Research on Validation Metrics for Multiple Dynamic Response Comparison under Uncertainty 2015-01-0443

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty. In this paper, some researches on investigating validation method and process for dynamic systems with multiple functional outputs are conducted to improve the robustness of existing metrics previously developed by the authors. A vehicle case study is used to demonstrate the comparison study.