Robust and universal predictive models for frictional pressure drop during two-phase flow in smooth helically coiled tube heat exchangers

Abstract There is a lack of well-verified models in the literature for the prediction of the frictional pressure drop (FPD) in the helically coiled tubes at different conditions/orientations. In this study, the robust and universal models for estimating two-phase FPD in smooth coiled tubes with diff...

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Autores principales:	M. A. Moradkhani, Seyyed Hossein Hosseini, M. Mansouri, G. Ahmadi, Mengjie Song
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9f1c49713aa24accadf3e3d18f11165c
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spelling	oai:doaj.org-article:9f1c49713aa24accadf3e3d18f11165c2021-12-02T18:01:48ZRobust and universal predictive models for frictional pressure drop during two-phase flow in smooth helically coiled tube heat exchangers10.1038/s41598-021-99476-62045-2322https://doaj.org/article/9f1c49713aa24accadf3e3d18f11165c2021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99476-6https://doaj.org/toc/2045-2322Abstract There is a lack of well-verified models in the literature for the prediction of the frictional pressure drop (FPD) in the helically coiled tubes at different conditions/orientations. In this study, the robust and universal models for estimating two-phase FPD in smooth coiled tubes with different orientations were developed using several intelligent approaches. For this reason, a databank comprising 1267 experimental data samples was collected from 12 independent studies, which covers a broad range of fluids, tube diameters, coil diameters, coil axis inclinations, mass fluxes, saturation temperatures, and vapor qualities. The earlier models for straight and coiled tubes were examined using the collected database, which showed absolute average relative error (AARE) higher than 21%. The most relevant dimensionless groups were used as models’ inputs, and the neural network approach of multilayer perceptron and radial basis functions (RBF) were developed based on the homogenous equilibrium method. Although both intelligent models exhibited excellent accuracy, the RBF model predicted the best results with AARE 4.73% for the testing process. In addition, an explicit FPD model was developed by the genetic programming (GP), which showed the AARE of 14.97% for all data points. Capabilities of the proposed models under different conditions were described and, the sensitivity analyses were performed.M. A. MoradkhaniSeyyed Hossein HosseiniM. MansouriG. AhmadiMengjie SongNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q M. A. Moradkhani Seyyed Hossein Hosseini M. Mansouri G. Ahmadi Mengjie Song Robust and universal predictive models for frictional pressure drop during two-phase flow in smooth helically coiled tube heat exchangers
description	Abstract There is a lack of well-verified models in the literature for the prediction of the frictional pressure drop (FPD) in the helically coiled tubes at different conditions/orientations. In this study, the robust and universal models for estimating two-phase FPD in smooth coiled tubes with different orientations were developed using several intelligent approaches. For this reason, a databank comprising 1267 experimental data samples was collected from 12 independent studies, which covers a broad range of fluids, tube diameters, coil diameters, coil axis inclinations, mass fluxes, saturation temperatures, and vapor qualities. The earlier models for straight and coiled tubes were examined using the collected database, which showed absolute average relative error (AARE) higher than 21%. The most relevant dimensionless groups were used as models’ inputs, and the neural network approach of multilayer perceptron and radial basis functions (RBF) were developed based on the homogenous equilibrium method. Although both intelligent models exhibited excellent accuracy, the RBF model predicted the best results with AARE 4.73% for the testing process. In addition, an explicit FPD model was developed by the genetic programming (GP), which showed the AARE of 14.97% for all data points. Capabilities of the proposed models under different conditions were described and, the sensitivity analyses were performed.
format	article
author	M. A. Moradkhani Seyyed Hossein Hosseini M. Mansouri G. Ahmadi Mengjie Song
author_facet	M. A. Moradkhani Seyyed Hossein Hosseini M. Mansouri G. Ahmadi Mengjie Song
author_sort	M. A. Moradkhani
title	Robust and universal predictive models for frictional pressure drop during two-phase flow in smooth helically coiled tube heat exchangers
title_short	Robust and universal predictive models for frictional pressure drop during two-phase flow in smooth helically coiled tube heat exchangers
title_full	Robust and universal predictive models for frictional pressure drop during two-phase flow in smooth helically coiled tube heat exchangers
title_fullStr	Robust and universal predictive models for frictional pressure drop during two-phase flow in smooth helically coiled tube heat exchangers
title_full_unstemmed	Robust and universal predictive models for frictional pressure drop during two-phase flow in smooth helically coiled tube heat exchangers
title_sort	robust and universal predictive models for frictional pressure drop during two-phase flow in smooth helically coiled tube heat exchangers
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9f1c49713aa24accadf3e3d18f11165c
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Robust and universal predictive models for frictional pressure drop during two-phase flow in smooth helically coiled tube heat exchangers

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