Statistical analysis of enriched water heat transfer with various sizes of MgO nanoparticles using artificial neural networks modeling

Ali Akbar Abbasian Arani, Ali Alirezaie, Mohammad Hassan Kamyab and Sayyid Majid Motallebi

Physica A: Statistical Mechanics and its Applications, 2020, vol. 554, issue C

Abstract: In the present research, experimental data of the heat transfer coefficient for MgO aqueous nanofluids are modeled by the MLP artificial neural networks (ANN), for 4 types of nanoparticles with diameters of 20, 40, 50 and 60 nm, at 4 solid volume fractions of 0.5%, 1%, 1.5% and 2%, and at 11 Reynolds numbers from 3000 to 25,000. Modeling and predicting of the data like the empirical data, is proved the well capability of the ANN in modeling the data related to nanofluids’ heat transfer coefficient. Another interesting point is that, the heat transfer coefficient increases by decline of the nanoparticles’ diameter, and there is a direct relationship between the rise of solid volume fraction and the heat transfer coefficient. The increment rate of heat transfer coefficient, remained unchanged by increasing the Reynolds number, and increased with the rise of solid volume fraction. Present investigation showed that, ANN is able to save all the rules hidden in these changes with a high accuracy and take the advantage of them to predict the other data. In addition, a correlation in terms of variables affecting the heat transfer coefficient is obtained and presented in the article.

Keywords: Heat Transfer; Convection; Nanofluid; Artificial Neural Network (ANN); MgO nanoparticle (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:554:y:2020:i:c:s0378437119321909

DOI: 10.1016/j.physa.2019.123950

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