Vol. , No. , 200x FAN SHAPE OPTIMIZATION USING CFD AND GENETIC
ALGORITHMS FOR INCREASING THE EFFICIENCY OF ELECTRIC MOTORS. Noel
Leon1, Eduardo Uresti2, Waldo Arcos3 Center for Innovation in
Design and Technology, ITESM, Campus Monterrey, Ave. Eugenio Garza
Sada #2501, Colonia Tecnolgico, Monterrey, CP 64841, Monterrey,
Mxico. [email protected], [email protected] Centro de
Inteligencia Artificial, ITESM, Campus Monterrey, Sucursal de
Correos J, CP 64849 Monterrey, Mxico. [email protected]
1
The purpose of this paper is to introduce a new way to increase
the efficiency of electric motors through shape optimization using
Genetic Algorithms. As known, 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 or external fans, a better cooling system in the motor
adds up to better efficiency. In recent years, a number of attempts
to improve motor efficiency have been achieved without compromising
motor performance but at higher costs. By using genetics
algorithms, 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 CFD
simulation software. Keywords: Shape optimization, genetic
algorithms, Shape parameterization, CFD.
1. INTRODUCTION Improving the energy management of electric
motors is relevant because improved efficiency can lead to
increased performance and slower growth in electricity demand. In
recent years, efforts have been made to minimize the
electromagnetic losses. When electromagnetic losses are too high, a
significant rise in temperature can affect the motor operation.
Losses due to electrical resistance take the form of heat, which
has to be dissipated. Using a better cooling system, these losses
can be reduced and the electric motor efficiency is improved. There
are different suggestions on how to increase the efficiency of an
electric motor, such as higher core-steel grade, closer
manufacturing tolerances, better insulation systems, slots
redesign, better bearings and reduced windage design. All these
options have been used throughout the years for improving electric
motor efficiency, but even though all these methods have caused
increments in electric motor efficiency, they increment the total
cost of the motor. The idea of achieving higher efficiency through
fan shape optimization using genetic algorithms is obtaining the
benefits without increasing the costs. Genetic algorithms have been
successfully applied in other shape optimization cases. Using CFD
software it is possible to simulate the effect of changing the fan
blades shapes with genetic algorithms. This article describes a new
way to increase the efficiency of the electric motor using the same
raw materials and manufacturing processes. The electrical design of
the motor will not be modified as the shape optimization of the fan
blades will increase the cooling effect and, therefore, the
electric motor efficiency. 2. MOTOR EFFICIENCY Electric motor
efficiency is a measure of the effectiveness with which a motor
converts electrical energy to mechanical energy. It is defined as
the ratio of power output to power input or, in terms of electrical
power, Watts output to Watts input and can be restated as the ratio
of output + losses: Motor Efficiency = Output Input = Output Output
+ losses
The losses are due to electrical losses plus friction and
windage. Even thought higher horsepower motors are typically more
efficient, their losses are significant and should not be ignored.
In fact, higher horsepower motors offer the greatest savings
potential for the least analysis effort, since just one motor can
save more money energy than several smaller motors [15]. 2.1 Watts
Loss Determine Motor Efficiency Every AC motor has five components
of electrical losses, which are the reasons for its inefficiency.
Electrical losses are converted into heat which is dissipated by
the motor frame aided by internal or external fans. Stator and
rotor I2R losses are caused by current flowing through the motor
winding and are proportional to the current squared times the
winding resistance (I2R). Iron losses are mainly confined to the
laminated core of the stator and rotor and can be reduced by
utilizing steels with
Copyright 200x Inderscience Enterprises Ltd.
Noel Leon, Eduardo Uresti and Waldo Arcos low core loss
characteristics found in high grade silicon steel. Friction and
windage losses is due to all sources of friction and air movement
in the motor and may be appreciable in large high-speed or totally
enclosed fan-cooled motors. The stay load loss is due mainly to
high frequency flux pulsations caused by design and manufacturing
variations [15]. 2.2 Improving efficiency by minimizing electrical
losses. Improvements in motor efficiency can be achieved without
compromising motor performance at higher cost within the limits of
existing design and manufacturing technology. The formula for
efficiency shows that any improvement in motor efficiency must be
the results of reducing electrical losses. In terms of the existing
state of electric motor technology, a reduction in electrical
losses can be achieved in various ways. All of these changes to
reduce motor losses are possible with existing motor design and
manufacturing technology. They would, however, require additional
materials and/or the use of higher quality materials and improved
manufacturing processes resulting in increased motor cost [15].
Watts Loss Area 1. Iron Efficiency Improvement Use of thinner
gauge, lower loss core steel reduces Eddy-current losses. Longer
core adds more steel to the design, which reduces losses due to
lower operating flux densities. Use of more copper and larger
conductors increases cross sectional area of stator windings. This
lowers resistance (R) of the windings and reduces losses due to
current flow (I). Use of larger rotor conductor bars increases size
of cross section, lowering conductor resistance (R) and losses due
to current flow (I). Use of low loss fan design reduces losses due
to air movement. Use of optimized design and strict quality control
procedures minimizes stray load losses.
2. Stator I 2 R
3. Rotor I 2 R 4. Friction & Windage 5. Stray Load Loss
Figure 1: Representation of Losses in an Electric Motor.
3. 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, 1) the temperature gradient between stator
coil to body machine for heat flow by conduction, which can be
solved analytically, and 2) the temperature difference between body
and air for heat flow by convection and radiation. For a totally
enclosed fan cooled (TEFC) machine, the problem of heat transfer
from machine body 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.
FAN SHAPE OPTIMIZATION USING CFD AND GENETIC ALGORITHMS FOR
INCREASING THE EFFICIENCY OF ELECTRIC MOTORS
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 in the stator core
and frame 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 on the application and the environment where the
motor will be working. For this research, we will use an electric
motor with TEFC (totally enclosed fan cooled) enclosure. In this
type of enclosure, an external fan pulls air in through a fan cover
and blows it over the exterior (only) surface of the motor. Heat
transfer occurs by forced convention when the air supplied by the
fan to the fan cover blows over the frame motor. 4. GENETIC
ALGORITHMS Genetic algorithms (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 randomized 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 bits and the pieces of the fittest of
the old; an occasional new part is tried for good measure. While
randomized, GAs are no simple random walk. They efficiently exploit
historical information to speculate on new search points with
expected improved performance [6]. Normally, when a GA is used for
function optimization, 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. A number
of different GAs have been proposed. From the simple Genetic
Algorithm [6] to other with different selection schemes as Genitor
[15]. In the simple GA each generation, the whole population is
replaced by a set of new individuals. The new set of individuals is
produced in pairs. In order to produce two new individuals, a pair
of individuals (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 of a
pair of new individuals by taking parts of the genetic material of
both parents. The expected effect is the combination of the
characteristics being presented 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 joint 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
few changes in the genetic material in both resulting new
individuals with low probability. Compared with the simple GA,
Genitor algorithm has some differences. Genitor produces one new
individual each generation; such individual replaces the worst
individual in the current population. As in the simple GA two
parents are selected, but such individuals are selected according
to the 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 string of characters [2]-[7], in such
applications the use of different crossover operator is required.
5. THE BETTER WAY Loss calculation is required for determining the
efficiency of the motor during the optimization process. The
following losses must be included in determining the efficiency: -
Stator I2R (Winding loss) - Rotor I2R (Rotor loss) - Core loss -
Stray load loss - Friction and windage loss There are two ways to
increase the efficiency of an electric motor. One way to increase
the electric motor efficiency is to minimize the energy absorbed by
the fan in the motor, keeping the same air flow rate. It will only
benefit the windage loss. The rest of the losses will not be
benefited. The gain in the efficiency is based in the minimization
of losses due to air movement. Another way to increase electric
motor efficiency is to increase the air flow rate provided by the
fan over the motor without increasing the energy absorbed for
driving the fan. It will benefit the rest of the losses mentioned
earlier and the windage loss remains constant. Fan size variations
are a straightforward way for increasing air flow. However, fan
size is constrained by the space available in the fan cover and it
increases the energy required for driving the fan.
Noel Leon, Eduardo Uresti and Waldo Arcos 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 genetic algorithms may be useful in achieving the
best results considering the size constraints 6. 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 a
computational simulation. In the first test, a motor was tested in
a laboratory. One motor was tested eliminating energy consumption
of the fan by using an external cooling system. The fan was removed
from the motor and it was tested in normal operation conditions.
With this test, the fan motor energy consumption was zero (an ideal
situation). The efficiency increase measured was 1.09%. This test
was made in a short time to prevent possible damage to the motor
due heat transfer. The second test was an analytical simulation.
The airflow was increased in the model by 50% without increasing
the energy consumption of the fan. The efficiency increase
calculated was 1.35%. After these tests it was concluded that for
increasing the efficiency of the motor the best way is incrementing
the airflow through efficiency augmentation of the fan. 7.
SIMULATION USING TWO DIFFERENT COMPUTATIONAL FLUID DYNAMICS (CFD)
PACKAGES
Figure 2: Fan of the electric motor.
The simulation was made using 2 different CFD simulation
programs. In the first case we used a 3D CAD system for modeling
the geometry and computational fluid dynamics (CFD) simulation
software with a dedicated mesh generation software. In the second
case we performed the same case study using a computational fluid
dynamics (CFD) simulation software integrated into a 3D CAD system
with meshing capabilities. 7.1 Simulation using 3D CAD, dedicated
mesh generation software and computational fluid dynamics (CFD)
software. Taking advantage of the periodicity of the geometry, it
is sufficient to model only one-eighth on the actual flow
environment.
Figure 3: 3D CAD Geometry, Mesh Model and Display of Surface
Grid.
FAN SHAPE OPTIMIZATION USING CFD AND GENETIC ALGORITHMS FOR
INCREASING THE EFFICIENCY OF ELECTRIC MOTORS
Simulating only one-eighth of the geometry the total
computational time consumed by the CFD program is accordingly
reduced.. The grid is set up with periodic boundaries on either
side of the domain (turquoise). The upstream boundary (blue) is
defined by velocity and the downstream boundary (red) by pressure.
The lower wall is assumed to be rotating with the blade (white),
while the upper wall is stationary (white). The z axis is used as
rotation axis for the reference frame. The fan spins clockwise
(looking from the positive end of the z axis) at constant
rotational speed. The fan rotation speed defined by the motor at
full load. Standard k - turbulence model and sea-level conditions
were used. After that, the boundary conditions were established so
we could start with the analysis. The solution stops after
convergence criteria are met. The first convergence criterion is a
residual plot, where the residual values are printed. Another
convergence criterion is the net mass/heat imbalance that indicates
when the solution has converged. It should be a small fraction of
the total flux through the system. The residual plot and the net
balance are shown in Fig. 4.
(a) Figure 4: (a) Residual History Plot. (b) Flux Report.
(b)
In figure 5 are shown the velocity vectors as found at the
midspan of the rotational area. The velocity vector plot provides
insight into the behavior of the flow around the fan blade. This
analysis is the basis for finding the variations of blade profiles
that increase the air flow.
Figure 5: Velocity vectors.
The pressure distribution on the blade is shown in Fig. 6.
(a) (b) Figure 6: (a) Positive Pressures. (b) Negative Pressures
(suction).
Noel Leon, Eduardo Uresti and Waldo Arcos The contour plots
illustrate the pressure rise on the fluid caused by the fan blade.
Positive pressures values occur on the surface where the fan blade
pushes the air and negative pressures (suction) on the other
surface. 7.2 Simulation using computational fluid dynamics (CFD)
simulation software integrated into 3D CAD system with meshing
capabilities The following analysis was made with the same boundary
conditions, turbulence model and operation conditions as the first
simulation. In this analysis, also a single blade of the fan was
simulated. For this analysis, the geometry was constructed as a
single blade centered within the region divided by two even
adjacent passages, and the mesh was generated inside of the 3D CAD
system using an interface provided by the CFD software.
(a) (b) Figure 7: (a) 3D CAD Geometry. (b) Mesh Model in CFD
program.
After that, the mesh was generated and all conditions were
established so we could start the analysis to determine the
solution convergence. The 3D CAD model and the mesh generated are
shown in Fig. 7. The convergence criteria are shown in the Fig. 8.
Each curve should become horizontal as criterion that the
convergence was reached. A good guideline to follow is that the
quantities in the calculation progresses change less than 5% over
the last 20% of the total iterations.
Figure 8: Plot curve convergence.
The behavior of the flow around the fan blade in this analysis
is identical to the former analysis. The quantitative results
differences are very little as may be appreciated at the velocity
vector graphics shown in Fig. 9.
FAN SHAPE OPTIMIZATION USING CFD AND GENETIC ALGORITHMS FOR
INCREASING THE EFFICIENCY OF ELECTRIC MOTORS
Figure 9: Velocity vectors (Second Simulation).
In Fig.10 the pressure distribution on the blade is shown. The
contour plots illustrate the pressure rise which is imposed on the
fluid by the fan blade as in the first simulation. As may be seen,
the pressure distribution is very similar with respect the first
analysis. The pressure values also differ little with respect to
the results commented above.
Figure 10: Pressure Distribution on the Blade (Second
Simulation).
7.3 Profile modification As mentioned before, the purpose of
this paper is to describe a new way to increase the efficiency of
the electric motor using shape optimization through genetic
algorithms. For this reason the blade profile requires to be
modified. For a first trial the straight profile of the fan blade
was substituted by a warped profile.
Figure 11: Fan Profile modification.
This new fan profile shape was simulated and analyzed in both
CFD programs. Both analyses showed an increment in the velocity
vectors magnitude. The results are shown in Fig.12.
Noel Leon, Eduardo Uresti and Waldo Arcos
(a)
(b)
(c) (d) Figure 12: (a) Velocity vectors (Profile
Curve-Simulation 1). (b) Pressure Distribution on the Blade
(Profile Curve-Simulation 1). (c) Velocity vectors (Profile
Curve-Simulation 2). (d) Pressure Distribution on the Blade
(Profile Curve-Simulation 2).
The velocity vectors magnitude was increased 4.7% with respect
to the actual design. The increment of the parameters measured
shows that is possible to get higher air flow provided by the fan
over the motor. By increasing the air flow the electric motor
efficiency can be improved as was discussed in the section 6. 8.
REPRESENTATION OF FAN BLADE FOR OPTIMIZATION Shape optimization,
also called topology optimization, deals with variations of the
form: find a shape (in two or three dimensions) which is optimal in
a certain sense, while satisfying certain requirements. In the
preceding section was already mentioned that by taking advantage of
the periodicity of the geometry, it is sufficient to model only one
fan blade. As can be seen, the fan blade has several profiles to be
parameterized. In order to simplify the shape optimization problem
only one profile will be modified and parameterized: the midspan of
the rotational area (half way up the fan blade) as shown in Fig 13.
The fan blade profile as was analyzed in section 7 using the CFD
programs will be codified below as chromosome in the genetic
algorithm.
Figure 13: Fan Blade Profile
FAN SHAPE OPTIMIZATION USING CFD AND GENETIC ALGORITHMS FOR
INCREASING THE EFFICIENCY OF ELECTRIC MOTORS
The population of individuals represent fan profile shapes.
Where, each fan profile shape is evaluated and represents a
possible solution. Following a strategy adopted by several
researches and the concepts studied in the preceding sections, the
shape parameterization is based on B-splines curves. B-Splines are
commonly used to define curved bodies and profiles. Six control
points are used in this case to control the whole fan profile shape
modification by the crossover operator, as shown in Fig. 14.
Figure 14: Control Points along the Profile.
The control points shown above (P1, P2, P3, P4, P5 & P6) are
defined by its x and y coordinates respectively; thus, P1 = ( x1 ,
y1 ), ., P6 = ( x6 , y6 ) . For simplifying the shape
parameterizatio the x values are fixed, and only the y coordinates
are allowed to vary. This way P1 = y1 , P2 = y2 ., P6 = y6
represents the search space, while Pi, Pi, Pf and Pf are fixed.
Therefore the chromosome will be represented only by the y
coordinates:
Chromosome = y1, y 2, y 3, y 4, y 5, y 6
F E5555F E5555 Lower Upper
A genetic algorithm will now introduce changes on the position
of the control points resulting in profile variations.
Traditionally, GAs use binary numbers to represent such strings: a
string has a finite length, and each bit of a string can be either
0 or 1. For real-valued function optimization, however, it is more
natural to use real numbers (as might be done with evolution
strategies or evolutionary programming). Real-number coding is used
here. The length of the real-number string corresponds to the
number of design variables. Genitor is the genetic algorithm
selected for sampling the search space, but in our case we never
remove an individual in the population. In order to keep the
selection pressure high, we dynamically set the selection bias
parameter to be 1.5 in a growing population. In the present
investigation we use the operator BLX- [7]: New = *P1 + (1- )*P2
Where P1, P2 are the parents, New is the new individual and is a
random number in the real interval [-0.5, 1.5]. Mutation operations
consist in adding to the new individual a vector of small random
numbers. 9. EVALUATION PROCEDURE. Using the genetic algorithm
discussed in section 4, the process flow is constituted by the
steps shown in Fig. 15. The initial population was generated with
25 individuals, which were seeded. Because the evaluation process
is complicated and the cycle could not be done yet automatically,
it was necessary to use an evaluation team. The evaluation team was
a group of specialists (Master degree students). Each possible
solution has been evaluated by one person of this team group. For
this reason, it was also necessary to create a web database (See
Fig. 16a) where each person could check the pending designs
assigned by the genetic algorithm to each team member.
Noel Leon, Eduardo Uresti and Waldo Arcos
Figure 15: Process Flow Diagram from GA.
In Fig. 16b, is shown how the members of this team group report
the results obtained in the analysis (Section 7 describes the
analysis). All data obtained in the analysis was saved in the web
page.
FAN SHAPE OPTIMIZATION USING CFD AND GENETIC ALGORITHMS FOR
INCREASING THE EFFICIENCY OF ELECTRIC MOTORS
(a)
(b)
Noel Leon, Eduardo Uresti and Waldo Arcos
(c) Figure 16: (a) Database Web Page. (b) Report of results. (c)
GA Population.Fig. 16c shows some individuals of the GA population
in the web page. In the red circle is the best fan profile design
generated by the GA. It will be discussed in the next section. 10.
RESULTS This project has not been finished. The Genetic algorithm
is still running and giving new fan profile designs to be
evaluated. The evaluation team continues to work in the
simulations. The best fan profile generated by the genetic
algorithm until this moment presents a significant increment of
9.4% with respect to the actual design. While the profile curve
presented in section 8 had an increment of 4.7%. The results are
shown in Fig. 17.
(a) (b) Figure 17: (a) Velocity vectors (Profile Generated by
GA). (b) Pressure Distribution on the Blade (Profile Generated by
GA)
As we see in Fig. 17a, the topological changes in the fan
profile have been minimal with respect the original shape, and it
has been a product of the changes introduced by the GA on the
position of the control points in the fan profile.
FAN SHAPE OPTIMIZATION USING CFD AND GENETIC ALGORITHMS FOR
INCREASING THE EFFICIENCY OF ELECTRIC MOTORS
11. CONCLUSIONS The results obtained until now indicate that
optimizing the fan blade shape through genetic algorithms increment
the air flow in the motor. Moreover, this improvement can be
achieved by small shape modifications. The use of splines in
profiles seems to be an ideal solution to resolve problems where
the designers wish to control the geometry through control points.
Thermal evaluation of TEFC induction motor needs to be considered
in order to achieve an increment in the efficiency. The economic
benefits obtained through efficiency increment may be substantial.
Although the research is not yet finished, it represents a step
forward towards a new way to increase the efficiency of an electric
motor through shape optimization using Genetic Algorithms and CFD
techniques. 12. ACKNOWLEDGMENT The computational time was provided
by Center for Innovation in Products and Technology, Tec de
Monterrey, (CIDYT, ITESM). The authors would like to thank Motores
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WEBSITE
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http://www.usmotors.com/
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