Optimization of Electrical Machines in the Cloud with SyMSpace by LCM
•
1 j'aime•277 vues
Presented at NAFEMS DACH regional conference for numerical simulation methods by LCM and cloudSME in Wiesbaden on the 14th of November 2019. The Linz Center of Mechatronics GmbH showcased how they easily optimize electrical drive engines in the cloud. We supported LCM to work out the right cloud-based service solutions for their customers based on their existing software. By respecting the latest developments in the industry and science, including security and privacy compliance and hosting flexibility (free choice of data centre, no vendor lock-in). Check out their cool System Model Space "SyMSpace" for electrical drive engines and trusted by industrial partners! (https://bit.ly/2CKGphb) #poweredbycloudSME Yes, Cloud Computing is offering a broad range of actions and can be confusing. You want to dig deeper? Write us an email or give us a call so that we can work out how to approach the perfect cloud solution for your needs.
Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (18)
Similaire à Optimization of Electrical Machines in the Cloud with SyMSpace by LCM
Similaire à Optimization of Electrical Machines in the Cloud with SyMSpace by LCM (20)
Dernier
Dernier (20)
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
- 23. NAFEMS Cluster on demand portal
- 24. NAFEMS Cluster on demand portal
- 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