7 papers
Efficient Pareto-Front Generation for Electric Machines using IGA and Second Order Derivatives
Theodor Komann, Michael Wiesheu, Stefan Ulbrich +2
The multiobjective optimization of electric machines always involves a trade-off caused by various competing objectives such as performance and cost. A suitable design is usually d…
A Comparison of Multirate Co-Simulation Techniques for Field-Circuit Coupled Problems
Michael Wiesheu, Sebastian Schöps, Idoia Cortes Garcia
This paper compares three different multirate splitting approaches for the application on field-circuit coupled magnetoquasistatic simulations. For these methods, again three diffe…
Gradient-Informed Machine Learning in Electromagnetics
Matteo Zorzetto, Merle Backmeyer, Michael Wiesheu +3
Simulation techniques such as the finite element method are essential for designing electrical devices, but their computational cost can be prohibitive for repeated or real-time co…
Learning electromagnetic fields based on finite element basis functions
Merle Backmeyer, Michael Wiesheu, Sebastian Schöps
Parametric surrogate models of electric machines are widely used for efficient design optimization and operational monitoring. Addressing geometry variations, spline-based computer…
An Air-Gap Element for the Isogeometric Space-Time-Simulation of Electric Machines
Michael Reichelt, Michael Wiesheu, Melina Merkel +2
Space-time methods promise more efficient time-domain simulations, in particular of electrical machines. However, most approaches require the motion to be known in advance so that…
Isogeometric Analysis for 2D Magnetostatic Computations with Multi-level Bézier Extraction for Local Refinement
Andreas Grendas, Michael Wiesheu, Sebastian Schöps +1
Local refinement is vital for efficient numerical simulations. In the context of Isogeometric Analysis (IGA), hierarchical B-splines have gained prominence. The work applies the me…