Space-time shape optimization of rotating electric machines
<|reference_start|>Space-time shape optimization of rotating electric machines: This article is devoted to the shape optimization of the internal structure of an electric motor, and more precisely of the arrangement of air and ferromagnetic material inside the rotor part with the aim to increase the torque of the machine. The governing physical problem is the time-dependent, non linear magneto-quasi-static version of Maxwell's equations. This multiphase problem can be reformulated on a 2d section of the real cylindrical 3d configuration; however, due to the rotation of the machine, the geometry of the various material phases at play (the ferromagnetic material, the permanent magnets, air, etc.) undergoes a prescribed motion over the considered time period. This original setting raises a number of issues. From the theoretical viewpoint, we prove the well-posedness of this unusual non linear evolution problem featuring a moving geometry. We then calculate the shape derivative of a performance criterion depending on the shape of the ferromagnetic phase via the corresponding magneto-quasi-static potential. Our numerical framework to address this problem is based on a shape gradient algorithm. The non linear time periodic evolution problems for the magneto-quasi-static potential is solved in the time domain, with a Newton-Raphson method. The discretization features a space-time finite element method, applied on a precise, meshed representation of the space-time region of interest, which encloses a body-fitted representation of the various material phases of the motor at all the considered stages of the time period. After appraising the efficiency of our numerical framework on an academic problem, we present a quite realistic example of optimal design of the ferromagnetic phase of the rotor of an electric machine.<|reference_end|>
@article{cesarano2024space-time,
    title={Space-time shape optimization of rotating electric machines},
    author={Alessio Cesarano and Charles Dapogny and Peter Gangl},
    journal={arXiv preprint arXiv:2402.07017},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07017},
    primaryClass={math.OC cs.NA math.NA}
}