Bubble transport during SGTR accident in lead-cooled fast reactor : A machine learning

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Detail(s)

Original languageEnglish
Article number112724
Journal / PublicationNuclear Engineering and Design
Volume415
Online published31 Oct 2023
Publication statusPublished - 15 Dec 2023

Abstract

Steam generator tube rupture (SGTR) is one of the safety issues for pool-type lead-cooled fast reactors (LFR). Accurately quantifying and predicting the bubble transport result is essential for evaluating the accident. This paper addresses tracking the bubble motion using an Eulerian-Lagrangian method in CFD based on the Europe Lead cooling System (ELSY) primary system model at 1/8 centrosymmetric structure. The steady and transient bubble distributions in the system under different leakage heights are obtained. Furthermore, the simulation results are predicted by machine learning, where Gaussian Process Regression (GPR) is employed for both steady conditions and transient conditions. The data-driven GPR model is established for the bubble transport prediction, which can capture complex non-linear relationships between input variables (i.e. bubble diameter, SG leakage height, final position, system contaminate) and output (reached bubble percentage) responses directly from data. For steady conditions, the prediction results by the kernel function of Automatic Relevance Determination (ARD) Rational Quadratic show the best accuracy in predicting the percentages of bubbles reaching the core, top of the steam generator, and staying in the system, with a total Root Mean Square Error (RMSE) of 3.22%. Four typical transient cases of bubble accumulation in the core are selected for prediction. All the cases are well predicted by the kernel function of ARD Matern 3/2 with low mean averaged error. © 2023 Elsevier B.V.

Research Area(s)

  • Bubble transport, LFR, Machine learning, SGTR