Develop artificial neural network numerical modeling to study fluid flow and heat transfer of dispersed nanoparticles through base liquid

Abstract

Purpose

This paper aims to disperse the silicon dioxide in water (as the mono nanofluid [MN]) and then, carbon nanotube (CNT)-silica composite in water (as the hybrid nanofluid [HN]).

Design/methodology/approach

Nanofluids have gained lots of attention through the recent years. Due to their usage in the industries and also medical applications, they have high protentional to be studied in different aspects. The most common study for the nanofluids is to understand the heat transfer capacity for each material in each fluid. These material(s) or fluid(s) can be one (mono nanofluid) or more than one (hybrid nanofluid).

Findings

The mixture of two solids is to assess the unique properties of each material and also to decrease the cost of experiments. The heat transfers for both MN and HN were measured at volume fractions up to 1.0%, and temperatures up to 50°C. Also, the heat transfers were compared. By more CNT, thermal conductivity was enhanced about 17.39% (from 12.42% of MN to 29.81% of HN).

Originality/value

X-Ray diffraction and field emission scanning electron microscope (FESEM) were examined for mono solids and the composite. After the experimental study, for MN and HN, four novel correlations calculated.

Citation

Alkanhal, T.A. (2021), "Develop artificial neural network numerical modeling to study fluid flow and heat transfer of dispersed nanoparticles through base liquid", International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 31 No. 8, pp. 2733-2753. https://doi.org/10.1108/HFF-11-2020-0697