Ensemble-Based Data Assimilation for Material Model Characterization in High-Velocity Impact

Abstract

High-fidelity simulations are essential for predicting material behavior under high-velocity impact (HVI), but their accuracy depends on material models and parameters that are often calibrated by manual fitting to multiple costly experiments. In this study, we develop an ensemble-based data assimilation framework for automatic calibration of selected plasticity, fracture, and equation-of-state (EOS) parameters in HVI simulations using data from a single HVI test. The framework combines Smoothed Particle Hydrodynamics, the ensemble Kalman filter (EnKF), and adaptive covariance inflation to mitigate variance collapse. A simple benchmark first shows that the framework is at least one order of magnitude more computationally efficient than Markov chain Monte Carlo at comparable identification accuracy. We then use synthetic back-face deflection data for an AZ31B magnesium plate to identify representative parameters in the Johnson-Cook plasticity and fracture models and the Mie-Gruneisen EOS. Results under under-biased, over-biased, and limited-observation cases show that parameters to which the data are sufficiently sensitive can be accurately recovered within about five iterations, with convergent ensemble standard deviations. In contrast, insensitive parameters tend to converge to incorrect values and retain large ensemble spreads. With fewer observations, convergence is still achieved for sensitive parameters but requires more iterations. Under extreme prior bias, a parameter rejuvenation strategy drives sensitive parameters toward the true values even when the truth lies outside the initial ensemble spread. These results show that ensemble standard deviation provides a practical diagnostic for parameter sensitivity, identifiability, and potential non-uniqueness. Overall, the proposed framework offers an efficient and robust approach for material model characterization in HVI problems.