4.5 Article

Rheological Characteristics and Environmental Remediation Using Fe3O4-SiC Hybrid Nanomaterials in Heat Transfer Oil: Experimental Evaluation and Modeling

出版社

SPRINGER
DOI: 10.1007/s10904-022-02481-z

关键词

Nanofluid; Heat transfer oil; Iron oxide; Silicon carbide; Hybrid; Inorganic materials

资金

  1. University of Jeddah, Jeddah, Saudi Arabia [UJ-02-006-ICGR]

向作者/读者索取更多资源

This study experimentally investigated the rheological characteristics of Fe3O4-SiC hybrid nanoparticles in heat transfer oil, and found that the nanofluid exhibited non-Newtonian and shear thinning behavior. Both response surface method and artificial neural network models were used to predict the viscosity, and showed good agreement with the experimental results.
Nanoparticles have a wide range of industrial applications, including heat transfer, innovative materials, electronics, catalysis, and medical. The rheological characteristics of Fe3O4-SiC hybrid nanoparticles dispersions in heat transfer oil were experimentally investigated at varying loadings of nanoparticles ranging from 0 to 2 wt%. A mixture of ultrasonication and stabilizer addition was employed to produce stability. The nanofluid viscosity is determined at a variety of temperatures (ranging from 25 to 65 degrees C) and (1-2000 s(-1)) shear rates. The viscosity of nanofluid is measured at several temperatures (25-65 degrees C) and shear rates (1-2000 s(-1)). The acquired results suggested that nanofluids show non-Newtonian and shear thinning behavior. An increased of 33% in viscocity was observed at 65 degrees C, in 1 wt% Fe3O4-SiC nano-suspension. The viscosity data of hybrid nanofluid was subjected to empirical quadratic polynomial modeling with response surface method (RSM) and artificial neural network (ANN). Viscosity predictions were excellent for both models. ANN showed better agreement compared to RSM with R-2 0.999 and average absolute deviation (AAD) 1.2914%. [GRAPHICS] .

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据