Data analysis for parallel car-crash simulation results and model optimization

The paper discusses automotive crash simulation in a stochastic context, whereby the uncertainties in numerical simulation results generated by parallel computing. Since crash is a non-repeatable phenomenon, qualification for crashworthiness based on a single test is not meaningful, and should be replaced by stochastic simulation. But the stochastic simulations may generate different results on parallel machines, if the same application is executed more than once. For a benchmark car model, differences between the position of a node in two simulation runs of PAMCRASH or LS-DYNA of up to 10 cm were observed, just as a result of round-off differences in the case of parallel computing. In this paper, some data mining algorithms are described to measure the scatter of parallel simulation results of car-crash and then provide hints to overcome this scatter to get more stable car model.