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

Su Guanghui, Koji Morita, K. Fukuda, Mark Pidduck, Jia Dounan, Jaakko Miettinen

Research output: Contribution to journalReview article

55 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.CPT

Original languageEnglish
Pages (from-to)17-35
Number of pages19
JournalNuclear Engineering and Design
Volume220
Issue number1
DOIs
Publication statusPublished - Mar 1 2003

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pressure oscillations
low flow
artificial neural network
heat flux
low pressure
Heat flux
mass flow rate
oscillation
tubes
Neural networks
oscillations
Flow rate
neurons
Neurons
output
trends
analysis
curves
products
parameter

All Science Journal Classification (ASJC) codes

  • Nuclear and High Energy Physics
  • Nuclear Energy and Engineering
  • Materials Science(all)
  • Safety, Risk, Reliability and Quality
  • Waste Management and Disposal
  • Mechanical Engineering

Cite this

Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions. Applications of artificial neural network. / Guanghui, Su; Morita, Koji; Fukuda, K.; Pidduck, Mark; Dounan, Jia; Miettinen, Jaakko.

In: Nuclear Engineering and Design, Vol. 220, No. 1, 01.03.2003, p. 17-35.

Research output: Contribution to journalReview article

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