Study on tire‐ice traction using a combined neural network and secondary development finite element modeling method. (18th October 2018)

• Record Type: Journal Article
• Authors: Zhang, Yuanchao; Gao, Jingwei; Li, Qiao
• Other Names: Mei Lin guestEditor.; Yan Zhiguo guestEditor.; Shao Jie guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 9(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-10-18
• Subjects: FEM -- neural network -- tire‐ice model -- traction performance; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5045
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Research on tire‐ice traction theory is still in the basic stage, and there are large differences between the current theoretical calculation and engineering practice. Because testing of pneumatic tire performance on icy road is very expensive, and testing conditions are complex and variable, testing does not have higher repeatability. In this paper, a tire‐ice traction model is established based on a neural network and the finite‐element method. Through the model calculations, the traction characteristics of tires on ice can be well‐simulated. The method of combining the Levenberg‐Marquardt (LM) optimizing algorithm with the neural network is used to predict the friction between tires and ice. The attributive characteristics of tires based on testing results are then measured. A nonlinear finite‐element model of a 37 × 12.5R16.5 tire is established by adopting the Yeoh model of rubber and researching existing tire‐ice driving traction models. This tire‐ice finite‐element model can predict the changes of drawbar pull and torque when driving on icy roads, and two different tire working conditions, ie, straight line and turning, are taken into consideration simultaneously. The influence of slip ratio, speed, load on drawbar pull, and torque are also explored.