Abstract
This study investigates the impact of rotor structure, material selection, and cooling methods on ultra-high-speed motor performance, revealing performance variation laws under multi-physical field coupling. Considering mechanical boundary constraints, we propose an optimization design method based on a multi-physical field coupling model. Using a MaxPro experimental design, initial samples are obtained and fitted using a Kriging surrogate model. The NSGA-2 algorithm is then applied for optimization, with Relative Maximum Absolute Error (RMAE) and Relative Average Absolute Error (RAAE) employed for accuracy evaluation. The Kriging model is iteratively updated based on evaluation results until the optimal design is achieved. This method enhances motor performance, ensures mechanical boundary conditions, and reduces computational load. Experimental results show significant improvements in efficiency and power density. This study provides theoretical support and technical guidance for ultra-high-speed motor design and offers new ideas for related motor research and development. Future work will explore more efficient and intelligent optimization algorithms to continuously advance ultra-high-speed motor technology.
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