Many existing Computer-aided Fault Tree Analysis (CAFTA) and Fault Tree Reliability eXpert (FTREX)-based Probabilistic Risk Assessment (PRA) models have been developed, expanded and updated over several years without a strong focus on the ultimate use of the models. As a result, many of these PRA models have become unwieldy to maintain and can take a significant amount of time to update or quantify. As risk-informed applications become more time-constrained, some even requiring near real-time risk information to support plant decision-making, the need for streamlined models is becoming paramount.
Westinghouse’s PRA Model Optimization process provides a means to streamline PRA models to reduce update and/or quantification times and produce direct benefits across multiple hazard models. Our optimization efforts are initially based on internal event models with the downstream quantification impacts extrapolated to the external events and risk monitor models. These improvements allow plants to support the more rigorous, time-constrained risk applications and, as a result, deliver the associated significant operational flexibility and regulatory response capability available through risk-informed applications. Such improvements include control room-based risk quantifications (risk monitor models) that extend allowed outage times (AOTs) and completion times (CTs), and prompt model updates for emergent needs such as Notices of Enforcement Discretion (NOEDs) and Significance Determination Process evaluations (SDPs).
Optimized PRA models being used to provide timely input to the decision-making process.
The key steps and objectives of the optimization process are as follows:
Two major benefits:
Westinghouse’s PRA Model Optimization efforts have been specific to the CAFTA/FTREX suite, but optimization may be extended to other PRA software tools through assessment of targeted quantification processes and functions. To-date, Westinghouse has seen the following benefits from our PRA optimization solution:
*Reported results are based on Internal Events Models; downstream impacts on external events and risk monitor models quantification times were also realized.