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Use of Data Reduction Methods for Robust Optimization
Data reduction methods like principle component analysis, singular value decomposition and independent component analysis methods allow analyzing huge sets of data. Applied to simulation results they allow the characterization of major trends in the variation of these results. For the public Chevrolet Silverado example all thicknesses are initially varied independent of each other among a number of simulation results and its correlations are computed to the variation of the behavior of the firewall. The behavior of the firewall is approximated using data reduction methods. It turns out, that the variation of the firewall can be characterized by one basic deformation mode. The thickness variation of a part may show strong or weak correlation to the behavior of this deformation mode. In several steps now, the sensitivity analysis is repeated using only those parts for thickness variation, which had a strong correlation in the previous step. Finally it turns out, that the thicknesses of the longitudinal rails as well as certain bifurcation behavior of the longitudinal are responsible for this variation mode of the firewall.
Automated Generation of Robustness Knowledge for selected Crash Structures
Process to improve optimization with combined robustness analysis results