periodic boundaries on either side of the domain. The upstream boundary is defined by velocity and the downstream boundary by pressure. The single blade centred is assumed to be rotating, while the two even adjacent passages are stationary. The z-axis is used as a rotation axis for the reference frame. The fan spins clock-wise (looking from the positive end of the z-axis) at constant rotational speed. The fan rotation speed is defined by the motor at full load. The standard k –ε turbulence model (Abbott, 1989) and sea-level conditions were used. The 3D CAD model and the mesh generated are shown in Figure 1. The simulations were performed based on the defined boundary conditions. The simulation stops after convergence criteria are met. The convergence criteria are shown in the Figure 2. Each curve should become horizontal as criterion that the convergence was reached. A guideline followed is that the quantities in the calculation progresses change y6, y2 > y5 and y3 > y4, and minimum permitted thickness. Individuals with smaller thickness are considered invalids by the GA.
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Evaluation procedure
Using the GA described, the process flow is constituted by the steps shown in Figure 9. An initial population of 25 individuals was generated, which were seeded. As the evaluation process is complicated and the cycle could not be
GA depends on the data reported after the evaluation from each new individual has been done. Thus, it was constructed using PHP language where it runs on a web server, taking PHP code (GA) and a MySQL database as input and creating a web page to manage the information of the simulation. When a new evaluation is reported to the GA by any member of the team, the PHP script stores the information parameter and images related to the simulation process into the databases. A new individual is created, and the new parameters (six control points) are sent via e-mail to the team member with less evaluations pending. In order to assure that evaluations in the CFD package are consistent, a step-by-step manual was created and provided to each team member and exercises were performed before starting with the actual evaluation. Figure 10(a) shows how the members of this team report the results obtained in the analysis. All data obtained in the analysis were saved in the databases and it can be easily retrieved as a web page.
Fan shape optimisation using CFD and genetic algorithms Figure 10 (a) Report of results and (b) GA population
(a)
(b)
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N. León-Rovira, E. Uresti and W. Arcos
Figure 10(b) shows some individuals of the GA population in the web page. The best fan profile design generated by the GA is shown into the red circle. It will be discussed in the next section. The web page can be accessed in the following link http://lizt.mty.itesm.mx/~euresti/genetico/ home.htm.
GA shows that introducing changes in the profile it is possible to get a better aerodynamic performance than the results obtained by former profile curves. The results are shown in Figure 11. In Figure 12, the changes in the new fan profile generated by GA are shown. This kind of variation obtained by GA optimisation procedures is finer than the changes obtained by trial and error variations of experienced designers, who commonly do not have the patience and time for such minimal changes. The use of splines for changing profiles that are initially modelled with only circular arcs and lines seems to be a better solution for shape optimisation by using genetics algorithms for controlling the geometry through control points (Lamping, 2003).
10 Results The best fan profile generated by the GA presents a significant increase in the velocity vector’s magnitude of 9.4%, with respect to the current design, while the profile curve presented in Section 7.1 had an increment of 4.7%. Due to the time limits, the GA was not run until its final convergence; however, the best fan profile obtained from
Figure 11 (a) Velocity vectors (profile generated by GA) and (b) pressure distribution on the blade (profile generated by GA)
(a)
(b)
Fan shape optimisation using CFD and genetic algorithms
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Figure 12 (a) Original profile and (b) profile generated by GA
11 Conclusions Although requiring experimental validation, the results obtained by simulation indicate that optimising the fan blade shape through GAs increases the airflow in the motor. This improvement can be achieved by small shape modifications. The obtained shapes are not more expensive to manufacture than the original ones, as the fan is produced by injection moulding and the cost increment of the mould is negligible. The economic benefits obtained through efficiency increment may be substantial. The present research represents a step forward towards a new way of increasing the efficiency of TEFC induction motors through shape optimisation, using genetic algorithms and CFD techniques.
Acknowledgements The authors acknowledge the support received from Tecnológico de Monterrey through grant number CAT043, Research Chair Creativity and Innovation in Engineering of the Center for Innovation in Products and Technology (CIDYT) to carry out the research reported in this paper.
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Websites http://www.usmotors.com/. http://www.reliance.com/mtr/b7087_5/b7087_5_2.htm.
