Using Machine Learning to Predict the Fuel Peak Cladding Temperature for a Large Break Loss of Coolant Accident

Abstract

In this paper the use of machine learning (ML) is explored as an efficient tool for uncertainty quantification. A machine learning algorithm is developed to predict the peak cladding temperature (PCT) under the conditions of a large break loss of coolant accident given the various underlying uncertainties. The best estimate approach is used to simulate the thermal-hydraulic system of APR1400 large break loss of coolant accident (LBLOCA) scenario using the multidimensional reactor safety analysis code (MARS-KS) lumped parameter system code developed by Korea Atomic Energy Research Institute (KAERI). To generate the database necessary to train the ML model, a set of uncertainty parameters derived from the phenomena identification and ranking table (PIRT) is propagated through the thermal hydraulic model using the Dakota-MARS uncertainty quantification framework. The developed ML model uses the database created by the uncertainty quantification framework along with Keras library and Talos optimization to construct the artificial neural network (ANN). After learning and validation, the ML model can predict the peak cladding temperature (PCT) reasonably well with a mean squared error (MSE) of ∼0.002 and R2 of ∼0.9 with 9 to 11 key uncertain parameters. As a bounding accident scenario analysis of the LBLOCA case paves the way to using machine learning as a decision making tool for design extension conditions as well as severe accidents.

A deep learning-based framework for the operation prediction of primary heat transfer loop in nuclear power plants

“…SVM and ANN have also been applied to predicting the operation trend of NPPs (Sallehhudin and Diab, 2021;Moshkbar-Bakhshayesh and Ghofrani, 2022), except that their main purpose is to verify the accuracy and reliability of the single node prediction by the improved machine learning methods. The feasibility of applying predictions to the entire PHT loop was not considered yet in the previous researches.…”

Time-Series Forecasting of a Typical PWR Undergoing Large Break LOCA

“…Te thermal-hydraulics model is developed in RELAP5/MOD3.4 system code to simulate the nuclear power plant response under LBLOCA conditions [28]. Te plant of choice for the analysis is the Korean Advanced Pressurized Reactor (APR1400).…”

“…Te system is initialized using the nominal operative conditions provided for LOCA in Chapter 15 of APR1400 DCD, which are listed in Table 1. [28,30] as listed in Table 2.…”

“…In an extension of the previous work by Sallehhudin and Diab [28] where an artifcial neural network (ANN) was used to predict the maximum value of the peak cladding temperature (PCT) for diferent initial and boundary conditions, this research is aimed at forecasting the evolution of the PCT over time as the accident progresses. For this class of problems, time-series forecasting based on recurrent neural network (RNN) is the most efective ML tool.…”

“…Given the potential benefts of AI at large and specifcally of machine learning (ML), this study builds on the work of Sallehhudin and Diab [28] and follows the work of Radaideh [29] to develop a time-series forecasting ML model capable of predicting the NPP real-time response for APR1400 undergoing an LBLOCA. Unlike the work of Sallehhudin and Diab [28], the NPP response is predicted as a function of time rather than pointwise prediction of a characteristic safety criterion. Furthermore, the current work is considered an extension of the earlier work of Radaideh [29] whose work focused on small break LOCA.…”