Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results

Abstract Utilizing artificial intelligence algorithm of adaptive network-based fuzzy inference system (ANFIS) in combination with the computational lfuid dynamics (CFD) has recently revealed great potential as an auxiliary method for simulating challenging fluid mechnics problems. This research area...

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Autores principales:	Meisam Babanezhad, Iman Behroyan, Azam Marjani, Saeed Shirazian
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/39a2e879ba9145ed9a76b6040f4b4d79
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spelling	oai:doaj.org-article:39a2e879ba9145ed9a76b6040f4b4d792021-12-02T14:12:08ZVelocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results10.1038/s41598-020-79913-82045-2322https://doaj.org/article/39a2e879ba9145ed9a76b6040f4b4d792021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79913-8https://doaj.org/toc/2045-2322Abstract Utilizing artificial intelligence algorithm of adaptive network-based fuzzy inference system (ANFIS) in combination with the computational lfuid dynamics (CFD) has recently revealed great potential as an auxiliary method for simulating challenging fluid mechnics problems. This research area is at the beginning, and needs sophisticated algorithms to be developed. No studies are available to consider the efficiency of the other trainers like differential evolution (DE) integrating with the FIS for capturing the pattern of the simulation results generated by CFD technique. Besides, the adjustment of the tuning parameters of the artificial intelligence (AI) algorithm for finding the highest level of intelligence is unavailable. The performance of AI algorithms in the meshing process has not been considered yet. Therfore, herein the Al2O3/water nanofluid flow in a porous pipe is simulated by a sophisticated hybrid approach combining mechnsitic model (CFD) and AI. The finite volume method (FVM) is employed as the CFD approach. Also, the differential evolution-based fuzzy inference system (DEFIS) is used for learning the CFD results. The DEFIS learns the nanofluid velocity in the y-direction, as output, and the nodes coordinates (i.e., x, y, and z), as inputs. The intelligence of the DEFIS is assessed by adjusting the methd’s variables including input number, population number, and crossover. It was found that the DEFIS intelligence is related to the input number of 3, the crossover of 0.8, and the population number of 120. In addition, the nodes increment from 4833 to 774,468 was done by the DEFIS. The DEFIS predicted the velocity for the new dense mesh without using the CFD data. Finally, all CFD results were covered with the new predictions of the DEFIS.Meisam BabanezhadIman BehroyanAzam MarjaniSaeed ShirazianNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Meisam Babanezhad Iman Behroyan Azam Marjani Saeed Shirazian Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results
description	Abstract Utilizing artificial intelligence algorithm of adaptive network-based fuzzy inference system (ANFIS) in combination with the computational lfuid dynamics (CFD) has recently revealed great potential as an auxiliary method for simulating challenging fluid mechnics problems. This research area is at the beginning, and needs sophisticated algorithms to be developed. No studies are available to consider the efficiency of the other trainers like differential evolution (DE) integrating with the FIS for capturing the pattern of the simulation results generated by CFD technique. Besides, the adjustment of the tuning parameters of the artificial intelligence (AI) algorithm for finding the highest level of intelligence is unavailable. The performance of AI algorithms in the meshing process has not been considered yet. Therfore, herein the Al2O3/water nanofluid flow in a porous pipe is simulated by a sophisticated hybrid approach combining mechnsitic model (CFD) and AI. The finite volume method (FVM) is employed as the CFD approach. Also, the differential evolution-based fuzzy inference system (DEFIS) is used for learning the CFD results. The DEFIS learns the nanofluid velocity in the y-direction, as output, and the nodes coordinates (i.e., x, y, and z), as inputs. The intelligence of the DEFIS is assessed by adjusting the methd’s variables including input number, population number, and crossover. It was found that the DEFIS intelligence is related to the input number of 3, the crossover of 0.8, and the population number of 120. In addition, the nodes increment from 4833 to 774,468 was done by the DEFIS. The DEFIS predicted the velocity for the new dense mesh without using the CFD data. Finally, all CFD results were covered with the new predictions of the DEFIS.
format	article
author	Meisam Babanezhad Iman Behroyan Azam Marjani Saeed Shirazian
author_facet	Meisam Babanezhad Iman Behroyan Azam Marjani Saeed Shirazian
author_sort	Meisam Babanezhad
title	Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results
title_short	Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results
title_full	Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results
title_fullStr	Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results
title_full_unstemmed	Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results
title_sort	velocity prediction of nanofluid in a heated porous pipe: defis learning of cfd results
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/39a2e879ba9145ed9a76b6040f4b4d79
work_keys_str_mv	AT meisambabanezhad velocitypredictionofnanofluidinaheatedporouspipedefislearningofcfdresults AT imanbehroyan velocitypredictionofnanofluidinaheatedporouspipedefislearningofcfdresults AT azammarjani velocitypredictionofnanofluidinaheatedporouspipedefislearningofcfdresults AT saeedshirazian velocitypredictionofnanofluidinaheatedporouspipedefislearningofcfdresults
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Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results

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