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. (January 2023)
- Record Type:
- Journal Article
- 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. (January 2023)
- Main 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
- Authors:
- Hai, Tao
Dhahad, Hayder A.
Ali, Masood Ashraf
Goyal, Vishal
Almojil, Sattam Fahad
Almohana, Abdulaziz Ibrahim
Alali, Abdulrhman Fahmi
Almoalimi, Khaled Twfiq
Qasim Ahmed Alyousuf, Farah - Abstract:
- Abstract: 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.Abstract: 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. … (more)
- Is Part Of:
- Engineering analysis with boundary elements. Volume 146(2023)
- Journal:
- Engineering analysis with boundary elements
- Issue:
- Volume 146(2023)
- Issue Display:
- Volume 146, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 146
- Issue:
- 2023
- Issue Sort Value:
- 2023-0146-2023-0000
- Page Start:
- 252
- Page End:
- 262
- Publication Date:
- 2023-01
- Subjects:
- Machine learning -- Entropy generation -- Numerical calculations -- Convection -- Nanofluid
Boundary element methods -- Periodicals
Engineering mathematics -- Periodicals
Équations intégrales de frontière, Méthodes des -- Périodiques
Mathématiques de l'ingénieur -- Périodiques
Boundary element methods
Engineering mathematics
Periodicals
620.00151 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09557997 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.enganabound.2022.09.023 ↗
- Languages:
- English
- ISSNs:
- 0955-7997
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3753.350000
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British Library HMNTS - ELD Digital store - Ingest File:
- 24631.xml