Affiliation:
1. School of Mechanical Engineering University of Leeds Leeds UK
Abstract
AbstractThis 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.
Funder
Tertiary Education Trust Fund
Subject
Fluid Flow and Transfer Processes,Condensed Matter Physics
Cited by
2 articles.
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