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…
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…
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…
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…
Combined Parameter and Shape Optimization of Electric Machines with Isogeometric Analysis
Michael Wiesheu, Theodor Komann, Melina Merkel +3
In structural optimization, both parameters and shape are relevant for the model performance. Yet, conventional optimization techniques usually consider either parameters or the sh…
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…