Using Gaussian Process Regression (GPR) models with the Matérn covariance function to predict the dynamic viscosity and torque of SiO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1345" altimg="si4.svg"><mml:msub><mml:mrow /><mml:mrow><mml:mi mathvariant="bold">2</mml:mi></mml:mrow></mml:msub></mml:math>/Ethylene glycol nanofluid: A machine learning approach-Reference-Cited by-同舟云学术

Using Gaussian Process Regression (GPR) models with the Matérn covariance function to predict the dynamic viscosity and torque of SiO2/Ethylene glycol nanofluid: A machine learning approach

Published:2023-06 Issue: Volume:122 Page:106107
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Dai Xiaohong,Andani Hamid Taheri,Alizadeh As’ad,Abed Azher M.,Smaisim Ghassan Fadhil,Hadrawi Salema K.,Karimi Maryam,Shamsborhan Mahmoud,Toghraie D.

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference40 articles.

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3. Experimental study and ANN analysis of rheological behavior of mineral oil-based SiO2 nanofluids;Amizhtan;IEEE Trans. Dielectr. Electr. Insul.,2022

4. An experimental study on characterization, stability and dynamic viscosity of CuO-TiO2/water hybrid nanofluid;Asadi;J. Mol. Liq.,2020

5. Viscosity of hybrid nanofluids: a critical review;Babar;Therm. Sci.,2019

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