Experimental study of the rheological behavior of TiO2-Al2O3/mineral oil hybrid nanofluids

Shelton, John M.; Saini, Nanda K.; Hasan, Syed Mustajab

doi:10.1016/j.molliq.2023.121688

Cited by 12 publications

(1 citation statement)

References 85 publications

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“…It should be noted that there have been few attempts to study hybrid nanoliquid ow. [20][21][22][23][24][25][26][27][28][29][30] Entropy generation is the measurement of energy wastage during any thermal process. Entropy always increases in an irreversible process, and therefore, entropy generation is a positive quantity.…”

Section: Introductionmentioning

confidence: 99%

KHA model comprising MoS₄and CoFe₂O₃in engine oil invoking non-similar Darcy–Forchheimer flow with entropy and Cattaneo–Christov heat flux

Khan,

Hayat,

Alsaedi

2023

Nanoscale Adv.

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Section: Introductionmentioning

confidence: 99%

KHA model comprising MoS₄and CoFe₂O₃in engine oil invoking non-similar Darcy–Forchheimer flow with entropy and Cattaneo–Christov heat flux

Khan,

Hayat,

Alsaedi

2023

Nanoscale Adv.

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Stability and thermodynamic property of TiO₂/R141b nanorefrigerants by multi‐objective optimization

Xing,

Zhai

2024

Asia-Pacific J Chem Eng

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Utilizing nanorefrigerants as working fluids can significantly enhance the energy efficiency of low‐temperature waste heat recovery systems (≤ 50°C). Refrigerants with low viscosity and density require a substantial amount of surfactant to maintain a stable suspension of nanoparticles. However, the excessive use of surfactants, which have a notably low thermal conductivity, could lead to foam generation and reduce heat transfer coefficient. High viscosity lubricating oils with small amount of surfactant can prolong the stable suspension time and produce repulsive force. Therefore, a new combination of them improves the stability of TiO2/R141b nanorefrigerants. Additionally, viscosity and thermal conductivity of the nanorefrigerants were optimized using an implementation of a modified non‐dominated sorting genetic algorithm (NSGA‐II). The results show that adding lubricating oil inhibits aggregation of the nanoparticles leading to a stable suspension for more than 6 h at volumetric mixing ratios (lubricating oil: refrigerant) greater than 1:30. The best dispersion stability was achieved at surfactant polyvinyl pyrrolidone (PVP) mass ratio of 0.5, and the average absorbance value was increased by 65.45%. Compared with pure refrigerants, the thermal conductivity of TiO2/R141b (0.15 vol.%) nanorefrigerant was enhanced by up to 12.59% under the optimum mixing ratio. Moreover, the studied nanorefrigerants exhibited shear thickening behavior throughout the studied shear rate range, with increased non‐Newtonianization with decreasing temperature. Finally, the Pareto points were divided into three representative groups based on thermal conductivity and viscosity. These findings suggest enhanced high heat transfer efficiency with pumping power of nanorefrigerant in the waste heat recovery systems.

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Effects of temperature and nanoparticle mixing ratio on the thermophysical properties of GNP–Fe2O3 hybrid nanofluids: an experimental study with RSM and ANN modeling

Borode,

Tshephe,

Olubambi

et al. 2024

J Therm Anal Calorim

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This study investigated the impact of temperature and nanoparticle mixing ratio on the thermophysical properties of hybrid nanofluids (HNFs) made with graphene nanoplatelets (GNP) and iron oxide nanoparticles (Fe2O3). The results showed that increased temperature led to higher thermal conductivity (TC) and electrical conductivity (EC), and lower viscosity in HNFs. Higher GNP content relative to Fe2O3 also resulted in higher TC but lower EC and viscosity. Artificial neural network (ANN) and response surface methodology (RSM) were used to model and correlate the thermophysical properties of HNFs. The ANN models showed a high degree of correlation between predicted and actual values for all three properties (TC, EC, and viscosity). The optimal number of neurons varied for each property. For TC, the model with six neurons performed the best, while for viscosity, the model with ten neurons was optimal. The best ANN model for EC contained 18 neurons. The RSM results indicated that the 2-factor interaction term was the most significant factor for optimizing TC and EC; while, the linear term was most important for optimizing viscosity. The ANN models performed better than the RSM models for all properties. The findings provide insights into factors affecting the thermophysical properties of HNFs and can inform the development of more effective heat transfer fluids for industrial applications.

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Experimental study of the rheological behavior of TiO2-Al2O3/mineral oil hybrid nanofluids

Cited by 12 publications

References 85 publications

KHA model comprising MoS₄and CoFe₂O₃in engine oil invoking non-similar Darcy–Forchheimer flow with entropy and Cattaneo–Christov heat flux

KHA model comprising MoS₄and CoFe₂O₃in engine oil invoking non-similar Darcy–Forchheimer flow with entropy and Cattaneo–Christov heat flux

Stability and thermodynamic property of TiO₂/R141b nanorefrigerants by multi‐objective optimization

Effects of temperature and nanoparticle mixing ratio on the thermophysical properties of GNP–Fe2O3 hybrid nanofluids: an experimental study with RSM and ANN modeling

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Experimental study of the rheological behavior of TiO2-Al2O3/mineral oil hybrid nanofluids

Cited by 12 publications

References 85 publications

KHA model comprising MoS4and CoFe2O3in engine oil invoking non-similar Darcy–Forchheimer flow with entropy and Cattaneo–Christov heat flux

KHA model comprising MoS4and CoFe2O3in engine oil invoking non-similar Darcy–Forchheimer flow with entropy and Cattaneo–Christov heat flux

Stability and thermodynamic property of TiO2/R141b nanorefrigerants by multi‐objective optimization

Effects of temperature and nanoparticle mixing ratio on the thermophysical properties of GNP–Fe2O3 hybrid nanofluids: an experimental study with RSM and ANN modeling

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Product

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KHA model comprising MoS₄and CoFe₂O₃in engine oil invoking non-similar Darcy–Forchheimer flow with entropy and Cattaneo–Christov heat flux

KHA model comprising MoS₄and CoFe₂O₃in engine oil invoking non-similar Darcy–Forchheimer flow with entropy and Cattaneo–Christov heat flux

Stability and thermodynamic property of TiO₂/R141b nanorefrigerants by multi‐objective optimization