Back to results list
Please use this identifier to cite or link to this item:
|Title:||Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms||Authors:||Ahmadi, MH
Artificial neural network
Multivariate adaptive regression splines (MARS)
Multivariable polynomial regression (MPR)
|Issue Date:||2019||Publisher:||Hong Kong Polytechnic University, Department of Civil and Structural Engineering||Source:||Engineering applications of computational fluid mechanics, 1 Jan. 2019, v. 13, no. 1, p. 220-228 How to cite?||Journal:||Engineering applications of computational fluid mechanics||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.||URI:||http://hdl.handle.net/10397/80799||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
|Appears in Collections:||Journal/Magazine Article|
Show full item record
Files in This Item:
|Ahmadi_Applicability_MARS_MPR.pdf||1.53 MB||Adobe PDF||View/Open|
Citations as of Aug 21, 2019
Citations as of Aug 21, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.