The present paper proposes a tire model based in finite element analyses that predicts displacement on its outer shell as a result of inflation pressure variation. The model is composed of wire axisymmetric elements, under pressure, applying displacement on the edges to represent the wheel deformation. The next step is to divide the geometry in sections, addressing the problem of defining a virtual material for each section that represents with good fidelity the material combinations found on the actual tire. The classical Yeoh hyperelastic material model was adopted. One parameter per section has to be obtained based on physical tests. An initial set of parameters was chosen at random by a Design of Experiment methodology. The first step towards this objective was to measure displacement data for four different values of inflation pressures of a tire using optical 3D metrology technique. The resulting images were numerically treated to obtain the displacements and tire thickness for each section. A set of points on the tire was used as a reference to assess the tire deformation. The model was run and its results were compared to 3D experimental data in order to establish a discrepancy metric. This set of parameters was then fed into a Gaussian Process Regression Algorithm as inputs and the corresponding discrepancy values as outputs. The discrepancies were minimized in terms of the material parameters. The initial material model predictions were used to create a second, more refined, DoE for the Yeoh material parameters. This process was repeated iteratively in order to calibrate with increasing accuracy of the adopted material model.
Tire Material Characterization Using Gaussian Process Regression and Optimization
Sae Technical Papers
2020 SAE Brasil Congress & Exhibition ; 2020
26.03.2021
Aufsatz (Konferenz)
Englisch
Tire Material Characterization Using Gaussian Process Regression and Optimization
British Library Conference Proceedings | 2020
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