Thermal characteristics of in-tube upward supercritical CO2 flows and a new heat transfer prediction model based on artificial neural networks (ANN)
File(s)
Accepted version
Author(s)
Sun, Feng
Xie, Gongnan
Song, Jian
Li, Shulei
Markides, Christos N
Type
Journal Article
Abstract
The potential employment of supercritical carbon dioxide (sCO2) flows in heated tubes in many applications requires accurate and reliable predictions of the thermal characteristics of these flows. However, the ability to predict such flows remains limited due to a lack of a complete fundamental understanding, with traditional prediction capabilities relying on either simple empirical correlations or highly complex and computationally demanding simulation methods both of which limit the design of next-generation systems. To overcome this challenge, a prediction model based on artificial neural network (ANN) is proposed and trained by 5780 sets of experimental wall temperature data from upward flows with a very satisfactory root mean square error (RMSE) and mean relative error that are less than 1.9 °C and 1.8%, respectively. The results confirm that the structured model can provide satisfactory prediction capabilities overall, as well specific performance with mean relative error under the normal, enhanced and deteriorated heat transfer (NHT, EHT and DHT) conditions of 1.8%, 1.6% and 1.7%, respectively. The proposed model’s ability to predict the heat transfer coefficient in these flows is also considered, and it is shown that the mean relative error is less than 2.8%. Thus, it is confirmed that it has a better prediction accuracy than traditional empirical correlations. This work indicates that such ANN methods can provide a real alternative for adoption in select thermal science and engineering applications, shedding a new light and giving added insight into the thermal characteristics of heated supercritical fluids.
Date Issued
2021-07-25
Date Acceptance
2021-05-03
Citation
Applied Thermal Engineering, 2021, 194, pp.1-13
ISSN
1359-4311
Publisher
Elsevier BV
Start Page
1
End Page
13
Journal / Book Title
Applied Thermal Engineering
Volume
194
Copyright Statement
© 2021 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Identifier
https://www.sciencedirect.com/science/article/pii/S1359431121005111?via%3Dihub
Subjects
Energy
0913 Mechanical Engineering
0915 Interdisciplinary Engineering
Publication Status
Submitted
Article Number
117067
Date Publish Online
2021-05-08