An intelligent optimization method for stabilizing parameters in the maintenance of ballast particles

IF 2.8 3区工程技术 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Computational Particle Mechanics Pub Date : 2024-11-27 DOI:10.1007/s40571-024-00871-3

Shunwei Shi, Bowen Hou, Yixiong Xiao, Zhihan Zhang, Chunyu Wang, Liang Gao

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引用次数: 0

Abstract

Stabilizing operations are crucial for improving the mechanical states of ballast particles during railway maintenance. An intelligent optimization method for stabilizing parameters is proposed in this study. First, a ballasted track-stabilizing unit coupling model is constructed using DEM-MFBD, which is an effective method for the coupling simulation of discrete and continuous bodies. Subsequently, a surrogate model of stabilizing parameters-objective functions is established using RBF, which has high robustness and accuracy for little sample. Finally, the optimal stabilizing parameters are determined using MOGA, which has a good global search capability. The results indicate that the contact force, compactness, coordination number of the ballast, and the pressure on sleeper are the most sensitive to stabilizing parameters, and 32.59 Hz and 240 kN are recommended as the optimal stabilizing frequency and vertical force, respectively. The proposed intelligent optimization method involves the interaction of stabilizing parameters, and it offers higher accuracy and efficiency than traditional optimization methods. This study provides theoretical guidance for improving railway maintenance.

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一种稳定压载颗粒维修参数的智能优化方法

在铁路养护过程中，稳定作业是改善道砟颗粒力学状态的关键。本文提出了一种稳定参数的智能优化方法。首先，利用DEM-MFBD建立了有碴稳轨单元耦合模型，该模型是离散体与连续体耦合仿真的有效方法。随后，利用RBF建立了稳定参数-目标函数的代理模型，该模型在小样本条件下具有较高的鲁棒性和准确性。最后，利用遗传算法确定最优稳定参数，该算法具有良好的全局搜索能力。结果表明，接触力、压实度、镇流器配合数和轨枕压力对稳定参数最敏感，建议最佳稳定频率为32.59 Hz，最佳稳定垂直力为240 kN。所提出的智能优化方法涉及稳定参数的相互作用，具有比传统优化方法更高的精度和效率。本研究为提高铁路维修质量提供了理论指导。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Particle Mechanics Mathematics-Computational Mathematics

CiteScore

5.70

自引率

9.10%

发文量

期刊介绍： GENERAL OBJECTIVES: Computational Particle Mechanics (CPM) is a quarterly journal with the goal of publishing full-length original articles addressing the modeling and simulation of systems involving particles and particle methods. The goal is to enhance communication among researchers in the applied sciences who use "particles'''' in one form or another in their research. SPECIFIC OBJECTIVES: Particle-based materials and numerical methods have become wide-spread in the natural and applied sciences, engineering, biology. The term "particle methods/mechanics'''' has now come to imply several different things to researchers in the 21st century, including: (a) Particles as a physical unit in granular media, particulate ﬂows, plasmas, swarms, etc., (b) Particles representing material phases in continua at the meso-, micro-and nano-scale and (c) Particles as a discretization unit in continua and discontinua in numerical methods such as Discrete Element Methods (DEM), Particle Finite Element Methods (PFEM), Molecular Dynamics (MD), and Smoothed Particle Hydrodynamics (SPH), to name a few.