Journal
CASE STUDIES IN THERMAL ENGINEERING
Volume 26, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.csite.2021.101194
Keywords
Nanofluid; Thermal conductivity; ANN; Functionalized graphene
Categories
Ask authors/readers for more resources
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.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available