Modeling and Optimization of Air-Core Monopole Linear Motor Based on Multiphysical Fields

Abstract

The key technology of the air-core monopole linear motor (AMLM), a core component of precision motion systems, is the distribution of the electromagnetic structure and cooling structure. Thus, AMLM can achieve small thrust fluctuation, high thrust density, and low surface temperature rise. Consequently, the multiobjective optimal design of AMLM under the multiphysical fields has become the key technology of AMLM. Consideration of the strict requirements of AMLM model calculational accuracy will cause an excessively long computation time for the finite-element analysis (FEA) software three-dimensional (3-D) model, and it is not convenient for an optimal computation of the multiphysical fields model. Therefore, a surface magnetic charge model and an image method are proposed in the paper to establish an air gap magnetic field model of AMLM. The saturation characteristic of magnetic materials can be reflected through saturation coefficient of the model. In addition, the air gap field distribution status of AMLM can be better predicted. An AMLM thermal field model is established by adopting the thermal network method. In the model, the heat transfer coefficient of cooling water is calculated based on the current curve obtained from the FEA software. Parametric modeling was performed on the AMLM electromagnetic and cooling structure through definition of 7-D proportionality coefficients. Based on the aforementioned electromagnetic field and the thermal field model, genetic algorithm (GA) was adopted for AMLM optimization design by setting cooling capacity and size as constrain conditions as well as by setting thrust density, thrust fluctuation, mover's mass, and motor constants as optimization objects. The AMLM electromagnetic field, the accuracy of thermal field model, and the validity of the AMLM optimization method were verified through simulation and tests. The method proposed has significantly improved the efficiency of optimization calculation.