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

Yuemin Zhang, Junpeng Dai, Wei Hu, Dan Negrut

TL;DR

This work tackles the challenge of calibrating an expeditious terramechanics model (SCM) by leveraging high‑fidelity DEM data generated in a virtual bevameter setup within a multibody dynamics framework. A Bayesian inference workflow is used to estimate the six SCM parameters ($K_c$, $K_\phi$, $n$, $c$, $\varphi$, $K_s$) from ground‑truth data produced by DEM, yielding calibrated SCM that closely matches DEM across single‑wheel and full‑rover scenarios while delivering 2–3 orders of magnitude faster run times. The approach demonstrates that SCM, when properly calibrated with DEM ground truth, can serve as a reliable surrogate for design, control, and planning tasks in deformable terrains. All simulations are conducted in Chrono with open‑source scripts enabling reproducibility and extension to other soil types and geometries.

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.

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

TL;DR

This work tackles the challenge of calibrating an expeditious terramechanics model (SCM) by leveraging high‑fidelity DEM data generated in a virtual bevameter setup within a multibody dynamics framework. A Bayesian inference workflow is used to estimate the six SCM parameters (, , , , , ) from ground‑truth data produced by DEM, yielding calibrated SCM that closely matches DEM across single‑wheel and full‑rover scenarios while delivering 2–3 orders of magnitude faster run times. The approach demonstrates that SCM, when properly calibrated with DEM ground truth, can serve as a reliable surrogate for design, control, and planning tasks in deformable terrains. All simulations are conducted in Chrono with open‑source scripts enabling reproducibility and extension to other soil types and geometries.

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.
Paper Structure (15 sections, 20 equations, 51 figures, 4 tables)

This paper contains 15 sections, 20 equations, 51 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Numerical methods preamble
  3. Overview of the DEM method
  4. The Newton-Euler equations of motion
  5. The Hertz-Mindlin contact model
  6. Overview of the SCM method
  7. Overview of the Bayesian inference framework
  8. Calibration of the SCM model
  9. The plate sinkage virtual test, and the $K_c$, $K_\phi$, and $n$ calibration
  10. Annulus shear virtual test, and the $c$, $\varphi$, and $K_s$ calibration
  11. Validation of the calibrated SCM model
  12. Single wheel virtual experiments
  13. Full vehicle virtual experiments
  14. Conclusions and directions of future work
  15. Appendix

Figures (51)

  • Figure 1: Schematic of the contact between two spherical particles using the penalty-based DEM approach.
  • Figure 2: Schematic view of the SCM model for wheel-soil interaction; wheel can have arbitrary shape.
  • Figure 3: Screenshots of the terrain settling simulation with DEM particles. Animation of the settling simulation is provided in the supplementary materials.
  • Figure 4: Schematic of the plate sinkage test.
  • Figure 5: Screenshot of the plate sinkage test with 0.2 m radius plate. Animation of the plate sinkage test simulation is provided in the supplementary materials.
  • ...and 46 more figures