{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1642"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1642","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Collings, William"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Automotive Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["El-Gindy, Moustafa","Ren, Jing"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-07-01","date_published":"2023-07-01","updated_at":"2026-07-24T05:35:18Z","subjects":["FEA","Tire-rim slip","Terramechanics","Virtual sensor","Neural network"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1642","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["El-Gindy, Moustafa","Ren, Jing"]},{"key":"dc:creator","label":"Author","values":["Collings, William"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-08-22T14:51:26Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-08-22T14:51:26Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-07-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Automotive Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["FEA","Tire-rim slip","Terramechanics","Virtual sensor","Neural network"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1642"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Modelling and prediction of tire-rim slip with finite element analysis"]}]}],"canonical_facts":{"dc:contributor.advisor":["El-Gindy, Moustafa","Ren, Jing"],"dc:creator":["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: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."],"dc:identifier.uri":["https://hdl.handle.net/10155/1642"],"dc:language.iso":["en"],"dc:subject":["FEA","Tire-rim slip","Terramechanics","Virtual sensor","Neural network"],"dc:title":["Modelling and prediction of tire-rim slip with finite element analysis"],"dc:type":["Thesis"],"thesis:degree_discipline":["Automotive Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:18Z"}