Application of an artificial neural network in reactor thermohydraulic problem: Prediction of critical heat flux

Guanghui Su, Kenji Fukuda, Dounan Jia, Koji Morita

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)

Abstract

A new method for predicting Critical Heat Flux (CHF) with the Artificial Neural Network (ANN) method is presented in this paper. The ANNs were trained based on three conditions: type I (inlet or upstream conditions), II (local or CHF point conditions) and III (outlet or downstream conditions). The best condition for predicting CHF is type II, providing an accuracy of ±10%. The effects of main parameters such as pressure, mass flow rate, equilibrium quality and inlet subcooling on CHF were analyzed using the ANN. Critical heat flux under oscillation flow conditions was also predicted.

Original languageEnglish
Pages (from-to)564-571
Number of pages8
Journaljournal of nuclear science and technology
Volume39
Issue number5
DOIs
Publication statusPublished - Jan 1 2002

All Science Journal Classification (ASJC) codes

  • Nuclear and High Energy Physics
  • Nuclear Energy and Engineering

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