Heat transfer properties of metal, metal oxides, and carbon water-based nanofluids in the ethanol condensation process

Hybrid nanocomposites impact on heat transfer efficiency and pressure drop in turbulent flow systems: application of numerical and machine learning insights

This research explores the feasibility of using a nanocomposite from multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) for thermal engineering applications. The hybrid nanocomposites were suspended in water at various volumetric concentrations. Their heat transfer and pressure drop characteristics were analyzed using computational fluid dynamics and artificial neural network models. The study examined flow regimes with Reynolds numbers between 5000 and 17,000, inlet fluid temperatures ranging from 293.15 to 333.15 K, and concentrations from 0.01 to 0.2% by volume. The numerical results were validated against empirical correlations for heat transfer coefficient and pressure drop, showing an acceptable average error. The findings revealed that the heat transfer coefficient and pressure drop increased significantly with higher inlet temperatures and concentrations, achieving approximately 45.22% and 452.90%, respectively. These results suggested that MWCNTs-GNPs nanocomposites hold promise for enhancing the performance of thermal systems, offering a potential pathway for developing and optimizing advanced thermal engineering solutions.

Heat Transfer Enhancement in Industrial Heat Exchangers Using Graphene Oxide Nanofluids

In this study, the heat transfer characteristics within the heat exchanger using water-based GO nanofluids were comprehensively assessed. An apparatus was constructed by scaling down an industrial heat exchanger. The nanofluid's thermal conductivity, specific heat capacity, viscosity, density, Prandtl number, and Nusselt number were examined at varying temperatures and GO nanoparticle concentrations. The results revealed that the thermal conductivity of the nanofluid increased with both temperature and nanoparticle concentration, reaching a peak value of 0.380 W m-1 K-1 at 85 °C and 0.1 wt %, leading to enhanced heat transfer rates through conduction and convection mechanisms. The specific heat capacity increased with temperature but decreased with higher GO nanoparticle contents with a maximum value of 3403.821 J kg-1 K-1 recorded at 40 °C and 0.01 wt %. The viscosity of the nanofluid increased with higher concentrations of GO nanoparticles, and the minimum value of 0.83 mPa s was observed at 85 °C and 0.01 wt %. The Prandtl number decreased with the temperature but increased with increasing GO nanoparticle concentration, suggesting a transition from convective to conductive heat transfer. A newly derived correlation equation for the Nusselt number, Nu = 0.0059(1 + 7.62ϕ0.6886)Pe 0.001 Re 0.9238 Pr 0.4, allows predicting heat transfer enhancement in nanofluids. The findings emphasize the potential of nanofluids for improving heat exchanger performance and offer valuable insights into optimizing nanofluid applications in thermal systems.

Analysis of the mechanism of enhanced heat transfer by nanofluids

CONTEXT

Industrial production and humans cannot exist without energy, but the low efficiency of the heat transfer in the excessive use of energy is the most significant aspect of energy saving and emission reduction. Molecular dynamics simulation methods are devoted to simulate the heat transfer efficiency of a nanofluid system with different particle sizes, and the heat transfer enhancement mechanism of the nanofluid is simulated and studied from a microscopic perspective. The analysis showed that as nanoparticle size increases, the thermal conductivity of the Al-Ar nanofluid tends to decrease, but all of them are still higher than the thermal conductivity of the liquid argon system. According to the findings of the density and radial distribution function analyses, it can be seen that the microstructure of the system changes after putting solid nanoparticles to the original fluid. This alteration in the system's microstructure is the primary component responsible for the increased heat transfer efficiency of nanofluids.

METHODS

In this paper, based on the theory of molecular dynamics, the simulation calculations were mainly performed using LAMMPS software, which is a commonly used open source computational program in the field of MD simulation research. VMD is used for visualization and analysis. The Lennard-Jones potential function was used in the simulation to accurately describe the forces acting between the atoms.

A critical review on thermal conductivity enhancement of graphene-based nanofluids

Nanofluids which consist of nanoparticles added to conventional fluids (or base fluids) are considered as promising heat transfer fluids. Compared to metal, metal oxide nanoparticles and carbon nanotubes, graphene with its extremely high intrinsic thermal conductivity became the best candidate to design nanofluids. Such nanofluids have the potential to be highly-efficient heat transfer fluid by reducing loss of heat and increasing cooling rates. Over the last ten years, graphene-based nanofluids have shown significant thermal conductivity enhancements, however due to the numerous and interlinked parameters to consider, optimisation of their efficiency is still challenging. The present review article analyses and discusses the reported thermal conductivity in term of performance with respect to the amount of the used graphene to develop the prepared nanofluids. The enhancement of thermal conductivity must meet the minimal graphene amount due to its production cost and because graphene nanoparticles induces high viscosity in the nanofluid leading to higher energy consumption for the heat transfer systems. Unprecedented in the literature, this work proposes a simple approach to quantitatively compare the enhancement of the thermal conductivity of the nanofluids. The thermal conductivity performance parameter introduced could be applied to all nanofluid families and may become a reference tool in the nanofluid community. Such tool will help to determine the optimal preparation conditions without compromising the superior thermal performances.