Terramechanics models augmented by machine learning representations

IF 2.4 3区 工程技术 Q3 ENGINEERING, ENVIRONMENTAL Journal of Terramechanics Pub Date : 2023-06-01 DOI:10.1016/j.jterra.2023.03.002
Eric Karpman , Jozsef Kövecses , Marek Teichmann
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引用次数: 2

Abstract

The field of terramechanics focuses largely on two types of simulation approaches. First, the classical semi-empirical methods that rely on empirically determined soil parameters and equations to calculate the soil reaction forces acting on a wheel, track or tool. One major drawback to these methods is that they are only valid under steady-state conditions. The more flexible modelling approaches are discrete or finite element methods (DEM, FEM) that discretize the soil into elements. These computationally demanding approaches do away with the steady state assumption at the cost of including more model parameters that can be difficult to accurately tune. Model-free approaches in which machine learning algorithms are used to predict soil reaction forces have been explored in the past, but the use of these models comes at the cost of the valuable insight that the semi-empirical models provide. In this work, we presume that in a dynamic simulation, the soil reaction forces can be divided into a steady state component that can be captured using semi-empirical models and a dynamic component that cannot. We propose an augmented modelling approach in which a neural network is trained to predict the dynamic component of the reaction forces. We explore how this theory can be applied to the simulation of a soil-cutting blade using the Fundamental Earthmoving Equation and of a wheel driving over soft soil using the Bekker wheel-soil model.

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机器学习表示增强的Terramechanics模型
地形力学领域主要集中于两种类型的模拟方法。首先,经典的半经验方法依赖于经验确定的土壤参数和方程来计算作用在车轮、轨道或工具上的土壤反作用力。这些方法的一个主要缺点是它们只在稳态条件下有效。更灵活的建模方法是离散或有限元方法(DEM, FEM),它们将土壤离散成元素。这些计算要求很高的方法放弃了稳态假设,代价是包含了更多难以精确调整的模型参数。过去已经探索了使用机器学习算法来预测土壤反力的无模型方法,但是这些模型的使用是以半经验模型提供的宝贵见解为代价的。在这项工作中,我们假设在动态模拟中,土壤反作用力可以分为可以使用半经验模型捕获的稳态分量和不能使用半经验模型捕获的动态分量。我们提出了一种增强建模方法,其中训练神经网络来预测反作用力的动态分量。我们探讨了如何将这一理论应用于使用基本土方方程模拟土壤切割刀片和使用Bekker轮-土模型模拟车轮在软土上行驶。
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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.
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