An evaluation of critical heat flux prediction methods for the upward flow in a vertical narrow rectangular channel. (October 2021)
- Record Type:
- Journal Article
- Title:
- An evaluation of critical heat flux prediction methods for the upward flow in a vertical narrow rectangular channel. (October 2021)
- Main Title:
- An evaluation of critical heat flux prediction methods for the upward flow in a vertical narrow rectangular channel
- Authors:
- Yan, Meiyue
Ma, Zaiyong
Pan, Liangming
Liu, Wei
He, Qingche
Zhang, Rui
Wu, Qi
Xu, Wangtao - Abstract:
- Abstract: Narrow rectangular channels have widespread application in various domains owing to their significant enhancement in boiling heat transfer. In the present work, the CHF (critical heat flux) prediction method has been comprehensively evaluated and analyzed by the experimental data covering wide operating conditions in narrow rectangular channels. The Bubble Crowding Model can predict the high critical heat flux region. The prediction deviation of ANN (Artificial Neural Network) with two hidden layers and five neurons to predict CHF can reach 20 %, then the influences of thermal-hydraulic parameters on prediction of CHF were obtained based on this constructed ANN. Among the tens of CHF correlations, Tong's, Sudo's, and Mudawar's correlations were selected, and the results indicate that Sudo's correlation can forecast well in 1–4 MPa and Mudawar's correlation had relatively low prediction errors for a wide pressure range. The LUT (look-up table) needs proper correction factors to accurately predict the CHF in narrow rectangular channels.
- Is Part Of:
- Progress in nuclear energy. Volume 140(2021)
- Journal:
- Progress in nuclear energy
- Issue:
- Volume 140(2021)
- Issue Display:
- Volume 140, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 140
- Issue:
- 2021
- Issue Sort Value:
- 2021-0140-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-10
- Subjects:
- CHF -- Narrow rectangular channel -- Mechanism model -- Look-up table -- Empirical correlation -- Artificial neural network
Nuclear energy -- Periodicals
Nuclear engineering -- Periodicals
333.7924 - Journal URLs:
- http://www.sciencedirect.com/science/journal/01491970 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.pnucene.2021.103901 ↗
- Languages:
- English
- ISSNs:
- 0149-1970
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6870.542000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 19684.xml