Khetib, Yacine and Alahmadi, Ahmad and Alzaed, Ali and Sajadi, S. Mohammad (2021) Using neural network and RSM to evaluate improvement in thermal conductivity of nanodiamond-iron oxide/antifreeze. Chemical Engineering Communications, 210. pp. 596-606.
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Abstract
This study aimed to investigate the accuracy of artificial neural networks (ANN) in estimating the thermal conductivity (k) of ferrofluid-based nanofluids. The thermal conductivities of kND+Fe2O3/EG-water and kEG-water were measured at temperatures ranging from 20 to 60°C and at volume fractions of 0.05, 0.1, and 0.2%. Results showed that kFe2O3/EG-water was greater than kEG-water by 89%, achieved at 60°C and 0.2% volume fraction.To estimate kND+Fe3O4/EG-water, a three-layer ANN was developed with two, three, and one neuron in its respective layers. This neural network achieved an estimation error of less than 0.8%, yielding a coefficient of determination (R²) of 0.996. Additionally, response surface methodology (RSM) was employed, revealing that cubic polynomial models could predict kND+Fe3O4/EG-water with an error of less than 0.5% and an R² of 0.994
Item Type: | Article |
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Uncontrolled Keywords: | ANN, ferrofluid, nanofluid, neuron, RSM. |
Subjects: | Q Science > Q Science (General) T Technology > T Technology (General) |
Divisions: | Department of Communication and Computer Engineering > Research papers |
Depositing User: | ePrints Depositor |
Date Deposited: | 08 Oct 2024 10:29 |
Last Modified: | 08 Oct 2024 10:29 |
URI: | https://eprints.cihanuniversity.edu.iq/id/eprint/1620 |