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.
Original language | English |
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Pages (from-to) | 220-228 |
Number of pages | 9 |
Journal | Engineering Applications of Computational Fluid Mechanics |
Volume | 13 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Jan 2019 |
Keywords
- artificial neural network
- concentration
- dynamic viscosity
- multivariable polynomial regression (MPR)
- multivariate adaptive regression splines (MARS)
- nanofluid
ASJC Scopus subject areas
- General Computer Science
- Modelling and Simulation