An Integrated High-Performance Computing Reliability Prediction Framework for Ground Vehicle Design Evaluation 2010-01-0911
This paper addresses some aspects of an on-going multiyear research project for US Army TARDEC. The focus of the research project has been the enhancement of the overall vehicle reliability prediction process. This paper describes briefly few selected aspects of the new integrated reliability prediction approach. The integrated approach uses both computational mechanics predictions and experimental test databases for assessing vehicle system 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.
Citation: Ghiocel, D., Negrut, D., Lamb, D., and Gorsich, D., "An Integrated High-Performance Computing Reliability Prediction Framework for Ground Vehicle Design Evaluation," SAE Technical Paper 2010-01-0911, 2010, https://doi.org/10.4271/2010-01-0911. Download Citation
Dan M. Ghiocel, Dan Negrut, David Lamb, David Gorsich
GP Technologies, Inc., Univ. of Wisconsin, US ArmyTARDEC
SAE 2010 World Congress & Exhibition
Reliability and Robust Design in Automotive Engineering, 2010-SP-2272