These event proceedings were created in support of the NAFEMS DACH Seminar "Multiphysical simulations in the development of electric motors" held on the 13-14th of November 2019 in Wiesbaden, Germany.
Numerical simulation methods and engineering methods, such as the finite element method as well as parametric and non-parametric optimization, are nowadays almost impossible to imagine without the development of electromechanical products. Although Raymond Clough's first mention of the term finite elements in the 1950's was for mechanical applications, the method was rapidly extended to other physical domains. In addition to the analysis of thermal and fluid mechanical effects, it is also used for the simulation of electromagnetic fields and in particular for the development of electric motors.
In many classical mechanical applications, for example in the automotive and aerospace industries, electrical machines have become indispensable as system components. Since electric motors drive mechanical systems, there inevitably arises a multiphysical or multidisciplinary task requiring appropriate simulation approaches. The inclusion of electric motors in the development of electromechanical products thus becomes an integral part of overall physical considerations to be considered in system simulations of electromechanical products. Even the component-based simulation of the electric motors themselves is already a multiphysical application because friction losses, eddy current losses or vibrations directly influence the performance of electric motors.
The requirements for simulating physical processes in an electric motor are similarly demanding or perhaps even more complex than with an internal combustion engine. The task is to describe interactions between electromagnetic fields, electromechanical power losses, heat development, temperature distributions, cooling processes and oscillations due to electromagnetic excitations and mechanical stresses, to model them correctly and to calculate them with adequate multiphysics simulation approaches. The real challenge, however, is not only to perform individual calculations, but to reduce the simulation times so that screening and system optimization in finite time are possible to increase the robustness of the products.