基于梯度优化确定轮轨接触面积的物理驱动方法

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Long Liu, Bing Yi, Daping Li
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引用次数: 0

摘要

本文提出了一种基于物理的轮轨接触面积逆确定方法,该方法引入了包含滤波和投影过程的连续优化管道。首先,引入单元连通性参数化方法,构造具有离散接触对的连续目标,建立基于物理的优化模型;其次,采用基于半径的滤波方程对设计变量进行平滑处理以提高数值稳定性,并引入可微阶跃函数将平滑后的设计变量投影到0-1离散整数空间中,保证优化模型的解服从于离散接触对;最后通过分析松弛优化公式对设计变量的敏感性,构造了轮轨接触面积迭代更新的移动渐近线方法,直到算法收敛为止。实验结果表明,与线迹法相比,本文提出的方法能够有效地逆求轮轨接触点,并首次尝试利用基于梯度的优化方法,同时利用实测廓形和预测廓形数据考虑轮轨接触面积。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Physics-Driven Method for Determining Wheel - Rail Contact Area With Gradient-Based Optimization
In this paper, a physics-based method to inversely determine wheel-rail contact area in their lifecycle is proposed by introducing a continuous optimization pipeline including filtering and projection procedures. First, the element connectivity parameterization method is introduced to construct continuous objection with discrete contact pairs and formulate the physics-based optimization model. Second, the radius-based filter equation is employed for smoothing the design variables to improve the numerical stability and the differentiable step function is introduced to project smoothed design variables into 0-1 discrete integer space to ensure the solution of the optimization model yields to discrete contact pairs. Finally the method of moving asymptotes is constructed for iteratively updating wheel-rail contact area by analyzing the sensitivity of relaxed optimization formulation with respect to design variables until the algorithm converged. The experimental result shows the effectiveness of the proposed method to inversely determine the wheel-rail contact points in their lifecycle compared to the line tracing method, to the best of our knowledge, it is the first attempt to consider wheel-rail contact area in lifecycle service with both the measured profile and the predicted profile data by gradient-based optimization method.
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来源期刊
CiteScore
6.30
自引率
12.90%
发文量
100
审稿时长
6 months
期刊介绍: The ASME Journal of Computing and Information Science in Engineering (JCISE) publishes articles related to Algorithms, Computational Methods, Computing Infrastructure, Computer-Interpretable Representations, Human-Computer Interfaces, Information Science, and/or System Architectures that aim to improve some aspect of product and system lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, recycling etc.). Applications considered in JCISE manuscripts should be relevant to the mechanical engineering discipline. Papers can be focused on fundamental research leading to new methods, or adaptation of existing methods for new applications. Scope: Advanced Computing Infrastructure; Artificial Intelligence; Big Data and Analytics; Collaborative Design; Computer Aided Design; Computer Aided Engineering; Computer Aided Manufacturing; Computational Foundations for Additive Manufacturing; Computational Foundations for Engineering Optimization; Computational Geometry; Computational Metrology; Computational Synthesis; Conceptual Design; Cybermanufacturing; Cyber Physical Security for Factories; Cyber Physical System Design and Operation; Data-Driven Engineering Applications; Engineering Informatics; Geometric Reasoning; GPU Computing for Design and Manufacturing; Human Computer Interfaces/Interactions; Industrial Internet of Things; Knowledge Engineering; Information Management; Inverse Methods for Engineering Applications; Machine Learning for Engineering Applications; Manufacturing Planning; Manufacturing Automation; Model-based Systems Engineering; Multiphysics Modeling and Simulation; Multiscale Modeling and Simulation; Multidisciplinary Optimization; Physics-Based Simulations; Process Modeling for Engineering Applications; Qualification, Verification and Validation of Computational Models; Symbolic Computing for Engineering Applications; Tolerance Modeling; Topology and Shape Optimization; Virtual and Augmented Reality Environments; Virtual Prototyping
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