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    Hierarchical MPM-ANN Multiscale Terrain Model for High-Fidelity Off-Road Mobility Simulations: A Coupled MBD-FE-MPM-ANN Approach

    Source: Journal of Computational and Nonlinear Dynamics:;2023:;volume( 018 ):;issue: 007::page 71001-1
    Author:
    Chen, Guanchu
    ,
    Yamashita, Hiroki
    ,
    Ruan, Yeefeng
    ,
    Jayakumar, Paramsothy
    ,
    Gorsich, David
    ,
    Knap, Jaroslaw
    ,
    Leiter, Kenneth W.
    ,
    Yang, Xiaobo
    ,
    Sugiyama, Hiroyuki
    DOI: 10.1115/1.4062204
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: A new hierarchical multiscale terrain model is developed using the material point method (MPM) to enable effective modeling of large terrain deformation for high-fidelity off-road mobility simulations. Unlike the Lagrangian finite element (FE) model, MPM allows for modeling large deformation of a continuum without mesh distortion using material points as moving quadrature points for the background grid. This unique feature is extended to account for complex granular soil material behavior with the hierarchical multiscale modeling approach in the context of off-road mobility simulations. The grain-scale discrete-element (DE) representative volume element (RVE) and its neural network surrogate model (artificial neural network (ANN) RVE) are developed and introduced to the upper-scale MPM model through the scale-bridging algorithm. The DE RVE is used to generate training data for the ANN RVE, allowing for predicting the history-dependent grain-scale soil material behavior efficiently at every material point that moves through the upper-scale MPM background grid. A numerical procedure for modeling the interaction of the nonlinear FE tire model with the MPM-ANN multiscale terrain model is developed considering moving soil patches generalized for the upper-scale MPM terrain model. It is fully integrated into the general off-road mobility simulation framework by leveraging scalable high-performance computing techniques. The predictive ability of the proposed MPM-ANN multiscale off-road mobility model is examined and validated against the full-scale vehicle test data, involving large deformation of soft terrain. The computational benefit from the neural network surrogate model is also demonstrated.
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      Hierarchical MPM-ANN Multiscale Terrain Model for High-Fidelity Off-Road Mobility Simulations: A Coupled MBD-FE-MPM-ANN Approach

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4294867
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    contributor authorChen, Guanchu
    contributor authorYamashita, Hiroki
    contributor authorRuan, Yeefeng
    contributor authorJayakumar, Paramsothy
    contributor authorGorsich, David
    contributor authorKnap, Jaroslaw
    contributor authorLeiter, Kenneth W.
    contributor authorYang, Xiaobo
    contributor authorSugiyama, Hiroyuki
    date accessioned2023-11-29T19:34:08Z
    date available2023-11-29T19:34:08Z
    date copyright4/6/2023 12:00:00 AM
    date issued4/6/2023 12:00:00 AM
    date issued2023-04-06
    identifier issn1555-1415
    identifier othercnd_018_07_071001.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4294867
    description abstractA new hierarchical multiscale terrain model is developed using the material point method (MPM) to enable effective modeling of large terrain deformation for high-fidelity off-road mobility simulations. Unlike the Lagrangian finite element (FE) model, MPM allows for modeling large deformation of a continuum without mesh distortion using material points as moving quadrature points for the background grid. This unique feature is extended to account for complex granular soil material behavior with the hierarchical multiscale modeling approach in the context of off-road mobility simulations. The grain-scale discrete-element (DE) representative volume element (RVE) and its neural network surrogate model (artificial neural network (ANN) RVE) are developed and introduced to the upper-scale MPM model through the scale-bridging algorithm. The DE RVE is used to generate training data for the ANN RVE, allowing for predicting the history-dependent grain-scale soil material behavior efficiently at every material point that moves through the upper-scale MPM background grid. A numerical procedure for modeling the interaction of the nonlinear FE tire model with the MPM-ANN multiscale terrain model is developed considering moving soil patches generalized for the upper-scale MPM terrain model. It is fully integrated into the general off-road mobility simulation framework by leveraging scalable high-performance computing techniques. The predictive ability of the proposed MPM-ANN multiscale off-road mobility model is examined and validated against the full-scale vehicle test data, involving large deformation of soft terrain. The computational benefit from the neural network surrogate model is also demonstrated.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleHierarchical MPM-ANN Multiscale Terrain Model for High-Fidelity Off-Road Mobility Simulations: A Coupled MBD-FE-MPM-ANN Approach
    typeJournal Paper
    journal volume18
    journal issue7
    journal titleJournal of Computational and Nonlinear Dynamics
    identifier doi10.1115/1.4062204
    journal fristpage71001-1
    journal lastpage71001-13
    page13
    treeJournal of Computational and Nonlinear Dynamics:;2023:;volume( 018 ):;issue: 007
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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