Sequential Experimental Design for Materials Strength Model Calibration

Due to the time and expense associated with physical experiments, there is significant interest in optimal selection of the conditions for future experiments. Selection based on reduction in parameter uncertainty provides a natural path forward. We consider this type of optimal sequential design in the context of Bayesian calibration of materials strength models with the strength model characterizing the evolving resistance of a material to permanent strain. This problem is particularly challenging because different types of experiments and associated diagnostics are employed across strain rate regimes. For lower-strain-rate experiments, stress-strain curves can be measured directly. For higher-strain-rate experiments, strength must be inferred (e.g., from the deformation of a cylinder of material in a Taylor cylinder experiment). We employ data fusion in our sequential design methodology to incorporate these multiple experimental modalities.

Speaker: Katie Schmidt, Lawrence Livermore National Laboratory