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Improvement of Response Surface Quality for Full Car Frontal Crash Simulations by suppressing Bifurcation using Statistical Approach
In recent years, importance of optimization is rising in automotive industry, since needs in fulfilling conflicting requirement such as light weight, rigidity, and safety in high level are continuously increasing, while car structure becomes complex due to new material and new connection techniques. RSM (Response Surface Method) is one of key technology for the purpose, and various approaches have been made. However, quality of response surfaces tend to be poor when it comes to frontal or rear crash where contact and buckling is dominant, since bifurcations in behavior bring high non-linearity to response surfaces. One measure is to increase the number of simulation runs in order to improve the accuracy of response surface, but as the size of full car simulation models becomes bigger, it is not realistic to run over 100 times. The fundamental problem is that the response surface is with high complexity due to bifurcations such as buckling and contact so that trying to fit highly non-linear response surface by adding points is not the absolute solution, but to reduce non-linearity of the surface in order to make it easy to fit. In this study, scatter propagation mechanism is visualized based on statistical calculations, and structural design of front structure of an automobile is enhanced in order to suppress bifurcations with help from a statistical analysis software DIFFCRASH. Triggers of bifurcation are located and mechanisms of the bifurcations are studied, and design modifications are made to stabilize the deformation modes. As a result, the complexity of response surface has been reduced, and accuracy of the response surface has been improved.
Combined Analysis of LS-DYNA Crash-Simulations and Crash-Test Scans
In robustness campaigns and optimization processes metamodels are created out of a set of crash- simulations. With the help of such analyses the models used for the simulations can be improved. For example, instabilities can be found and explained or the needed material can be minimized under certain safety restrictions. An important question in this context is: How good can these metamodels represent the reality? To answer this question, one can compare the crash-simulations to the real crash-tests, which were recorded by camera systems after the crash. To be able to compare the test-data with the LS-DYNA crash-simulations, we first need to convert the test-data by matching the geometries and transferring the part information from the simulation to the crash-test. Afterwards one can calculate the combination of the simulations, which approximates geometry and deformation behavior of the test- data as close as possible. The distance and difference in behavior between this calculated Best Fit and the actual crash-test can be used to measure the quality of the simulation model. Once the evaluation of the model is finished, the test-data can also be added to a robustness campaign as an additional simulation and used for further analysis. This allows us to answer questions such as: How does the test fit into the simulation subspace? Which simulation runs are similar to the test for a certain crash event? Which of the dominating crash events found in the simulation can also be found in the test? Thus, the described matching procedure combined with exemplary further analysis methods on the one hand allow for a quick and automated matching between test and simulation and on the other hand a more detailed validation of the simulation model in comparison to the actual test. Due to the conversion of the test-data, the same post-processors can be used for both the simulations and the test-data, resulting in a smoother workflow.