Modelling and prediction of tire-rim slip with finite element analysis
dc.contributor.advisor | El-Gindy, Moustafa | |
dc.contributor.advisor | Ren, Jing | |
dc.contributor.author | Collings, William | |
dc.date.accessioned | 2023-08-22T14:51:26Z | |
dc.date.available | 2023-08-22T14:51:26Z | |
dc.date.issued | 2023-07-01 | |
dc.identifier.uri | https://hdl.handle.net/10155/1642 | |
dc.description.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. | en |
dc.description.sponsorship | University of Ontario Institute of Technology | en |
dc.language.iso | en | en |
dc.subject | FEA | en |
dc.subject | Tire-rim slip | en |
dc.subject | Terramechanics | en |
dc.subject | Virtual sensor | en |
dc.subject | Neural network | en |
dc.title | Modelling and prediction of tire-rim slip with finite element analysis | en |
dc.type | Thesis | en |
dc.degree.level | Master of Applied Science (MASc) | en |
dc.degree.discipline | Automotive Engineering | en |
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