Parametric Sensitivity Analysis of Pneumatic Tire–Soil Traction Interaction Under Controlled-Slip Conditions Using Meshed and Meshless Methods

Accurate tire–soil traction prediction is critical for agricultural and off-road vehicle design, yet rigorous comparisons of advanced discretization strategies under controlled-slip conditions remain limited. This study compares MM-ALE and Hybrid FE-SPH (H-SPH) discretization in LS-DYNA for SRTT (225/60R16) traction prediction on sandy loam (0.4% gravimetric moisture content) across 5–40% slip ratios. A CT-scan-based tire model using Yeoh visco-hyperelastic rubber (Material_2) was validated against experimental data, achieving CORA scores of 0.989 (radial deflection), 0.999 (loaded radius), 0.947 (footprint area), and 0.985 (contact pressure), outperforming the Mooney–Rivlin formulation (Material_1; CORA = 0.618). Soil moisture content (0.4%, 8%, 14%) was included as a design variable through a Latin Hypercube Sampling framework. Both methods reproduced a monotonic increase in traction; inter-method differences ranged from 29 to 36% at low slip, converging to a 7.8% coefficient of variation at 40% slip. A 27-run full-factorial DOE-I identified normal load as the dominant traction driver (90.1%), followed by velocity (8.6%) and inflation pressure (1.3%). An LHS-based DOE-II revealed moisture content as the primary driver of traction coefficient (67.5%), via a non-monotonic cohesion mechanism peaking at 8% gravimetric moisture. H-SPH reduced runtime by 38% versus MM-ALE. The validated framework provides reusable traction prediction protocols for variable conditions.