Research Programs

Nuclear Systems Performance

Select project High Cycle Thermal Fatigue Due to Thermal Striping Risk-Informed Decision Making Risk Analysis in Infrastructure Protection Quantification of Safety Margins Incident Management Using Probabilistic Safety Assessment Organizational Factors in Nuclear Power Plants Outage Performance Evaluation of Operating US Light Water Reactor

Publications:

1. J. M. Reinert and G. E. Apostolakis, “Including Model Uncertainty in Risk-Informed Decision Making."

Investigators :

• Professor G.E. Apostolakis
• M. McGrath
• M. Presley
• J. Reinert

Support :

• Nuclear Regulatory Commission

Risk-Informed Decision Making

The utilization of risk information in regulatory decisions has been an NRC goal ever since the Commission’s Policy Statement that stated: “The use of PRA should be increased to the extent supported by the state of the art and data and in a manner that complements the defense-in-depth philosophy.” The most detailed guidance regarding RIDM is given in Regulatory Guide 1.174. The “integrated decision-making process” is intended to maintain the defense-in-depth and large safety-margin philosophies while, at the same time, to consider changes in the risk metrics of Core Damage Frequency (CDF) and Large Early Release Frequency (LERF). Although this process is still subjective to a large extent, it represents tremendous progress toward RIDM.

Uncertainty in the estimates of CDF and LERF can be separated into three categories: parameter uncertainty, model uncertainty, and completeness uncertainty. This project addresses the impact of model uncertainty on the decision-making process related to licensing basis changes. We are developing a methodology that identifies basic events in the risk assessment that have the potential to change the decision and are known to have significant model uncertainties. Because we work with basic event probabilities, this methodology is not appropriate for analyzing uncertainties that cause a structural change to the model, such as success criteria. We use the Risk Achievement Worth (RAW) importance measure with respect to both the CDF and the change in core damage frequency (CDF) to identify potentially important basic events. We cross-check these with generically important model uncertainties. Then, sensitivity analysis is performed on the basic event probabilities, which are used as a proxy for the model parameters, to determine how much error in these probabilities would need to be present in order to impact the decision.