Design Optimisation of Electric Machines under Uncertainty and Optimal Design of Experiments for Parameter Identification (D03)

• Subject Area: Mathematics
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 492661287

Production tolerances, material variations, usage scenarios and other influences lead to uncertainty in the optimal design of electric machines. The project considers the development, analysis and application of efficient derivative-based methods for the robust shape and topology optimisation under uncertainty. In the second phase more complex motor models including time-dependence, magnetic hysteresis models as well as thermal aspects will be addressed. To deal with the increased complexity, nonlinear manifold reduced order models with error control will be integrated in the optimisation methods. Moreover, the optimisation methods for optimal design of experiments will be extended to the accurate identification of local magnetic properties in the newly developed hysteresis and coupled magneto-mechanical material models of project D04.

• DFG Programme: CRC/Transregios
• Subproject of: TRR 361: Computational Electric Machine Laboratory: Thermal Modelling, Transient Analysis, Geometry Handling and Robust Design
• Applicant Institution: Technische Universität Darmstadt
• Project Head: Professor Dr. Stefan Ulbrich