Enhancement Assessment Framework for Probabilistic Risk Assessment Tools

Probabilistic Risk Assessment (PRA), also known as Probabilistic Safety Assessment (PSA), is a methodology endorsed by the United States (US) Nuclear Regulatory Commission (NRC) to support risk-informed, performance-based decision-making for nuclear facilities across all lifecycle phases, from design to decommissioning. PRA tools, essential to this process, enable analysts to assess and quantify risk efficiently. However, despite their critical role, many PRA tools, particularly their quantification engines, have not kept pace with advances in computational capabilities since their inception in the 1990s. The quantification engine is central to the PRA tool’s function, responsible for interpreting models, generating cut sets, and calculating risk metrics. Limited quantification capacity can compromise model reliability and, ultimately, decision-making accuracy. This paper introduces the Enhancement Assessment Framework (EAF), a structured approach to evaluate PRA tool performance systematically, pinpoint areas requiring improvement, and facilitate a comparative assessment across tools. Comprising five elements—model generation, model exchange, benchmarking, standard profiling, and deeper profiling—the EAF assesses and tracks the performance and enhancement needs of PRA tools. Model generation enables flexible model testing configurations, while model exchange expands testing to actual models for comprehensive validation. Benchmarking and profiling elements offer metrics to diagnose and address performance bottlenecks. The EAF is not intended as a single application but as a cyclic process for continuous tool enhancement. Applying the EAF to PRA tools such as SCRAM-CPP and SAPHSOLVE has underscored the potential for significant improvements.