Evaluation of the effects of the presence of ZnO -TiO2 (50 %–50 %) on the thermal conductivity of Ethylene Glycol base fluid and its estimation using Artificial Neural Network for industrial and commercial applications |
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| Institution: | 1. Department of Civil Engineering, College of Engineering, Cihan University-Erbil, Erbil, Iraq;2. Air Conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University College, Babylon. Iraq;3. Department of Mechanical Engineering, Faculty of Engineering, University of Kufa, Iraq;4. Nanotechnology and Advanced Materials Research Unit (NAMRU), Faculty of Engineering, University of Kufa, Iraq;5. Refrigeration and Air-conditioning Technical Engineering Department, College of Technical Engineering, The Islamic University, Najaf, Iraq;6. Computer Engineering Department, Imam Reza University, Mashhad, Iran;7. College of Engineering, The American University of Kurdistan, Duhok, Kurdistan Region, Iraq;8. Department of Mechanical Engineering, College of Engineering, University of Zakho, Zakho, Kurdistan Region, Iraq;9. Department of Electronic Engineering, Isfahan University of Technology, Isfahan, Iran;10. Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Iran;11. Department of Material Science and Engineering, Georgia Institute of Technology, Atlanta 30332, USA |
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| Abstract: | In this study, the thermal conductivity (knf) of ZnO -TiO2 (50 %–50 %)/ Ethylene Glycol hybrid nanofluid using Artificial Neural Networks (ANNs) was predicted. The nanofluid was prepared at different volume fractions (φ) of nanoparticles (φ = 0.001 to 0.035) and temperatures (T = 25 to 50 °C). In this study, an algorithm is presented to find the best neuron number in the hidden layer. Also, a surface fitting method has been applied to predict the knf of nanofluid. Finally, the correlation coefficients, performances, and Maximum Absolute Error (MAE) for both methods have been presented and compared. It could be understood that the ANN method had a better ability in predicting the knf of nanofluid compared to the fitting method. This method not only showed better performance but also reached a better MAE and correlation coefficient. |
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| Keywords: | ZnO Nanofluid Thermal conductivity Artificial Neural Networks |
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