期刊
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
卷 127, 期 -, 页码 1169-1179出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijheatmasstransfer.2018.08.112
关键词
Enhanced artificial neural network; Thermo-physical properties; Electrical conductivity; Nanocomposites
The new nanocomposite material of CuFe2O4 (copper ferrite) nanoparticles coated by SiO2 is synthesized. Then, this newly generated nanocomposite is dispersed in water/ethylene glycol (60:40) to make a new homogeneous nanofluid in order to avoid settling and agglomeration. Through suitable accurate experiments, density, viscosity and electrical conductivity of the mixture are measured at various temperatures and nanoparticles concentrations. Besides we empirical correlations for the same parameters developed via the curve fitting method. To have a better statistical view, the optimization procedure based on the enhanced artificial neural network (EANN), developed at present study, is performed. Furthermore, according to the obtained empirical results, the sensitivity analysis is provided and the margin of deviations is represented for each proposed correlation. Generation, stabilization and measuring the density, viscosity and electrical conductivity of the newly mentioned nanofluid, make present work different from the previous ones in this field. The highest amount of relative electrical conductivity is observed at T = 75 degrees C and phi = 0.02 (g/mL); however, the case of T = 30 degrees C and phi = 0.02 (g/mL) represents the maximum value of relative viscosity. Moreover, density is decreased by temperature augmentation, through all cases. (C) 2018 Elsevier Ltd. All rights reserved.
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