ANN modeling, cost performance and sensitivity analyzing of thermal conductivity of DWCNT–SiO2/EG hybrid nanofluid for higher heat transfer

In this study, the thermal conductivity of SiO2–DWCNT/ethylene glycol hybrid nanofluid has been experimentally investigated on 0.03–1.71% solid volume fraction and temperatures from 30 to 50 °C. SiO2 and DWCNT’s nanoparticles dispersed in EG as base fluid, and its thermal conductivity was measured....

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Bibliographic Details
Published in:Journal of thermal analysis and calorimetry 2018-01, Vol.131 (3), p.2381-2393
Main Authors: Esfe, Mohammad Hemmat, Ali Akbar Abbasian Arani, Rasool Shafiei Badi, Mousa Rejvani
Format: Article
Language:English
Subjects:
Artificial neural networks
Concentration (composition)
Cost analysis
Ethylene glycol
Heat conductivity
Heat transfer
Nanofluids
Neural networks
Sensitivity analysis
Silicon dioxide
Thermal conductivity
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Summary:In this study, the thermal conductivity of SiO2–DWCNT/ethylene glycol hybrid nanofluid has been experimentally investigated on 0.03–1.71% solid volume fraction and temperatures from 30 to 50 °C. SiO2 and DWCNT’s nanoparticles dispersed in EG as base fluid, and its thermal conductivity was measured. The thermal conductivity was obtained 38% more than ethylene glycol thermal conductivity at some temperatures. A new correlation (R2 = 0.9925) was proposed to predict experimental thermal conductivity ratio as a function of volume concentration and temperature. Also an artificial neural network was designed for thermal conductivity ratio data predicting. The best artificial neural network topology has two hidden layers with five neurons in each layer. Comparing the experimental thermal conductivity ratio with artificial neural network outputs and the correlation shows the high capacity and accuracy of artificial neural network in thermal conductivity ratio data predicting.
ISSN:1388-6150
1588-2926
DOI:10.1007/s10973-017-6744-z