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The problem of the computationally efficient analysis of the electromagnetic system with nonlinearity is considered in the proposed work. Unlike the previously proposed proper orthogonal decomposition (POD)-based model order reduction (MOR), the proposed method based on the dynamic mode decomposition works well even under the highly nonlinear system. Using the proposed method, a highly saturated interior permanent-magnet (IPM) motor model is analyzed. Comparisons in terms of the radial air-gap flux density and torque are made with that of the full and POD-based models. The results show that the proposed method is capable of giving a good approximation of the full model of the IPM motor. Also, a reduction in the computational time can be achieved compared with that of the traditional POD-based MOR. [ABSTRACT FROM AUTHOR]

This paper presents a novel parametric design analysis of the magnetic sensor module used to find mover positions in linear motors. The proposed framework utilizes a computationally inexpensive proper orthogonal decomposition-dynamic mode decomposition-based model order reduction (MOR), coupled with a multiparameter moment matching method. The model is then utilized by a moment-based probabilistic design optimization method that considers the manufacturing uncertainties to find the optimal sensor design. A magnetic sensor module is designed for employing the proposed framework, and the results show that: 1) the proposed MOR technique is accurate; 2) the reliability analysis performed during the probabilistic design optimization can help to reduce the excessive non-compliance costs for the manufacturers; and 3) the combined framework is able to significantly expedite the optimal design search. [ABSTRACT FROM AUTHOR]