Guanchu Chen, Hiroki Yamashita, Y. Ruan, P. Jayakumar, D. Gorsich, J. Knap, K. Leiter, Xiaobo Yang, H. Sugiyama
{"title":"高保真越野机动模拟的分层MPM-ANN多尺度地形模型:MBD-FE-MPM-ANN耦合方法","authors":"Guanchu Chen, Hiroki Yamashita, Y. Ruan, P. Jayakumar, D. Gorsich, J. Knap, K. Leiter, Xiaobo Yang, H. Sugiyama","doi":"10.1115/1.4062204","DOIUrl":null,"url":null,"abstract":"\n 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 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 (ANN) 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.","PeriodicalId":54858,"journal":{"name":"Journal of Computational and Nonlinear Dynamics","volume":"23 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Hierarchical MPM-ANN Multiscale Terrain Model for High-Fidelity Off-Road Mobility Simulations: A Coupled MBD-FE-MPM-ANN Approach\",\"authors\":\"Guanchu Chen, Hiroki Yamashita, Y. Ruan, P. Jayakumar, D. Gorsich, J. Knap, K. Leiter, Xiaobo Yang, H. Sugiyama\",\"doi\":\"10.1115/1.4062204\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n 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 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 (ANN) 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. 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Hierarchical MPM-ANN Multiscale Terrain Model for High-Fidelity Off-Road Mobility Simulations: A Coupled MBD-FE-MPM-ANN Approach
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 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 (ANN) 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.
期刊介绍:
The purpose of the Journal of Computational and Nonlinear Dynamics is to provide a medium for rapid dissemination of original research results in theoretical as well as applied computational and nonlinear dynamics. The journal serves as a forum for the exchange of new ideas and applications in computational, rigid and flexible multi-body system dynamics and all aspects (analytical, numerical, and experimental) of dynamics associated with nonlinear systems. The broad scope of the journal encompasses all computational and nonlinear problems occurring in aeronautical, biological, electrical, mechanical, physical, and structural systems.