Scenario Grouping and Classification Methodology for Postprocessing of Data Generated by Integrated Deterministic-Probabilistic Safety Analysis

Abstract EN

Integrated Deterministic-Probabilistic Safety Assessment (IDPSA) combines deterministic model of a nuclear power plant with a method for exploration of the uncertainty space.

Huge amount of data is generated in the process of such exploration.

It is very difficult to “manually” process and extract from such data information that can be used by a decision maker for risk-informed characterization, understanding, and eventually decision making on improvement of the system safety and performance.

Such understanding requires an approach for interpretation, grouping of similar scenario evolutions, and classification of the principal characteristics of the events that contribute to the risk.

In this work, we develop an approach for classification and characterization of failure domains.

The method is based on scenario grouping, clustering, and application of decision trees for characterization of the influence of timing and order of events.

We demonstrate how the proposed approach is used to classify scenarios that are amenable to treatment with Boolean logic in classical Probabilistic Safety Assessment (PSA) from those where timing and order of events determine process evolution and eventually violation of safety criteria.

The efficiency of the approach has been verified with application to the SARNET benchmark exercise on the effectiveness of hydrogen management in the containment.