Analysis of CHF in saturated forced convective boiling on a heated surface with impinging jets using artificial neural network and genetic algorithm

Tenglong Cong, Ronghua Chen, Guanghui Su*, Suizheng Qiu, Wenxi Tian

*この研究の対応する著者

研究成果: Article査読

25 被引用数 (Scopus)

抄録

In this paper, a three-layer Back Propagation (BP) algorithm artificial neural network (ANN) for predicting critical heat flux (CHF) in saturated forced convective boiling on a heated surface with impinging jets was trained successfully with a root mean square (RMS) error of 17.39%. The input parameters of the ANN are liquid-to-vapor density ratio, ρl/ ρv, the ratio of characteristic dimension of the heated surface to the diameter of the impinging jet, L/d, reciprocal of the Weber number, 2σ/ρlu2(L - d), and the number of impinging jets, Nj. The output is dimensionless heat flux, qco/ ρvHfgu. Based on the trained ANN, the influence of principal parameters on CHF has been analyzed as follows. CHF increases with an increase in jet velocity and decreases with an increase in L/d and N j. CHF increases with an increase in pressure at first and then decreases. Besides, a new correlation was generalized using genetic algorithm (GA) as a comparison with ANN to confirm the advantage of ANN.

本文言語English
ページ(範囲)3945-3951
ページ数7
ジャーナルNuclear Engineering and Design
241
9
DOI
出版ステータスPublished - 2011 9

ASJC Scopus subject areas

  • 原子力エネルギーおよび原子力工学
  • 機械工学
  • 安全性、リスク、信頼性、品質管理
  • 材料科学(全般)
  • 核物理学および高エネルギー物理学
  • 廃棄物管理と処理

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