Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

Abstract Computational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approa...

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Autores principales:	Meisam Babanezhad, Iman Behroyan, Ali Taghvaie Nakhjiri, Mashallah Rezakazemi, Azam Marjani, Saeed Shirazian
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/249deb056dff461db521f3f505934a62
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spelling	oai:doaj.org-article:249deb056dff461db521f3f505934a622021-12-02T14:12:45ZThermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system10.1038/s41598-020-80207-22045-2322https://doaj.org/article/249deb056dff461db521f3f505934a622021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80207-2https://doaj.org/toc/2045-2322Abstract Computational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.Meisam BabanezhadIman BehroyanAli Taghvaie NakhjiriMashallah RezakazemiAzam MarjaniSaeed ShirazianNature 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 Meisam Babanezhad Iman Behroyan Ali Taghvaie Nakhjiri Mashallah Rezakazemi Azam Marjani Saeed Shirazian Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system
description	Abstract Computational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.
format	article
author	Meisam Babanezhad Iman Behroyan Ali Taghvaie Nakhjiri Mashallah Rezakazemi Azam Marjani Saeed Shirazian
author_facet	Meisam Babanezhad Iman Behroyan Ali Taghvaie Nakhjiri Mashallah Rezakazemi Azam Marjani Saeed Shirazian
author_sort	Meisam Babanezhad
title	Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system
title_short	Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system
title_full	Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system
title_fullStr	Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system
title_full_unstemmed	Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system
title_sort	thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/249deb056dff461db521f3f505934a62
work_keys_str_mv	AT meisambabanezhad thermalpredictionofturbulentforcedconvectionofnanofluidusingcomputationalfluiddynamicscoupledgeneticalgorithmwithfuzzyinterfacesystem AT imanbehroyan thermalpredictionofturbulentforcedconvectionofnanofluidusingcomputationalfluiddynamicscoupledgeneticalgorithmwithfuzzyinterfacesystem AT alitaghvaienakhjiri thermalpredictionofturbulentforcedconvectionofnanofluidusingcomputationalfluiddynamicscoupledgeneticalgorithmwithfuzzyinterfacesystem AT mashallahrezakazemi thermalpredictionofturbulentforcedconvectionofnanofluidusingcomputationalfluiddynamicscoupledgeneticalgorithmwithfuzzyinterfacesystem AT azammarjani thermalpredictionofturbulentforcedconvectionofnanofluidusingcomputationalfluiddynamicscoupledgeneticalgorithmwithfuzzyinterfacesystem AT saeedshirazian thermalpredictionofturbulentforcedconvectionofnanofluidusingcomputationalfluiddynamicscoupledgeneticalgorithmwithfuzzyinterfacesystem
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Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

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