Rapid and precise calibration of soil microparameters for high-fidelity discrete element models in vehicle mobility simulation

IF 2.4 3区 工程技术 Q3 ENGINEERING, ENVIRONMENTAL
Chen Hua , Runxin Niu , Xinkai Kuang , Biao Yu , Chunmao Jiang , Wei Liu
{"title":"Rapid and precise calibration of soil microparameters for high-fidelity discrete element models in vehicle mobility simulation","authors":"Chen Hua ,&nbsp;Runxin Niu ,&nbsp;Xinkai Kuang ,&nbsp;Biao Yu ,&nbsp;Chunmao Jiang ,&nbsp;Wei Liu","doi":"10.1016/j.jterra.2024.100985","DOIUrl":null,"url":null,"abstract":"<div><p>In the realm of numerical simulations concerning vehicle mobility, the establishment of a high-fidelity soil discrete element model often necessitates substantial parameter adjustments to align with the mechanical responses of actual soil. In pursuit of a rapid and precise calibration of the microparameters of the soil model, this paper describes an approach rooted in machine learning surrogate models. This method calibrates the corresponding discrete element microparameters based on the macroscopic Mohr–Coulomb parameters derived from actual soil direct shear tests. The distinct contribution lies in the creation of a dataset that bridges the soil microparameters and macroparameters through simulated direct shear tests, which serves as training data for machine learning algorithms. Additionally, an adaptive particle swarm optimization neural network algorithm is proposed to represent the nonlinear relationships among parameters within the dataset, thus achieving intelligent calibration. To verify the reliability of the proposed soil calibration model in the context of vehicle mobility simulations, a co-simulation is conducted using a vehicle multibody dynamics simulation model and the calibrated soil model, with validation conducted across multiple criteria.</p></div>","PeriodicalId":50023,"journal":{"name":"Journal of Terramechanics","volume":"115 ","pages":"Article 100985"},"PeriodicalIF":2.4000,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Terramechanics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022489824000272","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0

Abstract

In the realm of numerical simulations concerning vehicle mobility, the establishment of a high-fidelity soil discrete element model often necessitates substantial parameter adjustments to align with the mechanical responses of actual soil. In pursuit of a rapid and precise calibration of the microparameters of the soil model, this paper describes an approach rooted in machine learning surrogate models. This method calibrates the corresponding discrete element microparameters based on the macroscopic Mohr–Coulomb parameters derived from actual soil direct shear tests. The distinct contribution lies in the creation of a dataset that bridges the soil microparameters and macroparameters through simulated direct shear tests, which serves as training data for machine learning algorithms. Additionally, an adaptive particle swarm optimization neural network algorithm is proposed to represent the nonlinear relationships among parameters within the dataset, thus achieving intelligent calibration. To verify the reliability of the proposed soil calibration model in the context of vehicle mobility simulations, a co-simulation is conducted using a vehicle multibody dynamics simulation model and the calibrated soil model, with validation conducted across multiple criteria.

快速精确校准土壤微参数,用于车辆移动模拟中的高保真离散元件模型
在有关车辆机动性的数值模拟领域,建立高保真土壤离散元件模型往往需要对参数进行大量调整,以符合实际土壤的机械响应。为了快速精确地校准土壤模型的微参数,本文介绍了一种基于机器学习代用模型的方法。该方法根据从实际土壤直接剪切试验中得出的宏观莫尔-库仑参数校准相应的离散元素微参数。其独特之处在于,通过模拟直接剪切试验创建了一个数据集,将土壤微观参数和宏观参数连接起来,作为机器学习算法的训练数据。此外,还提出了一种自适应粒子群优化神经网络算法,用于表示数据集中各参数之间的非线性关系,从而实现智能校准。为了验证所提出的土壤标定模型在车辆行驶模拟中的可靠性,我们使用车辆多体动力学模拟模型和标定的土壤模型进行了联合模拟,并通过多个标准进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Terramechanics
Journal of Terramechanics 工程技术-工程:环境
CiteScore
5.90
自引率
8.30%
发文量
33
审稿时长
15.3 weeks
期刊介绍: The Journal of Terramechanics is primarily devoted to scientific articles concerned with research, design, and equipment utilization in the field of terramechanics. The Journal of Terramechanics is the leading international journal serving the multidisciplinary global off-road vehicle and soil working machinery industries, and related user community, governmental agencies and universities. The Journal of Terramechanics provides a forum for those involved in research, development, design, innovation, testing, application and utilization of off-road vehicles and soil working machinery, and their sub-systems and components. The Journal presents a cross-section of technical papers, reviews, comments and discussions, and serves as a medium for recording recent progress in the field.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信