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pytom: Topology optimization for magnetic actuators in python
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DOI：10.1007/s00158-026-04422-7.png)
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Abstract
En 中文
This paper presents pyTOM, an open-source topology optimization (TO) framework developed for the design of magnetic actuators. The framework integrates magnetostatic vector potential formulations with a density-based TO scheme incorporating Helmholtz filtering, Heaviside projection, and SIMP interpolation of magnetic reluctivity. Magnetic forces are computed via the Maxwell stress tensor, and the design variables are updated using the Method of Moving Asymptotes. The framework addresses three distinctive characteristics of TO for electromechanical devices within a unified computational environment. First, both permanent magnets and coil excitations are modeled as field sources. Second, nonlinear magnetic behavior due to saturation effect is captured through a field-dependent reluctivity model solved via a Newton–Raphson scheme. Third, a multi-position analysis is introduced to evaluate actuator performance across multiple plunger positions. Several numerical examples demonstrate the accuracy and capability of the proposed framework. The accuracy of the magnetic field computation is first validated against COMSOL in an interior permanent magnet motor benchmark. Comparative studies between linear and nonlinear material models then illustrate the effect of magnetic saturation on the design result. Finally, TO results obtained under multiple plunger positions are presented and discussed. The pyTOM framework is intended to serve as an accessible educational platform for TO in electromechanical devices, including electric motors and actuators.
Keywords:
Topology optimization
Magnetic actuators
Nonlinear magnetostatics
Education
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4
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4.9K
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1.7W
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