Data-driven Surrogate Modelling and Uncertainty Quantification for Electric Machines (D01)

• Subject Area: Electronic Semiconductors, Components and Circuits, Integrated Systems, Sensor Technology, Theoretical Electrical Engineering
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 492661287

This project will develop data-driven surrogate modelling methods to enable uncertainty quantification (UQ) studies within the context of electric machine design. First, material and geometrical uncertainties in the form of random fields will be stochastically modelled. Next, machine learning regression algorithms will be designed to approximate the dependence of electric machine quantities of interest on uncertain design parameters. Last, surrogate-based UQ methods for failure probability estimation and multivariate sensitivity analysis, two important and computationally demanding UQ tasks, will be developed to quantify the impact of uncertainty, provide novel insights, and facilitate improved machine designs.

• 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 Heads: Dr.-Ing. Dimitrios Loukrezis; Professor Dr. Sebastian Schöps