Viscosity and isobaric specific heat capacity of alumina nanoparticle enhanced ionic liquids: An experimental approach

Cherecheş, Elena Ionela; Prado, Jose I.; Ibănescu, Constanţa; Danu, Maricel; Minea, Alina Adriana; Lugo, Luis

doi:10.1016/j.molliq.2020.114020

Cited by 33 publications

(19 citation statements)

References 41 publications

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“…Even though in the literature, there are numerous models for viscosity estimation, no theoretical correlation was found acceptable to estimate both nanofluids or other nanoparticle-enhanced fluids' viscosity behavior. However, a number of papers are proposing the Krieger-Dougherty or Pastorizza-Galllego models (see, for example, the work of Chereches et al [65] and Pastorizza-Galllego et al [76]), which seems to describe well the experimental results.…”

Section: Viscositymentioning

confidence: 99%

“…Most of the new fluids contain little concentration of nanoparticles, up to 1 %wt. with several exceptions when the nanoparticles fractions are up to 0.05 (see, for example, Franca et al [26], Paul [61], Paul et al [62], Chereches et al [47,65], Hosseinghorbani et al [68]). As for base fluids, several ionic liquids were considered and few authors, such as, for example, Xie et al [37] and Chereches et al [47,60], made mixtures between water and ILs.…”

Section: Thermal Conductivitymentioning

confidence: 99%

“…contains experimental data using Al2O3 as a nanoparticle in four base fluids: [C4mpyrr][NTf2], [C4mim][NTf2], [C2mim][CH3SO3] and a mixture between [C2mim][CH3SO3] and water (with 0.25 mole fraction)[47,62,63,65]. Results concluded that, for 1%wt.…”

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confidence: 99%

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Ionic Liquids-Based Nanocolloids—A Review of Progress and Prospects in Convective Heat Transfer Applications

Minea

Murshed

2021

Nanomaterials

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Ionic liquids are a new and challenging class of fluids with great and tunable properties, having the capability of an extensive area of real-life applications, from chemistry, biology, medicine to heat transfer. These fluids are often considered as green solvents. Several properties of these fluids can be enhanced by adding nanoparticles following the idea of nanofluids. These ionic liquids-based nanocolloids are also termed in the literature as ionanofluids or nanoparticles-enhanced ionic liquids. This review summarizes the findings in both areas of ionic liquids and ionic liquids nanocolloids (i.e., ionic liquids with nanoparticles in suspension) with direct applicability in convective heat transfer applications. The review presents in a unified manner the progress and prospects of ionic liquids and their nanocolloids from preparation, thermophysical properties and equally experimental and numerical works. As the heat transfer enhancement requires innovative fluids, this new class of ionic liquids-based nanocolloids is certainly a viable option, despite the noticed drawbacks. Nevertheless, experimental studies are very limited, and thus, extensive experiments are needed to elucidate ionic liquids interaction with nanoparticles, as well as their behavior in convective heat transfer.

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Section: Viscositymentioning

confidence: 99%

Section: Thermal Conductivitymentioning

confidence: 99%

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Ionic Liquids-Based Nanocolloids—A Review of Progress and Prospects in Convective Heat Transfer Applications

Minea

Murshed

2021

Nanomaterials

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“…The flow behavior and consistency index are evaluated at varying temperatures and hybrid nanomaterial concentrations. The most popular expression to correlate the viscosity-temperature relationship of liquids is the Vogel-Fulcher-Tammann (VFT) equation [60], which is a well-known modification of Andrade's equation [61]. In this research, the VFT model is applied to the experimental investigations, and the fitting parameters are tabulated for nanofluids sample.…”

Section: Rheology Parameters and Multivariable Correlationmentioning

confidence: 99%

“…In this research, the VFT model is applied to the experimental investigations, and the fitting parameters are tabulated for nanofluids sample. A similar methodology was also applied in a recent study by Cherecheş et al [61] to correlate the temperature dependence of dynamic viscosity for alumina-based nanofluids enhanced with ionic liquids. Experimental measurements of thermophysical properties are vital for the practical design of thermal systems.…”

Section: Rheology Parameters and Multivariable Correlationmentioning

confidence: 99%

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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Machine Learning Approach for Thermal Characteristics and Improvement of Heat Transfer of Nanofluids—A Review

Patel,

Kumar Singh,

Prakash Verma

et al. 2024

Lecture Notes in Networks and Systems

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Viscosity and isobaric specific heat capacity of alumina nanoparticle enhanced ionic liquids: An experimental approach

Cited by 33 publications

References 41 publications

Ionic Liquids-Based Nanocolloids—A Review of Progress and Prospects in Convective Heat Transfer Applications

Ionic Liquids-Based Nanocolloids—A Review of Progress and Prospects in Convective Heat Transfer Applications

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

Machine Learning Approach for Thermal Characteristics and Improvement of Heat Transfer of Nanofluids—A Review

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