Statistical modeling and investigation of thermal characteristics of a new nanofluid containing cerium oxide powder

Behrooz Ruhani; Mansour Taheri Andani; Azher M Abed; Nima Sina; Ghassan Fadhil Smaisim; Salema K Hadrawi; Davood Toghraie

doi:10.1016/j.heliyon.2022.e11373

Statistical modeling and investigation of thermal characteristics of a new nanofluid containing cerium oxide powder

Heliyon. 2022 Nov 2;8(11):e11373. doi: 10.1016/j.heliyon.2022.e11373. eCollection 2022 Nov.

Authors

Behrooz Ruhani¹, Mansour Taheri Andani², Azher M Abed³, Nima Sina⁴, Ghassan Fadhil Smaisim^{5

6}, Salema K Hadrawi^{7

8}, Davood Toghraie⁹

Affiliations

¹ Solar Energy Naqsh-e Jahan Company, Chahar Bagh St, Isfahan, Iran.
² Department of Material Science Engineering, Isfahan University of Technology, Isfahan, Iran.
³ Air Conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University College, Babylon 51001, Iraq.
⁴ Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
⁵ Department of Mechanical Engineering, Faculty of Engineering, University of Kufa, Iraq.
⁶ Nanotechnology and Advanced Materials Research Unit (NAMRU), Faculty of Engineering, University of Kufa, Iraq.
⁷ Refrigeration and Air-conditioning Technical Engineering Department, College of Technical Engineering, The Islamic University, Najaf, Iraq.
⁸ Computer Engineering Department, Imam Reza University, Mashhad, Iran.
⁹ Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Iran.

Abstract

In this paper, the thermal conductivity (k_nf) of cerium oxide/ethylene glycol nanofluid is extracted for different temperatures (T = 25, 30, 35, 40, 45, and 50 °C) and the volume fraction of nanoparticles ( $φ =$ 0, 0.25, 0.5, 0.75, 1, 1.5, 2 and 2.5%) and then k_nf is predicted by two methods including Artificial Neural Network (ANN) and fitting method. For both methods, the results have been presented and compared. The experiments showed that with increasing $φ$ and temperature, the thermal conductivity ratio (TCR) of nanofluid increases. It was also observed that when the experiments are performed at high temperatures, the rate of increase in k_nf is much higher than the change in the same amount of $φ$ change at low temperatures. An ANN with 7 neurons has a correlation coefficient very close to 1 and this proves that the outputs are compatible with experimental results. Also, it can be seen that the ANN could predict the thermal behavior of cerium oxide/ethylene glycol nanofluid more accurately.

Keywords: Artificial Neural Network (ANN); Cerium oxide; Ethylene glycol; Nanofluid; Thermal conductivity.