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<p>This paper presents a&#xa0;multiphysical design tool chain for electrical machines. The individual process steps are explained in detail, starting with the definition of geometry and parameters. The respective simulation environments, including boundary conditions and operating points, are presented for the physical sub-areas of electromagnetics, thermodynamics and mechanics. Particular focus is placed on the post-processing of data generated using the finite element analysis (FEA). Combined with a&#xa0;forward-looking selection of operating points in the simulation, the post-processing presented allows for extensive changes in terms of materials, skewing and winding configuration without additional FEA evaluations. For iron, magnetic and AC copper losses, a&#xa0;mesh-based approach is presented, which calculates the relevant quantities directly from the vector potential.</p><p>The iterative optimizer integrated into the toolchain uses surrogate models to identify optimized machine designs. Multiple target variables can be selected from the various physical domains. The active learning process employed in the creation of the models ensures high accuracy with minimal simulation effort.</p><p>The functional capabilities of the tool chain are demonstrated through a&#xa0;case study. This study focuses on optimizing the rotor geometry for an existing stator with a&#xa0;fixed winding configuration. This optimization considers objectives and constraints from multiple physical domains. The final design is validated using FEA. Additionally, the flexibility of the tool chain is illustrated through various adaptations of the optimized machine.</p>

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A highly flexible and modular multi-physical design and optimization tool for electric machines

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月23日

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  • Christian Digel,
  • Julius Kesten,
  • Martin Bremer,
  • Matthias Brodatzki,
  • Martin Doppelbauer

摘要

This paper presents a multiphysical design tool chain for electrical machines. The individual process steps are explained in detail, starting with the definition of geometry and parameters. The respective simulation environments, including boundary conditions and operating points, are presented for the physical sub-areas of electromagnetics, thermodynamics and mechanics. Particular focus is placed on the post-processing of data generated using the finite element analysis (FEA). Combined with a forward-looking selection of operating points in the simulation, the post-processing presented allows for extensive changes in terms of materials, skewing and winding configuration without additional FEA evaluations. For iron, magnetic and AC copper losses, a mesh-based approach is presented, which calculates the relevant quantities directly from the vector potential.

The iterative optimizer integrated into the toolchain uses surrogate models to identify optimized machine designs. Multiple target variables can be selected from the various physical domains. The active learning process employed in the creation of the models ensures high accuracy with minimal simulation effort.

The functional capabilities of the tool chain are demonstrated through a case study. This study focuses on optimizing the rotor geometry for an existing stator with a fixed winding configuration. This optimization considers objectives and constraints from multiple physical domains. The final design is validated using FEA. Additionally, the flexibility of the tool chain is illustrated through various adaptations of the optimized machine.