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International Heat Transfer Conference 16

ISSN: 2377-424X (online)
ISSN: 2377-4371 (flashdrive)

CHARACTERIZATION OF A TRIPLE CONCENTRIC-TUBE HEAT EXCHANGER WITH CORRUGATED TUBES USING ARTIFICIAL NEURAL NETWORKS (ANN)

Jose D. Moya
Renewable Energy Research Institute, Section of Solar and Energy Efficiency, C/ de la Investigacion s/n, 02071 Albacete, Spain

Antonio E. Molina
Renewable Energy Research Institute, Section of Solar and Energy Efficiency, C/ de la Investigacion s/n, 02071 Albacete, Spain; Escuela de Ingenieros Industriales, Dpto. de Mecanica Aplicada e Ingenieria de Proyectos, Castilla-La Mancha University, Campus universitario s/n, 02071 Albacete, Spain

Juan F. Belmonte
Renewable Energy Research Institute, Section of Solar and Energy Efficiency, C/ de la Investigacion s/n, 02071 Albacete, Spain; Escuela de Ingenieros Industriales, Dpto. de Mecanica Aplicada e Ingenieria de Proyectos, Castilla-La Mancha University, Campus universitario s/n, 02071 Albacete, Spain

Juan I. Corcoles-Tendero
Renewable Energy Research Institute, Section of Solar and Energy Efficiency, C/ de la Investigacion s/n, 02071 Albacete, Spain; Escuela de Ingenieros Industriales, Dpto. de Mecanica Aplicada e Ingenieria de Proyectos, Castilla-La Mancha University, Campus universitario s/n, 02071 Albacete, Spain

Jose A. Almendros-Ibanez
Renewable Energy Research Institute, Section of Solar and Energy Efficiency, C/ de la Investigacion s/n, 02071 Albacete, Spain; Escuela de Ingenieros Industriales, Dpto. de Mecanica Aplicada e Ingenieria de Proyectos, Castilla-La Mancha University, Campus universitario s/n, 02071 Albacete, Spain

DOI: 10.1615/IHTC16.her.024002
pages 4893-4903


SCHLÜSSELWÖRTER: Heat transfer enhancement, Computational methods, triple tube, corrugated tubes, non-Newtonian fluids, ANN

Abstrakt

This work presents a model of Artificial Neural Networks (ANNs) to predict the heat transfer rate and pressure drop in to a triple concentric-tube heat exchanger (TTHX) with corrugated and non-corrugated inner tubes. Pitch and depths are varied in case of corrugated tubes. A back-propagation algorithm, the most common learning method for ANNs, is used in the training and testing of the network. Different network configurations were tested, and the optimum ANNs configuration consist of a network with two hidden layers with 15 and 21 nodes in the first and second layer, respectively. The ANNs results were found to be in good agreement with the experimental data, being the absolute average relative deviation (AARD%) under 2.79% for heat transfer coefficient and under 3.85% for pressure drop, respectively.

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