Conductivity Of Nanofluids Research Articles

Computer Simulations of EMHD Casson Nanofluid Flow of Blood through an Irregular Stenotic Permeable Artery: Application of Koo-Kleinstreuer-Li Correlations.

A novel analysis of the electromagnetohydrodynamic (EMHD) non-Newtonian nanofluid blood flow incorporating CuO and Al2O3 nanoparticles through a permeable walled diseased artery having irregular stenosis and an aneurysm is analyzed in this paper. The non-Newtonian behavior of blood flow is addressed by the Casson fluid model. The effective viscosity and thermal conductivity of nanofluids are calculated using the Koo-Kleinstreuer-Li model, which takes into account the Brownian motion of nanoparticles. The mild stenosis approximation is employed to reduce the bi-directional flow of blood to uni-directional. The blood flow is influenced by an electric field along with a magnetic field perpendicular to the blood flow. The governing mathematical equations are solved using Crank-Nicolson finite difference approach. The model has been developed and validated by comparing the current results to previously published benchmarks that are peculiar to this study. The results are utilized to investigate the impact of physical factors on momentum diffusion and heat transfer. The Nusselt number escalates with increasing CuO nanoparticle diameter and diminishing the diameter of Al2O3 nanoparticles. The relative % variation in Nusselt number enhances with Magnetic number, whereas a declining trend is obtained for the electric field parameter. The present study's findings may be helpful in the diagnosis of hemodynamic abnormalities and the fields of nano-hemodynamics, nano-pharmacology, drug delivery, tissue regeneration, wound healing, and blood purification systems.

Finite element analysis of the impact of particles aggregation on the thermal conductivity of nanofluid under chemical reaction

A theoretical investigation is being conducted to determine the impact of nanoparticle aggregation (NPA) and thermal radiation on the radiant heat of nanofluids along with the vertical cylinder. An efficient thermal conductivity model for nanofluids is constructed based on the fractal distributive properties of nanoparticle aggregation. In view of two alternative methods of heat conduction, comprising particle aggregation and convection, the model is represented as a function of the fractal size and concentration. The contemporary models for nanofluid thermal conductivity include a significant role in nanoparticle aggregation. The impact of particle diffusion in the temperature field on aggregation and transport has yet to be investigated. The controlling boundary value problem is transformed into a system of nonlinear ordinary differential conditions, which are then solved numerically by utilizing the finite element technique with similarity transformations. The exceptional features of nanoparticles, such as high thermal conductivity, are highly significant in advancing nanotechnology, heat exchangers, material sciences, and electronics. The temperature field is observed higher in the presence of nanoparticle aggregation. The concentration gradient decreases the viscosity of water-based nanofluids, resulting in an increasing velocity. It is observed that the effect of nanoparticles aggregation on the boundary is descending by the including parameters causing an increment in heat transfer rate. The numerical approach to optimal meshing has reached convergence, and the accuracy of the presented results is supported by the fact that they are close to the results found in the relevant research.

Effects of Key Parameters on Nanofluid Thermal Performance in Heat Exchangers

AbstractNanofluids are utilized in cooling equipment such as heat exchangers and renewable energies such as solar cells as heat transfer fluid. The techniques for nanofluid stability enhancement and stability evaluations, such as zeta potential, electron microscopy, and photographic techniques for sedimentation, are reviewed as well as some recent achievements in nanofluid stabilization and research on the parameters affecting the thermal properties of nanofluids. Finally, the applications of nanofluids in cooling devices are described. Increased heat transfer, reduced heat transfer time and size of heat exchangers, and finally higher energy and heat efficiency could be the most significant achievements. The parameters affecting the thermal conductivity of nanofluids include concentration, temperature, particle fluid, type of base fluid, and nanoparticles.

Anomalous behavior of liquid molecules near solid nanoparticles: Novel interpretation on thermal conductivity enhancement in nanofluids

HypothesisRecently, it has been reported that anomalous improvement in the thermal conductivity of nanofluid composed of base liquids and dispersed solid nanoparticles, compared to the theoretically predicted value calculated from the particle fraction. Generally, the thermal conductivity values of gases and liquids are dominated by the mean free path of the molecules during translational motion. Herein, we present solid evidence showing the possible contribution of the vibrational behavior of liquid molecules around nanoparticles to increasing these thermal conductivities. ExperimentsThe behavior of liquid molecules in nanofluids containing SiO2 particles larger than 100 nm, which were dispersed in a 50 wt% aqueous solution of ethylene glycol, was investigated by means of small-angle neutron scattering, quasi-elastic neutron scattering, wide-angle X-ray scattering, and Raman spectroscopy. FindingsThe vibrational changes in the liquid molecules caused by the interactions between the nanoparticles and liquid molecules surrounding the nanoparticles contributed majorly to the increase in the thermal conductivity values of the SiO2 nanofluids. Because the vibration of liquid molecules is equivalent to phonon conduction in solids, the increase in thermal conductivity of the suspension due to the presence of nanoparticles was inferred to be derived from the limitation of the translational diffusion, which induces a solid-like behavior in the liquid.

Machine learning model to predict the efficiency of thermal conductivity of oxide nanofluids

This article gathers a comprehensive experimental dataset to obtain the efficiency of the thermal conductivity of oxide nanoparticles with the variations in particle size, particle volume concentration, and suspension temperature using Machine learning approach. ML algorithms can be explored to design and develop trans-disciplinary predictive or forecasting models to enhance the existing system performances and efficiencies. Because of the efficiency of the proposed model, it provides several applications in various areas in industries, biomedical, etc. The present investigation is expected to be a Gradient Boosting Regression (GBR), and a new approach that overrides the ‘Artificial Neural Network’ (ANN). It is presented by considering a data set of 240 using the Koo–Kleinsture–Li (KKL) model conductivity of Al2O3 and CuO-water-based nanofluids. Further, the computation of ‘Mean Absolute Error’ (MAE), ‘Root Mean Squared Error’ (RMSE), and the ‘Coefficient of Determination’ (COD) is obtained and compared with five different approaches that validate the performances of the present technique. It is concluded that the GBR Model is proven as one of the powerful promising approaches over the other approaches used for the predictive enhancements in the performances of several inputs.

Micro-mechanism Analysis of Heat conductivity Enhancement of Argon-based Copper Nanofluids

Enhancing heat transfer is the consistent goal in current industrial production. Nanofluids have greater advantages than traditional fluids.In this in the passage, MDS was used to research the mechanism of improving the heat conductivity of Cu-Ar nanofluids compared with conventional fluids. The effects of nanoparticles with various volume fractions (1% - 3%) and different sizes (r = 1.03 nm, 1.26 nm, 1.50 nm) on the heat conductivity of nanofluids were mainly discussed. The results showed that the insertion of Cu nanoparticles increased the heat conductivity of the base fluid in studied range. The heat conductivity of the nanofluid was proportional to the volume fraction of the nanoparticles, and the maximum increase was 29.1%. Inversely ratio to the particle size, the maximum increase was 15.3%. The microscopic mechanism of the systems was statistically analyzed by the radial distribution function. It was found that the addition of nanoparticles caused the change in the atomic arrangement of the nanofluids, which made the nanofluids exhibit a microstructure similar to that of the solid. This is why the heat transfer efficiency of nanofluids is higher.

Analytical-statistical review of selected researches in the field of thermal conductivity of nanofluids

Analytical-statistical review of selected researches in the field of thermal conductivity of nanofluids

Forced convection of nanofluids in metal foam: An essential review

The combination of the high conductivity of nanofluids with the huge accessible surface area of open-cell metal foam would indeed be a superb heat exchange technology. While individual ingredients of this technology have been separately investigated, the combination of the two has received only scant attention. Actually, there are only few studies that are focused on nanofluids flow and heat transfer in metal foam. This paper is intended to serve as an orientation for researchers who are interested in both nanofluids heat transfer and metal foam. The paper thus includes discussion of various aspects of nanofluids and their heat-transfer applications, as well as fundamentals of flow and heat transfer of ordinary fluids in metal foam. A section on recent heat transfer studies in metal foam is included, and another section on pore-scale modeling of transport in metal foam is provided. Nanofluids are presented in terms of their definition, brief history, preparation methods, theoretical models, properties, and engineering applications related to heat transfer. The paper then moves to describe metal foam and its morphology, as well as current understanding and theoretical treatment of flow and heat transfer in this class of highly-porous media. Key heat transfer and flow properties of the foam are outlined. The paper also presents the case for nanofluids forced convection heat transfer in open channels to serve as a background for the more complex case that involves heat transfer in porous media in general. The case of forced convection of nanofluids in metal foam in particular is presented next. Studies including complex effects, e.g. magnetism, are briefly described. Based on the above information, some key findings, conclusions and recommendations are made for interested researches.

Experimental examination of the properties of Fe3O4/water nanofluid, and an estimation of a correlation using an artificial neural network

Currently, considering that the addition of solid particles to normal fluids directs to a considerable increase in thermal conductivity, the utilization of nanofluids rather than typical liquids is extremely customary. This paper provides an experimental evaluation of the viscosity and thermal conductivity of Fe3O4/water nanofluid and provides an estimation model using an artificial neural network (ANN) and margin of deviation. For this purpose, Fe3O4 nanoparticles were synthesized by the co-precipitation method, and the size of nanoparticles was investigated by the XRD test. Also, the Fe3O4/water nanofluid was prepared in a two-step method and the stability of the nanofluid was evaluated by the zeta potential test. Then, thermal conductivity and viscosity of nanofluid were measured at temperatures of 25, 30, 35, 40, 45, and 50°Celsius and volume fractions of 0.1, 0.2, and 0.3%. The outcomes of this experiment indicated that the volume fraction of nanoparticles and temperature have an immediate relationship with the thermal conductivity coefficient of nanofluids because, with the increase of these two parameters, the thermal conductivity coefficient increases and vice versa. Also, the viscosity of the nanofluid decreases with the decrease in volume fraction of nanoparticles and increases with the decrease in temperature.Because of the absence of an exact and proper correlation to predict the viscosity and thermal conductivity of this nanofluid, two correlations are presented as a subordinate of temperature and volume fraction. Also, a analogy was made among the results of the experiment and the output of the proposed relationship and it was found that the maximum margin of deflection for thermal conductivity is equal to 0.42% and for viscosity is 0.06%. Toward the end of this research, the laboratory results were analyzed by neural networks. The designed neural network was compared with the values obtained from the suggested relationship and the outcomes indicated that in predicting the viscosity and thermal conductivity of nanofluids, the neural network has fewer blunders and higher accuracy.

Why can hybrid nanofluid improve thermal conductivity more? A molecular dynamics simulation

It has been widely recognized that the addition of nanoparticles to a base fluid can significantly enhance the thermal conductivity of nanofluids. However, the underlying mechanism is unclear, especially for hybrid nanofluids with nanoparticles composed of two or more materials. This is the first exploration of the microscopic mechanism responsible for the enhancement in the thermal conductivity of hybrid nanofluids. Firstly, the thermal conductivity and diffusion coefficient of Cu-Ag/Ar hybrid nanofluid were estimated by molecular dynamics. The results indicate that the maximum improvement in thermal conductivity of 69.72% can be achieved for a hybrid nanofluid with Cu-Ag 50% /Ar compared to liquid Ar, which is much larger than the 47.95% and 26.4% achieved for Ag/Ar and Cu/Ar nanofluids. Then, the radial distribution function demonstrates that the Ar atoms on the nanoparticle surface are in dynamical equilibrium. Finally, the nanolayer densities and diffusion coefficients are calculated for various hybrid nanofluids to explain the underlying mechanism of the enhancement in the thermal conductivity. The consistency of the thermal conductivity with the nanolayer density and diffusion coefficient indicates that nanolayer structure and diffusion of Ar are the two underneath mechanisms.

REVVING UP HEAT-TRANSFER PERFORMANCE OF DOUBLE PIPE HEAT EXCHANGER USING DIVERSE MOLAR Ag-GO HYBRID NANOFLUIDS: AN EMPIRICAL AND NUMERICAL STUDY USING CENTRAL COMPOSITE DESIGN

This study seamlessly integrates empirical and numerical approaches to enhance the efficiency of a double pipe heat exchanger (DPHX) using varied molar concentrations (0.03, 0.06, and 0.09 M) of Ag-doped GO hybrid nanofluids as the working fluids within the heat exchanger's annulus. Remarkable improvements in the heat exchanger's performance were achieved by increasing the molar concentration of Ag-GO hybrid nanoparticles, along with the Reynolds number (ranging from 250 to 1451) and the mass flow rate (ranging from 8 to 47 g/s) of the hybrid nanofluids. The utilization of a 0.09M Ag-GO hybrid nanofluid at a Reynolds number of 1451 and a flow rate of 47 g/s resulted in outstanding enhancements in heat-transfer coefficient (62.9%), and Nusselt number (33.55%) surpassing the base fluid. The empirical results of Nusselt number and heat-transfer coefficient were optimized and analyzed using the central composite design approach (CCD) with response surface method, incorporating the Graetz number, varied molar concentrations of Ag-GO, and thermal conductivity of the hybrid nanofluids as input factors. The optimized second-order polynomial quadratic equation model demonstrated excellent compatibility and optimal performance of the heat exchanger, supported by variance analysis. Additionally, CCD optimization confirmed a notable desirability function (0.99) and emphasized the significant influence of the input factors on the output responses. In conclusion, the Graetz number exhibited prominent influence among the input factors, alongside the molar concentration and thermal conductivity of hybrid nanofluids, effectively enhancing the performance of the DPHX.

THERMAL CONDUCTIVITY, RHEOLOGY AND ELECTRICAL CONDUCTIVITY OF WATER AND ETHYLENE GLYCOL BASED NANOFLUIDS WITH COPPER AND ALUMINUM PARTICLES

Nanoparticles are mesoobjects that occupy an intermediate position in size between ordinary molecules and macroscopic particles. Suspensions with nanoparticles, called nanofluids, are also specific mesoscopic suspensions. Today it is known that their thermophysical and mechanical properties are not described by classical theories. The unusual properties of these dispersed fluids make them extremely popular in a wide variety of applications. However, successful use of nanofluids involves the prediction of their properties, which in turn requires systematic studies. This paper presents an experimental study on the thermophysical properties of water and ethylene glycol based nanofluids with aluminum and copper particles. The thermal conductivity, rheology and electrical conductivity of nanofluids were systematically studied. The weight concentration of nanoparticles varied from 2.5 to 20%. It was shown that the thermal conductivity of these nanofluids significantly exceeds that of nanofluids with oxide particles. Its higher values are determined by the size of the nanoparticles and the thermal conductivity of the base fluid. The nanofluids studied are either pseudoplastic or viscoplastic. Their rheology is determined by the concentration and size of nanoparticles. The smaller the size of nanoparticles and the higher their concentration, the more likely the change in rheology is. Correlation curves were constructed for the rheological parameters of nanofluids versus the concentration and size of nanoparticles. It was found that the electrical conductivity of nanofluids increases almost linearly with increasing nanoparticle concentration and strongly depends on the nanoparticle size. The electrical conductivity mechanisms of nanofluids were discussed. The speed of sound in a nanofluid and its dependence on particle size were measured.

Thermal conductivity of ethylene glycol and propylene glycol nanofluids with boron nitride nano-barbs.

This study investigates the potential of composite allotrope boron nitride nanobarbs (BNNBs) as nanoparticles for enhancing the thermal conductivity of nanofluids based on mixtures of ethylene glycol and propylene glycol with water. BNNBs are allotrope composites composed of boron nitride nanotube cores with walls decorated with attached hexagonal boron nitride crystals, creating a jagged morphology that facilitates the formation of a connected network and contributes to the enhancement of thermal conductivity in nanofluids. BNNBs exhibit high thermal conductivity due to efficient phonon transfer and they are electrical insulators owing to their wide bandgap. The effect of BNNB concentration in carrier fluids on nanofluid thermal conductivity was investigated by introducing BNNBs into ethylene glycol-water and propylene glycol-water mixtures at 0-10 wt%. The results showed that BNNBs enhanced thermal conductivity of carrier fluids up to 45%, and the enhancement was proportional to the concentration of BNNBs in the carrier fluid. The study also investigated the dispersion stability of BNNBs in different solvents using Hansen Solubility Parameters, revealing that propylene glycol mixtures demonstrated better long-term stability compared to ethylene glycol mixtures. The findings suggest that BNNBs have great potential for use as thermally conductive nanoparticles in nanofluids for various heat transfer applications. Future research should focus on enhancing the dispersion stability of BNNB nanofluids and exploring the influence of BNNB morphology on the thermal conductivity and other thermophysical properties of nanofluids.

Experimental Evaluation of Thermal Conductivity and Other Thermophysical Properties of Nanofluids Based on Functionalized (-OH) Mwcnt Nanoparticles Dispersed in Distilled Water

A possible way to increase thermal conductivity of working fluids, while keeping pressure drop at acceptable levels, is through nanofluids. Nanofluids are nano-sized particles dispersed in conventional working fluids. A great number of materials have potential to be used in nanoparticles production and then in nanofluids; one of them is Multi-Walled Carbon Nano Tubes (MWCNT). They have thermal conductivity around 3000 W/mK while other materials used as nanoparticles like CuO have thermal conductivity of 76.5 W/mK. Due to this fact, MWCNT nanoparticles have potential to be used in nanofluids production, aiming to increase heat transfer rate in energy systems. In this context, the main goal of this paper is to evaluate from the synthesis to the experimental measurement of thermal conductivity of nanofluid samples based on functionalized (-OH) MWCNT nanoparticles. They will be analyzed nanoparticles with different functionalization degrees (4% wt, 6% wt, and 9% wt). In addition, it will be quantified other thermophysical properties (dynamic viscosity, specific heat and specific mass) of the synthetized nanofluids. So, the present work can contribute with experimental data that will help researches in the study and development of MWCNT nanofluids. According to the results, the maximum increment obtained in thermal conductivity was 10.65% in relation to the base fluid (water).

Synthesis, thermophysical characterization and thermal performance analysis of novel Cu-MXene hybrid nanofluids for efficient coolant applications.

Hybrid nanofluids are considered as an alternative for conventional heat transfer fluids and mono nanofluids due to its remarkable enhancement in thermo-physical properties. However, there are some limitations in achieving the better thermo-physical properties due to the stability of nanoparticles in different base fluids at higher concentration. This work aims at synthesizing, thermo-physical characterization and thermal performance estimation of stable Cu-MXene based hybrid nanofluids using various base fluids at very low volume concentration of Cu and MXene nanostructures. Two step method is employed to prepare Cu-MXene hybrid nanofluids by dispersing the low volume concentration of as prepared Cu and MXene nanostructures (ranging from 0.01-0.05 vol%) containing SDS surfactant in various base fluids such as water, methanol, castor oil and silicon oil. Synthesized mono and hybrid nanofluids shows excellent stability against aggregation up to 7 days as evidenced from higher zeta potential values. Wettability studies conducted using contact angle measurement suggests that the castor oil, methanol and silicon oil based hybrid nanofluids exhibits hydrophilic behavior (showing contact angle less than 90°). Hybrid nanofluids display excellent enhancement in thermal conductivity at very low concentration of nanostructures (more than 70% for methanol based Cu-MXene hybrid nanofluid). Viscosity of the silicone oil based hybrid nanofluids show a remarkable enhancement followed by water, methanol and castor oil based hybrid nanofluids. Thermal conductivity and viscosity of hybrid nanofluids are effectively validated with existing theoretical models. Moreover, specific heat and pumping power of the hybrid nanofluids with respect to volume concentration of nanostructures are determined using the existing theoretical equations. Thermal performance of hybrid nanofluids was successfully estimated using Figure of Merit (FOM) analysis and suggested the better heat transfer fluid for improving the heat transfer performance under laminar and turbulent flow conditions for efficient cooling applications.

Exploring dual solutions and thermal conductivity in hybrid nanofluids: a comparative study of Xue and Hamilton-Crosser models.

Hybrid nanofluids show great potential for heat transport applications such as solar thermal systems, car cooling systems, heat sinks, and thermal energy storage. They possess better thermal stability and properties compared to standard nanofluids. In this study, a base fluid, methanol, is injected into an electrically conducting heat-generating/absorbing disk of permeable boundary, and dual solutions are obtained. Two alternative models, Xue and Hamilton-Crosser are considered, and their thermal conductivities are contrasted. Furthermore, thermal radiation and ohmic heating are also considered, and convective boundary conditions are utilized to simulate overall heat gains or losses resulting from conduction, forced or natural convection between nearby objects of nearly constant temperature. Using a similarity transform, the governing equations are obtained and numerically solved via bvp4c, a finite difference method. It is observed that the presence of a magnetic field and the shrinking of the disk elevate the energy transport rate and wall stress. Additionally, the skin friction coefficient and thermal distribution rate increase with wall transmission constraint while fluid flow and energy transport diminish. Furthermore, particle clustering and nano-layer creation suggest that the Hamilton-Crosser model exhibits better thermal conductivity than the Xue model.

Can graphene improve the thermal conductivity of copper nanofluids?

Copper (Cu) nanofluids (NFs) have attracted attention due to their high thermal conductivity, which has conferred a wide variety of applications. However, their high reactivity favors oxidation, corrosion and aggregation, leading them to lose their properties of interest. Copper capped by graphene (Cu@G) core@shell nanoparticles (NPs) have also attracted interest from the medical and industrial sectors because graphene can shield the Cu NPs from undesired phenomena. Additionally, they share some properties that expand the range of applications of Cu NFs. In this work, new Morse potentials are reported to reproduce the behavior of Cu@G NPs through molecular dynamics. Coordination-dependent Morse parameters were fitted for C, H, and Cu based on density functional theory calculations. Then, these parameters were implemented to evaluate the thermal conductivity of Cu@G NFs employing the Green-Kubo formalism, with NPs from 1.5 to 6.1 nm at 100 to 800 K, varying the size, the number of layers and the orientation of the graphene flakes. It was found that Cu@G NFs are stable and have an improved thermal conductivity compared to the Cu NFs, being 3.7 to 18.2 times higher at 300 K with only one graphene layer and above 26.2 times higher for the graphene-trilayered NPs. These values can be higher for temperatures below 300 K. Oppositely, the size, homogeneity and orientations of the graphene flakes did not affect the thermal conductivity of the Cu@G NFs.

A molecular dynamic simulation of the influence of linear aggregations on heat flux direction on the thermal conductivity of nanofluids

To reveal abnormal enhancement of thermal conductivity of nanofluids, molecular dynamics simulation is employed to investigate effects of included angle and nanoparticles size arrangement on thermal conductivity of Cu/Ar nanofluids. Included angle θ ranged from 0 to 90°, and six types of size arrangements are designed using combinations of r 1 and r 2 of Cu nanoparticles of 11 Å and 8.48 Å, 10.5 Å and 9.45 Å. Results demonstrated by reducing θ from 90° to 0°, thermal conductivity improved from 0.165 to 0.184 W . m −1 ·K −1 as actual heat flux distance increasing. Moreover, larger Cu nanoparticles on either side attracted more Ar atoms to form a denser interfacial layer, creating a compact heat flux channel to transfer more heat. The optimum thermal conductivity is at θ = 0° and Type 1 (two larger Cu radius r 1 = 10.5 Å on either side and two smaller radius r 2 = 9.45 Å in the middle). • Abnormal enhancement of thermal conductivity was revealed from microscopic view. • Effect of included angle of linear aggregations on thermal conductivity was conducted. • Particle size arrangement on thermal conductivity was comparatively investigated. • Optimum thermal conductivity was obtained to guide design of nanofluids.

Comparison of thermal conductivity of water-based nanofluids with various combinations of MWCNT, CuO, and SiO2 nanoparticles for using in heating systems

The application of nanofluids (NFs) with improved efficiency and performance is currently a major problem for many industrial sectors. By comparing two water-based NFs (with identical nanoparticles (NPs) but with different participation percentages), this research is attempted to present a more optimal NF for industrial applications. As a result, MWCNT (50%)-CuO(25%)-SiO2(25%)/Water NFs and MWCNT (10%)-CuO (20%)-SiO2(70%)/Water NFs were synthesized, and their thermal conductivities (TC) were compared. In the same experimental settings as the second NF, the first NF with the mixture of MWCNT (50%)-CuO (25%)-SiO2(25%)/Water possesses more thermal conductivity enhancement (TCE). TCE increases by 37.10% for the first NF but only by 16.20% for the second.

Influence of thermal cycling on stability and thermal conductivity of nanofluid ice slurry

Nanofluid ice slurry has the advantages of large phase change latent heat, high energy storage density and cooling rate. In this article, a two-step approach was used to prepare water-based graphene oxide (GO), Al2O3, and GO-Al2O3 hybrid nanofluids. The effects of thermal cycling on the stability and thermal conductivity of nanofluid ice slurry were also investigated. Moreover, thermal conductivity model considering thermal cycling and mass fraction was proposed. Results show with the increase of the number of phase change cycles, the stability of nanofluids decreases gradually, and tends to be stable after 6 cycles. The lower the minimum temperature of the thermal cycling, the more serious the segregation of nanoparticles, the worse the stability of nanofluids after thermal cycling. In addition, multi-cycle of phase change has less influence on thermal conductivity of GO nanofluid than that of non-phase-change, although the influence is a little larger at the dilute concentration range. The thermal conductivity of GO-Al2O3 hybrid nanofluid ice slurry is between that of GO and Al2O3 aqueous single nanofluid after phase change thermal cycling. The influence of the phase change cycles on thermal conductivity decreases with the increase of concentration, however, the smaller the concentration, the larger the thermal conductivity reduction. The hybrid nanofluid ice slurry can ensure the dispersion stability and thermal conductivity of the fluid, and its comprehensive performance is better than that of nanofluid with one type of nanoparticle.