Ramp-driven experiments offer possibilities to explore material response under conditions distinct from those accessed by shock-driven loading conditions. Use of the data from ramp-driven experiments is challenging in a variety of ways. The resistance of material to distortional deformation can be assessed from velocimetry in compression-release experiments. However, analysis methods that employ experimental data alone make use of assumptions that limit the utility of the inferences. Forward modeling of the experimental velocity profile offers an alternative path. We examine data from compression-release experiments that are sensitive to material strength and assess strength model fidelity in relation to experimental observations. Recent observations indicate that anelastic effects are important during load reversals such as those in ramp compression-release. Thus, we incorporate a novel anelasticity model to capture these effects. We aim to characterize the effects of inelasticity and anelasticity given known experimental data while taking into account experimental error and model uncertainty – possibly from a variety of data sources. We utilize concepts from Bayesian analysis to perform Bayesian model calibration to compute posterior parameter distributions to accomplish this task. We conjecture that incorporation of a physically based anelasticity model reduces the uncertainty in inferred strength model and consequently any application of that strength model.
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 (LLNL-ABS-798000).