Statistical developments for target and conditional sensitivity analysis: Application on safety studies for nuclear reactor

Abstract

In the framework of uncertainty treatment in numerical simulation, Global sensitivity analysis (GSA) aims at determining (qualitatively or quantitatively) how the variability of the uncertain inputs affects the model output. However, from reliability and risk management perspectives, GSA might be insufficient to capture the influence of the inputs on a restricted domain of the output (e.g., critical event).To remedy this, we define target (TSA) and conditional sensitivity analysis (CSA) to measure respectively the influence of the inputs on the occurrence of the critical event, and on the output within the critical domain. From existing GSA measures, new operational tools are proposed for TSA and CSA, based on Sobol’ index and Hilbert–Schmidt Independence Criterion (HSIC). Moreover, to cope with the loss of information (especially when the critical domain is of low probability) and reduce the variability of estimators, transformation of the output using weight functions is also proposed.These new TSA and CSA tools are tested and compared on analytical examples. The efficiency of HSIC-based indices clearly appear, as well as the relevancy of smooth relaxation. Finally, these latter indices are applied and interpreted on a nuclear engineering use case simulating a severe accidental scenario on a pressurized water reactor.