| description abstract | Recently, there has been an increasing use of advanced modeling and simulation in the nuclear domain across academia, industry, and regulatory agencies to improve the realism in capturing complex and highly spatiotemporal phenomena within the probabilistic risk assessment (PRA) of existing nuclear power plants (NPPs). Advanced modeling and simulation have also been used to accelerate the risk-informed design, licensing, and operationalization of advanced nuclear reactors. Validation of simulation models traditionally relies on empirical validation approaches which require enough validation data. Such validation data are, however, usually costly to obtain in the contexts of the nuclear industry. To overcome this challenge and to effectively support the use of simulation models in PRA and risk-informed decision-making applications, a systematic and scientifically justifiable validation methodology, namely, the probabilistic validation (PV) methodology, has been developed. This methodology leverages uncertainty analysis to support the validity assessment of the simulation prediction. The theoretical foundation and methodological platform of the PV methodology have been reported in the first paper of this two-part series. The purpose of this second paper is to computationalize the PV methodology, embedded in an integrated PRA framework, and apply it for a hierarchical fire simulation model used in NPP Fire PRA. | |
