Inference of strength and phase transition kinetics in dynamically-compressed tin

• Published in: Journal of applied physics 2023-06, Vol.133 (24), p.245903
• Main Authors: Schill, W. J., Schmidt, K. L., Austin, R. A., Anderson, W. W., Belof, J. L., Brown, J. L., Barton, N. R.
• Format: Article
• Language: English
• Subjects: Applied physics; Bayesian analysis; Calibration; Compressive strength; CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; Cross correlation; ENGINEERING; Gas guns; Inference; Kinetics; Model forms; Phase transitions
• ISSN: 0021-8979; 1089-7550
• DOI: 10.1063/5.0150749

Dynamic compression experiments in condensed matter are of interest in part because they provide opportunities to examine material response under extreme conditions; however, the inference of material behavior from dynamic experiments is challenging in the presence of phase transitions exhibiting kinetic processes. Demonstrating an approach to quantitative interpretation of such dynamic experiments, we present a Bayesian model calibration of strength and phase transformation parameters to data drawn from pulsed power and gas gun shot experiments. The posterior predictions of the Bayesian model capture the experimental measurements and account for the various uncertainties in the experimental configurations. This holistic approach to model calibration utilizing multiple types of experimental data identifies important cross correlations among kinetics, strength, and the phase boundary. Improved insight into potential sources of current model form error is provided by comparing the differences between calibrations against different subsets of the experimental data.