Multi-objective parameter optimization of gapping device for electromagnetic hybrid coupler

doi:10.3785/j.issn.1008-973X.2025.05.014

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (5): 1007-1017 DOI: 10.3785/j.issn.1008-973X.2025.05.014

Multi-objective parameter optimization of gapping device for electromagnetic hybrid coupler

Shuang WANG1,2,3(

),Shousuo SUN2,3,Yongcun GUO1,2,3,Zeyong HU2,3

1. State Key Laboratory of Digital and Intelligent Technology for Unmanned Coal Mining, Anhui University of Science and Technology, Huainan 232001, China
2. Collaborative Innovation Center of Mine Intelligent Equipment and Technology, Anhui University of Science and Technology, Huainan 232001, China
3. School of Electrical and Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China

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Abstract

A new electromagnetic hybrid magnetic coupler was proposed aiming at the problems of large volume and low adjustment precision that generally exist in the gapping mechanism of the double-disk magnetic coupler. The precise gapping of the magnetic coupler can be achieved through electromagnetic drive. Multi-objective optimization of the core component electromagnetic gapping device was conducted with average thrust and thrust fluctuation as the objectives. The dung beetle optimization algorithm optimization BP neural network model (DBO-BP) and the multi-objective golden jackal optimization algorithm (MOGJO) were proposed based on the sensitivity analysis for hierarchical optimization of design parameters in order to determine the optimal parameters of the electromagnetic gapping device by combining the response surface method and the scanning method. The thrust waveform, induced electromotive force, magnetic induction and magnetic field line distribution were analyzed based on the finite element method. The optimized radial air-gap magnetic induction was improved by 19%, the average thrust was improved by 57.8%, and the thrust fluctuation ratio was reduced by 28.3%. The excellent performance of the final design with respect to the initial design and the correctness of the multi-objective parameter grading optimization of the new magnetic coupler were verified.

Key words： magnetic coupler electromagnetically regulated air gap DBO-BP neural network multi-objective golden jackal optimization (MOGJO) algorithm multi-objective parameter optimization

Received: 12 March 2024 Published: 25 April 2025

CLC:

TH 133

Fund: 安徽省高校杰出青年科研资助项目（2022AH020056）；国家自然科学基金资助项目（52274152, 52404160）；安徽省自然科学优秀青年科研基金资助项目（2308085Y37）.

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Cite this article:

Shuang WANG,Shousuo SUN,Yongcun GUO,Zeyong HU. Multi-objective parameter optimization of gapping device for electromagnetic hybrid coupler. Journal of ZheJiang University (Engineering Science), 2025, 59(5): 1007-1017.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.05.014 OR https://www.zjujournals.com/eng/Y2025/V59/I5/1007

电磁混合式耦合器调隙装置多目标参数优化

针对双盘式磁力耦合器的调隙机构普遍存在的体积大、调节精度低的问题，提出新型的电磁混合式磁力耦合器，通过电磁驱动可以实现磁力耦合器的精准调隙. 以平均推力和推力波动为目标，对核心构件电磁调隙装置进行多目标优化. 基于敏感度分析对设计参数进行分级优化，提出蜣螂优化算法优化BP神经网络模型(DBO-BP)和多目标金豺优化算法(MOGJO)，结合响应面法和扫描法，确定电磁调隙装置的最优参数. 基于有限元法对推力波形、感应电动势、磁感应强度及磁场线分布进行分析，优化后径向气隙磁感应强度提升了19%，平均推力提升了57.8%，推力波动比值降低了28.3%，验证了最终设计相对于最初设计的优异性能以及新型磁力耦合器多目标参数分级优化的正确性.

关键词： 磁力耦合器, 电磁调隙, DBO-BP神经网络, 多目标金豺优化(MOGJO)算法, 多目标参数优化

Fig.1 Structure of new electromagnetic hybrid magnetic coupler

Fig.2 Structural diagram of regulating air gap device

Fig.3 Two-dimensional analytical model of magnetic field of electromagnetically regulated air gap device

Fig.4 Magnetic induction of radial air gap

Fig.5 Magnetic induction of axial air gap

Fig.6 Initial model thrust of electromagnetic gap device

Fig.7 Schematic representation of structural parameter of electromagnetic gap shifter

Tab.1 Initial value and variation range of structural parameters to be optimized

Tab.2 Variance of mean thrust and thrust fluctuation

Fig.8 Sensitivity analysis result for structural parameter

Fig.9 Flowchart for hierarchical optimization

Tab.3 Test of fitting effectiveness of different proxy models

Fig.10 Pareto chart for first level

Tab.4 Initial point for level 2 optimization

Tab.5 Box-Behnken experimental design and result of medium sensitivity parameter

Fig.11 Partial response surface at point A, B, C

Fig.12 Pareto chart for secondary optimization

Tab.6 Initial point for level 3 optimization

Fig.13 Time comparison box-plot

Tab.7 Scanning table of weak sensitivity design parameter

Fig.14 Scanning result graph of weak sensitivity parameter

Tab.8 Comparison of parameter before and after optimization

Fig.15 Magnetic induction of radial air gap after optimization

Fig.16 Magnetic induction of axial air gap after optimization

Fig.17 Thrust waveform before and after optimization

Fig.18 Waveform of induced electromotive force before and after optimization

Fig.19 Comparison of magnetic lines of force

Fig.20 Comparison of magnetic induction


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