Locally weighted moving regression: A non-parametric method for modeling nanofluid features of dynamic viscosity

Li Wei, Hossein Arasteh, Ali Abdollahi, Amir Parsian, Abdolmajid Taghipour, Ramin Mashayekhi and Iskander Tlili

Physica A: Statistical Mechanics and its Applications, 2020, vol. 550, issue C

Abstract: The objective of this study is to evaluate the variations of CuO/ ethanol nanofluid dynamic viscosity with temperature and shear rate experimentally. In this study, the nanoparticles average size, their mass fraction, temperature, and shear rate are varying from 10 to 50 nm, 0.005 to 5 wt. %, 25 to 70°C, and 2.6 to 64.6 s−1, respectively. The obtained results showed that as the nanoparticles mass fraction increases and the temperature decreases, the nanofluid dynamic viscosity enhances. Then to predict the nanofluid dynamic viscosity as a function of temperature and nanoparticles mass fraction, the locally weighted moving regression (LWMR) method is applied to the data alongside with applying a classic cubic interpolation for comparison. It was found that although both methods have acceptable performance in dealing with the trained data, the LWMR model results in a more precise prediction. In addition, the numerical viscosities obtained by both methods were evaluated at the trained dataset. Finally, it was concluded that the LWMR may be a suitable model for the investigation of nanofluid features.

Keywords: Locally weighted moving regression; Non-parametric method; Nanofluid; Viscosity (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437119322769
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:550:y:2020:i:c:s0378437119322769

DOI: 10.1016/j.physa.2019.124124

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().