Download - Chicago UGM-8_ANSYS Conference - 08292011
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2011 ANSYS, Inc. November 23, 2014
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Scripting in Maxwell 2D for Computationally Efficient FEA with Optimization Algorithms
Gennadi Sizov and Peng Zhang
Marquette University Electrical and Computer Engineering
advisors: Professors Demerdash and Ionel
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2011 ANSYS, Inc. November 23, 2014
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Introduction Model-based optimization
difficulties choice of a modeling approach
Computationally Efficient Finite Element Analysis (CEFEA) Electric circuit symmetry
vector potentials, flux linkages, induced voltages
Magnetic circuit symmetry core loss calculation
Torque calculation
Design Optimization Objective function selection (single weighted
function vs. multi-objective) Optimization algorithm
Differential Evolution
Optimization of 9-slot, 6-pole IPM Motor Design objectives Optimization study (10,000 candidate designs)
eleven independent stator and rotor variables
significant design improvements are achieved
Scripting Maxwell 2D RMxprt geometric primitives Simple interfacing of Maxwell 2D to Matlab Optimization of Toyota Prius IPM motor
Overview
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Model-based optimization of electric machinery Difficulties:
Complex geometries (large number of geometric variables) Nonlinear material properties Multi-domain (physics) modeling (electromagnetic, thermal, stress, etc.) Multiple design objectives Population based design is problematic Search of large design spaces
Choice of modeling approach Tradeoffs
Speed vs. Accuracy Analytic/Lumped Parameter (MEC) vs. FEA
Computationally Efficient FEA [Trans. on IA 2010/2011, ECCE 2010, IEMDC 2011] Fast simulation of synchronous machinery Accurately estimates EMFs, average torques, cogging torque, on-load torque ripple, and
core losses.
Introduction
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Maximum Torque per Ampere MTPA Optimum operating point depends on design
variables Every design requires additional model evaluations increasing computational time
Field Weakening Operation Depends on design variables. Requires further design evaluations increasing computational time Computational savings are possible by employing
solutions from MTPA search
Difficulties in Optimization of Interior-PM Machines
0
5
10
15
20
0 30 60 90 120 150 180
Ave
rage
torq
ue [N
m]
Torque angle, [deg. el.]
0
0.25
0.5
0.75
1
1.25
0 1 2 3 4 5 6
Torq
ue [p
u]
Speed [pu]
Field Weakening Operation
Maximum Torque per Ampere MTPA
MTPA
MTPA
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10,000 designs in less than 51 hours! 6 static FE solutions (20 seconds/per design) CE-FEA (seconds) vs. time-stepping FE (minutes) several days vs. months! large scale FEA-based optimization studies
The fundamentals of CE-FEA fully exploits symmetries of electric and
magnetic circuits to reduce simulation time minimum number of magnetostatic solutions,
correlated with the maximum order of significant field harmonics
one-to-two orders of magnitude reduction of simulation time
suitable for most types of synchronous machines.
Computationally Efficient Finite Element Analysis (CEFEA)
5 10 15 20 250
5
10
15
20
25
30
35
Shaft Torque [Nm]
Torq
ue R
ippl
e [%
]
Goo
dnes
s [N
m/s
qrt(W
loss
)]
0.4
0.5
0.6
0.7
0.8
0.9
1
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What is not included in CE-FEA?
Why is time-stepping (transient) FEA still needed? PM loss Eddy currents in conductors Current regulation in unconventional controllers (which may
require coupled simulations with Simplorer) Motor-drive-controller simulations
Fault conditions Unbalanced operation
Machine asymmetries, for example due to tolerances, e.g. air-gap eccentricity
More detailed space (i.e. multiple points) and time (e.g. PWM switching) info for core losses
Computationally Efficient Finite Element Analysis (CEFEA)
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CE-FEA is applicable to design and analysis of both surface-PM and interior-PM machines.
Assumptions: Machine model is excited by instantaneous values of a set of
balanced three-phase sinusoidal currents. Assumption is well justified in motor-drive systems with well tuned
current regulators.
Principle: 2-D magnetostatic finite element formulation is utilized:
Instantaneous values of rotor position, mech, phase currents, iR, iY, iB,
are inputs to the model and the magnetic vector potentials (MVPs), A, are the outputs used in the post-processing stage to extract flux-densities, flux-linkages, energies (energy/co-energy).
Computationally Efficient Finite Element Analysis (CEFEA)
PMJJyA
yxA
x=
+
11
Determination of number of solutions and rotor positions
FEA 2-D (static)
Post Processing: Flux linkages, emfs Flux densities Energies (energy/co-energy) Torques Losses
mechiR ()
A
iY () iB ()
CE FEA
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Principle (flux linkages and back emfs): Symmetry of electric circuit results in the following:
where, A, is the average MVP in the coil side. Similar expressions can be developed for all the other coil sides.
From the coil side MVPs tooth fluxes, , and phase flux linkages, , can be estimated as follows:
where, lFe, is the effective stack length and, Nph, is the number of series turns per phase.
Computationally Efficient Finite Element Analysis (CEFEA)
( ) ( ) ++ =+ YoR AA 60( ) ( ) ++ =+ BoR AA 120
( ) ( ) ( )( ) + = RRFeR AAl
( ) ( ) RphR N =
AY+
AB+
AR+
AR-
AR+
R
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Principle (flux linkages and back emfs):
Flux linkages, , can be written in Fourier series form as:
Accordingly, resulting back emfs, e, can be written in Fourier series form as:
Here, the maximum harmonic order, vM, is related to the number of magnetostatic FE solutions, s, as follows:
Note: due to aliasing, the minimum number of FE solutions is limited by the highest harmonic present in the flux linkage waveform.
Computationally Efficient Finite Element Analysis (CEFEA)
( ) ( )=
+=M
R
1cos
13 = sM
( ) ( )=
+==M
dtd
dde RR
1sin
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( ) ( ) ++ =+ YoR AA 60( ) ( ) ++ =+ BoR AA 120
Principle (flux linkages and back emfs): Coil side MVP, AR+
-0.0100
-0.0080
-0.0060
-0.0040
-0.0020
0.0000
0.0020
0.0040
0.0060
0.0080
0.0100
0 20 40 60 80 100 120 140 160 180
[Wb/
m]
[deg. el.]
R+
Y+
B+
-0.01
-0.008
-0.006
-0.004
-0.002
1.2E-17
0.002
0.004
0.006
0.008
0.01
0 20 40 60 80 100 120 140 160 180
[Wb/
m]
[deg. el.]
R+
Y+
B+
-0.0100
-0.0080
-0.0060
-0.0040
-0.0020
0.0000
0.0020
0.0040
0.0060
0.0080
0.0100
0 20 40 60 80 100 120 140 160 180
[Wb/
m]
[deg. el.]
R+
Y+
B+
Computationally Efficient Finite Element Analysis (CEFEA)
Five magnetostatic FE solutions
AY+
AB+
AR+
R
One magnetostatic FE solution
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Principle (flux linkages and back emfs): Coil side MVP, AR+ 5 - solutions Assuming lack of even order harmonics (half-wave symmetry)
Computationally Efficient Finite Element Analysis (CEFEA)
Five magnetostatic FE solutions
-0.0100-0.0080-0.0060-0.0040-0.00200.00000.00200.00400.00600.00800.0100
0 60 120 180 240 300 360
[Wb/
m]
[deg.el.]
Single magnetostatic FE solution yields six equally spaced points on MVP waveform! Using five static FE solutions yeilds thirty samples of MVP Flux EMF!
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Principle (flux linkages and back emfs):
Selection of the number of static solutions Has to be chosen to avoid aliasing in Fourier series Example 9-slot, 6-pole IPM with 5-static FE solutions (no aliasing) Spectrum of back emf, eR Rated-load, 3600 r/min
Computationally Efficient Finite Element Analysis (CEFEA)
0255075
100125150175200225250
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
EM
F H
arm
onic
Mag
nitu
de, |
e a| [
V]
Harmonic Order
Estimated from 5 solutions
No significant harmonic content
beyond 13th order
no aliasing
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Principle (flux linkages and back emfs): Verification (5-solutions): With respect to time-stepping FE Phase flux-linkage, R, and back emf, eR Open-Circuit, 3600 r/min
Computationally Efficient Finite Element Analysis (CEFEA)
( ) ( )=
+=M
R
1cos ( ) ( )
=
+==M
dtd
dde RR
1sin
-300
-200
-100
0
100
200
300
0.00 2.78 5.56 8.33 11.11 13.89 16.67Time, [ms]
EMF,
eR
, [V]
-300
-200
-100
0
100
200
3000 60 120 180 240 300 360
Rotor Position, [el. deg.]
TSFEeq. (3)
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.00 2.78 5.56 8.33 11.11 13.89 16.67Time, [ms]
Flux
Lin
kage
, R
, [W
b]
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.20 60 120 180 240 300 360
Rotor Position, [el. deg.]
TSFEeq. (2)
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Principle (flux linkages and back emfs): Verification (5-solutions): With respect to time-stepping FE Phase flux-linkage, R, and back emf, eR Rated-load, 3600 r/min
Computationally Efficient Finite Element Analysis (CEFEA)
( ) ( )=
+=M
R
1cos ( ) ( )
=
+==M
dtd
dde RR
1sin
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.20 60 120 180 240 300 360
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.00 0.93 1.85 2.78 3.70 4.63 5.56
Rotor Position [deg. el.]
Flux
Lin
kage
, a
[Wb]
Time [ms]
TSFESamples (5 solutions)eq. (2)
-300
-200
-100
0
100
200
3000 60 120 180 240 300 360
-300
-200
-100
0
100
200
300
0.00 0.93 1.85 2.78 3.70 4.63 5.56
Rotor Position [el. deg.]
Indu
ced
Vol
tage
, ea
[V]
Time [ms]
TSFEeq. (3)
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Principle (stator core flux densities): Magnetic circuit symmetry
Symmetry of magnetic circuit [PAS 1981, IAS 2011] results in the following relationships for elemental radial and tangential components of stator core flux-densities at different rotor positions:
where, k, is a positive integer, s, is the slot-pitch in electrical measure.
Computationally Efficient Finite Element Analysis (CEFEA)
( )strstr krtBrktB
+=
+ ,,,, ,,
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Principle (stator core flux densities): Magnetic circuit symmetry
Using the same magnetostatic solutions used for estimation of flux-linkages and back emfs and assuming the lack of even order harmonics (half-wave symmetry), Fourier series of elemental flux densities can be created:
Radial and tangential components of elemental flux densities will be used for estimation of stator core losses (efficiency).
Computationally Efficient Finite Element Analysis (CEFEA)
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
0 60 120 180 240 300 360
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
0.00 2.78 5.56 8.33 11.11 13.89 16.67
Rotor Position, [el. deg.]
Flux
Den
sity,
[T]
Time, [ms]
s 2s
e1
e2
e3
( ) ( )=
+=M
BB tr
1, cos
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Principle (stator core flux densities): Magnetic circuit symmetry Verification (5-solutions):
With respect to time-stepping FE Flux densities at locations: 1, 2 Full-load, 3600 r/min
Computationally Efficient Finite Element Analysis (CEFEA)
Location 1 (Yoke) Location 2 (Tooth-Yoke Junction)
-2
-1.5
-1
-0.5
0
0.5
1
1.5
20 60 120 180 240 300 360
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
0.00 0.93 1.85 2.78 3.70 4.63 5.56
Rotor Position [el. deg.]
Flux
Den
sity
[T]
Time [ms]
B r TSFE magnify x10B r eq. (11) magnify x10B t TSFEB t eq. (11)
-2
-1.5
-1
-0.5
0
0.5
1
1.5
20 60 120 180 240 300 360
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
0.00 0.93 1.85 2.78 3.70 4.63 5.56
Rotor Position [el. deg.]
Flux
Den
sity
[T]
Time [ms]
B r TSFE B t TSFEB r eq. (11) B t eq. (11)
4
3
2
1
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Principle (stator core flux densities): Magnetic circuit symmetry Verification (5-solutions):
With respect to time-stepping FE Flux densities at locations: 1, 2 Full-load, 3600 r/min
Computationally Efficient Finite Element Analysis (CEFEA)
Location 3 (Tooth) Location 4 (Tooth Tip)
-2.5-2-1.5-1-0.500.511.522.5
0 60 120 180 240 300 360
-2.5-2
-1.5-1
-0.50
0.51
1.52
2.5
0.00 0.93 1.85 2.78 3.70 4.63 5.56
Rotor Position [el. deg.]
Flux
Den
sity
[T]
Time [ms]
B r TSFEB t TSFE magnify x10B r eq. (11)B t eq. (11) magnify x10
-2.5-2-1.5-1-0.500.511.522.5
0 60 120 180 240 300 360
-2.5-2
-1.5-1
-0.50
0.51
1.52
2.5
0.00 0.93 1.85 2.78 3.70 4.63 5.56
Rotor Position [el. deg.]
Flux
Den
sity
[T]
Time [ms]
B r TSFE B t TSFEB r eq. (11) B t eq. (11)
4
3
2
1
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( ) ( )=
+=W
mcogmstored N
1
cosWW
( )=
+=W
mcogcogm
stored NNd
d
1
sin WW
Principle (torque calculation): Creating a Fourier series of the energy stored in the magnetic circuit:
Taking the derivative:
Using flux-linkages obtained earlier the electromagnetic torque can be estimated using the following expression:
Computationally Efficient Finite Element Analysis (CEFEA)
( ) ( ) ( ) ( )( ) ( ) ( )( )
( )
=
===
++
+++++=
++=
W
MMM
mech
oB
oYR
mech
storedBB
YY
RRem
W
iiiP
ddW
ddi
ddi
ddiPT
1
111
sin
240sin120sinsin2
2
alignment and reluctance cogging
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Principle (torque calculation): Cogging torque With respect to time-stepping FE Open-circuit, 3600 r/min
Computationally Efficient Finite Element Analysis (CEFEA)
16.7
16.8
16.9
17
17.1
17.2
17.3
17.40 10 20 30 40 50 60
-3-2.5
-2-1.5
-1-0.5
00.5
11.5
22.5
3
0 10 20 30 40 50 60
Stored E
nergy [J]
Cog
ging
Tor
que
[Nm
]
Rotor Position [el. deg.]
Cogging - TSFE Cogging - eq. (8)Energy (5 solutions) Energy - eq. (5)
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Principle (torque calculation): On-load With respect to time-stepping FE Electromagnetic torque Open-circuit, half-load, rated-load (3600 r/min)
Computationally Efficient Finite Element Analysis (CEFEA)
-5
0
5
10
15
200 60 120 180 240 300 360
-5
0
5
10
15
20
0 60 120 180 240 300 360
Torq
ue [N
m]
Rotor Position [el. deg.]
eq. (8)TSFE
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Principle (torque calculation):
Average torque calculation Error in estimation of the average torque as a function of number of static FE
solutions. Compared to TSFE (based on Maxwell Stress Tensor)
Computationally Efficient Finite Element Analysis (CEFEA)
-1-0.5
00.5
11.5
22.5
33.5
44.5
55.5
1 2 3 4 5 6 7 8 9 10
Ave
rage
torq
ue e
stim
atio
n er
ror [
%]
Number of magnetostatic FE solutions, s
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Model-based optimization: Conflicting requirements on the modeling approach used for optimization
(accuracy vs. execution time). Need for search of very large design spaces to find optimum design. Large number of design variables (geometry, materials, etc.) CE-FEA can be used for fast and accurate evaluation of very large number of
candidate designs.
Design Optimization
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Objective function selection: Single weighted objective function:
Pros: simple implementation Cons: choice of weights, wn, conflicting vs. non-conflicting objectives
Multi-Objective (Pareto-based Optimization)
Pros: meaningfully accounts for tradeoffs between multiple design goals, no weights, results in a family of best compromise designs
Cons: implementation
Design Optimization
=
=N
nnn fwf
11 )(x
FeCu
em
pkpk( em
PPT
f :maximize
Tf :minimize
+=
=
2
)1 min(Torque Ripple)
max(Goodness) measure of average electromagnetic torque with respect to total losses
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][ qPPMPMTTSSTSi w, ,h , w, ,d ,d ,l , wg, ,D =x
9-slot, 6-pole IPM: 11 geometric variables
Only outer diameter is fixed 7Arms/mm2, 0.3 slot fill factor Search of MTPA for every design
Optimization of 9-slot, 6-pole IPM Motor
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Optimization results: Generations = 100, Population = 100 Total of 10,000 candidate design evaluations
51 hours on a single core (single license) Search of maximum torque per amp (MTPA ) for every design
Optimization of 9-slot, 6-pole IPM Motor
M-1
M-2 M-3
Typ
M-1: High Specific Torque
M-2: Compromise between Specific Torque and Ripple
M-3: Low Ripple
Typ: Machine of Normal Proportions
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Optimized machines:
Optimization of 9-slot, 6-pole IPM Motor
M-1: High Specific Torque
M-2: Compromise Specific Torque and Ripple
M-3: Low Ripple
Typ: Machine of Normal Proportions
max. Bmid-tooth = 1.75T, min. BPM = 0.76T
max. Bmid-tooth = 1.71T, min. BPM = 0.75T
max. Bmid-tooth = 1.65T, min. BPM = 0.73T
max. Bmid-tooth = 1.67T, min. BPM = 0.78T
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Electromagnetic Torque at Rated-Load (MTPA): Verified with time-stepping FEA (2nd order elements)
Optimization of 9-slot, 6-pole IPM Motor
14
15
16
17
18
19
20
21
22
0 10 20 30 40 50 60
Elec
tro m
agne
tic to
rque
[Nm
]
Position [deg. el.]
M-1 Tem = 20.84Nm, Ripple = 4.93%
M-2 Tem = 18.55Nm, Ripple = 2.72%
M-3 Tem = 15.79Nm, Ripple = 0.45%
Typ Tem = 15.21Nm, Ripple = 10.64%
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Electromagnetic Torque at Open-Circuit (Cogging): Verified with time-stepping FEA (2nd order elements)
Optimization of 9-slot, 6-pole IPM Motor
-1.00
-0.75
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
0 10 20 30 40 50 60
Elec
tro m
agne
tic to
rque
[Nm
]
Position [deg. el.]
M-1 Tpk-pk = 0.41Nm M-2 Tpk-pk = 0.29Nm M-3 Tpk-pk = 0.19Nm Typ Tpk-pk = 1.87Nm
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7.5Hp rating (15Nm, 3600 r/min) axial-length is scaled to achieve the desired rating
Optimization of 9-slot, 6-pole IPM Motor
Axial length PM Mass
0.931.05
1.25
1.00
0
0.25
0.5
0.75
1
1.25
1.5
M-1 M-2 M-3 Typ.
PM M
ass
[pu]
0.75 0.810.92
1.00
0
0.25
0.5
0.75
1
1.25
M-1 M-2 M-3 Typ.
Tota
l Mac
hine
Mas
s [pu
]
Total Mass
0.730.82
0.96 1.00
0
0.25
0.5
0.75
1
1.25
M-1 M-2 M-3 Typ.
Axia
l Len
gth
[pu]
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Separation of Losses 7.5Hp rating (15Nm, 3600 r/min) Equal split between copper and core losses for optimized designs
Optimization of 9-slot, 6-pole IPM Motor
182.7 167.4 170.8 206.1
164.9 170.5 184.0167.2
050
100150200250300350
M-1 M-2 M-3 Typ.
Pow
er L
oss
[W]
Copper
Core
347.6W 337.9W 354.8W 373.3W
Note a 9.5% reduction of losses with optimized machine M-2!
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RMxprt geometric primitives Large number of predefined parameterized
geometries: Stator slot shapes Rotor topologies (various interior-PM and
surface-PM layouts) Primitives are accessible through scripting Custom geometries can be created through low-
level primitives such as lines, arcs, etc.
Simple interfacing of Maxwell 2D to third party software Communicates to any software that uses
Common Object Module (COM) Matlab, MS Office Excel, Visual Basic, etc.
Optimization exercise for Toyota Prius IPM type motor 48-slot, 8-pole, V-shaped IPM rotor topology Three parameters:
PM width, PM length, V depth
Scripting Maxwell 2D
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Advice/hint: Use Tools\Record Script To File to get an idea of how to write your own script!
ENABLE Record Script To File PERFORM the functions using Graphical User Interface (GUI)
THEN look at the recorded text file to get an idea how to script.
Opening Maxwell from Matlab environment Create COM object, open Maxwell, start new Maxwell 2D project, select transient
solver % Maxwell COM object: iMaxwell=actxserver('AnsoftMaxwell.MaxwellScriptInterface'); Desktop=iMaxwell.GetAppDesktop(); % remove set from the object definitions Desktop.RestoreWindow % Create project in Maxwell or open an existing project Project=Desktop.NewProject; invoke(Project,'Rename','C:\Users\labadmin\Desktop\Optimization
Project\Scripting\ANSOFT\Scripting\IPM2.mxwl',true) invoke(Project,'InsertDesign','Maxwell 2D','Design1','Transient','') Design=Project.SetActiveDesign('Design1'); invoke(Design,'SetSolutionType','Transient','XY') Editor=Design.SetActiveEditor('3D Modeler'); invoke(Editor,'SetModelUnits',{'NAME:Units Parameter','Units:=','mm','Rescale:=',false}) %
Setup units [mm]
Scripting Maxwell 2D
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Execution of Maxwell models from Matlab environment Changing Maxwell params. from Matlab
% Input parameters Imax=250; % Imax: the magnitude of phase currents Thet_deg=20; % Thet_deg: the load angle in degree, initial value: 20deg O2=7.28; % O2: Distance form duck bottom to shaft surface TM=6.48; % TM: Magnet thickness WM=32; % WM: Total width of all magnet per pole
Passing values to Maxwell using invoke/ChangeProperty commands Make sure to add units to all dimensions (using Matlabs strcat function). Also change all numerical values to strings (using Matlabs num2str function) before passing
them to Maxwell. O2S=strcat(num2str(O2),'mm');TMS=strcat(num2str(TM),'mm');WMS=strcat(num2str(WM),'mm'); % Change these 5 parameters: invoke(Design,'ChangeProperty',{'NAME:AllTabs',{'NAME:LocalVariableTab',{'NAME:PropServers','LocalVariab
les'},... {'NAME:ChangedProps',{'NAME:Imax','Value:=',num2str(Imax)}}}}) invoke(Design,'ChangeProperty',{'NAME:AllTabs',{'NAME:LocalVariableTab',{'NAME:PropServers','LocalVariab
les'},... {'NAME:ChangedProps',{'NAME:Thet_deg','Value:=',num2str(Thet_deg)}}}}) invoke(Design,'ChangeProperty',{'NAME:AllTabs',{'NAME:LocalVariableTab',{'NAME:PropServers','LocalVariab
les'},... {'NAME:ChangedProps',{'NAME:O2','Value:=',num2str(O2S)}}}}) invoke(Design,'ChangeProperty',{'NAME:AllTabs',{'NAME:LocalVariableTab',{'NAME:PropServers','LocalVariab
les'},... {'NAME:ChangedProps',{'NAME:TM','Value:=',num2str(TMS)}}}}) invoke(Design,'ChangeProperty',{'NAME:AllTabs',{'NAME:LocalVariableTab',{'NAME:PropServers','LocalVariab
les'},... {'NAME:ChangedProps',{'NAME:WM','Value:=',num2str(WMS)}}}})
Scripting Maxwell 2D
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Execution of Maxwell models from Matlab environment Running the simulation
% Run program: invoke(Design,'Analyze','Setup1');
Extracting results (Post-processing) % Create reports of flux linkages, energy and torque: Module=Design.GetModule('ReportSetup'); % Create the report of three phase flux linkages: invoke(Module,'CreateReport','XY Plot 1','Transient','Rectangular Plot','Setup1 :
Transient',{'Domain:=','Sweep'},... 'Time:=',{'All'},'Poles:=',{'Nominal'},'Speed_rpm:=',{'Nominal'},'Thet_deg:=',{'Nominal'},'Imax:=',{'Nominal
'}},... {'X Component:=','Time','Y
Component:=',{'FluxLinkage(PhaseA)','FluxLinkage(PhaseB)','FluxLinkage(PhaseC)'}},{}) invoke(Module,'RenameReport','XY Plot 1','FluxLinkages') % Create the report of torque: invoke(Module,'CreateReport','Torque','Transient','Rectangular Plot','Setup1 :
Transient',{},{'Time:=',{'All'},'Poles:=,{'All'},'Speed_rpm:=',{'All'},'Thet_deg:=',{'All'},'Imax:=',{'All'}},{'X Component:=','Time','Y Component:=',{'Moving1.Torque'}},{})
% Calculate the energy: Module=Design.GetModule('FieldsReporter'); invoke(Module,'EnterQty','Energy') invoke(Module,'EnterVol','AllObjects') invoke(Module,'CalcOp','Integrate') invoke(Module,'AddNamedExpression','WEnergy','Fields') % Create the report of energy: invoke(Module,'CreateReport','XY Plot 2','Fields','Rectangular Plot','Setup1 :
Transient',{'Domain:=','Sweep'},... {'Time:=',{'All'},'Poles:=',{'Nominal'},'Speed_rpm:=',{'Nominal'},'Thet_deg:=',{'Nominal'},'Imax:=',{'Nomina
l'}, 'O2:=',{'Nominal'},'TM:=',{'Nominal'},'WM:=',{'Nominal'}},{'X Component:=','Time','Y Component:=',{'WEnergy'}},{})
invoke(Module,'RenameReport','XY Plot 2','Energy')
Scripting Maxwell 2D
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Execution of Maxwell models from Matlab environment
Saving results % Export all data to .csv files: invoke(Module,'ExportToFile','FluxLinkages','C:\Users\labadmin\Desktop\Optimization
Project\Scripting\ANSOFT\Scripting\Fluxlinkages.csv') % The file path can be changed invoke(Module,'ExportToFile','Energy','C:\Users\labadmin\Desktop\Optimization
Project\Scripting\ANSOFT\Scripting\Energy.csv') % The file path can be changed invoke(Module,'ExportToFile','Torque','C:\Users\labadmin\Desktop\Optimization
Project\Scripting\ANSOFT\Scripting\Torque.csv') % The file path can be changed
These basic step summarize show how to:
Create a Maxwell 2D model using RMxprt primitives Change model parameters from Matlab environment Solve using Maxwell Extract the results
Scripting Maxwell 2D
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Toyota Prius IPM type motor: Objective function: Single objective function:
Single constraint maintain torque of 243Nm Goal reduce PM usage while maintaining same torque!
Differential Evolution Optimizer Settings Three design variables (rotor only)
PM length, PM width, V-depth Strategy DE/best/1 with jitter, F = 0.85, Cr = 1 Np = 15 candidates per population Ng = 10 generations Total candidate design evaluations = Ng*Np = 150 designs Simulation time approximately 30 minutes
Optimization exercise
PM Mass :maximize 1
emTf =
PM length
PM depth
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Optimization exercise
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Toyota Prius IPM type motor:
Optimization results Original motor (Toyota)
PM mass = 2.1 kg @ 243Nm Optimized motor
PM mass = 1.75 kg @ 245Nm Possible reduction of up to 20% of PM mass
Optimization exercise
Original (Toyota) Optimized
min. BPM = 0.65 T min. BPM = 0.61 T
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1. F. A. Fouad, T. W. Nehl, and N. A. O. Demerdash, Magnetic field modeling of permanent magnet type electronically operated synchronous machines using finite elements, IEEE Trans. on PAS, vol. PAS-100, no. 9, pp. 4125-4133, 1981
2. D. M. Ionel and M. Popescu, Finite element surrogate model for electric machines with revolving field - application to IPM motors, IEEE Trans on Ind. Apps., vol. 46, no.6, pp. 2424-2433, Nov/Dec 2010.
3. G. Y. Sizov, D. M. Ionel, and N.A.O. Demerdash, Modeling and design optimization of PM AC machines using computationally efficient finite element analysis, IEEE Energy Conversion Congress and Exposition ECCE, pp. 578-585, Atlanta, Georgia, September 2010, updated version accepted for publication at IEEE Transactions on IES.
4. G. Y. Sizov, D. M. Ionel, and N.A.O. Demerdash, Multi-Objective Optimization of PM AC Machines Using Computationally Efficient - FEA and Differential Evolution, IEEE International Conference on Electric Machines and Drives IEMDC, pp. 1537-1542, Niagara Falls, Canada, May 2011.
5. G. Y. Sizov, D. M. Ionel, and N.A.O. Demerdash, A Review of Efficient FE Modeling Techniques with Applications to PM AC Machines, IEEE Power and Energy Society General Meeting (PES-2011), Detroit, Michigan, July 24-28 2011.
6. G. Y. Sizov, P. Zhang, D. M. Ionel, N.A.O. Demerdash, and M. Rosu, Automated Multi-Objective Design Optimization of Integral-MW Direct-Drive PM Machines Using CE-FEA, IEEE Energy Conversion Congress and Exposition ECCE 2011, Phoenix, Arizona, September 2011.
7. K. V. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution - A Practical Approach to Global Optimization, Springer-Verlag Berlin Heidelberg, 2005.
References
Scripting in Maxwell 2D for Computationally Efficient FEA with Optimization AlgorithmsOverviewIntroductionDifficulties in Optimization of Interior-PM MachinesComputationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Computationally Efficient Finite Element Analysis (CEFEA)Design OptimizationDesign OptimizationOptimization of 9-slot, 6-pole IPM MotorOptimization of 9-slot, 6-pole IPM MotorOptimization of 9-slot, 6-pole IPM MotorOptimization of 9-slot, 6-pole IPM MotorOptimization of 9-slot, 6-pole IPM MotorOptimization of 9-slot, 6-pole IPM MotorOptimization of 9-slot, 6-pole IPM MotorScripting Maxwell 2DScripting Maxwell 2DScripting Maxwell 2DScripting Maxwell 2DScripting Maxwell 2DOptimization exerciseOptimization exerciseOptimization exerciseReferences