Investigation of Different Snow Material Models and Simulation Techniques for Enhanced Snow-Tire Interaction Study

Abstract In regions that experience significant snowfall, the interaction between tires and snow-covered surfaces is critical for vehicle dynamics, safety, and performance. Advanced material models of snow with reliable parameters are required for simulation of this complex interaction. A comprehensive study on the material characterization of compacted snow and parameter identification using an optimization-based approach are presented in this study. Then, the calibrated snow models are applied in various computational models for simulating rubber block-snow interactions. The characterization process involves Direct Shear Tests (DST) and Confined Compression Tests (CCT) to determine important snow properties. The model parameters are optimized using Multi-Objective Design of Experiments (MODoE) algorithm and experimental data. The study used two advanced computational techniques: Arbitrary Lagrangian-Eulerian (ALE) and Smoothed Particle Hydrodynamics (SPH), evaluating their efficacy in simulating traction in snow-rubber interaction conditions. The work enhances understanding of the complex mechanical behavior of compressed snow that could be used to improve tire design for snow traction.