A comprehensive study of effect of concentration, particle size and particle shape on thermal conductivity of titania/water based nanofluid

Maheshwary, Prashant; Handa, C. C.; Nemade, K.R.

doi:10.1016/j.applthermaleng.2017.03.054

Cited by 154 publications

(67 citation statements)

References 35 publications

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“…7 shows for the TiO 2 -based systems with a λ p /λ bf ratio of about 20 that most of the experimental data in literature [6,62,77,78] associated with particle volume fractions up to 0.06 provide moderate enhancement factors which scatter around the HC model [18] and our model [21]. Only the data obtained by Reddy and Rao [79] with the steady-state concentric-cylinder method with a stated uncertainty of 0.6 % and especially those obtained by Maheshway et al [80] with a hot-wire method of unknown uncertainty show clearly larger enhancement factors. Our measurement results follow the data of Fedele et al [62] based on the hot-disk technique and extend the data range up to larger particle volume fractions.…”

Section: Data Comparisonmentioning

confidence: 48%

Effective Thermal Conductivity of Nanofluids: Measurement and Prediction

et al. 2020

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In the present study, the effective thermal conductivity of nanoparticle dispersions, so-called nanofluids, is investigated experimentally and theoretically. For probing the influence of the nanoparticles on the effective thermal conductivity of dispersions with water as liquid continuous phase, nearly spherical and monodisperse titanium dioxide (TiO 2 ), silicon dioxide (SiO 2 ), and polystyrene (PS) nanoparticles with strongly varying thermal conductivities were used as model systems. For the measurement of the effective thermal conductivity of the nanofluids with particle volume fractions up to 0.31, a steady-state guarded parallel-plate instrument was applied successfully at temperatures between (298 and 323) K. For the same systems, dynamic light scattering (DLS) was used to analyze the collective translational diffusion, which provided information on the dispersion stability and the distribution of the particle size as essential factors for the effective thermal conductivity. The measurement results for the effective thermal conductivity show no temperature dependency and only a moderate change as a function of particle volume fraction, which is positive or negative for particles with larger or smaller thermal conductivities than the base fluid. Based on these findings, our theoretical model for the effective thermal conductivity originally developed for nanofluids containing fully dispersed particles of large thermal conductivities was revisited and also applied for a reliable prediction in the case of particles of relatively low thermal conductivities.

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Section: Data Comparisonmentioning

confidence: 48%

Effective Thermal Conductivity of Nanofluids: Measurement and Prediction

et al. 2020

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“…The simulation was run between temperature and performance parameters for a particle volume fraction ranging between 0.2% and 1.8%. Maheshwary et al [24] proposed an analysis of the influence of the particle volume fraction on the pumping power. They found that pumping power is insignificant until 1.8% particle volume fraction and shoots up after this volume fraction; therefore, in this study, a maximum particle volume fraction of 1.8% was considered to keep the pumping power consistent by the addition of nanoparticles.…”

Section: Parametric Study For Alumina Oxide Nanoparticles In the Wet mentioning

confidence: 99%

“…Therefore, this area requires further concentration; specifically, no analysis is available for a cross-flow configuration for different types, concentrations, and particle diameter of nanofluids. This analysis is important because a cross-flow configuration is preferred for commercial and residential installation [24] of indirect evaporative coolers. Additionally, the selection of the most appropriate type of nanofluid is also not well researched.…”

Section: Introductionmentioning

confidence: 99%

Thermal Performance Enhancement of a Cross-Flow-Type Maisotsenko Heat and Mass Exchanger Using Various Nanofluids

et al. 2018

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The incorporation of a Maisotsenko (M) Cycle into an indirect evaporative cooler has led to the achievement of sub-wet bulb temperature without any humidification, thus making it a possible green and sustainable alternative for handling the cooling load of a building. In this work, the thermal performance of a cross-flow heat and mass exchanger (HMX) is enhanced by the addition of nanoparticles in the wet channel because they significantly influence the heat and mass transfer characteristics of the base fluid. A governing model for the temperature and humidity variations of the HMX is numerically simulated. Initial benchmarking is achieved using water properties. Afterward, a comparative study is conducted using aluminum-oxide-, copper-oxide-, and titanium-oxide-based nanofluids. Enhancements of 24.2% in heat flux, 19.24% in wet bulb effectiveness, 7.04% in dew point effectiveness, 29.66% in cooling capacity, and 28.43% in energy efficiency ratio are observed by using alumina-based nanofluid as compared to water in the wet channel of the cross-flow HMX. Furthermore, a particle volume concentration of 1% and a particle diameter of 20nm are recommended for maximum performance.

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“…Literally, many researchers have been interested in this field. Elena et al, Jisun et al, Gaganpreet and Sunita, and more recently Maheshwary et al have reported about the effects of the shape of particles on the nanofluids viscosity. Other papers have studied the effect of the particle size property, such as the works of Prasher et al, Anoop et al, and Jarahnejad et al The effects of temperature and volume fraction are also considered in different research …”

Section: Introductionmentioning

confidence: 99%

A new empirical correlating equation for calculating effective viscosity of nanofluids

Dhahri

Aouinet

Sammouda

2019

Heat Trans. Asian Res.

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In this paper, an empirical correlation for the nanofluid viscosity is proposed. The new equation for the nanofluid effective dynamic viscosity, normalized by the dynamic viscosity of the base liquid, is derived from a wide selection of experimental data available in the literature. This correlation presented the viscosity of the nanofluid as a function of the base fluid viscosity, nanoparticle volume fraction, nanoparticle diameter, nanoparticle temperature, and mass density of the base fluid. The new correlation was evaluated against 898 experimental data for the viscosities of nanofluid collected from the literature. The experimental data included different working nanofluids, such as alumina, Iron, and silica, where the diameter of nanoparticles was ranging between 10 and 350 nm, suspended in water, propylene glycol, and kerosene. The predicted results were then compared with many other published experimental results for different nanofluids and very good concordance between these results was observed. In general, this correlation has higher accuracy and precision.

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A comprehensive study of effect of concentration, particle size and particle shape on thermal conductivity of titania/water based nanofluid

Cited by 154 publications

References 35 publications

Effective Thermal Conductivity of Nanofluids: Measurement and Prediction

Effective Thermal Conductivity of Nanofluids: Measurement and Prediction

Thermal Performance Enhancement of a Cross-Flow-Type Maisotsenko Heat and Mass Exchanger Using Various Nanofluids

A new empirical correlating equation for calculating effective viscosity of nanofluids

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