Cascade forward Artificial Neural Network to estimate thermal conductivity of functionalized graphene-water nanofluids

In the present study, estimation and prediction of thermal conductivity (k(nf)) of functionalized Graphene were prepared by the alkaline method in water has been conducted using experimental data using Artificial Neural Network (ANN). k(nf) of four types of functionalized Graphene-water nanofluid has been modeled in 5 different temperatures ranging from 10 to 50 degrees C as the input of ANN. The finding shows that the Relative Thermal Conductivity (RTC) of nanofluids in sample 1 has a little decrease with a reduction in temperature, while the other samples had an increase in RTC with an increase in temperature. Also, after training the network and testing the data associated with network testing, the difference between experimental data and the values obtained from modeling (outputs) is obtained. The results show the acceptable precision of modeling and confirm its results.

Automated summary

In this study, thermal conductivity of functionalized Graphene-water nanofluids was estimated and predicted using experimental data with the help of Artificial Neural Network (ANN). The results showed acceptable precision of the modeling.