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Performance predictions of laminar and turbulent heat transfer and fluid flow of heat exchangers having large tube-diameter and large tube-row by artificial neural networks

Xie, Gongnan LU ; Sundén, Bengt LU ; Wang, Qiuwang and Tang, Linghong (2009) In International Journal of Heat and Mass Transfer 52(11-12). p.2484-2497
Abstract
In this work an artificial neural network (ANN) is used to correlate experimentally determined and numerically computed Nusselt numbers and friction factors of three kinds of fin-and-tube heat exchangers having plain fins, slit fins and fins with longitudinal delta-winglet vortex generators with large tube-diameter and large the number of tube rows. First the experimental data for training the network was picked up from the database of nine samples with tube outside diameter of 18 mm, number of tube rows of six, nine, twelve, and Reynolds number between 4000 and 10,000. The artificial neural network configuration under consideration has twelve inputs of geometrical parameters and two outputs of heat transfer Nusselt number and fluid flow... (More)
In this work an artificial neural network (ANN) is used to correlate experimentally determined and numerically computed Nusselt numbers and friction factors of three kinds of fin-and-tube heat exchangers having plain fins, slit fins and fins with longitudinal delta-winglet vortex generators with large tube-diameter and large the number of tube rows. First the experimental data for training the network was picked up from the database of nine samples with tube outside diameter of 18 mm, number of tube rows of six, nine, twelve, and Reynolds number between 4000 and 10,000. The artificial neural network configuration under consideration has twelve inputs of geometrical parameters and two outputs of heat transfer Nusselt number and fluid flow friction factor. The commonly-implemented feed-forward back propagation algorithm was used to train the neural network and modify weights. Different networks with various numbers of hidden neurons and layers were assessed to find the best architecture for predicting heat transfer and flow friction. The deviation between the predictions and experimental data was less than 4%. Compared to correlations for prediction, the performance of the ANN-based prediction exhibits ANN superiority. Then the ANN training database was expanded to include experimental data and numerical data of other similar geometries by computational fluid dynamics (CFD) for turbulent and laminar cases with the Reynolds number of 1000-10,000. This in turn indicated the prediction has a good agreement with the combined database. The satisfactory results suggest that the developed ANN model is generalized to predict the turbulent or/and laminar heat transfer and fluid flow of such three kinds of heat exchangers with large tube-diameter and large number of tube rows. Also in this paper the weights and biases corresponding to the neural network architecture are provided so that future research can be carried out. It is recommended that ANNs might be used to predict the performances of thermal systems in engineering applications, especially to model heat exchangers for heat transfer analysis. (C) 2009 Elsevier Ltd. All rights reserved. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Correlations, tube-diameter and large number of tube rows, Large, Artificial neural network (ANN), Heat transfer, Friction
in
International Journal of Heat and Mass Transfer
volume
52
issue
11-12
pages
2484 - 2497
publisher
Pergamon
external identifiers
  • wos:000265807200009
  • scopus:63049128665
ISSN
0017-9310
DOI
10.1016/j.ijheatmasstransfer.2008.10.036
language
English
LU publication?
yes
id
cead276b-da88-4ad6-8b96-4007366b5616 (old id 1426333)
date added to LUP
2009-06-29 15:41:51
date last changed
2017-04-23 03:27:52
@article{cead276b-da88-4ad6-8b96-4007366b5616,
  abstract     = {In this work an artificial neural network (ANN) is used to correlate experimentally determined and numerically computed Nusselt numbers and friction factors of three kinds of fin-and-tube heat exchangers having plain fins, slit fins and fins with longitudinal delta-winglet vortex generators with large tube-diameter and large the number of tube rows. First the experimental data for training the network was picked up from the database of nine samples with tube outside diameter of 18 mm, number of tube rows of six, nine, twelve, and Reynolds number between 4000 and 10,000. The artificial neural network configuration under consideration has twelve inputs of geometrical parameters and two outputs of heat transfer Nusselt number and fluid flow friction factor. The commonly-implemented feed-forward back propagation algorithm was used to train the neural network and modify weights. Different networks with various numbers of hidden neurons and layers were assessed to find the best architecture for predicting heat transfer and flow friction. The deviation between the predictions and experimental data was less than 4%. Compared to correlations for prediction, the performance of the ANN-based prediction exhibits ANN superiority. Then the ANN training database was expanded to include experimental data and numerical data of other similar geometries by computational fluid dynamics (CFD) for turbulent and laminar cases with the Reynolds number of 1000-10,000. This in turn indicated the prediction has a good agreement with the combined database. The satisfactory results suggest that the developed ANN model is generalized to predict the turbulent or/and laminar heat transfer and fluid flow of such three kinds of heat exchangers with large tube-diameter and large number of tube rows. Also in this paper the weights and biases corresponding to the neural network architecture are provided so that future research can be carried out. It is recommended that ANNs might be used to predict the performances of thermal systems in engineering applications, especially to model heat exchangers for heat transfer analysis. (C) 2009 Elsevier Ltd. All rights reserved.},
  author       = {Xie, Gongnan and Sundén, Bengt and Wang, Qiuwang and Tang, Linghong},
  issn         = {0017-9310},
  keyword      = {Correlations,tube-diameter and large number of tube rows,Large,Artificial neural network (ANN),Heat transfer,Friction},
  language     = {eng},
  number       = {11-12},
  pages        = {2484--2497},
  publisher    = {Pergamon},
  series       = {International Journal of Heat and Mass Transfer},
  title        = {Performance predictions of laminar and turbulent heat transfer and fluid flow of heat exchangers having large tube-diameter and large tube-row by artificial neural networks},
  url          = {http://dx.doi.org/10.1016/j.ijheatmasstransfer.2008.10.036},
  volume       = {52},
  year         = {2009},
}