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Open AccessArticle

Heat Transfer Coefficients Analysis in a Helical Double-Pipe Evaporator: Nusselt Number Correlations through Artificial Neural Networks

1
CONACyT—Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos, Cuernavaca C.P. 62209, Mexico
2
Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos, Cuernavaca C.P. 62209, Mexico
3
CONACyT—Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Col. Palmira, C.P. Cuernavaca 62490, Mexico
4
Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Col. Palmira, C.P. Cuernavaca 62490, Mexico
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(7), 689; https://doi.org/10.3390/e21070689
Received: 28 May 2019 / Revised: 28 June 2019 / Accepted: 9 July 2019 / Published: 14 July 2019
(This article belongs to the Special Issue Thermodynamic Optimization)
In this study, two empirical correlations of the Nusselt number, based on two artificial neural networks (ANN), were developed to determine the heat transfer coefficients for each section of a vertical helical double-pipe evaporator with water as the working fluid. Each ANN was obtained using an experimental database of 1109 values obtained from an evaporator coupled to an absorption heat transformer with energy recycling. The Nusselt number in the annular section was estimated based on the modified Wilson plot method solved by an ANN. This model included the Reynolds and Prandtl numbers as input variables and three neurons in their hidden layer. The Nusselt number in the inner section was estimated based on the Rohsenow equation, solved by an ANN. This ANN model included the numbers of the Prandtl and Jackob liquids as input variables and one neuron in their hidden layer. The coefficients of determination were R 2 > 0.99 for both models. Both ANN models satisfied the dimensionless condition of the Nusselt number. The Levenberg–Marquardt algorithm was chosen to determine the optimum values of the weights and biases. The transfer functions used for the learning process were the hyperbolic tangent sigmoid in the hidden layer and the linear function in the output layer. The Nusselt numbers, determined by the ANNs, proved adequate to predict the values of the heat transfer coefficients of a vertical helical double-pipe evaporator that considered biphasic flow with an accuracy of ±0.2 for the annular Nusselt and ±4 for the inner Nusselt. View Full-Text
Keywords: heat transfer coefficients; artificial neural network; Nusselt number; helical heat exchangers heat transfer coefficients; artificial neural network; Nusselt number; helical heat exchangers
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MDPI and ACS Style

Parrales, A.; Hernández-Pérez, J.A.; Flores, O.; Hernandez, H.; Gómez-Aguilar, J.F.; Escobar-Jiménez, R.; Huicochea, A. Heat Transfer Coefficients Analysis in a Helical Double-Pipe Evaporator: Nusselt Number Correlations through Artificial Neural Networks. Entropy 2019, 21, 689. https://doi.org/10.3390/e21070689

AMA Style

Parrales A, Hernández-Pérez JA, Flores O, Hernandez H, Gómez-Aguilar JF, Escobar-Jiménez R, Huicochea A. Heat Transfer Coefficients Analysis in a Helical Double-Pipe Evaporator: Nusselt Number Correlations through Artificial Neural Networks. Entropy. 2019; 21(7):689. https://doi.org/10.3390/e21070689

Chicago/Turabian Style

Parrales, Arianna; Hernández-Pérez, José A.; Flores, Oliver; Hernandez, Horacio; Gómez-Aguilar, José F.; Escobar-Jiménez, Ricardo; Huicochea, Armando. 2019. "Heat Transfer Coefficients Analysis in a Helical Double-Pipe Evaporator: Nusselt Number Correlations through Artificial Neural Networks" Entropy 21, no. 7: 689. https://doi.org/10.3390/e21070689

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