Prediction of in-vessel debris bed properties in BWR severe accident scenarios using MELCOR and neural networks

Abstract

Severe accident management strategy in Nordic boiling water reactors (BWRs) employs ex-vessel corium debris coolability. In-vessel core degradation and relocation provide initial conditions for further accident progression. Outcomes of core relocation depend on the interplay between (i) accident scenarios, e.g. timing and characteristics of failure and recovery of safety systems and (ii) accident phenomena. Uncertainty analysis is necessary for comprehensive risk assessment. However, computational efficiency of system analysis codes such as MELCOR is one of the big obstacles.The goal of this work is to develop a computationally efficient surrogate model (SM) for prediction of main characteristics of corium debris in the vessel lower plenum of a Nordic BWR. The SM has been developed using artificial neural networks (ANNs). The networks were trained with a database of MELCOR solutions. The effect of the noisy data in the full model (FM) database was addressed by introducing scenario classification (grouping) according to the ranges of the output parameters. SMs using different number of scenario groups with/without weighting between predictions of different ANNs were compared. The obtained SM can be used for failure domain and failure probability analysis in the risk assessment framework for Nordic BWRs.