Rolling resistance prediction of off-road tire using advanced simulation and analytical techniques

Abstract

This paper focuses on developing a relationship between the rolling resistance coefficient of an off-road truck tire running over different terrains and at various operating conditions. The various operating conditions include tire speed, vertical load, and inflation pressure. The different terrains include dry and moist sand, flooded surface, snow, dense sand, clayey soil, and sandy loam with moisture. The off-road truck tire size 315/80R22.5 is modeled and validated using the finite element analysis technique, while the terrains are modeled and calibrated using smoothed-particle hydrodynamics technique. Artificial neural network and genetic algorithm (GA) are then used to develop a relationship between the rolling resistance coefficient and the terrains and operating conditions. The results of both algorithms are compared to the simulation results in terms of R-square goodness of fit and the mean squared error. Finally, a numerical equation is presented that determines the rolling resistance coefficient as a function of the terrains parameters and the operating conditions. It was found that both techniques provide a suitable solution, however, the GA provides an explicit equation.