Artificial neural network modeling for predicting thermal conductivity of EG/water-based CNC nanofluid for engine cooling using different activation functions
A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature range. In most radiators that are used to cool an engine, water serves as a cooling fluid. Performance of a radiator in terms of heat transmission is significantly influenced by the incor...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Tech Science Press
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/40574/1/Artificial%20neural%20network%20modeling%20for%20predicting%20thermal%20conductivity.pdf |
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| author | Hasan, Md. Munirul Rahman, Md Mustafizur Suraya, Abu Bakar Islam, Mohammad Saiful Wong, Hung Chan Alginahi, Yasser M. Kabir, M. N. Devarajan, R. |
| author_facet | Hasan, Md. Munirul Rahman, Md Mustafizur Suraya, Abu Bakar Islam, Mohammad Saiful Wong, Hung Chan Alginahi, Yasser M. Kabir, M. N. Devarajan, R. |
| author_sort | Hasan, Md. Munirul |
| collection | UMP |
| description | A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature range. In most radiators that are used to cool an engine, water serves as a cooling fluid. Performance of a radiator in terms of heat transmission is significantly influenced by the incorporation of nanoparticles into the cooling water. Concentration and uniformity of nanoparticle distribution are the two major factors for practical use of nanofluids. The shape and the size of nanoparticles also have a great impact on the performance of heat transfer. Many researchers are investigating the impact of nanoparticles on heat transfer. This study aims to develop an artificial neural network (ANN) model for predicting the thermal conductivity of an ethylene glycol (EG)/water-based crystalline nanocellulose (CNC) nanofluid for cooling internal combustion engine. The implementation of an artificial neural network considering different activation functions in the hidden layer is made to find the best model for the cooling of an engine using the nanofluid. Accuracies of the model with different activation functions in artificial neural network are analyzed for different nanofluid’s concentrations and temperatures. In artificial neural network, Levenberg–Marquardt is an optimization approach used with activation functions including Tansig and Logsig functions in the training phase. The findings of each training, testing, and validation phase are presented to demonstrate the network that provides the highest level of accuracy. The best result was obtained with Tansig, which has a correlation of 0.99903 and an error of 3.7959 ×10-8. It has also been noticed that Logsig function can also be a good model due to its correlation of 0.99890 and an error of 4.9218 ×10-8. Thus our ANN with Tansig and Logsig functions demonstrates a high correlation between the actual output and the predicted output. |
| first_indexed | 2024-09-25T03:48:10Z |
| format | Article |
| id | UMPir40574 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-09-25T03:48:10Z |
| publishDate | 2024 |
| publisher | Tech Science Press |
| record_format | dspace |
| spelling | UMPir405742024-07-19T04:15:35Z http://umpir.ump.edu.my/id/eprint/40574/ Artificial neural network modeling for predicting thermal conductivity of EG/water-based CNC nanofluid for engine cooling using different activation functions Hasan, Md. Munirul Rahman, Md Mustafizur Suraya, Abu Bakar Islam, Mohammad Saiful Wong, Hung Chan Alginahi, Yasser M. Kabir, M. N. Devarajan, R. QA75 Electronic computers. Computer science A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature range. In most radiators that are used to cool an engine, water serves as a cooling fluid. Performance of a radiator in terms of heat transmission is significantly influenced by the incorporation of nanoparticles into the cooling water. Concentration and uniformity of nanoparticle distribution are the two major factors for practical use of nanofluids. The shape and the size of nanoparticles also have a great impact on the performance of heat transfer. Many researchers are investigating the impact of nanoparticles on heat transfer. This study aims to develop an artificial neural network (ANN) model for predicting the thermal conductivity of an ethylene glycol (EG)/water-based crystalline nanocellulose (CNC) nanofluid for cooling internal combustion engine. The implementation of an artificial neural network considering different activation functions in the hidden layer is made to find the best model for the cooling of an engine using the nanofluid. Accuracies of the model with different activation functions in artificial neural network are analyzed for different nanofluid’s concentrations and temperatures. In artificial neural network, Levenberg–Marquardt is an optimization approach used with activation functions including Tansig and Logsig functions in the training phase. The findings of each training, testing, and validation phase are presented to demonstrate the network that provides the highest level of accuracy. The best result was obtained with Tansig, which has a correlation of 0.99903 and an error of 3.7959 ×10-8. It has also been noticed that Logsig function can also be a good model due to its correlation of 0.99890 and an error of 4.9218 ×10-8. Thus our ANN with Tansig and Logsig functions demonstrates a high correlation between the actual output and the predicted output. Tech Science Press 2024 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/40574/1/Artificial%20neural%20network%20modeling%20for%20predicting%20thermal%20conductivity.pdf Hasan, Md. Munirul and Rahman, Md Mustafizur and Suraya, Abu Bakar and Islam, Mohammad Saiful and Wong, Hung Chan and Alginahi, Yasser M. and Kabir, M. N. and Devarajan, R. (2024) Artificial neural network modeling for predicting thermal conductivity of EG/water-based CNC nanofluid for engine cooling using different activation functions. Frontiers in Heat and Mass Transfer (FHMT), 22 (2). pp. 1-21. ISSN 2151-8629. (Published) https://doi.org/10.32604/fhmt.2024.047428 https://doi.org/10.32604/fhmt.2024.047428 |
| spellingShingle | QA75 Electronic computers. Computer science Hasan, Md. Munirul Rahman, Md Mustafizur Suraya, Abu Bakar Islam, Mohammad Saiful Wong, Hung Chan Alginahi, Yasser M. Kabir, M. N. Devarajan, R. Artificial neural network modeling for predicting thermal conductivity of EG/water-based CNC nanofluid for engine cooling using different activation functions |
| title | Artificial neural network modeling for predicting thermal conductivity of EG/water-based CNC nanofluid for engine cooling using different activation functions |
| title_full | Artificial neural network modeling for predicting thermal conductivity of EG/water-based CNC nanofluid for engine cooling using different activation functions |
| title_fullStr | Artificial neural network modeling for predicting thermal conductivity of EG/water-based CNC nanofluid for engine cooling using different activation functions |
| title_full_unstemmed | Artificial neural network modeling for predicting thermal conductivity of EG/water-based CNC nanofluid for engine cooling using different activation functions |
| title_short | Artificial neural network modeling for predicting thermal conductivity of EG/water-based CNC nanofluid for engine cooling using different activation functions |
| title_sort | artificial neural network modeling for predicting thermal conductivity of eg water based cnc nanofluid for engine cooling using different activation functions |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/40574/1/Artificial%20neural%20network%20modeling%20for%20predicting%20thermal%20conductivity.pdf |
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