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NAFEMS
Optimization of Electrical
Machines in the Cloud with
SyMSpace
Siegfried Silber
LCM GmbH
Andreas Ocklenburg
CloudSME UG
NAFEMS
SyMSpace Simulation Environment
SyMSpace
Center
COMPONENT
SPACE
WEB
INTERFACE
TOOL SPACE
COMPUTING
RESOURCES
Center slave /
Tool slaves
Storage
database
interface
model
configurator
data
visualization
multi-parameter
optimization
academia business
open-source-
community
rotor
dynamicsmagnetic bearing
PMSM
design
pump design
antenna design
ANSYS
CAD
Software
X2C
HOTINT
MODELICA
Cloud resources: amazon,
Cloudsigma, ...
Local CPU
JKU-LCM cluster
NAFEMS
SyMSpace Center
• Add simulation components to
project structure
• Configure and control component
interaction
• Create user defined functions,
customized post-processing, extra
visualizations, etc.
DetailbereichModellbaum,
Wertedarstellung
Designvarianten
Mitteilungsbereich
Model tree,
parameter setting
Design variants
Detail view
Log area,
python console
NAFEMS
Setup of Simulation Workflow
• Setup of a project with predefined simulation
Components.
• Simulation Components are available for various fields
of engineering.
• Simulation chains can be set up by combining
Components.
NAFEMS
Combination of Components
Stator
Interior
Magnetic
FEA
Rotor PM
Interior
requires
is
is
dq-Model
provides
requires
NAFEMS
Simulation based on Components
Example: Turbo Generator
ShaftTurbo
Control
Unit
Bearing
B
NAFEMS
Scripting Interfaces for SyMSpace
• SyMSpace offers scripting and script embedding
NAFEMS
Simulation of Permanent Magnet Synchronous
Machines (PMSM)
• Winding is designed fully automatically.
• To speed up simulation only a sector is calculated based on the winding design.
• Example: 12 slots, 5 pol pairs, 3 phases
Negative symmetry is used for simulation
NAFEMS
PMSM Examples
N=12, p=5, m=3 N=12, p=4, m=3
NAFEMS
Simulation Workflow
Specification
Tn=2Nm
n=3000rpm …
Material
Model Setup
Geometry
Winding
FE-Simulation
Motor Loadpoint Short Circuit
Demagnetisation
Stack length, Skewing, …
MagTwin
( , , )
( , , )
d d s d d q q
q q s q d q d
u r i
u r i
 
 


       
       
NAFEMS
Motor Model for fast Simulation: MagTwin
Generic MagTwin model structure
NAFEMS
Motor Model for fast Simulation: MagTwin
• Interpolation of simulated flux in dq-rotor reference frame with radial basis functions (RBF) of
the form:
• For functions of two variables
the thin-plate spline kernel is used
with
• Linear term
NAFEMS
Motor Model for fast Simulation: MagTwin
Specification
– Generalized Digital Twin which implements the physical behavior of an electromagnetic
system
Model
– Functional Mockup Unit implementation
Steady State Analysis Transient Simulation
NAFEMS
AC Loss Calculation (I)
Proximity losses in slot area
– Losses due to PM excitation
are included
– Displacement currents are considered
Losses in parallel wires
– E.g. insert winding with parallel wires
B
worst case
distribution
realistic
distribution
NAFEMS
AC Loss Calculation (II)
Eddy current losses caused by PWM modulation
– Includes losses in laminated stack, wires, permanent magnet, solid materials, …
NAFEMS
Multiphysics Simulations for PMSM
Rotor stress simulation
– Calculation of rotor stress due to
centrifugal force and shrink fit
– Evaluation stress, strain,
plastic deformation and
transmittable torque
Thermal simulation
– Steady-state heat conduction with
finite element analysis
– Thermal networks to consider 3D effects
Rotor stress distribution
StressMises/N/m2
NAFEMS
Multi-Objective Optimization (I)
0 60 120 180 240 300 360 420 480 540 600 660 720
-50
-40
-30
-20
-10
0
10
20
30
40
50
el
/ °
U
emf
/V
Current = 0.0 Arms
Current = 0.7 Arms
SyMSpace
NAFEMS
Multi-Objective Optimization (II)
Pareto optimal design
Pareto optimal solution:
A solution is Pareto optimal if there
exists no feasible solution for
which an improvement in one
objective does not lead to a
simultaneous degradation in one
(or more) of the other objectives.
Pareto
optimal designs
Material
Costs
Verluste
Design variants
Losses
Solution space
NAFEMS
Multi-Objective Optimization Algorithms
• Grid
Calculates any possible parameter combination.
Requires huge amount of calculation power
• Generational NSGA-II
(Non-dominated Sorting Genetic Algorithm II)
Steady State Async NSGA-II
• Generational SPEA2
(Strength Pareto Evolutionary Algorithm 2)
Steady State Async SPEA2
• DECMO
(Differential Evolution-based, Coevolutionary
Multi-objective Optimization algorithm)
see next page
Generation based vs. steady state
algorithm
NAFEMS
Multi-Objective Optimization Algorithms
DECMO algorithm
Improved convergence of the Pareto front in comparison to the other algorithms.
Combines two different multi-objective optimization algorithms.
NAFEMS
Hybrid Optimization Method
For complex simulations a surrogate model based on
artificial neural networks (ANNs) is created.
This surrogate model is created during the
optimization fully automatic on-the-fly.
Optimization speed can significantly
be improved.
NAFEMS
Cluster on demand portal
22
Created & owned by LCM
Accessed by LCM customers
(end users): engineers
Created by cloudSME,
hosted by LCM
accessed by LCM staff
Hosted by cloudSME in Germany
Accessed by LCM & cloudSME staff
NAFEMS
Cluster on demand portal
NAFEMS
Cluster on demand portal
NAFEMS
Thanks for your interest!
Responsible for SyMSpace
LINZ CENTER OF MECHATRONICS GMBH
Science Park I
Altenberger Straße 66
4040 Linz
Austria
+43 732 2468-6002
office@lcm.at
www.lcm.at
Responsible for the cloud concept &
technology:
CloudSME UG
TecTower
Bismarckstr. 142
47057 Duisburg
Germany
+49 203 3639 9955
ocklenburg@cloudsme.eu
www.cloudsme.eu
25

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Optimization of Electrical Machines in the Cloud with SyMSpace by LCM

  • 1. NAFEMS Optimization of Electrical Machines in the Cloud with SyMSpace Siegfried Silber LCM GmbH Andreas Ocklenburg CloudSME UG
  • 2. NAFEMS SyMSpace Simulation Environment SyMSpace Center COMPONENT SPACE WEB INTERFACE TOOL SPACE COMPUTING RESOURCES Center slave / Tool slaves Storage database interface model configurator data visualization multi-parameter optimization academia business open-source- community rotor dynamicsmagnetic bearing PMSM design pump design antenna design ANSYS CAD Software X2C HOTINT MODELICA Cloud resources: amazon, Cloudsigma, ... Local CPU JKU-LCM cluster
  • 3. NAFEMS SyMSpace Center • Add simulation components to project structure • Configure and control component interaction • Create user defined functions, customized post-processing, extra visualizations, etc. DetailbereichModellbaum, Wertedarstellung Designvarianten Mitteilungsbereich Model tree, parameter setting Design variants Detail view Log area, python console
  • 4. NAFEMS Setup of Simulation Workflow • Setup of a project with predefined simulation Components. • Simulation Components are available for various fields of engineering. • Simulation chains can be set up by combining Components.
  • 5. NAFEMS Combination of Components Stator Interior Magnetic FEA Rotor PM Interior requires is is dq-Model provides requires
  • 6. NAFEMS Simulation based on Components Example: Turbo Generator ShaftTurbo Control Unit Bearing B
  • 7. NAFEMS Scripting Interfaces for SyMSpace • SyMSpace offers scripting and script embedding
  • 8. NAFEMS Simulation of Permanent Magnet Synchronous Machines (PMSM) • Winding is designed fully automatically. • To speed up simulation only a sector is calculated based on the winding design. • Example: 12 slots, 5 pol pairs, 3 phases Negative symmetry is used for simulation
  • 9. NAFEMS PMSM Examples N=12, p=5, m=3 N=12, p=4, m=3
  • 10. NAFEMS Simulation Workflow Specification Tn=2Nm n=3000rpm … Material Model Setup Geometry Winding FE-Simulation Motor Loadpoint Short Circuit Demagnetisation Stack length, Skewing, … MagTwin ( , , ) ( , , ) d d s d d q q q q s q d q d u r i u r i                      
  • 11. NAFEMS Motor Model for fast Simulation: MagTwin Generic MagTwin model structure
  • 12. NAFEMS Motor Model for fast Simulation: MagTwin • Interpolation of simulated flux in dq-rotor reference frame with radial basis functions (RBF) of the form: • For functions of two variables the thin-plate spline kernel is used with • Linear term
  • 13. NAFEMS Motor Model for fast Simulation: MagTwin Specification – Generalized Digital Twin which implements the physical behavior of an electromagnetic system Model – Functional Mockup Unit implementation Steady State Analysis Transient Simulation
  • 14. NAFEMS AC Loss Calculation (I) Proximity losses in slot area – Losses due to PM excitation are included – Displacement currents are considered Losses in parallel wires – E.g. insert winding with parallel wires B worst case distribution realistic distribution
  • 15. NAFEMS AC Loss Calculation (II) Eddy current losses caused by PWM modulation – Includes losses in laminated stack, wires, permanent magnet, solid materials, …
  • 16. NAFEMS Multiphysics Simulations for PMSM Rotor stress simulation – Calculation of rotor stress due to centrifugal force and shrink fit – Evaluation stress, strain, plastic deformation and transmittable torque Thermal simulation – Steady-state heat conduction with finite element analysis – Thermal networks to consider 3D effects Rotor stress distribution StressMises/N/m2
  • 17. NAFEMS Multi-Objective Optimization (I) 0 60 120 180 240 300 360 420 480 540 600 660 720 -50 -40 -30 -20 -10 0 10 20 30 40 50 el / ° U emf /V Current = 0.0 Arms Current = 0.7 Arms SyMSpace
  • 18. NAFEMS Multi-Objective Optimization (II) Pareto optimal design Pareto optimal solution: A solution is Pareto optimal if there exists no feasible solution for which an improvement in one objective does not lead to a simultaneous degradation in one (or more) of the other objectives. Pareto optimal designs Material Costs Verluste Design variants Losses Solution space
  • 19. NAFEMS Multi-Objective Optimization Algorithms • Grid Calculates any possible parameter combination. Requires huge amount of calculation power • Generational NSGA-II (Non-dominated Sorting Genetic Algorithm II) Steady State Async NSGA-II • Generational SPEA2 (Strength Pareto Evolutionary Algorithm 2) Steady State Async SPEA2 • DECMO (Differential Evolution-based, Coevolutionary Multi-objective Optimization algorithm) see next page Generation based vs. steady state algorithm
  • 20. NAFEMS Multi-Objective Optimization Algorithms DECMO algorithm Improved convergence of the Pareto front in comparison to the other algorithms. Combines two different multi-objective optimization algorithms.
  • 21. NAFEMS Hybrid Optimization Method For complex simulations a surrogate model based on artificial neural networks (ANNs) is created. This surrogate model is created during the optimization fully automatic on-the-fly. Optimization speed can significantly be improved.
  • 22. NAFEMS Cluster on demand portal 22 Created & owned by LCM Accessed by LCM customers (end users): engineers Created by cloudSME, hosted by LCM accessed by LCM staff Hosted by cloudSME in Germany Accessed by LCM & cloudSME staff
  • 25. NAFEMS Thanks for your interest! Responsible for SyMSpace LINZ CENTER OF MECHATRONICS GMBH Science Park I Altenberger Straße 66 4040 Linz Austria +43 732 2468-6002 office@lcm.at www.lcm.at Responsible for the cloud concept & technology: CloudSME UG TecTower Bismarckstr. 142 47057 Duisburg Germany +49 203 3639 9955 ocklenburg@cloudsme.eu www.cloudsme.eu 25