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

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.

Name thesis:degree_name

Master of Applied Science (MASc)

Discipline thesis:degree_discipline

Automotive Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Collings, William

Advisors dc:contributor.advisor

• El-Gindy, Moustafa
• Ren, Jing

• FEA
• Tire-rim slip
• Terramechanics
• Virtual sensor
• Neural network

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1642

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1642