Incorporating uncertainty into the validation of automotive crash simulation models for component analysis

The design and development of crashworthy vehicles depend increasingly on credible crash simulation results. This credibility requires a careful process of verification and validation (V&V). The former relates to assessing the correctness of the calculation while the latter relates to the appropriateness of the physical and numerical test results. In this context, various sources of uncertainty exist in crash tests as well as in simulation models, which can influence the validation outcomes. Hence, it is essential to study statistical validation approaches for crash applications. In this paper, we elaborate on two important aspects of validation for crash simulation models: quantification of the credibility of simulation models, and consideration of uncertainties in the validation. Limited and noisy experimental data, typical in crash applications, impose a challenge on the investigation of validation approaches. To address these challenges, we use synthetic experimental results in our study. A crash-box model is validated using the V&V20-2009 validation standard. Two case studies are considered. The first case study aims to show the application of the V&V20 approach to a crash problem. The second case is a more complex scenario and aims to explore the impact of different experimental factors, such as experimental noise, hidden uncertainties, and the number of experiments on the validation results. In the end, we examine how underlying assumptions in the validation approach affect the conservativity of the validation results.