Modelling and prediction of tire-rim slip with finite element analysis

Abstract

In this thesis, tire-rim slip was simulated with a FEA model of a RHD truck tire. Multiple simulations were conducted to validate the model and investigate the effects of different parameters such as terrain type, tire-rim friction coefficient, drawbar load, vertical load, inflation pressure, and longitudinal wheel speed. Two terrain types were used: a high-friction hard surface and a soft SPH soil calibrated to represent upland sandy loam. An additional step was the design and training of a neural network-based virtual sensor for the prediction of tire-rim slip based on the parameters with significant effects. Two important findings were that tire-rim slip was higher on the soft soil than on the hard surface, and that the longitudinal wheel speed had negligible effect. Finally, a neural network with 31 neurons was trained using Bayesian regularization to predict the tire-rim slip with a correlation coefficient of 0.99431.