Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

Abstract The heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media....

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Autores principales:	Tiziana Ciano, Massimiliano Ferrara, Meisam Babanezhad, Afrasyab Khan, Azam Marjani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/64e7da7f2de447f4b2337f1bda2bca3e
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spelling	oai:doaj.org-article:64e7da7f2de447f4b2337f1bda2bca3e2021-12-02T14:58:53ZPrediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm10.1038/s41598-021-90201-x2045-2322https://doaj.org/article/64e7da7f2de447f4b2337f1bda2bca3e2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90201-xhttps://doaj.org/toc/2045-2322Abstract The heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.Tiziana CianoMassimiliano FerraraMeisam BabanezhadAfrasyab KhanAzam MarjaniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tiziana Ciano Massimiliano Ferrara Meisam Babanezhad Afrasyab Khan Azam Marjani Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm
description	Abstract The heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.
format	article
author	Tiziana Ciano Massimiliano Ferrara Meisam Babanezhad Afrasyab Khan Azam Marjani
author_facet	Tiziana Ciano Massimiliano Ferrara Meisam Babanezhad Afrasyab Khan Azam Marjani
author_sort	Tiziana Ciano
title	Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm
title_short	Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm
title_full	Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm
title_fullStr	Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm
title_full_unstemmed	Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm
title_sort	prediction of velocity profile of water based copper nanofluid in a heated porous tube using cfd and genetic algorithm
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/64e7da7f2de447f4b2337f1bda2bca3e
work_keys_str_mv	AT tizianaciano predictionofvelocityprofileofwaterbasedcoppernanofluidinaheatedporoustubeusingcfdandgeneticalgorithm AT massimilianoferrara predictionofvelocityprofileofwaterbasedcoppernanofluidinaheatedporoustubeusingcfdandgeneticalgorithm AT meisambabanezhad predictionofvelocityprofileofwaterbasedcoppernanofluidinaheatedporoustubeusingcfdandgeneticalgorithm AT afrasyabkhan predictionofvelocityprofileofwaterbasedcoppernanofluidinaheatedporoustubeusingcfdandgeneticalgorithm AT azammarjani predictionofvelocityprofileofwaterbasedcoppernanofluidinaheatedporoustubeusingcfdandgeneticalgorithm
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Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

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