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

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

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 uncerta...

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Bibliographic Details
Main Authors: Wazif Sallehhudin, Aya Diab
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-10-01
Series:Frontiers in Energy Research
Subjects:
nuclear safety
large break LOCA
artificial neural network
machine learning
uncertainty quantification
peak cladding temperature
Online Access:https://www.frontiersin.org/articles/10.3389/fenrg.2021.755638/full

Internet

https://www.frontiersin.org/articles/10.3389/fenrg.2021.755638/full

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