Alizadeh, As'ad and Jasim Mohammed, Khidhair and Fadhil Smaisim, Ghassan and Hadrawi, Salema K. and Zekri, Hussein and Taheri Andani, Hamid and Nasajpour-Esfahani, Navid and Toghraie, Davood (2023) 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. Journal of Saudi Chemical Society, 27 (2). pp. 1-10. ISSN 13196103
![[thumbnail of Research Article]](https://eprints.cihanuniversity.edu.iq/style/images/fileicons/text.png)
Article_JSCS_26-01-2023pdf.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Download (2MB)
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.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | ZnO; TiO2; Thermal conductivity; Artificial neural network (ANN); ZnO-TiO2 composite |
Subjects: | Q Science > QA Mathematics > QA76 Computer software T Technology > TA Engineering (General). Civil engineering (General) |
Divisions: | Department of Civil Engineering > Research papers |
Depositing User: | ePrints Depositor |
Date Deposited: | 30 Oct 2024 21:43 |
Last Modified: | 30 Oct 2024 21:43 |
URI: | https://eprints.cihanuniversity.edu.iq/id/eprint/2201 |