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Advanced Structures & Materials

Annual Plan

Enhanced Multiscale Off-Road Mobility Prediction Capability with Machine Learning Constitutive Modeling for Large Deformable Granular Terrain

Project Team

Principal Investigator

Hiroyuki Sugiyama, University of Iowa

Government

Paramsothy Jayakumar, Yeefeng Ruan, Thomas Skorupa, U.S. Army GVSC

Jaroslaw Knap, Kenneth W. Leiter, U.S. Army Research Lab

Faculty

Hiroki Yamashita, University of Iowa

Industry

Xiaobo Yang, Oshkosh Corporation

Student

Guanchu Chen, University of Iowa

Project Summary

Project begin in 2020.

A high-fidelity off-road mobility model is an integral part of the Next-Generation NATO Reference Mobility Model (NG-NRMM) to enable reliable operational planning. Whereas discrete-element (DE) terrain models have been utilized to predict granular soil behavior, the computational cost increases prohibitively high as the number of DE particles increases to consider a wider range of terrain; especially when constructing stochastic mobility maps, requiring a large number of simulation runs at different locations in the map for different soil at different speeds to predict distribution of “speed-made-good”.

The primary objective of this study is to develop a new hierarchical multiscale off-road mobility modeling procedure, enhanced by Material Point Method (MPM) and the machine learning (ML) soil constitutive modeling for the lower-scale representative volume element (RVE), to eliminate limitations of the existing finite element discrete element (FE-DE) multiscale and single-scale DE terrain models. The proposed model will enable quick and reliable vehicle mobility prediction on large deformable granular terrain in challenging missions.

This work builds upon the PI and his team’s previous ARC project on the FE-DE multiscale off-road mobility model. The new contributions of this work lie in the following points:

  • New development of the ML soil constitutive model and its training strategies for lower-scale RVEs to speed up online RVE calculations for large deformable granular terrain
  • New development of the MPM macro-scale terrain model for off-road mobility simulation to eliminate the limitation of the FE macro-scale model in predicting large terrain deformation
  • New development of the MPM-ML multiscale off-road mobility simulation framework with scalable parallel computing scheme to enable quick and reliable mobility prediction on large deformable granular terrain