Estimating the error in simulation prediction over the design space

This study addresses the assessrnent of accuracy of simulation predictions. A procedure is developed to validate a simple non-linear model defined to capture the hardening behavior of a foam material subjected to a short-duration transient impact. Validation means that the predictive accuracy of the model must be established, not just in the vicinity of a single testing condition, but for all settings or configurations of the system. The notion of validation domain is introduced to designate the design region where the model's predictive accuracy is appropriate for the application of interest. Techniques brought to bear to assess the model's predictive accuracy include test-analysis coi-relation, calibration, bootstrapping and sampling for uncertainty propagation and metamodeling. The model's predictive accuracy is established by training a metalnodel of prediction error. The prediction error is not assumed to be systcmatic. Instead, it depends on which configuration of the system is analyzed. Finally, the prediction error's confidence bounds are estimated by propagating the uncertainty associated with specific modeling assumptions

Model calibration and validation of an impact test simulation

This paper illustrates the methodology being developed at Los Alamos National Laboratory for the validation of numerical simulations for engineering structural dynamics. The application involves the transmission of a shock wave through an assembly that consists of a steel cylinder and a layer of elastomeric (hyper-foam) material. The assembly is mounted on an impact table to generate the shock wave. The input acceleration and three output accelerations are measured. The main objective of the experiment is to develop a finite element representation of the system capable of reproducing the test data with acceptable accuracy. Foam layers of various thicknesses and several drop heights are considered during impact testing. Each experiment is replicated several times to estimate the experimental variability. Instead of focusing on the calibration of input parameters for a single configuration, the numerical model is validated for its ability to predict the response of three different configurations (various combinations of foam thickness and drop height). Design of Experiments is implemented to perform parametric and statistical variance studies. Surrogate models are developed to replace the computationally expensive numerical simulation. Variables of the finite element model are separated into calibration variables and control variables, The models are calibrated to provide numerical simulations that correctly reproduce the statistical variation of the test configurations. The calibration step also provides inference for the parameters of a high strain-rate dependent material model of the hyper-foam. After calibration, the validity of the numerical simulation is assessed through its ability to predict the response of a fourth test setup