高速磁浮列车悬浮间隙仿真预测
CSTR:
作者:
作者单位:

1.中国科学院力学研究所 流固耦合系统力学重点实验室,北京 100190;2.中国科学院大学 工程科学学院,北京 100049

作者简介:

吴 晗(1988—),男,高级工程师,工学博士,主要研究方向为结构动力学与控制(高速列车、磁悬浮列车等)、非线性振动与模型预测控制。E-mail:wuhan@imech.ac.cn

通讯作者:

刘梦娟(1998—),女,硕士生,主要研究方向为磁浮列车动力学与控制。E-mail:liumengjuan@imech.ac.cn

中图分类号:

TH212;TH213.3

基金项目:

中国科学院稳定支持基础研究领域青年团队计划(YSBR-045)


Suspension Gap Prediction of High-speed Maglev Train
Author:
Affiliation:

1.Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics of Chinese Academy of Sciences, Beijing 100190, China;2.School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, China

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    摘要:

    基于长短时记忆(LSTM)神经网络提出了一种可用于高速磁浮列车的电磁铁悬浮间隙预测方法。考虑高速磁浮列车运行过程中受到的气动荷载,建立了列车仿真模型并计算列车的动态响应;通过PyCharm建立LSTM神经网络,并以高速磁浮列车仿真模型计算结果为样本集,构建了高速磁浮列车电磁铁悬浮间隙预测模型。最后,通过对预测模型计算结果和评价指标进行评判,验证了所提出的电磁铁间隙预测算法的准确性。

    Abstract:

    An electromagnet suspension gap prediction method was proposed based on long short-term memory (LSTM) neural network for high-speed maglev trains. Firstly, considering the aerodynamic load during the operation of high-speed maglev train, a train simulation model was established and the dynamic response of the train was calculated. Secondly, the LSTM neural network was established by PyCharm and the prediction model of the electromagnet suspension gap for high-speed maglev trains was built by taking the simulation calculation results as the sample set. Finally, the accuracy of the proposed electromagnet suspension gap prediction algorithm was verified by judging the calculation results and evaluation indexes of the prediction model.

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吴晗,刘梦娟,曾晓辉.高速磁浮列车悬浮间隙仿真预测[J].同济大学学报(自然科学版),2023,51(3):351~359

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  • 收稿日期:2023-01-01
  • 在线发布日期: 2023-03-29
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