Abstract
Severe accident mitigation strategy adopted in Nordic type Boiling Water Reactors (BWRs) employs ex-vessel core melt cooling in a deep pool of water below reactor vessel. Energetic fuel–coolant interaction (steam explosion) can occur during molten core release into water. Dynamic loads can threaten containment integrity increasing the risk of fission products release to the environment. Comprehensive uncertainty analysis is necessary in order to assess the risks. Computational costs of the existing fuel–coolant interaction (FCI) codes is often prohibitive for addressing the uncertainties, including the effect of stochastic triggering time.This paper discusses development of a computationally efficient surrogate model (SM) for prediction of statistical characteristics of steam explosion impulses in Nordic BWRs. The TEXAS-V code was used as the Full Model (FM) for the calculation of explosion impulses. The surrogate model was developed using artificial neural networks (ANNs) and the database of FM solutions. Statistical analysis was employed in order to treat chaotic response of steam explosion impulse to variations in the triggering time. Details of the FM and SM implementation and their verification are discussed in the paper.
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