Single phase nanofluid thermal conductivity and viscosity prediction using neural networks and its application in a heated pipe of a circular cross section

Abstract

This study investigates the single‐phase simulation of nanofluid with a neural network incorporated into the thermophysical properties in governing equations for the single‐phase treatment. The thermophysical properties affected are the viscosity, and the thermal conductivity, as both properties have been the area of contention in the study of nanofluid. The neural network is trained from experimental data gleaned from the available literature. The single phase and neural network are set up and solved using the finite element method in available commercial code. Grid independence was carried out, and the results were validated with experimental data that the neural networks were not trained with. It was observed that the lowest accuracy from the several simulations was 0.679% average percentage error. The results obtained agreed that nanofluids' thermal conductivity and viscosity can be accurately modeled for most single‐material nanofluids and hence reducing the error in the simulations of nanofluids using the single‐phase model which assumes the nanofluids are homogeneous and their properties are enhanced and effective.

Modelling Nanofluid Viscosity: Comparing Models and Optimizing Feature Selection - A Novel Approach

“…Predicting viscosity in nano uids plays a crucial role in understanding their ow behaviour and optimising their applications in various industries [1][2][3][4][5][6][7][8]. Nano uids, suspensions of nanoparticles in base uids, exhibit unique rheological properties that differ from those of conventional uids [4].…”