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

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.