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25 January 2026

Parameters Identification of Tire–Clay Contact Angle Based on Numerical Simulation

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1
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
China North Artificial Intelligence & Innovation Research Institute, Beijing 100072, China
*
Author to whom correspondence should be addressed.
Machines2026, 14(2), 139;https://doi.org/10.3390/machines14020139 
(registering DOI)
This article belongs to the Special Issue Emerging Research in Autonomous Vehicle Technology: Innovations in On-Road and Off-Road Driving Challenges

Abstract

The predictive accuracy of the Bekker–Wong model for wheel traction is highly dependent on the precision of the wheel–soil contact angle parameters. These parameters are typically identified through extensive and costly single wheel–soil tests, which are limited by poor experimental repeatability and site-specific constraints. This study proposes a method for obtaining contact angle parameters through numerical simulation. Firstly, a finite element model of an off-road tire is established. The Drucker–Prager (D-P) constitutive model parameters of clay under different moisture were calibrated by soil mechanical tests. And then the moist clay was modeled through the SPH algorithm. An FEM–SPH interaction model was developed to define the tire–moist clay interaction. Meanwhile, the tire–moist clay interaction model was verified by a single wheel–soil test device. To identify the empirical parameters of tire–soil interaction, numerical simulations were conducted for multiple operating conditions involving different slip ratios, soil moisture contents, and vertical loads. By processing the simulated wheel–soil contact characteristic images, the contact angles for each condition were extracted. Finally, the contact angle parameters in the Bekker–Wong model were identified. The empirical parameters were integrated into the Bekker–Wong model to predict traction. The results indicate that the maximum relative error of traction force between the prediction and experiment did not exceed 13.6%, which validated the reliability of the proposed method.

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