Polenz, Björn (2024)
Robust Shape Optimization of Electromechanical Energy Converters.
Technische Universität Darmstadt
doi: 10.26083/tuprints-00028161
Dissertation, Erstveröffentlichung, Verlagsversion
Kurzbeschreibung (Abstract)
This work deals with the simulation and shape optimization of electromechanical energy converters under uncertainty. More precisely, an asynchronous machine is considered, whose electromagnetic fields can be described by the magnetoquasistatic approximation of Maxwell’s equations, which are coupled with network equations for the rotor cage and for the exciting three-phase current. The state system is completed by an equation of motion which is excited by the torque. This leads to a system of partial differential algebraic equations. A finite element approach with a time-stepping method is used to solve the equation numerically. We consider uncertainties in the material and geometry of the machine and use a worst-case approach to address these uncertainties. This leads to a bi-level structured optimization problem. Since these problems are difficult to solve numerically, we use approximations up to second order as surrogate models. In particular, we use Taylor models in combination with an adaptive strategy to improve the approximation quality and derivative-free interpolation models that can also be improved iteratively. Both the problem formulation and the consideration of uncertainty in the optimization lead to a high computational cost. To speed up our computations, we apply model dimension reduction techniques.
Typ des Eintrags: | Dissertation | ||||
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Erschienen: | 2024 | ||||
Autor(en): | Polenz, Björn | ||||
Art des Eintrags: | Erstveröffentlichung | ||||
Titel: | Robust Shape Optimization of Electromechanical Energy Converters | ||||
Sprache: | Englisch | ||||
Referenten: | Ulbrich, Prof. Dr. Stefan ; Schöps, Prof. Dr. Sebastian | ||||
Publikationsjahr: | 7 Oktober 2024 | ||||
Ort: | Darmstadt | ||||
Kollation: | x, 167 Seiten | ||||
Datum der mündlichen Prüfung: | 27 Oktober 2023 | ||||
DOI: | 10.26083/tuprints-00028161 | ||||
URL / URN: | https://tuprints.ulb.tu-darmstadt.de/28161 | ||||
Kurzbeschreibung (Abstract): | This work deals with the simulation and shape optimization of electromechanical energy converters under uncertainty. More precisely, an asynchronous machine is considered, whose electromagnetic fields can be described by the magnetoquasistatic approximation of Maxwell’s equations, which are coupled with network equations for the rotor cage and for the exciting three-phase current. The state system is completed by an equation of motion which is excited by the torque. This leads to a system of partial differential algebraic equations. A finite element approach with a time-stepping method is used to solve the equation numerically. We consider uncertainties in the material and geometry of the machine and use a worst-case approach to address these uncertainties. This leads to a bi-level structured optimization problem. Since these problems are difficult to solve numerically, we use approximations up to second order as surrogate models. In particular, we use Taylor models in combination with an adaptive strategy to improve the approximation quality and derivative-free interpolation models that can also be improved iteratively. Both the problem formulation and the consideration of uncertainty in the optimization lead to a high computational cost. To speed up our computations, we apply model dimension reduction techniques. |
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Alternatives oder übersetztes Abstract: |
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Status: | Verlagsversion | ||||
URN: | urn:nbn:de:tuda-tuprints-281612 | ||||
Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 500 Naturwissenschaften und Mathematik > 510 Mathematik | ||||
Fachbereich(e)/-gebiet(e): | 04 Fachbereich Mathematik 04 Fachbereich Mathematik > Optimierung 04 Fachbereich Mathematik > Optimierung > Nonlinear Optimization |
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TU-Projekte: | Bund/BMBF|05M18RDA|PASIROM | ||||
Hinterlegungsdatum: | 07 Okt 2024 13:34 | ||||
Letzte Änderung: | 08 Okt 2024 09:31 | ||||
PPN: | |||||
Referenten: | Ulbrich, Prof. Dr. Stefan ; Schöps, Prof. Dr. Sebastian | ||||
Datum der mündlichen Prüfung / Verteidigung / mdl. Prüfung: | 27 Oktober 2023 | ||||
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