Artificial Intelligent Model to Enhance Thermal Conductivity of TiO<sub>2</sub>-Al<sub>2</sub>O<sub>3</sub>/Water-Ethylene Glycol-Based Hybrid Nanofluid for Automotive Radiator
Vehicular cooling system is one of the priorities for the automobile industry, aiming to achieve sustainability and energy efficiency. Currently, coolants are being utilized in cooling systems that exhibit super heat transfer capabilities. Hybrid nanofluids as a coolant is offer enhanced heat transf...
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2024-01-01
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| author | Md. Munirul Hasan Md. Mustafizur Rahman Md. Arafatur Rahman Suraya Abu Bakar Mohammad Saiful Islam Tarek Khalifa |
| author_facet | Md. Munirul Hasan Md. Mustafizur Rahman Md. Arafatur Rahman Suraya Abu Bakar Mohammad Saiful Islam Tarek Khalifa |
| author_sort | Md. Munirul Hasan |
| collection | DOAJ |
| description | Vehicular cooling system is one of the priorities for the automobile industry, aiming to achieve sustainability and energy efficiency. Currently, coolants are being utilized in cooling systems that exhibit super heat transfer capabilities. Hybrid nanofluids as a coolant is offer enhanced heat transfer rate and improved efficiency and eco-friendliness of vehicle engine cooling systems. This study aims to analyze the conductivity of a hybrid nanofluid consisting of distilled water and ethylene glycol (in a ratio of 40 and 60) with Al2O3 and TiO2 particles to evaluate its suitability as a coolant for vehicle engines using intelligent techniques. The volume concentration and the temperature varied from 0.02%-0.1% and <inline-formula> <tex-math notation="LaTeX">$30~^{\circ }$ </tex-math></inline-formula> C-<inline-formula> <tex-math notation="LaTeX">$80~^{\circ }$ </tex-math></inline-formula> C, respectively. The experimental findings led us to develop an artificial neural network (ANN) model. This model consists of a layer containing 9 neurons designed to estimate thermal conductivity. ANN model was constructed using input parameters such as volume concentration and temperature, with the output being the conductivity. Furthermore, apart from utilizing the ANN, we employed techniques like support vector machine (SVM) and curve fitting (CF) approaches to analyze the experimental data. This allowed us to calculate values such as the correlation coefficient (R) and mean square error (MSE). The increase in thermal conductivity reached a maximum of 40.86% when the temperature was <inline-formula> <tex-math notation="LaTeX">$80~^{\circ }$ </tex-math></inline-formula> C, and the volume concentration was 0.1%. The results obtained indicate that the suggested ANN model aligns closely with the experimental data. Based on the assessment of the highest R-value and lowest MSE, this analysis demonstrates performance, with an R-value of 0.9998 and an MSE of <inline-formula> <tex-math notation="LaTeX">$3.87415\times 10^{-06}$ </tex-math></inline-formula>. The training and testing phases exhibit remarkable performances with values of <inline-formula> <tex-math notation="LaTeX">$4.86256\times 10^{-07}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$2.540599\times 10^{-06}$ </tex-math></inline-formula>, respectively. Moreover, when comparing the SVM and CF approaches, it was found that ANN modelling provided a level of accuracy in predicting the enhancement of conductivity in the hybrid nanofluid. These results demonstrate that the ANN can accurately predict thermal conductivity. |
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| id | doaj-art-6c292cd82fe847128e1cfe6835cfa705 |
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| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
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| spelling | doaj-art-6c292cd82fe847128e1cfe6835cfa7052025-08-20T01:54:38ZengIEEEIEEE Access2169-35362024-01-011217916417918910.1109/ACCESS.2024.349678610750796Artificial Intelligent Model to Enhance Thermal Conductivity of TiO<sub>2</sub>-Al<sub>2</sub>O<sub>3</sub>/Water-Ethylene Glycol-Based Hybrid Nanofluid for Automotive RadiatorMd. Munirul Hasan0https://orcid.org/0000-0003-0406-037XMd. Mustafizur Rahman1https://orcid.org/0000-0002-4424-8345Md. Arafatur Rahman2https://orcid.org/0000-0002-8221-6168Suraya Abu Bakar3https://orcid.org/0000-0002-1116-6570Mohammad Saiful Islam4https://orcid.org/0009-0002-5589-514XTarek Khalifa5https://orcid.org/0000-0002-0467-805XFaculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, MalaysiaFaculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, MalaysiaSchool of Mathematics and Computer Science, University of Wolverhampton, Wolverhampton, U.K.Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, MalaysiaDepartment of Management and Information Technology, St. Francis College, Brooklyn, NY, USACollege of Engineering and Technology, American University of the Middle East, Egaila, KuwaitVehicular cooling system is one of the priorities for the automobile industry, aiming to achieve sustainability and energy efficiency. Currently, coolants are being utilized in cooling systems that exhibit super heat transfer capabilities. Hybrid nanofluids as a coolant is offer enhanced heat transfer rate and improved efficiency and eco-friendliness of vehicle engine cooling systems. This study aims to analyze the conductivity of a hybrid nanofluid consisting of distilled water and ethylene glycol (in a ratio of 40 and 60) with Al2O3 and TiO2 particles to evaluate its suitability as a coolant for vehicle engines using intelligent techniques. The volume concentration and the temperature varied from 0.02%-0.1% and <inline-formula> <tex-math notation="LaTeX">$30~^{\circ }$ </tex-math></inline-formula> C-<inline-formula> <tex-math notation="LaTeX">$80~^{\circ }$ </tex-math></inline-formula> C, respectively. The experimental findings led us to develop an artificial neural network (ANN) model. This model consists of a layer containing 9 neurons designed to estimate thermal conductivity. ANN model was constructed using input parameters such as volume concentration and temperature, with the output being the conductivity. Furthermore, apart from utilizing the ANN, we employed techniques like support vector machine (SVM) and curve fitting (CF) approaches to analyze the experimental data. This allowed us to calculate values such as the correlation coefficient (R) and mean square error (MSE). The increase in thermal conductivity reached a maximum of 40.86% when the temperature was <inline-formula> <tex-math notation="LaTeX">$80~^{\circ }$ </tex-math></inline-formula> C, and the volume concentration was 0.1%. The results obtained indicate that the suggested ANN model aligns closely with the experimental data. Based on the assessment of the highest R-value and lowest MSE, this analysis demonstrates performance, with an R-value of 0.9998 and an MSE of <inline-formula> <tex-math notation="LaTeX">$3.87415\times 10^{-06}$ </tex-math></inline-formula>. The training and testing phases exhibit remarkable performances with values of <inline-formula> <tex-math notation="LaTeX">$4.86256\times 10^{-07}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$2.540599\times 10^{-06}$ </tex-math></inline-formula>, respectively. Moreover, when comparing the SVM and CF approaches, it was found that ANN modelling provided a level of accuracy in predicting the enhancement of conductivity in the hybrid nanofluid. These results demonstrate that the ANN can accurately predict thermal conductivity.https://ieeexplore.ieee.org/document/10750796/Thermal conductivityhybrid nanofluidsartificial neural networksupport vector machinecurve fitting |
| spellingShingle | Md. Munirul Hasan Md. Mustafizur Rahman Md. Arafatur Rahman Suraya Abu Bakar Mohammad Saiful Islam Tarek Khalifa Artificial Intelligent Model to Enhance Thermal Conductivity of TiO<sub>2</sub>-Al<sub>2</sub>O<sub>3</sub>/Water-Ethylene Glycol-Based Hybrid Nanofluid for Automotive Radiator IEEE Access Thermal conductivity hybrid nanofluids artificial neural network support vector machine curve fitting |
| title | Artificial Intelligent Model to Enhance Thermal Conductivity of TiO<sub>2</sub>-Al<sub>2</sub>O<sub>3</sub>/Water-Ethylene Glycol-Based Hybrid Nanofluid for Automotive Radiator |
| title_full | Artificial Intelligent Model to Enhance Thermal Conductivity of TiO<sub>2</sub>-Al<sub>2</sub>O<sub>3</sub>/Water-Ethylene Glycol-Based Hybrid Nanofluid for Automotive Radiator |
| title_fullStr | Artificial Intelligent Model to Enhance Thermal Conductivity of TiO<sub>2</sub>-Al<sub>2</sub>O<sub>3</sub>/Water-Ethylene Glycol-Based Hybrid Nanofluid for Automotive Radiator |
| title_full_unstemmed | Artificial Intelligent Model to Enhance Thermal Conductivity of TiO<sub>2</sub>-Al<sub>2</sub>O<sub>3</sub>/Water-Ethylene Glycol-Based Hybrid Nanofluid for Automotive Radiator |
| title_short | Artificial Intelligent Model to Enhance Thermal Conductivity of TiO<sub>2</sub>-Al<sub>2</sub>O<sub>3</sub>/Water-Ethylene Glycol-Based Hybrid Nanofluid for Automotive Radiator |
| title_sort | artificial intelligent model to enhance thermal conductivity of tio sub 2 sub al sub 2 sub o sub 3 sub water ethylene glycol based hybrid nanofluid for automotive radiator |
| topic | Thermal conductivity hybrid nanofluids artificial neural network support vector machine curve fitting |
| url | https://ieeexplore.ieee.org/document/10750796/ |
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