Application of An Integrated HPC Reliability Prediction Framework to HMWWV Suspension System

This research paper addresses the ground vehicle reliability prediction process based on a new integrated reliability prediction framework. The paper is an extension of the paper presented last year at the GVSETS symposium. 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 integrated HPC stochastic approach combines the computational stochastic mechanics predictions with available statistical experimental databases for assessing vehicle system reliability. The paper illustrates the application of the integrated approach to evaluate the relliability of the HMMWV front-left suspension system.

DISCLAIMER: The HMMWV dynamic model and the suspension system configuration used in this research are slightly different than the actual HMMWV hardware. Thus, the presented results do not reflect in detail the real HMMWV suspension system behavior. The intent of the paper is to discuss the integrated reliability methodology and to highlight qualitative aspects.