Preliminary list of abstracts

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Difference principle component analysis for crash simulation results
Tue, 14:00--14:25
  • Thole, Clemens-August (Numerische Software, Fraunhofer Institut SCAI, Germany)

Potential scatter of simulation results caused for example by buckling, is still a challenging issue for the predictability of crash simulation results. Principle component analysis (PCA) is a well-known mathematical method for data analysis. In order to characterize scatter PCA analysis was applied to the simulation results from a number of runs using all node positions at all time steps. For industrials relevant problems the size of the data base is larger than 100 GBytes (even, if compressed by FEMzip). As a result the major components dominating the differences between the simulation results are available. Since PCA is a mathematically based method, the selected modes do not separate different physical effects like buckling at different parts of the model. PCA rather tries to maximize the variations by combining several physical effects into one mode.

Difference PCA (DPCA) applies PCA analysis to the results for each part and time step. By analysis of the related covariance matrices, the local dimension of the scatter subspace can be identified and correlation between the scatter at different places can be analyzed. Using DPCA, different origins of scatter can be identified and physically meaningful components can be determined. The talk introduces the approach and shows results for an industrial model.