SNN-Based Surrogate Modeling of Electromagnetic Force and Its Application in Maglev Vehicle Dynamics Simulation

The majority of electromagnetic force calculation models employed in maglev vehicle system dynamics focus exclusively on vertical and lateral movement while neglecting the nonlinear magnetization properties of ferromagnetic materials. This oversight leads to discrepancies between the dynamics simula...

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Main Authors: Yang Feng, Chunfa Zhao, Xin Liang, Zhan Bai
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Actuators
Subjects:
Online Access:https://www.mdpi.com/2076-0825/14/3/112
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author Yang Feng
Chunfa Zhao
Xin Liang
Zhan Bai
author_facet Yang Feng
Chunfa Zhao
Xin Liang
Zhan Bai
author_sort Yang Feng
collection DOAJ
description The majority of electromagnetic force calculation models employed in maglev vehicle system dynamics focus exclusively on vertical and lateral movement while neglecting the nonlinear magnetization properties of ferromagnetic materials. This oversight leads to discrepancies between the dynamics simulations and actual conditions. To enhance the accuracy of dynamics simulations and evaluate the performance of maglev vehicle systems under various operational conditions, it is imperative to identify an electromagnetic force calculation model that combines accuracy and applicability. To address this objective, this paper examines a U-shaped electromagnet in medium–low-speed maglev vehicles as a case study. It constructs a spatial electromagnetic force calculation surrogate model using a Shallow Neural Network. The surrogate model is capable of accurately calculating electromagnetic forces considering relative position deviations in the lateral, vertical, rolling, pitching, and shaking directions. Moreover, it can be integrated into vehicle system dynamics simulations. The accuracy of the electromagnetic force calculation surrogate model is confirmed by extensive comparisons with finite element simulation results across various conditions, achieving an impressive concordance rate of up to 95%. An illustrative application of the electromagnetic force calculation surrogate model in maglev vehicle system dynamics simulation is provided to showcase its practical utility.
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spelling doaj-art-d88cb0cce1ab427f8de1dad49fbf0ca82025-08-20T02:11:04ZengMDPI AGActuators2076-08252025-02-0114311210.3390/act14030112SNN-Based Surrogate Modeling of Electromagnetic Force and Its Application in Maglev Vehicle Dynamics SimulationYang Feng0Chunfa Zhao1Xin Liang2Zhan Bai3State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610031, ChinaState Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610031, ChinaState Key Laboratory of High-Speed Maglev Transportation Technology, Qingdao 266111, ChinaState Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610031, ChinaThe majority of electromagnetic force calculation models employed in maglev vehicle system dynamics focus exclusively on vertical and lateral movement while neglecting the nonlinear magnetization properties of ferromagnetic materials. This oversight leads to discrepancies between the dynamics simulations and actual conditions. To enhance the accuracy of dynamics simulations and evaluate the performance of maglev vehicle systems under various operational conditions, it is imperative to identify an electromagnetic force calculation model that combines accuracy and applicability. To address this objective, this paper examines a U-shaped electromagnet in medium–low-speed maglev vehicles as a case study. It constructs a spatial electromagnetic force calculation surrogate model using a Shallow Neural Network. The surrogate model is capable of accurately calculating electromagnetic forces considering relative position deviations in the lateral, vertical, rolling, pitching, and shaking directions. Moreover, it can be integrated into vehicle system dynamics simulations. The accuracy of the electromagnetic force calculation surrogate model is confirmed by extensive comparisons with finite element simulation results across various conditions, achieving an impressive concordance rate of up to 95%. An illustrative application of the electromagnetic force calculation surrogate model in maglev vehicle system dynamics simulation is provided to showcase its practical utility.https://www.mdpi.com/2076-0825/14/3/112electromagnetic levitation systemelectromagnetic force calculationnonlinear magnetization characteristicssurrogate modelmaglev system dynamics simulation
spellingShingle Yang Feng
Chunfa Zhao
Xin Liang
Zhan Bai
SNN-Based Surrogate Modeling of Electromagnetic Force and Its Application in Maglev Vehicle Dynamics Simulation
Actuators
electromagnetic levitation system
electromagnetic force calculation
nonlinear magnetization characteristics
surrogate model
maglev system dynamics simulation
title SNN-Based Surrogate Modeling of Electromagnetic Force and Its Application in Maglev Vehicle Dynamics Simulation
title_full SNN-Based Surrogate Modeling of Electromagnetic Force and Its Application in Maglev Vehicle Dynamics Simulation
title_fullStr SNN-Based Surrogate Modeling of Electromagnetic Force and Its Application in Maglev Vehicle Dynamics Simulation
title_full_unstemmed SNN-Based Surrogate Modeling of Electromagnetic Force and Its Application in Maglev Vehicle Dynamics Simulation
title_short SNN-Based Surrogate Modeling of Electromagnetic Force and Its Application in Maglev Vehicle Dynamics Simulation
title_sort snn based surrogate modeling of electromagnetic force and its application in maglev vehicle dynamics simulation
topic electromagnetic levitation system
electromagnetic force calculation
nonlinear magnetization characteristics
surrogate model
maglev system dynamics simulation
url https://www.mdpi.com/2076-0825/14/3/112
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AT chunfazhao snnbasedsurrogatemodelingofelectromagneticforceanditsapplicationinmaglevvehicledynamicssimulation
AT xinliang snnbasedsurrogatemodelingofelectromagneticforceanditsapplicationinmaglevvehicledynamicssimulation
AT zhanbai snnbasedsurrogatemodelingofelectromagneticforceanditsapplicationinmaglevvehicledynamicssimulation