Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions: Applications of artificial neural network

Su. Guanghui (Corresponding Author), K. Morita, K. Fukuda, Mark Pidduck, Jia Dounan, Jaakko Miettinen

Research output: Contribution to journalReview ArticleScientificpeer-review

58 Citations (Scopus)

Abstract

Artificial neural networks (ANNs) for predicting critical heat flux (CHF) under low pressure and oscillation conditions have been trained successfully for either natural circulation or forced circulation (FC) in the present study. The input parameters of the ANN are pressure, mean mass flow rate, relative amplitude, inlet subcooling, oscillation period and the ratio of the heated length to the diameter of the tube, L/D. The output is a nondimensionalized factor F, which expresses the relative CHF under oscillation conditions. Based on the trained ANN, the influences of principal parameters on F for FC were analyzed. The parametric trends of the CHF under oscillation obtained by the trained ANN are as follows: the effects of pressure below 500 kPa are complex due to the influence of other parameters. F will increase with increasing mean mass flow rate under any conditions, and will decrease generally with an increase in relative amplitude. F will decrease initially and then increase with increasing inlet subcooling. The influence curves of mean mass flow rate on F will be almost the same when the period is shorter than 5.0 s or longer than 15 s. The influence of L/D will be negligible if L/D>200. It is found that the minimum number of neurons in the hidden layer is a product of the number of neurons in the input layer and in the output layer.
Original languageEnglish
Pages (from-to)17-35
JournalNuclear Engineering and Design
Volume220
Issue number1
DOIs
Publication statusPublished - 2003
MoE publication typeA2 Review article in a scientific journal

Keywords

  • turbulent flow
  • boiling water reactors
  • nuclear reactors
  • mass flow
  • critical heat flux
  • artificial neural networks
  • neural networks

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