Improving thermal conductivity of a ferrofluid-based nanofluid using Fe₃O₄- challenging of RSM and ANN methodologies

Abstract

The thermal conductivity of Fe₃O₄/water nanofluid was forecasted using two methods of artificial neural network (ANN) along with response surface method (RSM). For ANN methods, the optimal neurons number and for RSM, the usefulness of several predicting function was specified using R-square criteria, and margin of deviation (MOD). It was found that $R^2$ for ANN was 0.999 while for RSM, this figure was 0.998. The mean square error for the former and latter methods was 0.00038 and 0.0013, respectively. Taking into account 0.964% and 1.895% for ANN and RSM, it was concluded that ANN efficacy was superior to RSM. Moreover, ANN was able to predict all points with a MOD below 1%, while 70% of data points in the RSM technique have a MOD of less than 1%.

Keywords:

• Artificial neural network
• ferrofluid-based nanofluid
• heat transfer
• nanoparticles
• response surface methodology
• thermal conductivity

Acknowledgment

The authors, therefore, acknowledge with thanks DSR technical and financial support.

Additional information

Funding

This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant no. (RG-11-135-40).