Using high-fidelity discrete element simulation to calibrate an expeditious terramechanics model in a multibody dynamics framework

TL;DR

This paper introduces a high-fidelity simulation-based calibration method for terramechanics models, significantly reducing computational costs while maintaining accuracy in off-road vehicle dynamics simulations.

Contribution

It presents a novel approach combining multibody and discrete element simulations to calibrate empirical soil contact models efficiently.

Findings

SCM parameters calibrated using high-fidelity DEM simulations

SCM results closely match DEM-based simulations

Simulation time for SCM is 100 to 1000 times lower than DEM

Abstract

The wheel-soil interaction has great impact on the dynamics of off-road vehicles in terramechanics applications. The Soil Contact Model (SCM), which anchors an empirical method to characterize the frictional contact between a wheel and soil, has been widely used in off-road vehicle dynamics simulations because it quickly produces adequate results for many terramechanics applications. The SCM approach calls for a set of model parameters that are obtained via a bevameter test. This test is expensive and time consuming to carry out, and in some cases difficult to set up, e.g., in extraterrestrial applications. We propose an approach to address these concerns by conducting the bevameter test in simulation, using a model that captures the physics of the actual experiment with high fidelity. To that end, we model the bevameter test rig as a multibody system, while the dynamics of the soil is captured using a discrete element model (DEM). The multibody dynamics--soil dynamics co-simulation is used to replicate the bevameter test, producing high-fidelity ground truth test data that is subsequently used to calibrate the SCM parameters within a Bayesian inference framework. To test the accuracy of the resulting SCM terramechanics, we run single wheel and full rover simulations using both DEM and SCM terrains. The SCM results match well with those produced by the DEM solution, and the simulation time for SCM is two to three orders of magnitude lower than that of DEM. All simulations in this work are performed using Chrono, an open-source, publicly available simulator. The scripts and models used are available in a public repository for reproducibility studies and further research.