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Title: Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms
Authors: Ahmadi, MH
Mohseni-Gharyehsafa, B
Farzaneh-Gord, M
Jilte, RD
Kumar, R
Chau, KW 
Issue Date: 2019
Source: Engineering applications of computational fluid mechanics, 1 Jan. 2019, v. 13, no. 1, p. 220-228
Abstract: 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 R-2 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.
Keywords: Nanofluid
Dynamic viscosity
Artificial neural network
Concentration
Multivariate adaptive regression splines (MARS)
Multivariable polynomial regression (MPR)
Publisher: Taylor & Francis
Journal: Engineering applications of computational fluid mechanics 
ISSN: 1994-2060
EISSN: 1997-003X
DOI: 10.1080/19942060.2019.1571442
Rights: © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Mohammad Hossein Ahmadi, Behnam Mohseni-Gharyehsafa, MahmoodFarzaneh-Gord, Ravindra D. Jilte, Ravinder Kumar & Kwok-wing Chau (2019) Applicability ofconnectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP,MARS and MPR algorithms, Engineering Applications of Computational Fluid Mechanics, 13:1,220-228 is available at https://dx.doi.org/10.1080/19942060.2019.1571442
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