Modeling the thermal conductivity ratio of an antifreeze-based hybrid nanofluid containing graphene oxide and copper oxide for using in thermal systems

According to laboratory data, the thermal conductivity ratio of an antifreeze-based hybrid nanofluid containing graphene oxide (GO) and Copper oxide (CuO) was modeled using mathematical methods, one is based on an artificial brain structure model, and the other is based on curve-fitting method. A two-variable empirical based correlation (R-2 = 0.996) as a function of temperature and volume fraction suggested from the curve-fitting method. In the brain structure-based section, an artificial neural network employed by applying temperature and concentration as input variables and thermal conductivity ratio as the desired output. The correlation coefficient (R) values of designed ANN are 0.999963, 0.999409, and 0.999103 for train, validation and test, respectively. Mean squared errors (MSE) values of designed ANN are 1.01743e-6, 5.01019e-5, and 2.90237e-5 for train, validation and test, respectively. The findings indicated that the artificial neural network and the proposed correlation can predict the thermal conductivity ratio of GO-CuO (50:50%)/EGWater (50:50%) hybrid nanofluid with high accuracy. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

The study utilized mathematical methods to model the thermal conductivity ratio of a hybrid nanofluid containing graphene oxide and Copper oxide. Two approaches were used: one based on an artificial neural network structure, and the other based on curve-fitting method. Both methods showed high accuracy in predicting the thermal conductivity ratio of the nanofluid.