Issue |
MATEC Web Conf.
Volume 347, 2021
12th South African Conference on Computational and Applied Mechanics (SACAM2020)
|
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Article Number | 00027 | |
Number of page(s) | 12 | |
DOI | https://doi.org/10.1051/matecconf/202134700027 | |
Published online | 23 November 2021 |
Identifiability of Tyre Force Contact Prediction from Deformation Measurements
Department of Mechanical and Aeronautical Engineering, University of Pretoria Private Bag X20, Hatfield 0002, South Africa
* e-mail: robin.gast@tuks.co.za
** e-mail: schalk.els@up.ac.za
*** e-mail: schalk.kok@up.ac.za
**** e-mail: nico.wilke@up.ac.za
† e-mail: theunis.botha@up.ac.za
The possibility of accurately inferring the external forces applied to a vehicle can directly contribute to better safety systems and thus lowers the chance of injury or loss of life. These external forces are applied to a vehicle through the tyres and are challenging to measure directly. Still, it is possible to measure acceleration, deformation, or strain on the inner surface of a tyre. These measurements are theorized to be strongly linked to the forces produced by the tyre. However, it is still unknown whether or not one can always identify external forces from internal measurements in this way. Research has mainly focused on obtaining estimates of tyre forces rather than establishing to what extent these tyre forces are identifiable. This paper investigates this by conducting a virtual experiment that simulates known external forces applied to the tyre and computes the strains and displacements inside the tyre. A virtual inverse simulation then recovers the external forces from either the deformation or strain computed on the inside of the tyre. The identifiability of the forces recovered by the virtual inverse simulation is investigated by adding artificial measurement noise and initial guess perturbations to quantify the variance in the identified forces.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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