Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact

This paper examines the mixed convective heat transfer (HTR) of nanofluid (NFD) flow in a rectangular enclosure with the upper moving wall numerically. The lower wall has a high temperature and a number of semi-circular obstacles with the same temperature are installed on it. The upper moving wall h...

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Published in:Engineering Analysis with Boundary Elements
Main Author: Hai T.; Dhahad H.A.; Ali M.A.; Goyal V.; Almojil S.F.; Almohana A.I.; Alali A.F.; Almoalimi K.T.; Qasim Ahmed Alyousuf F.
Format: Retracted
Language:English
Published: Elsevier Ltd 2023
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140908788&doi=10.1016%2fj.enganabound.2022.09.023&partnerID=40&md5=80bde90a16a2aa4bfe9b1168e9dab5e0
id 2-s2.0-85140908788
spelling 2-s2.0-85140908788
Hai T.; Dhahad H.A.; Ali M.A.; Goyal V.; Almojil S.F.; Almohana A.I.; Alali A.F.; Almoalimi K.T.; Qasim Ahmed Alyousuf F.
Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact
2023
Engineering Analysis with Boundary Elements
146

10.1016/j.enganabound.2022.09.023
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140908788&doi=10.1016%2fj.enganabound.2022.09.023&partnerID=40&md5=80bde90a16a2aa4bfe9b1168e9dab5e0
This paper examines the mixed convective heat transfer (HTR) of nanofluid (NFD) flow in a rectangular enclosure with the upper moving wall numerically. The lower wall has a high temperature and a number of semi-circular obstacles with the same temperature are installed on it. The upper moving wall has a low temperature and the other two walls are insulated. The enclosure can change from horizontal to vertical. Radiation HTR is considered in the enclosure and there is a magnetic field (MGF) that can change the angle from horizontal to vertical affecting the NFD. This study is carried out for different angles of the enclosure and MGF from horizontal to vertical for radiation parameters (RDP) of 0 to 3 and a constant MGF with Hartmann number of 20 and Richardson number of 10. The aim is to estimate the Nusselt number (Nu), entropy generation (ETG), and Bejan number (Be). The SIMPLE algorithm is utilized using FORTRAN software, and optimization is done using artificial intelligence to find the maximum and minimum output values. The results demonstrate that the maximum value of Nu and Bes corresponds to the MGF angle and enclosure angle of 90°. The minimum value of the Nu and the maximum amount of ETG corresponds to the horizontal MGF and horizontal enclosure when the RDP is 1.5. An increment in the RDP enhances the amount of Nu. The maximum amount of ETG, i.e. 12.87, corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. It was also found that most environmental impacts, and hence values for different environmental factors, arise from the production of nanoparticles; thus, it is a significant contributor to environmental impacts. © 2022 Elsevier Ltd
Elsevier Ltd
9557997
English
Retracted

author Hai T.; Dhahad H.A.; Ali M.A.; Goyal V.; Almojil S.F.; Almohana A.I.; Alali A.F.; Almoalimi K.T.; Qasim Ahmed Alyousuf F.
spellingShingle Hai T.; Dhahad H.A.; Ali M.A.; Goyal V.; Almojil S.F.; Almohana A.I.; Alali A.F.; Almoalimi K.T.; Qasim Ahmed Alyousuf F.
Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact
author_facet Hai T.; Dhahad H.A.; Ali M.A.; Goyal V.; Almojil S.F.; Almohana A.I.; Alali A.F.; Almoalimi K.T.; Qasim Ahmed Alyousuf F.
author_sort Hai T.; Dhahad H.A.; Ali M.A.; Goyal V.; Almojil S.F.; Almohana A.I.; Alali A.F.; Almoalimi K.T.; Qasim Ahmed Alyousuf F.
title Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact
title_short Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact
title_full Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact
title_fullStr Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact
title_full_unstemmed Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact
title_sort Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact
publishDate 2023
container_title Engineering Analysis with Boundary Elements
container_volume 146
container_issue
doi_str_mv 10.1016/j.enganabound.2022.09.023
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140908788&doi=10.1016%2fj.enganabound.2022.09.023&partnerID=40&md5=80bde90a16a2aa4bfe9b1168e9dab5e0
description This paper examines the mixed convective heat transfer (HTR) of nanofluid (NFD) flow in a rectangular enclosure with the upper moving wall numerically. The lower wall has a high temperature and a number of semi-circular obstacles with the same temperature are installed on it. The upper moving wall has a low temperature and the other two walls are insulated. The enclosure can change from horizontal to vertical. Radiation HTR is considered in the enclosure and there is a magnetic field (MGF) that can change the angle from horizontal to vertical affecting the NFD. This study is carried out for different angles of the enclosure and MGF from horizontal to vertical for radiation parameters (RDP) of 0 to 3 and a constant MGF with Hartmann number of 20 and Richardson number of 10. The aim is to estimate the Nusselt number (Nu), entropy generation (ETG), and Bejan number (Be). The SIMPLE algorithm is utilized using FORTRAN software, and optimization is done using artificial intelligence to find the maximum and minimum output values. The results demonstrate that the maximum value of Nu and Bes corresponds to the MGF angle and enclosure angle of 90°. The minimum value of the Nu and the maximum amount of ETG corresponds to the horizontal MGF and horizontal enclosure when the RDP is 1.5. An increment in the RDP enhances the amount of Nu. The maximum amount of ETG, i.e. 12.87, corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. It was also found that most environmental impacts, and hence values for different environmental factors, arise from the production of nanoparticles; thus, it is a significant contributor to environmental impacts. © 2022 Elsevier Ltd
publisher Elsevier Ltd
issn 9557997
language English
format Retracted
accesstype
record_format scopus
collection Scopus
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