Abstract
Abstract The dynamic stability of heavy trucks, as spinout, jackknifing, and rollover, is highly dependent on vehicle configuration, driving maneuver, and the force and moment characteristics of tires. Increasing safety requirements on the handling performance of heavy trucks demand tools that allow a tire design engineer to predict tire influences on the tire/vehicle system dynamic behavior. The computer simulation of handling performance of vehicles offers possibilities of evaluating influences of tire design changes on handling properties in any developing stage of new tire lines. Thus, modern simulation techniques may contribute to the building and testing of tires in an early design stage. This paper presents results from a recent program of tire/vehicle system research, applying tire/vehicle testing and simulation techniques to a 40 ton truck-semitrailer combination. The goal of this work is to visualize the possibilities of state-of-the-art simulation technologies on the tire design process. Tire force and moment characteristics can be calculated from the tire layout by an advanced tire model. The tire model for this type of calculation is a multibody system. Calculated and measured dynamic tire characteristics are used for the full vehicle handling simulation in ADAMS. Extensive tire characteristic testing on the road and test stand was done to improve and validate the tire model. Vehicle handling tests as steady state circular and lateral transient response tests were done for the empty and laden vehicle with different tires to prove the vehicle model. With the use of the simulation of the tire and vehicle behavior, the tire design engineer will be able to judge tire characteristics of different variants in an early design stage. Vehicle dynamic simulation studies up to instability as spinout, jackknifing, and rollover can be performed using modern CAE methods without harming man and environment, but subjective and objective tire evaluation still remains necessary for approving and validating the predicted results.
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