Destiny Mason, Varsha S Swamy, Corina Sandu, Alba Yerro, David Gorsich, and Katie Sebeck
Paper presented at ISTVS 2024 | 21st International and 12th Asia-Pacific Regional Conference of the ISTVS Keywords: Finite Element Analysis (FEA); Tire Modeling; Sensitivity analysis https://doi.org/10.56884/0AXMP2K2
Military and agriculture vehicles alike often experience uncertainties regarding sinkage and traction while traversing non-linear deformable soils. The off-road tires of these vehicles are equipped with stiffer sidewall characteristics to reduce puncture risk, increase load bearing capacity, and improve stability and traction. Since physically testing tires is time-consuming and costly, enhancing the virtual physics-based modeling capabilities can be beneficial for design decisions. Identification of crucial design parameters is essential to ensure that modelers prioritize these parameters to improve the model's accuracy. As a result, a tire sensitivity analysis is proposed to evaluate the tire model's robustness. The tire used for this study is a lugged bias-ply tire with improved self-cleaning characteristics for muddy terrains. An advanced FE tire model is developed with detailed modeling of the distinct tire parts, including the bead, apex, inner layer, 2-ply carcass, sidewalls, under tread, and lugs. Two types of parameters are of focus: (a) tire operational parameters and (b) tire design parameters. The influence of tire operational parameters, i.e., tire normal load, inflation pressure, and velocity are first conducted and verified with existing studies in the literature. As a novel contribution, the tire design parameters, i.e., tire width, tire diameter, number of lugs, orientation of lugs, and lug size, are also considered. The advanced FE tire is made to negotiate on the rigid ground first and then on low plasticity CL clay to analyze the trends in sensitivities. The results of this sensitivity analysis provide valuable insights into the reliability of the FE tire, enabling more informed conclusions to be drawn from simulated data.
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