Application of Modern Calculation Methods in the Development and Improvement of Automobile Tire Designs

Abstract

Automobile tire is a vital component of a vehicle. It is the only one that comes into contact with the road surface also. All engine power is transmitted to the tires through the transmission and wheels. Therefore, the range of consumer requirements for tires is quite broad, including safety, comfort, durability, cost, and appearance. Therefore, creating a tire that meets customer requirements at the design stage is a complex and non-trivial task, requiring significant time and financial investment. At "Cordiant" Company this problem is addressed through the extensive use of modern calculation methods and software for computer simulation of both individual tire and tire within a vehicle. Before the widespread adoption of computer technology, tire configuration calculations were based on equilibrium contour theory, which was determined using nomograms proposed by a group of authors led by the prominent Soviet scientist V.L. Biderman [1]. Key tire performance indicators included mileage and durability. Currently, the finite element method (FEM) -- a numerical method for solving partial differential equations arising in applied physics -- is widely used in tire design. Specialized software is used for these calculations at "Cordiant" Company. The input information for the calculations includes tire geometry (profile, material distribution, 3D tread pattern model), material properties, loads, and boundary conditions. The result of the FEM calculation includes displacements, deformations, and stresses of each structural component. Processing this information allows the characteristics of the future tire to be determined in terms understandable to engineers. Various models are used to describe the properties of rubber in simulation: Hyperelastic model proposed by Yeoh, which accounts for the nonlinearity of changes in their properties under various stress states [2], and viscoelasticity models. Cord materials are defined by Young's modulus. The first and most important class of problems, upon which further calculations are based, are static problems, including: calculating the structural safety margins, the contact patch shape analysis and contact pressure distribution, and determining the longitudinal, angular, lateral, and radial stiffness of the tire. This data set allows the engineers to obtain initial information about the tire's performance on the road and eliminate obviously unsuccessful options. The set of dynamic simulations includes: the tire hydroplaning, rolling on mud and snow, thermal analysis, and wearing. This group of calculations is quite complex and resource-intensive, but it provides important information about tire's performance under real operating conditions. A new milestone in modeling development has been the use of a test track simulation system, which allows the replication of real road tests through the analysis of the kinematics of multi-mass structures. Most automakers worldwide, as well as tire testing centers, use similar software to optimize suspension performance and vehicle handling precision. To create a reliable, working model, it is crucial to incorporate real data on tire behavior. In collaboration with our partners, we developed a method for estimating vehicle speed during a "double lane change" test. The input information consists of the track configuration and the vehicle's suspension characteristics. "TMeasy", a semi-physical approach (gray box method), was used to model tires within a vehicle. To prepare the "TMeasy" input file, tests were conducted on a specialized Skid-trailer - a trolley that records changes in forces and moments under various rolling conditions. The simulation results and the actual testing on the track showed satisfactory correlation. In conclusion, it can be noted that the development of calculation methods and the creation of a unified digital calculation environment make it possible to: • improve the validity of design decisions; • consider the tire as a whole and address the challenges of developing new products with improved performance; • increase the intensity of development by replacing some full-scale testing with virtual experiments.