Information-gap robustness for the test analysis correlation of nonlinear transient simulation

An alternative to the theory of probability is applied to the problem of assessing the robustness of test-analysis correlation to parametric sources of uncertainty. The analysis technique is based on the theory of information-gap, which models the clustering of uncertain events in families of nested sets instead of assuming a probability structure. The system investigated is the propagation of a transient impact through a layer of hyper-elastic material. The two sources of non-linearity are the softening of the constitutive law implemented to model the hyper-elastic material and contact dynamics at the interface between metallic and crushable materials. The robustness of test-analysis correlation to sources of parametric variability is first studied to identify the parameters of the model that significantly influence the agreement between measurements and predictions. Calibration under non-probabilistic uncertainty is then illustrated. Finally, two information-gap models of uncertainty are embedded to represent uncertainty not only in the knowledge of the model's parameters but also in the form of the model itself. Although computationally expensive, it is demonstrated that the information-gap reasoning can greatly enhance our understanding of a moderately complex system when the theory of probability cannot be applied due to insufficient information.