Study on pool boiling and flow boiling with artificial neural networks

Rong Hua Chen, Guang Hui Su, Sui Zheng Qiu

Research output: Contribution to journalArticle

Abstract

In this paper, two artificial neural networks (ANNs) are trained successfully to predict the CHF of thermosyphon and heat transfer coefficient of pool nucleate boiling respectively. The root mean square of predicated value are 16.43% and 19.57%, respectively. The analysis results indicate that CHF would be improved by inserting an inner tube in the thermosyphon. CHF increases initially as inner tube diameter increases and then decreases with the further increase of inner tube diameter. The heat transfer coefficient of pool nucleate boiling increases linearly as pressure increases, and when the pressure is close to the critical pressure, the increasing rate increases.

Original languageEnglish
Pages (from-to)49-52
Number of pages4
JournalHedongli Gongcheng/Nuclear Power Engineering
Volume31
Issue numberSUPPL. 1
Publication statusPublished - 2010 May

Keywords

  • Artificial neural network
  • CHF
  • Pool nucleate boiling
  • Thermosyphon

ASJC Scopus subject areas

  • Nuclear Energy and Engineering

Fingerprint Dive into the research topics of 'Study on pool boiling and flow boiling with artificial neural networks'. Together they form a unique fingerprint.

  • Cite this