Modeling and optimization of impinging jet pressure using artificial intelligence
Artificial Intelligence (AI) can be used to model efficient processes. In this paper, AI and CFD are employed to maximize the wall pressure of the impinging jet, which has a wide range of industrial and technological applications. Firstly, the CFD model is validated with the experimental results for...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823011407 |
| _version_ | 1827370631150174208 |
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| author | Sajjad Miran Muhammad Imtiaz Hussain Tahir Abbas Jauhar Tayybah Kiren Waseem Arif Gwi Hyun Lee |
| author_facet | Sajjad Miran Muhammad Imtiaz Hussain Tahir Abbas Jauhar Tayybah Kiren Waseem Arif Gwi Hyun Lee |
| author_sort | Sajjad Miran |
| collection | DOAJ |
| description | Artificial Intelligence (AI) can be used to model efficient processes. In this paper, AI and CFD are employed to maximize the wall pressure of the impinging jet, which has a wide range of industrial and technological applications. Firstly, the CFD model is validated with the experimental results for various geometrical and flow rate configurations (H/D= {1, 2, 3, 4, 5, 10, 20}, where H is nozzle to flat plate distance and D is nozzle diameter, and Reynolds Number (Re) ranges {Re= 15,000, 25,000, 30,000}. In explanatory data analysis, Pressure and D are slightly negatively correlated. Re and D show a negative relation of −0.2 whereas a slight negative relation appears for D vs H/D. Re vs H/D have a positive correlation of 0.2. Various activation functions were explored to find that tangent hyperbolic performed best model fit under AI. The Artificial Neural Networks (ANNs) are applied to train the model with minimized least squared error. Finally, the trained model is optimized for the maximum wall pressure over the distance. The maximum pressure obtained from the trained model is 383.83 KPa. |
| first_indexed | 2024-03-08T10:18:21Z |
| format | Article |
| id | doaj.art-045eec778eed4fe9815b2a99b28aeb48 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-08T10:18:21Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-045eec778eed4fe9815b2a99b28aeb482024-01-28T04:21:00ZengElsevierAlexandria Engineering Journal1110-01682024-01-0187489500Modeling and optimization of impinging jet pressure using artificial intelligenceSajjad Miran0Muhammad Imtiaz Hussain1Tahir Abbas Jauhar2Tayybah Kiren3Waseem Arif4Gwi Hyun Lee5Department of Mechanical Engineering, University of Gujrat, PakistanAgriculture and Life Sciences Research Institute, Kangwon National University, Chuncheon, South KoreaDepartment of Mechanical Engineering, University of Gujrat, PakistanDepartment of Computer Science (RCET), University of Engineering and Technology Lahore, PakistanDepartment of Mechanical Engineering, University of Gujrat, PakistanInterdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon, South Korea; Corresponding author.Artificial Intelligence (AI) can be used to model efficient processes. In this paper, AI and CFD are employed to maximize the wall pressure of the impinging jet, which has a wide range of industrial and technological applications. Firstly, the CFD model is validated with the experimental results for various geometrical and flow rate configurations (H/D= {1, 2, 3, 4, 5, 10, 20}, where H is nozzle to flat plate distance and D is nozzle diameter, and Reynolds Number (Re) ranges {Re= 15,000, 25,000, 30,000}. In explanatory data analysis, Pressure and D are slightly negatively correlated. Re and D show a negative relation of −0.2 whereas a slight negative relation appears for D vs H/D. Re vs H/D have a positive correlation of 0.2. Various activation functions were explored to find that tangent hyperbolic performed best model fit under AI. The Artificial Neural Networks (ANNs) are applied to train the model with minimized least squared error. Finally, the trained model is optimized for the maximum wall pressure over the distance. The maximum pressure obtained from the trained model is 383.83 KPa.http://www.sciencedirect.com/science/article/pii/S1110016823011407Impinging jetPressure distributionNozzle to Plate DistanceArtificial Intelligence |
| spellingShingle | Sajjad Miran Muhammad Imtiaz Hussain Tahir Abbas Jauhar Tayybah Kiren Waseem Arif Gwi Hyun Lee Modeling and optimization of impinging jet pressure using artificial intelligence Alexandria Engineering Journal Impinging jet Pressure distribution Nozzle to Plate Distance Artificial Intelligence |
| title | Modeling and optimization of impinging jet pressure using artificial intelligence |
| title_full | Modeling and optimization of impinging jet pressure using artificial intelligence |
| title_fullStr | Modeling and optimization of impinging jet pressure using artificial intelligence |
| title_full_unstemmed | Modeling and optimization of impinging jet pressure using artificial intelligence |
| title_short | Modeling and optimization of impinging jet pressure using artificial intelligence |
| title_sort | modeling and optimization of impinging jet pressure using artificial intelligence |
| topic | Impinging jet Pressure distribution Nozzle to Plate Distance Artificial Intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823011407 |
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