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

Mahmood Farzaneh-Gord¹  Kwok-wing Chau²  Ravindra D. Jilte³  Ravinder Kumar³  Behnam Mohseni-Gharyehsafa⁴  Mohammad Hossein Ahmadi⁴ 

[1] Ferdowsi University of Mashhad;Hong Kong Polytechnic University;Lovely Professional University;Shahrood University of Technology;

关键词: nanofluid;    dynamic viscosity;    artificial neural network;    concentration;    multivariate adaptive regression splines (mars);    multivariable polynomial regression (mpr);   

DOI  :  10.1080/19942060.2019.1571442

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown