Rheological behavior of dilute graphene-water nanofluids using various surfactants: An experimental evaluation

被引:15
|
作者
Ebrahim, Shikha A. [1 ]
Pradeep, Emil [1 ]
Mukherjee, Sayantan [2 ]
Ali, Naser [3 ]
机构
[1] Kuwait Univ, Coll Engn & Petr, Mech Engn Dept, POB 5969, Safat 13060, Kuwait
[2] Inst Plasma Res IPR, Gandhinagar 382428, India
[3] Kuwait Inst Sci Res, Energy & Bldg Res Ctr, Nanotechnol & Adv Mat Program, Safat 13109, Kuwait
关键词
Graphene nanoplatelets; Nanofluids; Stability; Viscosity; Surfactants; Rheology of nanofluids; THERMAL-CONDUCTIVITY; HEAT-TRANSFER; SOLAR COLLECTOR; THERMOPHYSICAL PROPERTIES; VISCOSITY; NANOPLATELETS; OXIDE; ENHANCEMENT; PERFORMANCE; MORPHOLOGY;
D O I
10.1016/j.molliq.2022.120987
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
This study aims to experimentally investigate the effects of temperature and nanoparticle concentration on dynamic viscosity, which is one of the most significant thermophysical properties. Diluted water -based graphene nanoplatelets (GNP) nanofluids are prepared using a two-step approach, with concentra-tions ranging from 0.00005 to 0.001 vol.%. Surfactants such as Gum Arabic (GA) and Sodium dodecyl sul-fate (SDS) are dispersed in the nanofluid medium at 1:1 weight ratios with respect to GNP. The suspensions are rheologically characterized from 20 degrees C to 50 degrees C using a rotational rheometer at shear rates ranging from 10 to 100 (s-1). The rheological behavior of GNP nanofluids is examined to ultimately develop a regression model for viscosity, that considers the effects of nanoparticle concentration and temperature for different surfactant type. Results indicate that GNP-GA and GNP-SDS nanofluids at 0.001 vol.% retained their stability over a time frame of 21 days. An increase in viscosity with the increase in nanoparticle concentration and a decrease in viscosity with the rise in temperature is reported. GNP -GA nanofluid at 0.001 vol.% concentration depicts the highest viscosity value. The rheological analysis demonstrates a Newtonian flow behavior for GNP nanofluids throughout the studied shear rate range, except for GNP-SDS nanofluids that exhibit shear thinning behavior at highest nanoparticle loading, and GNP-GA nanofluids that exhibit shear thickening behavior at the lowest nanoparticle loading. The proposed regression model has high prediction accuracy (R2 > 99%) for GNP nanofluids with different surfactants. The outcomes of this work are anticipated to aid several industrial and engineering applica-tions like heat exchangers, refrigeration systems, cryogenic systems, air-conditioning units, power plants and solar panels. (c) 2022 The Author(s). Published by Elsevier B.V.
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页数:14
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