Robustness Analysis in Safety Simulations
Today safety engineers have to deal with the fear of uncertainly of their simulation results. Complex safety analysis processes, such as FMVSS201U (Interior safety), Pedestrian Impact and dummy positioning and restraining, have a high degree of uncertainty because there are highly dependent on positioning parameters. In these types of loadcases, a testing device needs to be positioned in multiple areas on the testing vehicle, and everyone of these positions has certain tolerances. In addition, safety loadcases set-up require a high degree of automation by the CAE pre-processor in order to perform the required multi-positioning. All of the above are contributing factors of uncertainty. BETA CAE Systems researches methodologies to address this problem. A tool that can set up stochastic processes for robustness analysis, within ANSA, is under development. In this processes ANSA uses the currently available tools to perform auto-positioning, and with the use of computational algorithms (such as Monte Carlo) the stochastic processes are created. The results are evaluated and a study of the influence of the stochastic input parameters to the performance of the model is evaluated. The analyst then can have a high confidence level of the validity of his/her simulation results.
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Robustness Analysis in Safety Simulations
Today safety engineers have to deal with the fear of uncertainly of their simulation results. Complex safety analysis processes, such as FMVSS201U (Interior safety), Pedestrian Impact and dummy positioning and restraining, have a high degree of uncertainty because there are highly dependent on positioning parameters. In these types of loadcases, a testing device needs to be positioned in multiple areas on the testing vehicle, and everyone of these positions has certain tolerances. In addition, safety loadcases set-up require a high degree of automation by the CAE pre-processor in order to perform the required multi-positioning. All of the above are contributing factors of uncertainty. BETA CAE Systems researches methodologies to address this problem. A tool that can set up stochastic processes for robustness analysis, within ANSA, is under development. In this processes ANSA uses the currently available tools to perform auto-positioning, and with the use of computational algorithms (such as Monte Carlo) the stochastic processes are created. The results are evaluated and a study of the influence of the stochastic input parameters to the performance of the model is evaluated. The analyst then can have a high confidence level of the validity of his/her simulation results.