How to effectively compute the reliability of a thermal–hydraulic nuclear passive system

The computation of the reliability of a thermal–hydraulic (T–H) passive system of a nuclear power plant can be obtained by (i) Monte Carlo (MC) sampling the uncertainties of the system model and parameters, (ii) computing, for each sample, the system response by a mechanistic T–H code and (iii) comparing the system response with pre-established safety thresholds, which define the success or failure of the safety function. The computational effort involved can be prohibitive because of the large number of (typically long) T–H code simulations that must be performed (one for each sample) for the statistical estimation of the probability of success or failure. The objective of this work is to provide operative guidelines to effectively handle the computation of the reliability of a nuclear passive system. Two directions of computation efficiency are considered: from one side, efficient Monte Carlo Simulation (MCS) techniques are indicated as a means to performing robust estimations with a limited number of samples: in particular, the Subset Simulation (SS) and Line Sampling (LS) methods are identified as most valuable; from the other side, fast-running, surrogate regression models (also called response surfaces or meta-models) are indicated as a valid replacement of the long-running T–H model codes: in particular, the use of bootstrapped Artificial Neural Networks (ANNs) is shown to have interesting potentials, including for uncertainty propagation. The recommendations drawn are supported by the results obtained in an illustrative application of literature.

Research highlights▶ Optimized LS is the preferred choice for failure probability estimation. ▶ Two alternative options are suggested for uncertainty and sensitivity analyses. ▶ SS for simulation codes requiring seconds or minutes to run. ▶ Regression models (e.g., ANNs) for simulation codes requiring hours or days to run.