Search Results

This paper discusses an approach to construct a high fidelity surrogate tire model using a two-phase optimization-based algorithm that draws on data generated by off-line nonlinear ABAQUS tire simulations. It subsequently describes the process of Simulink-based interfacing of the resulting surrogate model to a full ADAMS vehicle model to enable accurate and expeditious durability studies. The two-phase surrogate model construction relies on an identification method that draws on the Instantaneous Center Manifold (ICM) theory. In the proposed method, a generally forced non-autonomous nonlinear structural system is represented as a sequence of harmonically excited autonomous nonlinear systems. The close-form solution of each of these systems is produced using the ICM theory. The first phase of the surrogate model construction uses an optimal Orthogonal Matching Pursuit (OMP) algorithm to unify all ICMs used to approximate the reaction force of the tire at its spindle.

This research paper addresses the ground vehicle reliability prediction process based on a new integrated reliability prediction framework. The integrated stochastic framework combines the computational physics-based predictions with experimental testing information for assessing vehicle reliability. The integrated reliability prediction approach incorporates the following computational steps: i) simulation of stochastic operational environment, ii) vehicle multi-body dynamics analysis, iii) stress prediction in subsystems and components, iv) stochastic progressive damage analysis, and v) component life prediction, including the effects of maintenance and, finally, iv) reliability prediction at component and system level. To solve efficiently and accurately the challenges coming from large-size computational mechanics models and high-dimensional stochastic spaces, a HPC simulation-based approach to the reliability problem was implemented.

The paper describes a methodology to co-simulate, with high fidelity, simultaneously and in one computational framework, all of the main vehicle subsystems for improved engineering design. The co-simulation based approach integrates in MATLAB/Simulink a physics-based tire model with high fidelity vehicle dynamics model and an accurate powertrain model allowing insights into 1) how the dynamics of a vehicle affect fuel consumption, quality of emission and vehicle control strategies and 2) how the choice of powertrain systems influence the dynamics of the vehicle; for instance how the variations in drive shaft torque affects vehicle handling, the maximum achievable acceleration of the vehicle, etc. The goal of developing this co-simulation framework is to capture the interaction between powertrain and rest of the vehicle in order to better predict, through simulation, the overall dynamics of the vehicle.