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

被引:8
作者
Alsaady, Mustafa [1 ]
Ilyas, Suhaib Umer [2 ]
Ali, Abulhassan [1 ]
Maqsood, Khuram [1 ]
Yan, Yuying [3 ]
Show, Pau Loke [4 ,5 ,6 ]
机构
[1] Univ Jeddah, Dept Chem Engn, Jeddah, Saudi Arabia
[2] Univ Gujrat, Dept Chem Engn, Jalalpur Jattan Rd, Gujrat 50700, Pakistan
[3] Univ Nottingham, Fac Engn, Fluids & Thermal Engn Res Grp, Nottingham NG7 2RD, England
[4] Wenzhou Univ, Zhejiang Prov Key Lab Subtrop Water Environm & Ma, Wenzhou 325035, Peoples R China
[5] Univ Nottingham Malaysia, Fac Sci & Engn, Dept Chem Engn, Semenyih 43500, Selangor Darul, Malaysia
[6] SIMATS, Saveetha Sch Engn, Dept Sustainable Engn, Chennai 602105, Tamil Nadu, India
关键词
Nanofluid; Heat transfer oil; Iron oxide; Silicon carbide; Hybrid; Inorganic materials; PHYSICAL PROPERTY EVALUATION; STABILITY; NANOFLUIDS; BEHAVIOR; VISCOSITY; NANOPARTICLES;
D O I
10.1007/s10904-022-02481-z
中图分类号
O63 [高分子化学(高聚物)];
学科分类号
070305 ; 080501 ; 081704 ;
摘要
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] .
引用
收藏
页码:48 / 60
页数:13
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