Impact of Uncertainty on Calculations for Recovery from Loss of Offsite Power

Uncertainty, both aleatory and epistemic, can have a significant impact on estimated probabilities of recovering from loss of offsite power within a specified time window, and such probabilities are an input to risk-informed decisions as to the significance of inspection findings in the U.S. Nuclear Regulatory Commission’s Reactor Oversight Process. In particular, the choice of aleatory model for offsite power recovery time can have a significant impact on the estimated nonrecovery probability, especially if epistemic uncertainty regarding parameters in the aleatory model is accounted for properly. In past and current analyses, such uncertainty has largely been ignored. This paper examines the impact of both aleatory and epistemic uncertainty on the results, using modern open-source Bayesian inference software, which implements Markov chain Monte Carlo sampling. It includes examples of time-dependent convolution calculations to show the impact that uncertainty can have on this increasingly frequent type of calculation, also. The results show that the “point estimate” result, which is an input to risk-informed decisions, can easily be uncertain by a factor of 10 if both aleatory and epistemic uncertainties are considered. The paper also illustrates the use of Bayesian model selection criteria to aid in the choice of aleatory model