A Toolbox for Validation of Nonlinear Finite Element Models

With increasingly powerful computational resources at our disposal, it is becoming common- place to use analytical predictions in lieu of experimentation for characterization of physical systems and events. This trend, with its perceived potential for reducing costs, is the basis for the simulation-based procurement initiatives currently gaining momentum within the Gov- ernment and industry. However, a simulation-based approach is often sold to decision-makers via plausible visualizations of model simulation results; the simulations themselves having only been validated in an ad hoc manner using anecdotal comparisons with real events. Ex- perience has shown that, while simulation using physics-based models may lead to qualita- tively correct results, there can be large quantitative discrepancies between simulation and experimental results for a given physical event, and between simulation results from different analysts for the same event. Obviously, for simulation-based procurement to be a viable alter- native to more traditional test-based procurement, the quantitative accuracy of the simulations must be insured. To do this requires at least a modicum of experimental data (perhaps at the component or subsystem level) to serve as a yardstick with which the accuracy of simulation results can be measured. And it requires minimizing the differences between corresponding analytical and experimental results in physically meaningful ways, as well as characterizing the ability of the models to predict future events. This paper describes a toolbox for the vali- dation of nonlinear finite element models. The toolbox includes tools for quantitatively up- dating model parameters based on the differences between test results and analytical predic- tions, as well as estimating the predictive accuracy of the model based on generically similar comparisons. Use of the toolbox is illustrated for a DYNA model of a reinforced concrete wall subjected to blast loading.