Maqola

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

Asˈad AlizadehDepartment of Civil Engineering, College of Engineering, Cihan University-Erbil, Erbil, IraqKhidhair Jasim MohammedAir Conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University College, Babylon. IraqGhassan Fadhil SmaisimDepartment of Mechanical Engineering, Faculty of Engineering, University of Kufa, IraqSalema K. HadrawiComputer Engineering Department, Imam Reza University, Mashhad, IranHussein ZekriCollege of Engineering, The American University of Kurdistan, Duhok, Kurdistan Region, IraqHamid Taheri AndaniDepartment of Electronic Engineering, Isfahan University of Technology, Isfahan, IranNavid Nasajpour-EsfahaniDepartment of Material Science and Engineering, Georgia Institute of Technology, Atlanta 30332, USADavood ToghraieDepartment of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Iran

2023en

ABI

Annotatsiya

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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Identifikatorlar

DOI: 10.1016/j.jscs.2023.101613

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