Artificial neural network (ANN) approach in predicting the thermo-solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolant
This study focuses on enhancing heat and mass transfer in an electronic cooling system such as a rectangular cavity that contains equidistant heated chips along the bottom wall. The cavity of the present study is filled with ethylene glycol (30:70) based hybrid nano coolants of different volume frac...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | International Journal of Thermofluids |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266620272400363X |
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| author | Tawsif Mahmud Jiaul Haque Saboj Preetom Nag Goutam Saha Bijan K. Saha |
| author_facet | Tawsif Mahmud Jiaul Haque Saboj Preetom Nag Goutam Saha Bijan K. Saha |
| author_sort | Tawsif Mahmud |
| collection | DOAJ |
| description | This study focuses on enhancing heat and mass transfer in an electronic cooling system such as a rectangular cavity that contains equidistant heated chips along the bottom wall. The cavity of the present study is filled with ethylene glycol (30:70) based hybrid nano coolants of different volume fractions (ϕ) of Multi-walled Carbon Nanotube (MWCNT), Aluminum Oxide (Al2O3), and Copper Oxide (CuO). The set of equations controlling the thermo-solutal natural convection within the enclosure is simulated using the Galerkin weighted residual finite element method (FEM). The study has shown a good agreement of numerical results with different experimental reports within the framework of the present study. A solution space is constructed based on governing parameters such as Rayleigh number, buoyancy ratio, and Lewis number. A hybrid nano-coolant containing ϕMWCNT = 1.5 %, ϕCuO = 0.5 %, and ϕAl2O3 = 2 % showed a 3.11% improvement in heat transfer rate compared to the base fluid, highlighting its potential for thermal management applications. This study also investigates various machine learning models for predicting the heat and mass transfer rate, and an error analysis is conducted on the K-Nearest Neighbour Regressor, Random Forest Regressor, Decision Tree Regressor, and ANN model. The ANN model with 6-50-100-50-2 architecture showcases the best fit with the mean squared error of 0.8923 and an R² value of 99.96 % on testing data. The ANN model exhibits its capability to predict heat transfer and mass transfer rates within the error ranges from 1–2 % and 2–3 %, respectively, even at a strong thermal buoyancy force (Ra = 10⁵). This accuracy showcases a novel use of ANN to efficiently predict thermo-fluidic transport behaviors of hybrid nanofluids, offering a faster alternative to resource-intensive simulations. The present study opens new possibilities for real-time, cost-effective cooling solutions, particularly in microelectronics and renewable energy, while maintaining high prediction accuracy by integrating machine learning with the nanotechnology approach. |
| format | Article |
| id | doaj-art-1e8ac9aad27c496e907ae90eca8fb165 |
| institution | OA Journals |
| issn | 2666-2027 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Thermofluids |
| spelling | doaj-art-1e8ac9aad27c496e907ae90eca8fb1652025-08-20T02:38:10ZengElsevierInternational Journal of Thermofluids2666-20272024-11-012410092310.1016/j.ijft.2024.100923Artificial neural network (ANN) approach in predicting the thermo-solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolantTawsif Mahmud0Jiaul Haque Saboj1Preetom Nag2Goutam Saha3Bijan K. Saha4Department of Mathematics & Physics, North South University (NSU), Dhaka, 1229, Bangladesh; Center of Applied & Computational Sciences (CACS), NSU, Dhaka, 1229, BangladeshDepartment of Mathematics & Physics, North South University (NSU), Dhaka, 1229, Bangladesh; Center of Applied & Computational Sciences (CACS), NSU, Dhaka, 1229, BangladeshDepartment of Mathematics & Physics, North South University (NSU), Dhaka, 1229, Bangladesh; Center of Applied & Computational Sciences (CACS), NSU, Dhaka, 1229, Bangladesh; Corresponding author at: Department of Mathematics & Physics, North South University (NSU), Dhaka, 1229, Bangladesh.Department of Mathematics, University of Dhaka, Dhaka, 1000, BangladeshDepartment of Mathematics, University of Barishal, Barishal, 8254, BangladeshThis study focuses on enhancing heat and mass transfer in an electronic cooling system such as a rectangular cavity that contains equidistant heated chips along the bottom wall. The cavity of the present study is filled with ethylene glycol (30:70) based hybrid nano coolants of different volume fractions (ϕ) of Multi-walled Carbon Nanotube (MWCNT), Aluminum Oxide (Al2O3), and Copper Oxide (CuO). The set of equations controlling the thermo-solutal natural convection within the enclosure is simulated using the Galerkin weighted residual finite element method (FEM). The study has shown a good agreement of numerical results with different experimental reports within the framework of the present study. A solution space is constructed based on governing parameters such as Rayleigh number, buoyancy ratio, and Lewis number. A hybrid nano-coolant containing ϕMWCNT = 1.5 %, ϕCuO = 0.5 %, and ϕAl2O3 = 2 % showed a 3.11% improvement in heat transfer rate compared to the base fluid, highlighting its potential for thermal management applications. This study also investigates various machine learning models for predicting the heat and mass transfer rate, and an error analysis is conducted on the K-Nearest Neighbour Regressor, Random Forest Regressor, Decision Tree Regressor, and ANN model. The ANN model with 6-50-100-50-2 architecture showcases the best fit with the mean squared error of 0.8923 and an R² value of 99.96 % on testing data. The ANN model exhibits its capability to predict heat transfer and mass transfer rates within the error ranges from 1–2 % and 2–3 %, respectively, even at a strong thermal buoyancy force (Ra = 10⁵). This accuracy showcases a novel use of ANN to efficiently predict thermo-fluidic transport behaviors of hybrid nanofluids, offering a faster alternative to resource-intensive simulations. The present study opens new possibilities for real-time, cost-effective cooling solutions, particularly in microelectronics and renewable energy, while maintaining high prediction accuracy by integrating machine learning with the nanotechnology approach.http://www.sciencedirect.com/science/article/pii/S266620272400363XHeated chipsThermo-solutal convectionArtificial neural network (ANN)Hybrid nano-coolantRegressor model |
| spellingShingle | Tawsif Mahmud Jiaul Haque Saboj Preetom Nag Goutam Saha Bijan K. Saha Artificial neural network (ANN) approach in predicting the thermo-solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolant International Journal of Thermofluids Heated chips Thermo-solutal convection Artificial neural network (ANN) Hybrid nano-coolant Regressor model |
| title | Artificial neural network (ANN) approach in predicting the thermo-solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolant |
| title_full | Artificial neural network (ANN) approach in predicting the thermo-solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolant |
| title_fullStr | Artificial neural network (ANN) approach in predicting the thermo-solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolant |
| title_full_unstemmed | Artificial neural network (ANN) approach in predicting the thermo-solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolant |
| title_short | Artificial neural network (ANN) approach in predicting the thermo-solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolant |
| title_sort | artificial neural network ann approach in predicting the thermo solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolant |
| topic | Heated chips Thermo-solutal convection Artificial neural network (ANN) Hybrid nano-coolant Regressor model |
| url | http://www.sciencedirect.com/science/article/pii/S266620272400363X |
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