Deep learning with multilayer perceptron for optimizing the heat transfer of mixed convection equipped with MWCNT-water nanofluid
In the modern era, Artificial Intelligence (AI) has emerged as a powerful tool that can rapidly generate highly accurate data, offering tremendous potential for optimizing system performance. This study focuses on harnessing the capabilities of an artificial neural network (ANN) to determine optimal...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-05-01
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| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X2400340X |
| _version_ | 1827278485680291840 |
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| author | Xiaogang Dong Salah Knani Hamdi Ayed Abir Mouldi Ibrahim Mahariq Javid Alhoee |
| author_facet | Xiaogang Dong Salah Knani Hamdi Ayed Abir Mouldi Ibrahim Mahariq Javid Alhoee |
| author_sort | Xiaogang Dong |
| collection | DOAJ |
| description | In the modern era, Artificial Intelligence (AI) has emerged as a powerful tool that can rapidly generate highly accurate data, offering tremendous potential for optimizing system performance. This study focuses on harnessing the capabilities of an artificial neural network (ANN) to determine optimal parameter values for heat transfer in a mixed convection mechanism. The first step of this research involved conducting CFD simulations to investigate the impact of varying Grashof numbers (102,103,and104), Reynolds numbers (1,10,and100), and MWCNT volume fractions (0, 0.01, 0.02, and 0.03) on the Nusselt number within an elliptical enclosure containing a centrally located rotational cylinder. A total of 48 simulations were performed, generating a comprehensive dataset for training the ANN-Multilayer Perceptron (MLP). In the second step, the trained ANN was utilized to generate an additional 700 data points with remarkable accuracy. This enabled efficient exploration of the parameter space, providing valuable insights into the system behavior and facilitating optimization efforts. The findings of this study revealed a 0.03 vol fraction of MWCNT into the water in Grashof numbers of 102,103,and104, the average Nusselt number increased by approximately 46%, 31%, and 12%, respectively. The ANN-based approach successfully identified optimal values for the variables that maximize the Nusselt number. |
| first_indexed | 2024-04-24T07:58:37Z |
| format | Article |
| id | doaj.art-c5573ba8ae2340318ff594a19ad49be1 |
| institution | Directory Open Access Journal |
| issn | 2214-157X |
| language | English |
| last_indexed | 2024-04-24T07:58:37Z |
| publishDate | 2024-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj.art-c5573ba8ae2340318ff594a19ad49be12024-04-18T04:20:32ZengElsevierCase Studies in Thermal Engineering2214-157X2024-05-0157104309Deep learning with multilayer perceptron for optimizing the heat transfer of mixed convection equipped with MWCNT-water nanofluidXiaogang Dong0Salah Knani1Hamdi Ayed2Abir Mouldi3Ibrahim Mahariq4Javid Alhoee5Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang, 262700, ChinaDepartment of Physics, College of Science, Northern Border University, Arar, Saudi ArabiaDepartment of Civil Engineering, College of Engineering, King Khalid University, Abha, 61421, Saudi ArabiaDepartment of Industrial Engineering, College of Engineering, King Khalid University, Abha, 61421, Saudi ArabiaElectrical and Computer Engineering Department, Gulf University for Science and Technology, Mishref, Kuwait; Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan; Corresponding author. Electrical and Computer Engineering Department, Gulf University for Science and Technology, Mishref, Kuwait.Department of Mechanical Engineering, Faculty of Engineering, Sana'a University, P.O. Box 12544, Sana'a, Yemen; Sustainable Management of Natural Resources and Environment Research Group, Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Corresponding author. Department of Mechanical Engineering, Faculty of Engineering, Sana'a University, P.O. Box 12544, Sana'a, Yemen.In the modern era, Artificial Intelligence (AI) has emerged as a powerful tool that can rapidly generate highly accurate data, offering tremendous potential for optimizing system performance. This study focuses on harnessing the capabilities of an artificial neural network (ANN) to determine optimal parameter values for heat transfer in a mixed convection mechanism. The first step of this research involved conducting CFD simulations to investigate the impact of varying Grashof numbers (102,103,and104), Reynolds numbers (1,10,and100), and MWCNT volume fractions (0, 0.01, 0.02, and 0.03) on the Nusselt number within an elliptical enclosure containing a centrally located rotational cylinder. A total of 48 simulations were performed, generating a comprehensive dataset for training the ANN-Multilayer Perceptron (MLP). In the second step, the trained ANN was utilized to generate an additional 700 data points with remarkable accuracy. This enabled efficient exploration of the parameter space, providing valuable insights into the system behavior and facilitating optimization efforts. The findings of this study revealed a 0.03 vol fraction of MWCNT into the water in Grashof numbers of 102,103,and104, the average Nusselt number increased by approximately 46%, 31%, and 12%, respectively. The ANN-based approach successfully identified optimal values for the variables that maximize the Nusselt number.http://www.sciencedirect.com/science/article/pii/S2214157X2400340XDeep learningMultilayer perceptronMixed convectionNanofluidNumerical study |
| spellingShingle | Xiaogang Dong Salah Knani Hamdi Ayed Abir Mouldi Ibrahim Mahariq Javid Alhoee Deep learning with multilayer perceptron for optimizing the heat transfer of mixed convection equipped with MWCNT-water nanofluid Case Studies in Thermal Engineering Deep learning Multilayer perceptron Mixed convection Nanofluid Numerical study |
| title | Deep learning with multilayer perceptron for optimizing the heat transfer of mixed convection equipped with MWCNT-water nanofluid |
| title_full | Deep learning with multilayer perceptron for optimizing the heat transfer of mixed convection equipped with MWCNT-water nanofluid |
| title_fullStr | Deep learning with multilayer perceptron for optimizing the heat transfer of mixed convection equipped with MWCNT-water nanofluid |
| title_full_unstemmed | Deep learning with multilayer perceptron for optimizing the heat transfer of mixed convection equipped with MWCNT-water nanofluid |
| title_short | Deep learning with multilayer perceptron for optimizing the heat transfer of mixed convection equipped with MWCNT-water nanofluid |
| title_sort | deep learning with multilayer perceptron for optimizing the heat transfer of mixed convection equipped with mwcnt water nanofluid |
| topic | Deep learning Multilayer perceptron Mixed convection Nanofluid Numerical study |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X2400340X |
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