Paper Title:
Applications of Wavelet Neural Network for Prediction of CHF
  Abstract

In this study, a wavelet neural network (WNN) model for predicting critical heat flux (CHF) is set up. The WNN mode combining the properties of the wavelet transform and the advantages of Artificial Neural Networks (ANN) has some advantages of its globe optimal searching, quick convergence speed and solving non-linear problem. The database used in the analysis is from the 1960’s, including 126 data points which cover these parameter ranges: pressure P=100–1,000 kPa, mass flow rate G=40–500 kgm-2s-1, inlet subcooling ΔTsub=0–35C and heat flux Q=20–8,000 kWm-2. The WNN prediction results have a good agreement with experimental data. Simulation and analysis results show that the network model can effectively predict CHF.

  Info
Periodical
Edited by
Qi Luo
Pages
1282-1286
DOI
10.4028/www.scientific.net/AMM.58-60.1282
Citation
H. M. Wei, Y. Tian, J. Zhong, X. Zhang, "Applications of Wavelet Neural Network for Prediction of CHF", Applied Mechanics and Materials, Vols. 58-60, pp. 1282-1286, 2011
Online since
June 2011
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