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Predicting terrain parameters for physics-based vehicle mobility models from cone index data

机译:从圆锥指数数据预测基于物理的车辆机动性模型的地形参数

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摘要

To provide terrain data for the development of physics-based vehicle mobility models, such as the Next Generation NATO Reference Mobility Model, there is a desire to make use of the vast amount of cone index (CI) data available. The challenge is whether the terrain parameters for physics-based vehicle mobility models can be predicted from CI data. An improved model for cone-terrain interaction has been developed that takes into account both normal pressure and shear stress distributions on the cone-terrain interface. A methodology based on Derivative-Free Optimization Algorithms (DFOA) has been developed in combination with the improved model to make use of continuously measured CI vs. sinkage data for predicting the three Bekker pressure-sinkage parameters, k(o) k(phi) and n, and two cone-terrain shear strength parameters, c(c), and phi(c). The methodology has been demonstrated on two types of soil, LETE sand and Keweenaw Research Center (KRC) soils, where continuous CI vs. sinkage measurements and continuous plate pressure vs. sinkage measurements are available. The correlations between the predicted pressure-sinkage relationships based on the parameters derived from continuous CI vs. sinkage measurements using the DFOA-based methodology and that measured were generally encouraging. (C) 2020 ISTVS. Published by Elsevier Ltd. All rights reserved.
机译:为了提供地形数据以用于基于物理的车辆机动性模型的开发,例如下一代北约参考机动性模型,希望利用大量可用的圆锥指数(CI)数据。挑战在于,是否可以从CI数据中预测基于物理的车辆机动性模型的地形参数。已经开发了一种改进的锥体-地形相互作用模型,该模型考虑了锥体-地形界面上的法向压力和切应力分布。结合改进模型,开发了一种基于无导数优化算法(DFOA)的方法,以利用连续测量的CI与沉降数据来预测三个Bekker压力沉降参数k(o)k(phi)和n,以及两个锥面抗剪强度参数c(c)和phi(c)。该方法已在两种类型的土壤(LETE沙土和Keweenaw研究中心(KRC))上得到了证明,其中可以进行连续CI与下沉测量以及连续板压力与下沉测量。基于使用基于DFOA的方法从连续CI与下沉测量得出的参数的预测压力-下沉关系之间的相关性与所测得的相关性通常令人鼓舞。 (C)2020年ISTVS。由Elsevier Ltd.出版。保留所有权利。

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