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.
|ジャーナル||Hedongli Gongcheng/Nuclear Power Engineering|
|出版ステータス||Published - 2010 5|
ASJC Scopus subject areas
- Nuclear Energy and Engineering