Bayesian model selection for metal yield models in high-velocity impact

Abstract The shock hydrodynamics code ALEGRA and the optimization and uncertainty quantification toolkit Dakota are used to calibrate and select between three competing steel yield models, taking uncertainties in the system into account. A Bayesian model selection procedure is used to choose between the models in a systematic, automated fashion, within an uncertainty quantification workflow. Time-series penetration data of a long tungsten-alloy rod impacting a hardened steel plate at approximately 1250 m/s, along with their measurement uncertainty, are used to calibrate and select between the models. The procedure finds that between the Johnson–Cook, Steinberg–Guinan–Lund, and Zerilli–Armstrong stress models, Zerilli–Armstrong performs the best.