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Simulation and Robust Optimization for Electric Devices with Uncertainties

Bontinck, Zeger (2018):
Simulation and Robust Optimization for Electric Devices with Uncertainties.
Darmstadt, Technische Universität, [Online-Edition: https://tuprints.ulb.tu-darmstadt.de/8330],
[Ph.D. Thesis]

Abstract

This dissertation deals with modeling, simulation and optimization of low-frequency electromagnetic devices and quantification of the impact of uncertainties on these devices. The emphasis of these methods is on their application for electric machines.

A Permanent Magnet Synchronous Machine (PMSM) is simulated using Iso-Geometric Analysis (IGA). An efficient modeling procedure has been established by incorporating a harmonic stator-rotor coupling. The procedure is found to be stable. Furthermore, it is found that there is strong reduction in computational time with respect to a classical monolithic finite element method. The properties of the ingredients of IGA, i.e. B-splines and Non-Uniform B-Splines, are exploited to conduct a shape optimization for the example of a Stern-Gerlach magnet. It is shown that the IGA framework is a reliable and promising tool for simulating and optimizing electric devices.

Different formulations for robust optimization are recalled. The formulations are tested for the optimization of the size of the permanent magnet in a PMSM. It is shown that under the application of linearization the deterministic and the stochastic formulation are equivalent. An efficient deterministic optimization algorithm is constructed by the implementation of an affine decomposition. It is shown that the deterministic algorithm outperforms the widely used stochastic algorithms for this application.

Finally, different models to incorporate uncertainties in the simulation of PMSMs are developed. They incorporate different types of rotor eccentricity, uncertainties in the permanent magnets (geometric and material related) and uncertainties that are introduced by the welding processes during the manufacturing. Their influences are studied using stochastic collocation and using the classical Monte Carlo method. Furthermore, the Multilevel Monte Carlo approach is combined with error estimation and applied to determine high dimensional uncertainties in a PMSM.

Item Type: Ph.D. Thesis
Erschienen: 2018
Creators: Bontinck, Zeger
Title: Simulation and Robust Optimization for Electric Devices with Uncertainties
Language: English
Abstract:

This dissertation deals with modeling, simulation and optimization of low-frequency electromagnetic devices and quantification of the impact of uncertainties on these devices. The emphasis of these methods is on their application for electric machines.

A Permanent Magnet Synchronous Machine (PMSM) is simulated using Iso-Geometric Analysis (IGA). An efficient modeling procedure has been established by incorporating a harmonic stator-rotor coupling. The procedure is found to be stable. Furthermore, it is found that there is strong reduction in computational time with respect to a classical monolithic finite element method. The properties of the ingredients of IGA, i.e. B-splines and Non-Uniform B-Splines, are exploited to conduct a shape optimization for the example of a Stern-Gerlach magnet. It is shown that the IGA framework is a reliable and promising tool for simulating and optimizing electric devices.

Different formulations for robust optimization are recalled. The formulations are tested for the optimization of the size of the permanent magnet in a PMSM. It is shown that under the application of linearization the deterministic and the stochastic formulation are equivalent. An efficient deterministic optimization algorithm is constructed by the implementation of an affine decomposition. It is shown that the deterministic algorithm outperforms the widely used stochastic algorithms for this application.

Finally, different models to incorporate uncertainties in the simulation of PMSMs are developed. They incorporate different types of rotor eccentricity, uncertainties in the permanent magnets (geometric and material related) and uncertainties that are introduced by the welding processes during the manufacturing. Their influences are studied using stochastic collocation and using the classical Monte Carlo method. Furthermore, the Multilevel Monte Carlo approach is combined with error estimation and applied to determine high dimensional uncertainties in a PMSM.

Place of Publication: Darmstadt
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Accelerator Science and Electromagnetic Fields > Computational Electromagnetics
18 Department of Electrical Engineering and Information Technology > Institute for Accelerator Science and Electromagnetic Fields
Date Deposited: 10 Feb 2019 20:55
Official URL: https://tuprints.ulb.tu-darmstadt.de/8330
URN: urn:nbn:de:tuda-tuprints-83302
Referees: Schöps, Prof. Dr. Sebastian and De Gersem, Prof. Dr. Herbert
Refereed / Verteidigung / mdl. Prüfung: 2 November 2018
Alternative Abstract:
Alternative abstract Language
Diese Dissertation befasst sich mit der Modellierung, Simulation und Optimierung niederfrequenter elektromagnetischer Geräte, unter Berücksichtigung von Unsicherheiten. Der Schwerpunkt liegt in die Anwendung auf elektrische Maschinen. Eine Permanentmagnetsynchronmaschine (PMSM) wird mit isogeometrischer Analyse (IGA) simuliert. Ein numerisch effizientes Verfahren erhält man mit einer harmonischen Stator-Rotor-Kopplung. Das Verfahren ist stabil und die Rechenzeit lässt sich im Vergleich mit der klassischen Finite-Elemente-Methode weitgehend reduzieren. Die inhärenten Strukturen der B-Splines in der IGA ermöglichen die Entwicklung eines effizienten Optimierungsverfahrens für die Optimierung der Geometrie elektrischer Geräte. Verschiedene etablierte Optimierungsverfahren werden vorgestellt und zur Optimierung einer PMSM angewendet. Es wird gezeigt, dass die deterministische und stochastische Formulierung mit einander äquivalent sind. Ein effizientes Optimierungsverfahren wird durch eine affine Dekomposition bewirkt. Abschließend werden Unsicherheiten während der Simulation elektrischer Maschinen berücksichtigt. Diese Unsicherheiten treten durch Rotor-Exzentrizitäten und Materialeigenschaften auf. Sie werden mit stochastischer Kollokation und mit einem Monte Carlo Verfahren untersucht. Zur Reduktion der numerischen Kosten wird die Anwendbarkeit des Multilevel Monte Carlo Verfahrens untersucht. Dieses Verfahren wird mit einem Fehlerschätzer kombiniert.German
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