Abstract: The goal of this work is to reduce the time required to qualify new materials for nuclear service by reducing the lead time required for dedicated, long-term material testing to establish key long-term material properties. We use Bayesian inference to find the statistical distribution of the model parameters that best explain the short-term rupture data. The Bayesian prior distributions provide a means for incorporating material characterization data into the final model to improve the accuracy of the long-term model predictions. We find that the developed framework makes more accurate predictions than a Larson-Miller approach using actual long-term rupture data available for 316H, including tests with rupture times greater than 200,000 hours.

The general approach developed here could be applied to other materials and other time-dependent material properties. Applying this new technique to develop long-term qualified material properties, potentially in conjunction with other accelerated qualification approaches like staggered qualification test programs, could greatly reduce the time required to qualify new materials for nuclear service.