6 research outputs found
Stability and Thermophysical Properties of GNP-Fe<sub>2</sub>O<sub>3</sub> Hybrid Nanofluid: Effect of Volume Fraction and Temperature
No full textThe study focused on the impact of concentration and temperature on the electrical conductivity, viscosity, and thermal conductivity of GNP/Fe2O3 hybrid nanofluids. The study found that nanofluids have better electrical conductivity, viscosity, and thermal conductivity than water. The electrical conductivity and thermal conductivity increase linearly with concentration for a constant temperature. However, the nanofluid’s viscosity increases with the addition of the hybrid nanoparticles and decreases as the temperature increases. Furthermore, the study shows that the thermal conductivity of the nanofluid is enhanced with increased addition of hybrid nanoparticles in the base fluid and that the thermal conductivity ratio increases with increased addition of nanoparticles. Overall, the results suggest that GNP/Fe2O3 hybrid nanofluids could be used in various industrial applications to improve the heat transfer and energy efficiency of systems
Nanoindentation mechanical properties on spark plasma sintered 48Ti-48Al-2Cr-2Nb alloy
No full textThis study aims to investigate the microstructure, plastic (H) properties, elastic (E) properties, reduced elastic (Er) properties the strain-to-break parameter (H/Er), and the resistance to plastic deformation parameter (H3/Er2) of the Ti-48Al-2Cr-2Nb alloy by use of scanning electron microscopy, nanoindentation and micro-indentation techniques. The results show that the sintering parameters had significant effect on the resulting microstructure. Desirable mechanical properties were obtained with the sample sintered at temperature of 1200 °C, pressure of 50 MPa, holding time of 7.5 min and a heating rate of 50 °C/min which had a near lamellar structure, resulting from the grain boundary pinning effect of the fine equiaxed gamma grains and the impartation of ductility due to the coarsened lamellar colonies. The nano-hardness and elastic modulus were observed to be about 4GPa and 31GPa for the near lamellar microstructure, respectively, with the microhardness of about 4.4GPa. While the duplex and the near gamma microstructures possessed the least nano-hardness (3.65–3.78GPa) and elastic modulus (3.6–29.5GPa) with the exception of sample sintered at temperature of 1150 °C, pressure of 50 MPa, holding time of 7.5 min and a heating rate of 100 °C/min., with nano-hardness and elastic modulus of 4.05GPa and 31.25GPa, respectively, however it had the lowest micro-hardness of 2.7GPa. Furthermore, the ratios H/Er and H3/Er2 values were observed to be greater for the same sample suggesting good wear resistance of the alloy
Experimental study and ANFIS modelling of the thermophysical properties and efficacy of GNP‑Al2O3 hybrid nanofuids of different concentrations and temperatures
No full textDATA AVAILABITY STATEMENT: The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.This study delves into an extensive investigation of the thermophysical properties and heat transfer efficacy of a
hybrid nanofluid incorporating graphene nanoplatelets and γ-Al2O3 nanoparticles dispersed in deionised water. The
nanofluids were characterised for their viscosity (µ), thermal conductivity (λ), and electrical conductivity (σ) over
a 15–40 °C temperature range for varying nanoparticle loading (0.1–0.4 volume%). The experimental results revealed
notable enhancements in µ, λ, and σ with increasing nanoparticle concentration, while µ decreased at elevated
temperatures as λ and σ increased. At the highest concentration (0.4 vol%), µ increased by 21.74%, while λ and σ
exhibited peak enhancements of 17.82% and 393.36% at 40 °C. An Adaptive Neuro-fuzzy Inference System (ANFIS)
model was devised to enhance predictive precision by meticulously optimising the number of membership functions (MFs) and input MF type. The ANFIS architecture that exhibited the most remarkable agreement with the
experimental data for µ, λ, and σ was found to utilise the Product of Sigmas, Difference of Sigmas, and Generalized
Bell MFs, respectively, with corresponding input MF numbers being 2–3, 3–2, and 3–2. The optimal ANFIS model
for µ, λ, and σ exhibits a higher prediction accuracy with an R2
value of 0.99965, 0.99424 and 0.99995, respectively.
The Figure of Merit analysis using Mouromtseff Number identified an optimal nanoparticle concentration range of
0.1–0.2 volume% for enhanced heat transfer performance with a reasonable µ increase. This range guides practitioners
in utilising hybrid nanofluids effectively while managing potential drawbacks.The University Research Council of the University of Johannesburg.https://www.springer.com/journal/42452Mechanical and Aeronautical EngineeringSDG-09: Industry, innovation and infrastructur
Effects of temperature and nanoparticle mixing ratio on the thermophysical properties of GNP-Fe2O3 hybrid nanofluids : an experimental study with RSM and ANN modeling
No full textThis 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.The University Research Council (URC) of the University of Johannesburg. Open access funding provided by University of Pretoria.https://www.springer.com/journal/10973am2024Mechanical and Aeronautical EngineeringSDG-09: Industry, innovation and infrastructur
Stability and thermophysical properties of GNP-Fe2O3 hybrid nanofluid : effect of volume fraction and temperature
No full textDATA AVAILABILITY STATEMENT : The data presented in this study are available in the article.The study focused on the impact of concentration and temperature on the electrical conductivity,
viscosity, and thermal conductivity of GNP/Fe2O3 hybrid nanofluids. The study found that
nanofluids have better electrical conductivity, viscosity, and thermal conductivity than water. The
electrical conductivity and thermal conductivity increase linearly with concentration for a constant
temperature. However, the nanofluid’s viscosity increases with the addition of the hybrid nanoparticles
and decreases as the temperature increases. Furthermore, the study shows that the thermal
conductivity of the nanofluid is enhanced with increased addition of hybrid nanoparticles in the
base fluid and that the thermal conductivity ratio increases with increased addition of nanoparticles.
Overall, the results suggest that GNP/Fe2O3 hybrid nanofluids could be used in various industrial
applications to improve the heat transfer and energy efficiency of systems.https://www.mdpi.com/journal/nanomaterialsam2024Mechanical and Aeronautical EngineeringSDG-09: Industry, innovation and infrastructur
