Imperial College London
Alternate

ProfessorChristosMarkides

Faculty of Engineering, Department of Chemical Engineering

Professor of Clean Energy Technologies
 
 
 
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Contact

 

+44 (0)20 7594 1601c.markides Website

 
 
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Location

 

404ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Sun:2021:10.1016/j.applthermaleng.2021.117067,
author = {Sun, F and Xie, G and Song, J and Li, S and Markides, CN},
doi = {10.1016/j.applthermaleng.2021.117067},
journal = {Applied Thermal Engineering},
pages = {1--13},
title = {Thermal characteristics of in-tube upward supercritical CO2 flows and a new heat transfer prediction model based on artificial neural networks (ANN)},
url = {http://dx.doi.org/10.1016/j.applthermaleng.2021.117067},
volume = {194},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Sun,F
AU  - Xie,G
AU  - Song,J
AU  - Li,S
AU  - Markides,CN
DO  - 10.1016/j.applthermaleng.2021.117067
EP  - 13
PY  - 2021///
SN  - 1359-4311
SP  - 1
TI  - Thermal characteristics of in-tube upward supercritical CO2 flows and a new heat transfer prediction model based on artificial neural networks (ANN)
T2  - Applied Thermal Engineering
UR  - http://dx.doi.org/10.1016/j.applthermaleng.2021.117067
UR  - https://www.sciencedirect.com/science/article/pii/S1359431121005111?via%3Dihub
UR  - http://hdl.handle.net/10044/1/88735
VL  - 194
ER  -
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