Application of Neural Network for estimation of heat transfer treatment of Al2O3-H2O nanofluid through a channel

By Mohsen Sheikholeslami, M. Barzegar Gerdroodbary, R. Moradi, Ahmad Shafee, Zhixiong Li. Computer Methods in Applied Mechanics and Engineering, 2019

268 citations recorded by the source corpus.

Abstract Heat transfer studying in channels is crucial for transport of the fluids in the oil and gas industry. In this study, numerical approach is applied to analyze the thermal behavior of alumina nanofluid in a duct. Brownian motion impact has been included for predicting nanofluid properties. Neural Network was employed to estimate the heat transfer rate. Numerical data has been obtained via Runge–Kutta method. Our outputs display that GMDH achieved an operative method for an efficient recognition of trends in data. Impact of expansion ratio, nanoparticle concentration, power law index and Reynolds number on Nu was also studied. Our findings reveal that heat transfer intensifies by rise of nanoparticle concentration while it has a reducing trend with rise of expansion ratio.

Subfields: Biomedical Engineering, Computational Mechanics, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/cd3dbd1ff6540a88637a3fccfc1b99507b5636ae

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Computational Mechanics

In Mechanical Engineering

Citations in the corpus

Impact of Lorentz forces on Fe3O4-water ferrofluid entropy and exergy treatment within a permeable semi annulus (2019, methodology): “If domain is porous space, several models can be used for simulation (Zin et al. (2017), Sheikholeslami et al. (2019b), Soomro et al. (2019), Sheikholeslami (2019b)).”