Mathematical and artificial brain structure-based modeling of heat conductivity of water based nanofluid enriched by double wall carbon nanotubes

Mohammad Hemmat Esfe and Masoud Afrand

Physica A: Statistical Mechanics and its Applications, 2020, vol. 540, issue C

Abstract: Mathematical modeling based on response surface method and also an artificial brain structure-based model was applied to predict thermal properties of a water-based nanofluid earned by a set of laboratory results. Functionalized DWCNTs were used to enrich the conventional fluid (Water in the present study). Using response surface method a two-variable experimental based correlation proposed as a function of concentration and temperature. Also, artificial neural network employed as a brain structure-based approach with two inputs (temperature and concentrations) and thermal conductivity ratio as the desired output. Using mathematical and artificial based methods help to improve the economic efficiency of experimental studies.

Keywords: Artificial neural network; DWCNTs; Empirical correlation; Thermal conductivity; Nanofluid (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:540:y:2020:i:c:s0378437119303668

DOI: 10.1016/j.physa.2019.04.002

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