基于Kriging代理模型的变流器吊耳结构强度分析与优化

贺冠强; 刘永江; 李华; 陈力铭

doi:10.13890/j.issn.1000-128X.2022.04.015

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基于Kriging代理模型的变流器吊耳结构强度分析与优化

铁道机车车辆 | 更新时间：2024-08-02

- 基于Kriging代理模型的变流器吊耳结构强度分析与优化
- Structural strength analysis and optimization of converter lug based on Kriging model
- 机车电传动 2022年第4期页码：104-110
- 作者机构：
  
  株洲中车时代电气股份有限公司轨道交通技术中心，湖南株洲　　412001
- 作者简介：
  
  陈力铭（1995—），男，博士，工程师，主要从事基于机器学习的变流器设计优化方面的研究； E-mail: chenlm4@csrzic.com
- 基金信息：
- DOI：10.13890/j.issn.1000-128X.2022.04.015
  中图分类号： U264.3⁺7
- 纸质出版日期：2022-07-10，
  
  收稿日期：2022-02-27，
  
  修回日期：2022-04-10，
- 稿件说明：
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贺冠强, 刘永江, 李华, 等. 基于Kriging代理模型的变流器吊耳结构强度分析与优化[J]. 机车电传动, 2022,(4):104-110.

HE Guanqiang, LIU Yongjiang, LI Hua, et al. Structural strength analysis and optimization of converter lug based on Kriging model[J]. Electric drive for locomotives, 2022,(4):104-110.
贺冠强, 刘永江, 李华, 等. 基于Kriging代理模型的变流器吊耳结构强度分析与优化[J]. 机车电传动, 2022,(4):104-110. DOI： 10.13890/j.issn.1000-128X.2022.04.015.

HE Guanqiang, LIU Yongjiang, LI Hua, et al. Structural strength analysis and optimization of converter lug based on Kriging model[J]. Electric drive for locomotives, 2022,(4):104-110. DOI： 10.13890/j.issn.1000-128X.2022.04.015.

摘要

开展车下安装的轨道交通变流器结构分析通常涉及耗时的仿真计算，以评估结构强度是否符合安全要求。对于典型的轨道交通变流器产品，进行1次静强度分析（4个工况）需耗时1 h以上，进行1次单方向的随机振动分析需要8 h以上。当采用传统的工程优化方法对变流器进行结构优化时，由于需要大量调用仿真进行评估，优化效率受到严重限制。因此采用机器学习方法，通过拉丁超立方采样试验设计确定仿真方案（采样位置），根据仿真样本构建Kriging代理模型（高斯过程），以近似反映设计变量与响应之间的函数关系。在Kriging模型提供信息的基础上，实现高效的灵敏度分析、设计空间探索和全局优化，为设计人员提供优化设计的参考依据与推荐方案，避免出现安全裕量过低或设计冗余过大等极端情况，在提高产品性能的同时缩短设计周期，提升产品的竞争力。基于Kriging代理模型，文章给出了进行分析与优化的完整技术路线，并对其中关键步骤（试验设计、代理模型、自适应采样、灵敏度分析、设计空间探索、全局优化等）的具体实现方法进行了详细说明。通过对某牵引变流器吊耳结构进行分析与优化，验证了所提技术路线的有效性。

Abstract

The structural analysis of rail transit converters installed under the vehicle often involves time-consuming computer simulations to evaluate the strength of the structure for safety requirements. For typical rail transit converters

each static strength analysis with four working conditions costs at least 1 hour

and each random vibration analysis with one axis costs more than 8 hours. As a result

for using traditional engineering optimization methods to optimize the converter structure

the optimization efficiency will be severely limited

since these methods need to invoke a large amount of simulations as function evaluations. In this paper

machine learning methods were considered to approximate the functional relationships between design variables and responses. Specifically

the Latin Hypercube Sampling method was used to generate the samples and build Kriging surrogate models (also known as Gaussian Processes). With the information provided by the Kriging models

the sensitivity analysis

design space exploration and global optimization processes can all be facilitated so that the designers can avoid low safety allowance and over-redundant designs while the design circle is also shortened. A complete technical route of Kriging-based analysis and optimization was provided in this paper

and the key steps (design of experiments

surrogate model

adaptive sampling

sensitivity analysis

design space exploration

global optimization

etc.) were described in detail. Finally

the proposed technical route has been verified the validity by the analysis and optimization of a traction converter lug structure.

关键词

轨道交通装备变流器吊耳结构优化Kriging代理模型机器学习高斯过程仿真

Keywords

rail transit equipmentconverter lugstructural optimizationKriging surrogate modelmachine learningGaussian processessimulation

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