Compaction-Based Deformable Terrain Model as an Interface for Real-Time Vehicle Dynamics Simulations

Paper #:
  • 2013-01-1197

Published:
  • 2013-04-08
Citation:
Madsen, J., Seidl, A., and Negrut, D., "Compaction-Based Deformable Terrain Model as an Interface for Real-Time Vehicle Dynamics Simulations," SAE Technical Paper 2013-01-1197, 2013, https://doi.org/10.4271/2013-01-1197.
Pages:
6
Abstract:
This paper discusses the development of a novel deformable terrain database and its use in a co-simulation environment with a multibody dynamics vehicle model. The implementation of the model includes a general tire-terrain traction model which is modular to allow for any type of tire model that supports the Standard Tire Interface[1] to operate on the terrain. This allows arbitrarily complex tire geometry to be used, which typically has a large impact on the mobility performance of vehicles operating on deformable terrains. However, this gain in generality comes at the cost that popular analytical pressure-sinkage terramechanics models cannot be used to find the normal pressure and shear stress of the contact patch. Pressure and shear stress are approximated by combining the contributions from tire normal forces, shear stresses and bulldozing forces due to soil rutting. The governing equations of the terrain are based on a soil compaction model that includes both the propagation of subsoil stresses due to vehicular loads, and the resulting visco-elastic-plastic stress/strain on the affected soil volume. Pedo transfer functions allow for the calculation of the soil mechanics model parameters from existing soil measurements. This terrain model was implemented in a way that maps well to Graphics Processor Unit, which allows the model to run in real-time, enabling operator in the loop full vehicle simulations. Test simulations are run using a rigid tire with lugs to show the capability of the model to predict tire and terrain responses due to complex tractive element geometry. Run times and scaling analyses are presented to gauge the relative speedup of utilizing GPUs for computational acceleration.
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