Prediction of In-Vessel Debris Bed Properties in BWR Severe Accident Scenarios using MELCOR and Neural Networks
2017 (English)In: Annals of Nuclear Energy, ISSN 0306-4549, E-ISSN 1873-2100Article in journal (Refereed) Submitted
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. Characteristics of debris bed in the reactor vessel lower plenum affect corium interactions with the vessel structures, vessel failure and melt ejection mode. Melt release from the vessel determines conditions for ex-vessel accident progression and potential threats to containment integrity, i.e. steam explosion, debris bed formation and coolability. 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. Another problem is that code predictions can be quite sensitive to small variations of the input parameters and numerical discretization, due to the highly non-linear feedbacks and discrete thresholds employed in the code models.
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. Station black out scenario with a delayed power recovery is considered. 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.
Place, publisher, year, edition, pages
Core relocation; boiling water reactor; MELCOR; surrogate model; artificial neural network.
Other Engineering and Technologies not elsewhere specified
IdentifiersURN: urn:nbn:se:kth:diva-202955OAI: oai:DiVA.org:kth-202955DiVA: diva2:1079542
QC 201703092017-03-082017-03-082017-03-09Bibliographically approved