Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data

Lars Muth; Christian Noll; Walter Sextro

doi:10.1007/978-3-031-07305-2_92

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Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data

Lecture notes in mechanical engineering 2022

Lars Muth, Christian Noll, Walter Sextro

Summary

Tire and road wear particles represent the largest single source of microplastics in the environment, and this study developed a computationally efficient method using clustered driving data to evaluate and optimize tire wear during vehicle suspension design. Reducing tire wear through engineering optimization offers a meaningful pathway to cutting non-exhaust microplastic emissions at the source before particles enter road runoff and waterways.

Polymers

rubber

Tire and road wear are a major source of emissions of non-exhaust particulate matter (PM) and make up the largest share of microplastics in the environment. To reduce tire wear through numerical optimization of a vehicle’s suspension system, fast simulations of...

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