A new pressure and temperature dependent relative viscosity model for water-Al<sub>2</sub>O<sub>3</sub> nanofluids using GMDH neural network-Reference-Cited by-同舟云学术

A new pressure and temperature dependent relative viscosity model for water-Al₂O₃ nanofluids using GMDH neural network

Published:2022-01-27 Issue:14 Volume:40 Page:1735-1755
ISSN:1091-6466
Container-title:Petroleum Science and Technology
language:en
Short-container-title:Petroleum Science and Technology

Author:

Shirbazo Armin¹^ORCID,Keshavarz Moraveji Mostafa²^ORCID

Affiliation:

1. Department of Petroleum Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

2. Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

Publisher

Informa UK Limited

Subject

Geotechnical Engineering and Engineering Geology,Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering,General Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/10916466.2022.2030354

Reference24 articles.

1. Modeling the viscosity of nanofluids using artificial neural network and Bayesian support vector regression

2. Estimation of the viscosity of nine nanofluids using a hybrid GMDH-type neural network system

3. The effect of Brownian motion on the bulk stress in a suspension of spherical particles

4. The Viscosity of Concentrated Suspensions and Solutions

5. Experimental investigation toward obtaining a new correlation for viscosity of WO3 and Al2O3 nanoparticles-loaded nanofluid within aqueous and non-aqueous basefluids

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