Prediction of Thermal Conductivity of Litz Winding by Least Square Method and GA-BP Neural Network Based on Numerical Simulations

Qi Dong and Xiaoli Fu ()
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Qi Dong: Wuhan Institute of Marine Electric Propulsion, Wuhan 430064, China
Xiaoli Fu: College of Civil Engineering, Tongji University, Shanghai 200092, China

Energies, 2023, vol. 16, issue 21, 1-15

Abstract: This paper proposes a Litz winding numerical-simulation model considering the transposition effect, and uses the transient-plane-source method to verify the numerical-simulation method. In addition, numerical methods were adopted to further investigate the impact of filling rate and epoxy-resin type, and their combined effects, on thermal conductivity. To facilitate engineering design, the discrete data points were fitted using the least square method to obtain a straightforward and application-friendly polynomial empirical formula. On this basis, the GA-BP neural network was used to analyze the data in order to seek out more accurate prediction results for the entire data set. As a result, compared with the least square method, the error between the prediction result and the target value in the x direction was reduced by 87.04%, and the error in the z direction was reduced by 84.97%.

Keywords: transposition effect; Litz winding; epoxy-resin type; thermal conductivity; least square method; accurate prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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