Wu, J.; Zhang, Y.; Yang, B.; Li, S.; Song, H. Electromagnetic Fields Calculation and Optimization of Structural Parameters for Axial Helical and Radial Helical Air-Core Inductors. Preprints2024, 2024080416. https://doi.org/10.20944/preprints202408.0416.v1
APA Style
Wu, J., Zhang, Y., Yang, B., Li, S., & Song, H. (2024). Electromagnetic Fields Calculation and Optimization of Structural Parameters for Axial Helical and Radial Helical Air-Core Inductors. Preprints. https://doi.org/10.20944/preprints202408.0416.v1
Chicago/Turabian Style
Wu, J., Sihan Li and Haipeng Song. 2024 "Electromagnetic Fields Calculation and Optimization of Structural Parameters for Axial Helical and Radial Helical Air-Core Inductors" Preprints. https://doi.org/10.20944/preprints202408.0416.v1
Abstract
To improve the current density distribution and electromagnetic performance of air-core inductors, a structural optimization method combining BP neural network and genetic algorithm was proposed for the study of axial and radial spiral multi-winding inductors. The Monte Carlo method was used to extract the structural size samples of the inductors, and the training dataset was obtained through finite element calculation of electromagnetic fields. Based on BP neural networks, nonlinear mapping models between the inductance value, volumetric inductance density, current distribution non-uniformity coefficient, and inductor structural parameters were constructed. The sensitivity analysis of the inductor inductance value affected by structural parameters was conducted through the Sobol index calculation. Using the current distribution non-uniformity coefficient as the fitness function and the volumetric inductance density as the constraint condition, the genetic algorithm was applied to optimize the structural parameters of the inductor globally. The optimization results were verified through finite element comparison. The results show that, under the requirement of satisfying the volumetric inductance density, the current distribution non-uniformity coefficient of AHI type inductor was reduced by 4.57% compared with the best sample in the sampling, while that of RHI type inductor was reduced by 5.33%, demonstrating the practicality of the BP-GA joint algorithm in the structural optimization design of inductors.
Keywords
air-core inductor; finite element analysis; structural optimization; BP neural network; genetic algorithm
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
