Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms

Dynamic viscosity considerably affects the heat transfer and flow of fluids. Due to improved thermophysical properties of fluids containing nanostructures, these types of fluids are widely employed in thermal mediums. The nanofluid's dynamic viscosity relies on different variables including siz...

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Main Authors: Mohammad Hossein Ahmadi, Behnam Mohseni-Gharyehsafa, Mahmood Farzaneh-Gord, Ravindra D. Jilte, Ravinder Kumar, Kwok-wing Chau
Format: Article
Language:English
Published: Taylor & Francis Group 2019-01-01
Series:Engineering Applications of Computational Fluid Mechanics
Subjects:
Online Access:http://dx.doi.org/10.1080/19942060.2019.1571442
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spelling doaj-c977ea84788147878852ebd9a0aaae442020-11-25T02:01:42ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2019-01-0113122022810.1080/19942060.2019.15714421571442Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithmsMohammad Hossein Ahmadi0Behnam Mohseni-Gharyehsafa1Mahmood Farzaneh-Gord2Ravindra D. Jilte3Ravinder Kumar4Kwok-wing Chau5Shahrood University of TechnologyShahrood University of TechnologyFerdowsi University of MashhadLovely Professional UniversityLovely Professional UniversityHong Kong Polytechnic UniversityDynamic viscosity considerably affects the heat transfer and flow of fluids. Due to improved thermophysical properties of fluids containing nanostructures, these types of fluids are widely employed in thermal mediums. The nanofluid's dynamic viscosity relies on different variables including size of solid phase, concentration and temperature. In the present study, three algorithms including multivariable polynomial regression (MPR), artificial neural network–multilayer perceptron (ANN-MLP) and multivariate adaptive regression splines (MARS) are applied to model the dynamic viscosity of silver (Ag)/water nanofluid. Recently published experimental investigations are employed for data extraction. The input variables considered in the modeling process to be the most important ones are the size of particles, fluid temperature and the concentration of Ag nanoparticles in the base fluid. The R2 values for the studied models are 0.9998, 0.9997 and 0.9996 for the ANN-MLP, MARS and MPR algorithms, respectively. In addition, based on importance analysis, the temperature is highly effective and the dominant parameter for the dynamic viscosity of the nanofluid in comparison with size and concentration.http://dx.doi.org/10.1080/19942060.2019.1571442nanofluiddynamic viscosityartificial neural networkconcentrationmultivariate adaptive regression splines (mars)multivariable polynomial regression (mpr)
collection DOAJ
language English
format Article
sources DOAJ
author Mohammad Hossein Ahmadi
Behnam Mohseni-Gharyehsafa
Mahmood Farzaneh-Gord
Ravindra D. Jilte
Ravinder Kumar
Kwok-wing Chau
spellingShingle Mohammad Hossein Ahmadi
Behnam Mohseni-Gharyehsafa
Mahmood Farzaneh-Gord
Ravindra D. Jilte
Ravinder Kumar
Kwok-wing Chau
Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms
Engineering Applications of Computational Fluid Mechanics
nanofluid
dynamic viscosity
artificial neural network
concentration
multivariate adaptive regression splines (mars)
multivariable polynomial regression (mpr)
author_facet Mohammad Hossein Ahmadi
Behnam Mohseni-Gharyehsafa
Mahmood Farzaneh-Gord
Ravindra D. Jilte
Ravinder Kumar
Kwok-wing Chau
author_sort Mohammad Hossein Ahmadi
title Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms
title_short Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms
title_full Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms
title_fullStr Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms
title_full_unstemmed Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms
title_sort applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ann-mlp, mars and mpr algorithms
publisher Taylor & Francis Group
series Engineering Applications of Computational Fluid Mechanics
issn 1994-2060
1997-003X
publishDate 2019-01-01
description Dynamic viscosity considerably affects the heat transfer and flow of fluids. Due to improved thermophysical properties of fluids containing nanostructures, these types of fluids are widely employed in thermal mediums. The nanofluid's dynamic viscosity relies on different variables including size of solid phase, concentration and temperature. In the present study, three algorithms including multivariable polynomial regression (MPR), artificial neural network–multilayer perceptron (ANN-MLP) and multivariate adaptive regression splines (MARS) are applied to model the dynamic viscosity of silver (Ag)/water nanofluid. Recently published experimental investigations are employed for data extraction. The input variables considered in the modeling process to be the most important ones are the size of particles, fluid temperature and the concentration of Ag nanoparticles in the base fluid. The R2 values for the studied models are 0.9998, 0.9997 and 0.9996 for the ANN-MLP, MARS and MPR algorithms, respectively. In addition, based on importance analysis, the temperature is highly effective and the dominant parameter for the dynamic viscosity of the nanofluid in comparison with size and concentration.
topic nanofluid
dynamic viscosity
artificial neural network
concentration
multivariate adaptive regression splines (mars)
multivariable polynomial regression (mpr)
url http://dx.doi.org/10.1080/19942060.2019.1571442
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