Nanouptake - An European Network on Nanofluid Research

Buschmann, Matthias

doi:10.14311/tpfm.2018.004

Cited by 1 publication

(5 citation statements)

References 16 publications

(22 reference statements)

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“…Diamond nanoparticles are acquired from US Research Nanomaterials, Inc. (USA), having nearly spherical morphology with an average diameter size of 3-10 nm and specific surface area (SSA) of 272 m 2 /g. The true density of diamond nanoparticles is 3.05-3.30 g/cm 3 with a purity of >98.3%. Graphene nanoplatelets are acquired from Sigma-Aldrich Malaysia with 750m 2 /g SSA.…”

Section: Methodology 21 Materialsmentioning

confidence: 99%

“…The average size of the nanoplatelet is <2 μm, and an average thickness of 10 nm. The molecular weight of nanoplatelets is 12.01, and the true density is 2.25 g/cm 3 . A hybrid nanomaterial system is created by adding diamond nanoparticles and graphene nanoplatelets in an equal mass ratio (1:1) in the base fluid.…”

Section: Methodology 21 Materialsmentioning

confidence: 99%

“…Since recent developments in nanofluid technology have suggested that the conventional models may not be suitable for accurate predictions of temperature-dependent properties. This deviation can be attributed to the inadvertence of temperature, nanomaterial geometry, Brownian motion, thermophoresis, and agglomeration factors in the classical model [3,5,27]. A recently published comprehensive review [5] study [27], while the true densities of nanomaterials are used at ambient conditions.…”

Section: Rheology Parameters and Multivariable Correlationmentioning

confidence: 99%

“…The understanding and consideration of momentum transport during nanofluid operation in different applications is vital in achieving proficient thermal management of energy devices. A recent report [3] has stated several challenges in nanofluid technology towards industrial applications, such as low stability, increased pumping power for forced convective applications, reliability of conventional models, correlations, and reported experimental data.…”

Section: Introductionmentioning

confidence: 99%

“…The progression in modeling the nanofluids' viscosity has suggested that the conventional models may not accurately predict nanofluids' thermophysical properties [3,5,27]. These conventional models do not consider agglomeration effect, sorption, double electrical layers, temperature, Brownian motion, etc.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Rheological behavior of stabilized diamond-graphene nanoplatelets hybrid nanosuspensions in mineral oil

Ilyas

Ridha

Sardar

et al. 2021

Journal of Molecular Liquids

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Recent advancements in thermo-fluid technology assisted with highly thermal conductive nanomaterials have shown assuring outcomes. It is also proven that thermal conductivity alone cannot define the overall heat transfer characteristics, and the viscous properties are equally significant towards thermal management. Therefore, this research involves investigating the rheological behavior of hybrid nanosuspensions containing high thermally conductive diamond and graphene nanoplatelets (1:1). These nanomaterials are dispersed in mineral oil using a two-step technique. Hybrid nanofluids' stability is achieved using a non-ionic stabilizer Span85, exhibiting no sedimentation for a minimum of five months. Nanomaterial characterizations are performed to study morphology, purity, and chemical analysis. The flow behavior of hybrid nanosuspensions is investigated at varying nanomaterial mass concentrations (0-2 %), temperatures (298.15-338.15 K), and shear rates (1-2000 s -1 ). Hybrid nanofluids exhibit shear-thinning behavior, which is also correlated with the Ostwald-de-Waele model. The temperature-viscosity relationship is well predicted using the Vogel-Fulcher-Tammann model. Hybrid nanofluids show a maximum enhancement of 35% viscosity at 2% concentration. A generalized twovariable correlation is used to express viscosity as a function of temperature and nanofluid concentration with an excellent agreement. Three different machine learning methods, i.e., Artificial Neural Network (ANN), Gradient Boosting Machine (GBM), and Random Forest (RF) algorithms are also introduced to predict the viscosity of hybrid nanofluids based on the three input parameters (temperature, concentration, and shear rate). The parity plots conclude that all algorithms can predict big-data viscous behavior with high precision.

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Section: Methodology 21 Materialsmentioning

confidence: 99%

Section: Methodology 21 Materialsmentioning

confidence: 99%