Conductivity Of Nanofluids Research Articles

IMPROVING THERMAL AND ELECTRICITY GENERATION PERFORMANCE OF PHOTOVOLTAIC/THERMAL (PV/T) SYSTEMS USING HYBRID NANOFLUID

Solar energy is a safe and clean source of energy, available on the Earth throughout the year. A photovoltaic/thermal (PV/T) system is a device designed to take solar energy and convert it into electrical/thermal energy. Photovoltaic/ thermal systems can also be useful to produce hot fluid (usually water) along with the generation of electrical energy. In addition, the electric generating performance of PVs increases with heat discharging ability of thermal system, which also prevents overheating in PV systems. Nanofluid is a new generation heat transfer fluid that delivers higher thermal conductivity and heat transfer rate compared to conventional fluids. The thermal conductivity of the nanofluid depends on the size of the nanoparticles, concentration of the nanofluid, and the method of its preparation. In this study, it is aimed to increase the thermal heat transfer of the PV/T system by using hybrid nanofluids, manufactured by adding 0.5% Fe<sub>2</sub>O<sub>3</sub> and Fe<sub>3</sub>O<sub>4</sub> nanoparticles to the water as a working fluid. By using hybrid nanofluids, increase in bidirectional performance along with enhanced cooling is achieved. In the experimental study, more heat was withdrawn from the heated PV panels by utilizing the high thermal conductivity of the hybrid nanofluid, and the best improvement in total efficiency was obtained as 86% for the hybrid nanofluid. With the use of hybrid nanofluids in the cooling circuit, the electrical and thermal efficiency of the PV panel has reached to overall 81% on average basis.

Electrical conductivity of nanofluids based on water and Cu-C nanoparticles synthesized by the electric arc method

In this work, electric arc synthesis was carried out by sputtering a graphite electrode with copper powder in a helium environment at a pressure of 12 Torr, resulting in the production of copper nanoparticles coated with carbon, with an average size of 6 nm. The synthesized material was characterized by transmission electron microscopy, Raman spectroscopy, and X-ray phase analysis. The effect of nanoparticle concentration on the electrical conductivity of water-based nanofluids was studied. It was found that when nanoparticles are added to water, the electrical conductivity of nanofluids increases, however at concentrations above 0.01 wt%, the electrical conductivity begins to decrease.

Сравнительный анализ теоретических моделей определения теплопроводности наножидкости

This article is devoted to the study of thermal conductivity of nanofluid. A nanofluid is a liquid in which nanometer-sized solid particles are dispersed. These particles are called nanoparticles. Nanofluids have new promising thermophysical properties compared to conventional heat transfer fluids. Thermal conductivity is one of the main thermophysical properties of a liquid. Thermal conductivity is of great importance in processes where heat transfer and fluid flow occur. The article presents well-known theoretical models for determining the thermal conductivity of nanofluid. A brief description of these models is given. Some experimental work on determining the thermal conductivity of various nanofluids is considered. A computational study of the effect of aluminum oxide (Al2O3) and silicon dioxide (SiO2) nanoparticles on the change in thermal conductivity of a nanofluid has been performed. A comparative analysis of known computational models and experimental data is carried out. The accuracy of the calculated models is determined by determining the thermal conductivity of the nanofluid.

Experimental investigation of a biomass-derived nanofluid with enhanced thermal conductivity as a green, sustainable heat-transfer medium and qualitative comparison via mathematical modelling.

In this study, bio-based carbon nanospheres (CNSs) were synthesized from lignocellulosic-rich groundnut skin (Arachis hypogaea) and tested for their practical application in nanofluids (NFs) for enhanced heat transfer. The CNSs were characterized using various techniques, including FESEM, EDS, XRD, Raman spectroscopy, zeta potential analysis, and FTIR. Thermal conductivity (TC) and viscosity measurements were conducted using transient plane source (TPS) technique with a Hot Disk thermal analyser and discovery hybrid rheometer, respectively. The nanoparticles (NPs) were dispersed in two base fluids: ethylene glycol (EG) and a 60 : 40 mixture of deionized water (DI) and EG. Optimization studies were performed by varying the stirring and measurement times to improve TC values. The results showed that when a power source of 40 mW was applied at a high concentration of nanoparticles (i.e., 0.1 wt%), there was a 91.9% increment in thermal conductivity (TC) compared to the base fluid EG. DI-EG-based nanofluids (NFs) exhibited enhancements of up to 45% compared to the base fluid DI-EG (60 : 40), with a heating power of 80 mW and concentration of 0.1 wt%. These results demonstrated significant TC improvements with NP incorporation. Further experiments were performed by varying the temperature in the range of 30-80 °C with readings taken for every 10 °C increase, which showed a direct relation with the TC values. At 80 °C, EG-based NFs showed increments of 77%, 111.49%, 139.67% and 175% at 0.01, 0.02, 0.05 and 0.1 wt% concentrations of NPs, respectively. It was also found that with the increase in the concentration of NPs, viscosity increased, whereas an increase in the temperature led to a decrease in viscosity. The CNS nanofluid exhibited a Newtonian behaviour with the nanoparticle concentration and temperature, resulting in an approximately 114% enhancement compared to the base fluid when the concentration of CNSs was 0.1 wt% at 30 °C but decreased by up to 18% when the temperature was increased to 90 °C. Using appropriate mathematical models for assessing thermophysical quantities, it was discovered that the model values and experimental values correspond reasonably well. Our method thus validates our experimental results and deepens the understanding of the mechanisms behind enhancing thermal conductivity in biomass-derived nanofluids. In summary, our work advances sustainable nanomaterial synthesis, providing a new solution for boosting thermal conductivity while maintaining environmental integrity, thereby inspiring further research and innovation in this field.

Heat transfer and flow characteristics of cuo-al2o3 hybrid nanofluids in slotted finned channels

This study aimed to investigate the heat transfer coefficient of CuO-Al2O3 hybrid nanofluids within a microchannel heatsink, focusing on the impact of flow rate and volume fraction. The thermal conductivity and viscosity of hybrid nanofluids with different particle ratios were investigated. The results showed that the nanofluids have a more remarkable enhancement in thermal conductivity than water. Employing the hybrid nanofluids enhanced the heat transfer coefficient significantly. Furthermore, a slight growth in the friction factor was observed.

Coupling at the molecular scale between the graphene nanosheet and water and its effect on the thermal conductivity of the nanofluid.

Graphene nanofluid is a promising way to improve heat transfer in many situations. As a two-dimensional material, graphene's anisotropic thermal conductivity influences the heat transfer of nanofluids. In the present study, a nonequilibrium molecular dynamics (MD) simulation is adopted to study the interaction between graphene nanosheets (GNSs) and liquid water in water-based graphene nanofluids. Consequently, the coupling interaction between the orientation and length of GNSs and the thermal conductivity of nanofluids is then investigated. We discover that the molecular thermal coupling between GNSs and water can effectively influence the orientation angle of the GNSs. A preferential orientation angle of the GNSs inside the nanofluid is then observed during heat transfer. The preferential orientation angle decreases with the GNS length and has no apparent relation with the size of heat flux in this study. The overall thermal conductivity of the nanofluid decreases as the orientation angle of the GNS rises. Increasing the GNS length not only reduces the preferential orientation angle but also improves the thermal conductivity along the graphene length direction. The thermal conductivity of the nanofluid along the graphene length direction increases from 0.414 to 4.085 W m K-1 as the length increases from 103 to 3274 A. Our results provide the fundamental knowledge of the heat transfer performance of graphene nanofluids.

Recent development in fluid-flow problems: A brief review

This particular article is a collection of some recent research articles on fluid flow problems. The present article summarizes the recent research developments regarding the experimental and theoretical examination about thermal conductivity of different nanofluids. The current investigate analyzes several factors those strongly affecting thermal conductivity of nanofluids include temperature, solid volume fraction, particle type, particle size, particle shape, magnetic field, different base fluids, pH, ultrasonic time and surfactant. Other than, different justifiably attractive models contributing enlargement of thermal conductivity of nanofluids are invoked. Lastly, major heat transfer mechanisms namely Brownian motion, thermophoresis, nanoclustering, interfacial nano-layer and osmophoresis responsible for remarkable role in upgrade the thermal conductivity. Hence the heat transfer characteristics of nanofluids are considered.

Computational fluid dynamics approaches: An overview

This special article is a collection of 28 review articles from different fluid flow approaches. The present article summarizes the recent research developments regarding the theoretical and experimental investigations about thermal conductivity of different nanofluids. The current study analyzes several factors those strongly affecting thermal conductivity of nanofluids include solid volume fraction, temperature, particle size, particle type, particle shape, different base fluids, magnetic field, pH, surfactant and ultrasonic time. In addition, different reasonably attractive models contributing augmentation of thermal conductivity of nanofluids are invoked. Finally, important heat transfer mechanisms namely Brownian motion, nanoclustering, thermophoresis, osmophoresis and interfacial nano-layer responsible for significant role in ameliorating the thermal conductivity and therefore the heat transfer characteristics of nanofluids are discussed.

ANALYTICAL EXAMINATION OF NANO-FLUID-ASSISTED COOLING RATE ENHANCEMENT IN A CAR RADIATOR

In recent years, nanofluids have been extensively utilized to improve heat transmission in various mediums. The use of nanofluids can enhance the thermal efficiency of thermosyphons. This study focuses on analyzing the thermal efficiency of automobile radiators by utilizing three different types of nanofluids. In this study, we conducted a thermal performance analysis of a car radiator by examining the effects of three different types of nanoparticles mixed with water: CuO-water, ZnO-water, and MgO-water nanofluids. The thermal performance of these three nanofluids was compared and analyzed. The experimental setup included a radiator with a hydraulic diameter of 0.0191 m and a total length of the radiator channel of 10.5354 m. The analyses are conducted at different volume fractions, ranging from 0.5% to 3%, and a constant inlet velocity of 0.24 m/s is maintained. The findings demonstrated a substantial enhancement in thermal efficiency when employing CuO-water nanofluid as a coolant for vehicle engine cooling in the radiator. Conversely, ZnO-water and MgO-water nanofluids exhibited very limited improvements in comparison to pure water. The thermal conductivity of CuO-water nanofluid is greater than that of pure water. The CuO-water nanofluid yields the most significant enhancements in terms of the mass flow rate and heat transfer coefficient, as revealed by this investigation. Hence, it is advisable to utilize CuO-water nanofluid in automobile radiators to achieve optimal heat transfer efficiency in comparison to the base fluid.

Effect of interface layer on the enhancement of thermal conductivity of SiC-Water nanofluids: Molecular dynamics simulation

To investigate the impact of interfacial layer effects on the thermal conductivity of nanofluids and the microscopic mechanisms of enhanced thermal conductivity, this study employed non-equilibrium molecular dynamics to compute the thermal conductivity, number density, radial distribution function, and mean square displacement distribution of SiC nanofluids. The impact of nanoparticle volume fraction and particle size parameters on the thermal conductivity of nanofluids and the structure of interfacial adsorption layers was discussed. The simulation calculation results show that the coefficient of thermal conductivity of nanofluid is positively related to the volume fraction of nanoparticles, increasing from 0.6529 W/(m·K) to 0.8159 W/(m·K), and the enhancement of thermal conductivity by the volume fraction can be up to 33.97 %. The thermal conductivity is inversely correlated with the change in particle size, and the maximum improvement in thermal conductivity by particle size can reach up to 12.05 %. The simulated results of the thermal conductivity of nanofluid are almost consistent with the predicted results of the Yu&Choi model, and the error is controlled within 5 %. Simultaneously, the thickness of the interfacial adsorption layer decreases with an increase in particle size. This reduction arises due to larger particles having a smaller specific surface area, resulting in fewer particle surfaces covered by the interface layer. Moreover, the impact of particle size on the arrangement and affinity of molecules within the interface layer contributes to this decrease. Overall, interface layer effects exhibit a dual impact on the thermal conduction of nanofluids. The structured formation and high-density distribution of the adsorption layer contribute to enhanced heat transfer, while thermal resistance between nanoparticle surfaces and the fluid restricts heat transmission.

MAGNETOHYDRODYNAMIC TERNARY HYBRID NANOFLUID FLOW OVER A STRETCHING SURFACE SUBJECT TO THERMAL CONVECTIVE AND ZERO MASS FLUX CONDITIONS

Nanoparticles have the capability to augment the thermal conductivity of nanofluids. For the transmission of heat, the material’s low thermal conductivity is the key problem. Therefore, to increase the thermal conductivity, researchers mixed different nanoparticles in the base fluids. In this field of study, utilizing three different particles is the most recent strategy to form a ternary hybrid nanofluid that gives us better results in terms of heat transfer. The interaction of three different kinds of nanoparticles, i.e. copper, alumina and silver, is considered with water serving as the base fluid to form a ternary hybrid nanofluid. The paper explores the behavior of ternary hybrid nanofluids on heat and mass transportation phenomena of the two-dimensional magnetohydrodynamic (MHD) micropolar flow across a porous extending surface with zero mass flux and convective conditions. The Brownian motion, thermal radiation, heat source and sink, and joule heating are taken into consideration in the temperature equation. The chemical reaction is incorporated into the concentration equation. Appropriate similarity transformations are used to transform the system of partial differential equations (PDEs) to a coupled system of ordinary differential equations (ODEs). The homotopy analysis method (HAM) is used to solve the system of the flow equations. The effects of the nanoparticle’s volume fractions and other different physical parameters on the surface drag force, Nusselt number, velocities, microrotation, temperature and concentration profiles are scrutinized through figures and tables. The outcomes of the present investigation show that the heat transfer rate is augmented with the increasing value of thermophoresis parameter. The magnetic field has augmented temperature while the opposite result is seen in velocity and microrotation profiles. With the escalating values of thermophoresis parameter, the concentration and temperature of ternary hybrid nanofluids are boosted while the increasing Brownian and chemical reaction parameters have decreased the concentration profile. The surface friction coefficient exhibited by the ternary hybrid nanofluid is higher than hybrid and conventional nanofluids.

Is the Hamilton-Crosser model of any relevance? – A contribution dedicated to the 150th anniversary of Maxwell's model and the 100th anniversary of Fricke's model

The Hamilton-Crosser model (HC model) is designed to describe the thermal conductivity of suspensions and nanofluids with anisometric particles. It is one of the most widely cited models in the nanofluid community. In this paper we show, however, that the HC model (1962) is essentially scientific folklore. It is an incorrect reformulation of a correct model that has been derived almost a century ago, Fricke's model (1924). The latter is a generalization to ellipsoids, in particular spheroids, of Maxwell's model (1873) for spheres. Fricke's model is an effective medium approximation that provides admissible (though not necessarily realistic) predictions of the thermal conductivity of carbon nanotube or graphene nanoplatelet nanofluids, while the HC model is an inadmissible variant of the same model, because it cannot reproduce the correct predictions (Fricke's model) and under certain circumstances it even violates a basic law of physics, the upper Hashin-Shtrikman bound (1962). Thus, it cannot be used for any isotropic system. Therefore, in contrast to common belief, any comparison of experimental data with the HC model predictions is irrelevant and all attempts to “improve” or “renovate” the HC model should be abandoned.

Thermal Conductivity of Nanofluids: Influence of Particle Shape

Thermal Conductivity of Nanofluids: Influence of Particle Shape

The Influence of Nanofluid Characteristics on the Thermal Performance of Photovoltaic Thermal System

Optimal solar energy utilization can be achieved with a Photovoltaic Thermal (PVT) system, which simultaneously produces electricity and heat. PVT generally uses water as heat transfer fluid to cool the PV cells while transporting heat for other purposes. However, water has low thermal conductivity and capacity as a heat transfer fluid. Nanofluid, which is popular in heat exchanger applications, has also been researched and tested for use in improving the performance of PVT systems. The superior thermal conductivity of nanofluid could produce a PVT system efficiency of up to 89.75%. However, nanofluid applications in the PVT system still face challenges, especially in their preparation and properties. This study determines the properties of nanofluids that could improve the electrical and thermal performance of PVT systems. The authors found that preparation procedures for manufacturing nanofluids were essential for researchers to consider before applying nanofluids. Also, some disadvantages of nanofluids are still an obstacle in their application, especially economic issues and adverse environmental effects.

Development of a neural architecture to predict the thermal conductivity of nanofluids

Development of a neural architecture to predict the thermal conductivity of nanofluids

Evaluation of machining characteristics of SiO2 doped vegetable based nanofluids with Taguchi approach in turning of AISI 304 steel

In this study, nanofluids prepared by incorporating silicon dioxide (SiO2) nanoparticles into sunflower oil were used as cutting fluids for turning AISI 304 stainless steel. Dynamic viscosities and thermal conductivities of nanofluids prepared at two different concentrations (1 % and 0.5 % by volume) were measured at four different temperature conditions. The experiments were carried out in two stages: main experiments and additional experiments. In the main experiments, four different cooling conditions (dry, PureMQL, 0.5 % NanoMQL, 1 % NanoMQL), four different cutting speeds (80, 120, 160, 200 m/min), and four different feed rates (0.10; 0.15, 0.20, 0.25 mm/rev) were used. In additional experiments, the effect of 4 different cooling methods on machining performance was investigated by keeping the maximum cutting speed (200 m/min) and feed rate (0.25 mm/rev) constant. In all experiments, workpiece surface roughness (Ra), temperature in the cutting zone (T), and flank wear on cutting tools (Vb) were determined as performance criteria. Also, additional experiments were repeated 45 times, and wear was measured every ten experiments to get a clearer picture of the tool wear process. At the end of the study, it was determined that the most effective parameter on surface roughness was feed rate (76 %), while the temperature in the cutting zone and tool wear were mainly affected by the cooling method (80 % and 50.5 %, respectively). The best machining performance was observed in the 0.5 % nanoMQL method.

A sensitivity analysis on thermal conductivity of Al2O3-H2O nanofluid: A case based on molecular dynamics and support vector regression method

The current study examines the sensitivity study of the thermal conductivity of water-based alumina nanofluids with variations in concentration, sphericity, and temperature. Thermal conductivity data of alumina nanofluids were obtained by simulation using the non-equilibrium molecular dynamics method with the parameters of nanoparticle volume fraction (1.24–6.2%), particle sphericity (0.6975–1.0), and temperature (290–360 K). Volume fraction and temperature have been discovered to have a major effect on the thermal conductivity of nanofluids, but sphericity should also be taken into consideration. Further, for parameter sensitivity analysis, a support vector machine regression model of thermal conductivity versus parameters was created with a coefficient of determination up to 0.98. The findings show that an Al2O3-H2O nanofluid's thermal conductivity increases with temperature and volume fraction while decreasing with sphericity. Temperature, sphericity, and volume fraction are the variables that are most sensitive to modification, according to a sensitivity analysis. A change in thermal conductivity of up to 4 % can occasionally be brought on by temperature differences of merely 1 %. The use of spherical particles makes it possible for the thermal conductivity of the nanofluid to be more stable, and the sensitive volume fraction region that can cause thermal conductivity to fluctuate across a large range is between 2.5 % and 5.5 %.

Studying Alumina–Water Nanofluid Two-Phase Heat Transfer in a Novel E-Shaped Porous Cavity via Introducing New Thermal Conductivity Correlation

Investigating natural convection heat transfer of nanofluids in various geometries has garnered significant attention due to its potential applications across several disciplines. This study presents a numerical simulation of the natural convection heat transfer and entropy generation process in an E-shaped porous cavity filled with nanofluids, implementing Buongiorno’s simulation model. Analyzing the behavior of individual nanoparticles, or even the entire nanofluid system at the molecular level, can be extremely computationally intensive. Symmetry is a fundamental concept in science that can help reduce this computational burden considerably. In this study, nanofluids are frequently conceived of as a combination of water and Al2O3 nanoparticles at a concentration of up to 4% by volume. A unique correlation was proposed to model the effective thermal conductivity of nanofluids. The average Nusselt number, entropy production, and Rayleigh number have been illustrated to exhibit a decreasing trend when the volume concentration of nanoparticles inside the porous cavity rises; the 4% vol. water–alumina NFs yield 17.35% less average Nu number compared to the base water.

Computing of temperature-dependent thermal conductivity and viscosity correlation for solar energy and turbulence appliances via artificial neuro network algorithm

The growing popularity of artificial intelligence approaches has led to their application in a wide range of engineering fields. The most widely used artificial intelligence tool, artificial neural networks, can be used to predict data with high accuracy. An artificial neural network approach is being used to predict effective and accurate thermal conductivity and viscosity models for hybrid nanofluid systems. Here, new types of correlations relating to the thermophysical properties of Fly Ash–Cu nanoparticles with diameter sized 15.2[Formula: see text]nm and which are temperature-dependent are developed. The highest thermal conductivity and viscosity values were obtained for hybrid nanofluids with a mixture ratio of 20:80, with maximum amplification exceeding 83.2% and 65%, respectively, over the base fluid. The Fly Ash–Cu/water hybrid nanofluid’s viscosity and thermal conductivity are evaluated for a concentration range of 0–4%. The evaluation of the Fly Ash–Cu/water hybrid nanofluids system at concentrations ranging from 0 to 4% most likely entails a scientific or engineering study aimed at understanding the behavior and properties of this nanofluid mixture. Nanoparticles can agglomerate or settle in the base fluid over time, compromising the stability of the nanofluids. Researchers may be interested in determining how varied quantities of Fly Ash and Cu nanoparticles affect the nanofluid’s stability and sedimentation behavior. The heat transfer potential is examined within the optimistic range of temperatures of 30–80∘C. Many fruitful results for turbulence and solar energy have been drawn. The Mouromtseff number achieved an optimal value for all concentration levels. The heat transfers of turbulent flow and thermal conductivity of hybrid nanofluids increase with the augmented values of concentrations and temperature. Researchers found an increase in thermal conductivity of hybrid nanofluids at 0–4% concentrations, potentially impacting heat transfer applications. The conclusion explores the potential integration of the developed correlations and neural network model into practical engineering or industrial applications involving solar energy and turbulence appliances. In this work, we extend the work of Kanti et al. [Sol. Energy Mater. Sol. Cells 234 (2022) 111423] which is on the properties of water-based fly ash-copper hybrid nanofluid for solar energy applications.

Modelling the thermal conductivity of nanofluids using a novel model of models approach

In this study, a unique method for modelling the thermal conductivity of nanofluids is proposed using a "model of models" approach. Three distinct data streams are utilised to achieve this. The first stream uses experimental data to predict thermal conductivity, an input for the primary machine learning model. The other stream involves modelling correlations from previous studies and integrating them as an additional input. Lastly, theoretical data streams are modelled and included as a last stream. By training a model on these combined data streams, the study aims to overcome various challenges in modelling nanofluids' thermophysical properties. The research holds great significance as it can potentially reconcile and understand errors that come with various modelling methods. This could result in improved model performance that closely resembles experimental data. The presented model in the model of models’ approach achieves a remarkable coefficient of determination (R-squared) value of 0.999 on the test data set, showcasing its exceptional accuracy and effectiveness in handling complex data, particularly about the thermophysical properties of nanofluids. Furthermore, this implicit general model comprises of data models incorporating material properties and physical phenomena, offering broad applicability. It is recommended that this approach be extended to viscosity, enhancing the understanding and prediction of nanofluid properties.