Conductivity Of Nanofluids Research Articles

Multi-objective optimization and price performance factor evaluation of polyaniline nanofibers-palm oil nanofluids for thermal energy storage application

The application of nanofluid in thermal energy storage technology has attracted interest among researchers in the development of novel nanofluids with high thermal conductivity behavior. Therefore, it is crucial to study the thermal physical behavior of formulated nanofluids prior to extend use in greener energy production. In this study, response surface methodology (RSM) is employed to optimize the density, viscosity and thermal conductivity behavior of formulated polyaniline-palm oil nanofluids. RSM based central composite design (CCD) is applied to extract the significant impact of temperature in the range of 30–60 °C and volume concentration of nanoadditives in the range of 0.01–0.5 vol% to the thermal physical properties of polyaniline-palm oil nanofluids and to generate empirical mathematical model for prediction purpose. Finally, the price performance factor of the studied polyaniline-palm oil nanofluids is evaluated for the first time in this research. Analysis of variance is employed to verify that the generated mathematical regression model is reliable. The formation of 45° angle line in the middle of the predicted vs actual data graph with acceptable R2 of 94.43% for density model, 99.43% for viscosity model, and 94.18% for the thermal conductivity model showing an excellent agreement of both predicted and actual data and verified the reliability of the generated regression equation for response prediction. Optimal density, viscosity and thermal conductivity of polyaniline-palm oil nanofluids found to be 0.8878 g/mL, 25.8251 mPa s and 0.2877 W/mK respectively with the critical parameters for temperature and volume concentration of polyaniline are 60 °C and 0.0347 vol% respectively. The PPF evaluation shows that the higher thermal conductivity of nanofluids are not economical. The formulated polyaniline-palm oil nanofluid evaluated properties expose the possibility of alternative advanced heat transfer fluid for thermal energy storage application due to their superior inherent qualities.

Influence mechanism of functionalization of CNTs on the thermal transport property of their nanofluids

Carbon nanotube (CNT) nanofluids have promising application prospects as the working fluids in energy and power systems due to their prominent thermal properties, while the poor stability of CNT nanofluids is one of the biggest obstacles to their practical application. The addition of functional groups on CNTs is a good method to improve the dispersion of CNT nanofluids, while the influence mechanism of this strategy on the thermal transport property is not clear. In this work, the thermal conductivities of CNT, CNT-OH, and CNT-COOH nanofluids were investigated by experiments and molecular dynamics (MD) simulations to reveal the influence mechanism of functionalization on the thermal transport ability of CNT nanofluids. Both experimental measurements and MD simulations indicate the significant suppression of functionalization on the thermal transport property of CNT nanofluids. Furthermore, the influence mechanism of functional groups on the thermal conductivity of CNT nanofluids has been revealed by analyzing the effect of the thermal conductivities of CNTs, the formation of hydrogen bonds, the mean square displacement of water molecules, and the interlayer thermal resistance of CNT and water. This work is helpful to the utilization of CNT nanofluids as working fluids in energy systems.

A comprehensive experimental study on thermal conductivity of ZrO2-SiC /DW hybrid nanofluid for practical applications: Characterization, preparation, stability, and developing a new correlation

This study mainly intended to identify changes in the thermal conductivity of distilled water (DW) because of the addition of Zirconium oxide (ZrO2) and Silicon carbide (SiC) nanoparticles. Keeping this in view, the study considered hybrid nanofluid samples developed with the help of ZrO2 (dp: 20 nm)-SiC (dp: 45––60 nm) nanoparticles. Initially, the research involved conducting XRD, TEM, EDX and FESEM tests which allowed for conducting phase and structural analysis and also facilitated the study of nanoparticles’ microstructure. In the next step, a magnetic stirrer and ultrasonic vibrator were used in sequential steps for dispersing and homogenizing Nanomaterials in distilled water. The volume concentration (φ) was kept between 0.025 and 0.1 % during the synthesis of samples. Then the samples were checked to evaluate their thermal conductivity through the transient hot wire method. In this way, the thermal conductivity of all samples was evaluated for temperatures between 20 and 60 °C. The results indicated a positive association of the thermal conductivity of nanofluid with parameters like temperature and solid volume fraction. It was noted that ZrO2-SiC/DW hybrid nanofluid depicted a nearly 25.75 % increase in thermal conductivity at a 0.1 % volume fraction of nanoparticles and a temperature of 60 °C. The value of the thermal conductivity ratio of ZrO2-SiC/DW was determined using the proposed mathematical correlation. The fitting method was applied to experimental data which depicted the R-Square value of 0.9906 which is considerably high. It was found that the proposed equation gave an acceptable value of the maximum margin of deviation (i.e.,1.15 %).

Numerical investigations of ion slip and hall effects on Cattaneo-Christov heat and mass fluxes in darcy-forchheimer flow of Casson fluid within a porous medium, utilizing non-fourier double diffusion theories through artificial neural networks ANNs

The current research focuses on harnessing the computational power of neural networks, notably the Levenberg-Marquardt backpropagation optimization algorithm (ANN-LMBOA), to simulate the behavior of magnetohydrodynamic (MHD) three-dimensional non-Newtonian Darcy-Forchheimer flow of Casson nanofluid over a stretching surface situated within a porous medium. The fluid flow is subject to the influence of Hall and ion slip forces, contributing to its behavior. Additionally, the Cattaneo–Christov heat flux model is incorporated to represent the temperature balance in the boundary layer accurately. Non-Newtonian fluids improve thermal transmission due to their shear-thinning propensity, increased turbulence, and more excellent convective heat transfer. The thermal conductivity of nanofluids is increased by the inclusion of nanoparticles, enabling more effective heat transmission. Buongiorno's model is used for the nanofluid phenomena that concentrate on thermophoresis and Brownian motion. To tackle the flow problem efficiently, the governing equations are transformed into non-linear differential equations using self-similar variables. The ANN-LMBOA algorithm is then employed to solve these equations, providing a practical and accurate approach to the simulation. To train and validate the ANN-LMBOA model, a dataset is generated using the bvp4c numerical method, which can easily handle highly non-linear coupled differential equations. The dataset is created for different scenarios of flow parameters, facilitating comprehensive training, testing, and authentication of the neural network. The accuracy of the ANN-LMBOA model is assessed through various statistical neural network tools, such as MSE, RP, CF graphs, and EH. The study thoroughly investigates flow model parameters related to momentum, energy, and concentration profiles, which are presented using visual representations to understand the system's behavior and characteristics comprehensively. The effects of various parameters of interest like Casson, ion slip, porosity, thermal relaxation, and magnetic parameters on velocity, temperature, and concentration distribution have been studied numerically via ANN-LMBOA networks. The absolute error of reference and target data is obtained in the range of 10−4 − 10−5which proves the best accuracy performance of ANN-LMBOA networks. By raising the Prandtl and Schmidt numbers, solutal, thermal, and diffusional variables, one can reduce the diffusion of mass and heat.

Thermal Transport and Rheological Properties of Hybrid Nanofluids Based on Vegetable Lubricants.

Nanofluids based on vegetal oil with different wt.% of carbon nanotubes (CNT), hexagonal boron nitride (h-BN), and its hybrid (h-BN@CNT) were produced to investigate the effects of these nano-additives on the thermal conductivity and rheological properties of nanofluids. Stable suspensions of these oil/nanostructures were produced without the use of stabilizing agents. The dispersed nanostructures were investigated by SEM, EDS, XRD, and XPS, while the thermal conductivity and rheological characteristics were studied by a transient hot-wire method and steady-state flow tests, respectively. Increases in thermal conductivity of up to 39% were observed for fluids produced with 0.5 wt.% of the hybrid nanomaterials. As for the rheological properties, it was verified that both the base fluid and the h-BN suspensions exhibited Newtonian behavior, while the presence of CNT modified this tendency. This change in behavior is attributed to the hydrophobic character of both CNT and the base oil, while h-BN nanostructures have lip-lip "bonds", giving it a partial ionic character. However, the combination of these nanostructures was fundamental for the synergistic effect on the increase of thermal conductivity with respect to their counterparts.

Review on emission reduction of VCR system using various nano‐refrigerants

AbstractThe focus of the study is the exploration of the possible efficacy and environmental advantages of nanofluids, including hybrid nanoparticles, concerning the effectiveness of the Vapor Compression Refrigeration System (VCRS). This necessitates a thorough assessment of the stability qualities, synthesis techniques, thermal conductivity, viscosity, and heat transfer efficiency of nanofluids. This research also aims to explore different Nano‐Refrigerant combinations that influence the Coefficient of Performance (COP). According to some research findings, the use of copper nanoparticles improved COP by 8.1%, while the use of alumina nanoparticles raised COP values as high as 6.71%. Moreover, studies have demonstrated that various hybrid nanoparticles like silver‐graphene oxide also greatly improve outcomes concerning amounts used, raising the system's overall performance capabilities. Finally, it can be said that there is a wide range of applications for these fluids because they can function cost‐effectively and environmentally friendly while significantly reducing energy consumption, acting as refrigerants or secondary fluid systems, and having a positive impact on performance when used in deployed VCRS configurations.

Experimental studies on thermal conductivity of metal oxides/water-ethylene glycol (50%-50%) nanofluids with varying temperature and concentration using ultrasonic interferometer

The thermal conductivity of nanofluid has been measured using an ultrasonic interferometer with the help of Bridgman's equation. Due to the strong dependency of the thermal conductivity of nanofluid on base fluid, we have taken different combinations of water and ethylene glycol. An equal proportion of both fluids gives the highest thermal conductivity. The thermal conductivity of nanofluid with concentrations of 0.05%, 0.1%, and 0.2% have been measured within the temperature range of 30OC to 80OC. Measured thermal conductivity of the prepared nanofluids is increased up to 288% (CuO), 272% (MgO), and 234% (Fe2O3) and found better increment in comparison to the results reported by earlier researchers. Observed increment in the thermal conductivity of metal oxide nanofluids can be explained on the basis of static and dynamic parameters of the nanofluid. The high thermal conductivity of such nanofluids suggests it as a potential candidate for systems where heat transfer is required.

Modelling of Viscosity and Thermal Conductivity of Water-Based Nanofluids using Machine-Learning Techniques

In this study, a variety of machine-learning algorithms are used to predict the viscosity and thermal conductivity of several water-based nanofluids. Machine learning algorithms, namely decision tree, random forest, extra tree, KNN, and polynomial regression, have been used, and their performances have been compared. The input parameters for the prediction of the thermal conductivity of nanofluids include temperature, concentration, and the thermal conductivity of nanoparticles. A three-input and a two-input model were utilized in modelling the viscosity of nanofluid. Both models considered temperature and concentration as input parameters, and additionally, the type of nanoparticle was considered for the three-input model. The order of importance of the most influential parameters in predicting both viscosity and thermal conductivity was studied. A wider range of input parameters have been considered in an open-access database. With the existing experimental data, all of the developed machine learning models exhibit reasonable agreement. Extra trees were found to provide the best results for estimating thermal conductivity, with a value of 0.9403. In predicting viscosity using a three-input model, extra trees were found to provide the best result with a value of 0.9771, and decision trees were found to provide the best results for estimating the viscosity using a two-input model with a value of 0.9678. In order to study heat transport phenomena through mathematical modelling, it is important to have an explicit mathematical expression. Therefore, the formulation of mathematical expressions for predicting viscosity and thermal conductivity has been carried out. Additionally, a comparison with the Xue and Maxwell thermal conductivity models is made to validate the results of this study, and the results are observed to be reliable.

The Combined Effect of Gravity Modulation and Throughflow on Thermal Instability in the Hele-Shaw Cell Filled with Oldroyd-B Nanofluid

This paper shows the combined effect of throughflow and gravity modulation on the stability of Oldroyd-B nanofluid filled in Hele-Shaw cell. Nanofluid compared to the base fluid has higher thermal conduction. The thermal conductivity of nanofluid increased and thus increases the amount of energy transferred. The Oldroyd-B fluid model is important because of its numerous applications such as production of plastic sheet and extrusion of polymers through a slit die in polymer industry, biological solution pant tars glues, etc. In linear stability analysis, we found the expression of the critical Hele-Shaw Rayleigh number by using the normal mode method. Two-term Fourier series method is used for non-linear stability analysis and is also considered the Brinkman model for flow of nanofluid in Hele-Shaw cell. In linear stability analysis, we observed that there is no effect of Oldroyd-B nanofluid, which means that Deborah number (λ1) and retardation parameter (λ2) do not affect the stability analysis. Oldroyd-B nanofluid is similar to ordinary nanofluid in linear analysis. In non-linear analysis, Deborah number, retardation parameter, throughflow, gravity modulation, and Hele-Shaw number play a major role in heat/mass transfer. Enhancement in both heat/mass transfer in the system while increasing throughflow and Deborah number. An increment in Hele-Shaw number (Hs), decreases heat/mass transfer in the system.

Experimental comparison of the photothermal conversion performance of coal and plant soot nanofluids for direct absorption solar collectors

Direct absorption solar collectors (DASCs) based on nanofluids improve the thermal utilization efficiency of solar energy. In this study, the waste soot materials obtained from coal and plant biomass combustion were recycled. Deionized water was used as the basic fluid. A two-stage method was used to prepare coal soot (CS) and plant soot (PS) nanofluids. Detailed investigations and comparisons of the thermal conductivity and light-absorbing characteristics of the two fluids were performed. The results suggested that the introduction of soot nanoparticles improved the heat transfer and light-absorbing capacities of the base fluid and both thermal conductivity and optical absorption of soot nanofluids were positively correlated with the soot nanoparticle loading. Subsequently, photothermal conversion experiments were conducted internally and externally. The experimental results indicated that the PS nanofluids could absorb incident light radiation more efficiently and converted it into thermal energy. Specifically, the overall photothermal conversion efficiency of the PS nanofluid at a maximum mass fraction of 0.02% in the solar simulator was 55.7%, which was approximately 9.8% higher than that of the CS nanofluid. In addition, 42.3 °C and 44 °C were the maximum temperature increases under natural sunlight for the CS nanofluid and PS nanofluid, respectively. Notably, the waste soot materials are low cost and easily available. Therefore, this work demonstrates that recycled soot nanofluids, especially PS nanofluids, are promising candidates for application in DASCs systems.

Why is the thermal conductivity of Janus nanofluid larger? – From the perspective of aggregation morphology

The nanoparticle aggregation is a crucial factor for the thermal conductivity of nanofluids based on previous researches. Molecular dynamics simulations for the nanofluids involving 4–32 Janus nanoparticles (termed as Janus nanofluid) were conducted. The thermal conductivity of the Janus nanofluid was found to be larger than that of the ordinary nanofluid, approximately 0.3707 W/m.K for Janus, compared to 0.3521 W/m.K for Au and 0.3303 W/m.K for Cu nanofluid. Further analysis shows that the aggregation morphology in the Janus nanofluid is different from that in the ordinary one. For the better evaluation of the aggregation morphology, a term named degree of looseness was introduced to replace fractal dimension. The relationship between thermal conductivity and degree of looseness shows that the thermal conductivity of the Janus nanofluid first rises and then decreases with the degree of looseness. When 18 nanoparticles are included in the simulated system, the maximum thermal conductivity is 0.3249 W/m.K for a moderate degree of looseness of 10.841 at 4 vol%. Finally, the underlying mechanism why the Janus nanofluid has a larger thermal conductivity than the ordinary one is discussed. This can provide a useful reference for preparing Janus nanofluids with larger thermal conductivity and understanding the underlying mechanisms.

Predictive modelling of thermal conductivity in single-material nanofluids: a novel approach

BackgroundThis research introduces a novel approach for modelling single-material nanofluids, considering the constituents and characteristics of the fluids under investigation. The primary focus of this study was to develop models for predicting the thermal conductivity of nanofluids using a range of machine learning algorithms, including ensembles, trees, neural networks, linear regression, Gaussian process regressors, and support vector machines.The main body of the abstractTo identify the most relevant features for accurate thermal conductivity prediction, the study compared the performance of established feature selection algorithms, such as minimum redundancy maximum relevance, Ftest, and RReliefF, a newly proposed feature selection algorithm. The novel algorithm eliminated features lacking direct implications for fluid thermal conductivity. The selected features included temperature as a thermal property of the fluid itself, multiphase features such as volume fraction and particle size, and material features including nanoparticle material and base fluid material, which could be fixed based on any two intensive properties. Statistical methods were employed to select the features accordingly.ResultsThe results demonstrated that the novel feature selection algorithm outperformed the established approaches in predicting the thermal conductivity of nanofluids. The models were evaluated using fivefold cross-validation, and the best model was the model based on the proposed feature selection algorithm that exhibited a root-mean-squared error of validation of 1.83 and an R-squared value of 0.94 on validation set. The model achieved a root-mean-squared error of 1.46 and an R-squared value of 0.97 for the test set.ConclusionsThe developed predictive model holds practical significance by enabling nanofluids' numerical study and optimisation before their creation. This model facilitates the customisation of conventional fluids to attain desired fluid properties, particularly their thermal properties. Additionally, the model permits the exploration of numerous nanofluid variations based on permutations of their features. Consequently, this research contributes valuable insights to the design and optimisation of nanofluid systems, advancing our understanding and application of thermal conductivity in nanofluids and introducing a novel and methodological approach for feature selection in machine learning.

Analytical investigation of graphene oxide blood base nanofluid with the impact of dynamic viscosity and viscous dissipation

The aim of this research paper is to analytically investigate graphene oxide blood base nanofluid with the impact of dynamic viscosity and viscous dissipation. The increased thermal conductivity of nanofluids over regular fluids motivates this research. The basic flow equations are used to model the flow problem in nonlinear partial differential equations (NLPDEs). The dimensionless parameters, classical lie group, and thermo-physical properties are applied to transform the developed NLPDEs into dimensionless ordinary differential equations (ODEs). The resultant ODEs are resolved using the homotopy analysis method (HAM), and graphical and tabular interpretations are used to note the effects of contributing parameters including magnetic parameter, dynamic viscosity, nanoparticle volume friction, Eckert number, and Prandtl number on the velocity profile and temperature distribution. From the obtained results, we see that the velocity profile is decreasing by increasing magnetic parameter, dynamic viscosity, nanoparticle volume friction, and the temperature profile is increasing by increasing dynamic viscosity and Eckert number. The tabular descriptions of convergence of the presented fluid flow are also provided.

From experimental data to predictions: Artificial intelligence supported new mathematical approaches for estimating thermal conductivity, viscosity and zeta potential in Fe3O4-water magnetic nanofluids

Magnetic nanofluids (MNs) are considered advanced heat transfer fluids of the future due to their ability to function as intelligent fluids, with the applied external magnetic field effect being readily manageable. In this study, firstly, the stabilities of Fe3O4-water MNs prepared at 0.1, 0.25, 0.5, 0.75 and 1 mass ratios were determined by zeta potential measurement. The thermal conductivity and viscosities of MNs with appropriate stability were measured at 20–60 °C for all mass ratios. Secondly, using experimental data, two different artificial neural network (ANN) models were developed: one for thermal conductivity and viscosity depending on the temperature (20–60 °C) and mass ratio values and one for zeta potential depending on pH and mass ratio. Finally, using the obtained ANN data, two new mathematical correlations are proposed to predict thermal conductivity and viscosity. The study's results revealed that the developed ANN model has MSE and R values of 4.51E-06 and 0.99968, respectively, for thermal conductivity and viscosity of Fe3O4-water MNs can be accurately predicted by novel mathematical correlations.

Comparison of thermal conductivity of nanofluids with single-walled and multi-walled carbon nanotubes

The purpose of the present paper is to systematically study and compare the thermal conductivity of nanofluids based on water+surfactants, ethylene glycol, ethylene glycol+surfactants, and isopropyl alcohol with single-walled (SWCNT) and multi-walled carbon nanotubes (MWCNT). The weight concentration of carbon nanotubes varied from 0.01 to 1 %. In all cases, the excess of the thermal conductivity coefficient in the nanofluid with SWCNTs is significantly higher than with MWCNTs. However, it should be borne in mind that the volume concentrations of MWCNTs in this case are several times lower than those of SWCNTs. The maximum excess of thermal conductivity coefficient was registered in the nanofluids based on isopropyl alcohol and is over 50 % at a SWCNT weight concentration of only 0.2 %. The enhancement of the thermal conductivity coefficient is greater, the lower the thermal conductivity coefficient of based fluid. The effect of the surfactants on the thermal conductivity of nanofluids is discussed. The thermal conductivity coefficients of nanofluids based on ethylene glycol+surfactants are almost twice as high as those of nanofluids based on ethylene glycol. It is shown that at a fixed weight concentration, the thermal conductivity of nanofluids increases with a decrease in the nanotube length, but their volume concentration also increases in almost proportion to the length decreasing. Finally, the nanofluids with SWCNTs provide an excess of the thermal conductivity unattainable with the use of conventional spherical nanoparticles.

Effect of interfacial layer around core-shell nanoparticles on thermal conductivity of nanofluids

Core-shell nanoparticles are a special type of nanostructured materials characterized by excellent dispersion in nanofluids due to surface modification. In this work, we investigate the thermal conductivity of nanofluid considering different surface materials and core-shell ratios. It has been found by molecular dynamics simulations that the thermal conductivity of core-shell nanofluids can be significantly enhanced compared to that of ordinary nanofluids. The nanofluid with Cu@Au particles has a better thermal enhancement than the Cu@Ag nanofluid. The thermal conductivity of core-shell nanofluid exhibits a non-monotonic variation with increasing core-shell ratio, which cannot be explained by classical Maxwell model. Based on the analysis of phonon state density, it is found that higher thermal conductivity is consistent with more match degree of phonon coupling at the interface. The findings in the present paper have important implications for the design of nanofluids with better thermal properties..

MOLECULAR DYNAMICS SIMULATION OF THE VISCOSITY AND THERMAL CONDUCTIVITY OF AL2O3-H2O NANOFLUIDS

This research investigated Al2O3-H2O nanofluids’ conduction of heat and viscosity using molecular dynamics method. For nanofluids, consequences of the nanoparticles’ volume fraction, temperature, size, and shape were studied. EMD computed the Al2O3-H2O nanofluid viscosity, while NEMD calculated the thermal conductivity. The findings indicated the sensitivity of the nanofluid to temperature and volume fraction variations. Alongside rising volume fraction, viscosity and conduction of heat likewise rise. When the temperature rises, the viscosity will decrease, and the thermal conductivity will increase. And the variation trend is the same as the experimental value. At the same temperature, the particle size of the nanoparticles becomes smaller, the specific surface area of the particles becomes larger, and the viscosity and thermal conductivity of the nanofluids become larger. And get the shape of the nanoparticles at the time of low volume fraction of greater effects on the viscosity and thermal conductivity of nanofluids, but in the case of high-volume fraction, volume fraction increases nano fluid viscosity and thermal conductivity coefficient is bigger.

Investigation of thermal conductivity and thermal performance of heat pipes by structurally designed copolymer stabilized ZnO nanofluid

The present study concentrated on estimating the thermal conductivity, stability, efficiency, and resistance of a heat pipe for heat exchangers, which were essential for many industrial applications. To achieve this, copolymer of amphiphilic poly (styrene-co-2-Acrylamido-2-methylpropane sulfonic acid) poly (STY-co-AMPS) was synthesized by free radical polymerisation technique. The dispersant were used for homogeneous solution and stabilization of ZnO nanofluids. The effect of dispersant on the thermal conductivity of nanofluids was analysed using a KD2 pro thermal property analyser. There is a significant increase in fluid conductivity had a nonlinear relationship with the volume fraction. The maximum enhancement was observed at an optimized concentration of dispersant at 1.5 vol%. Same time, the influence of dispersant agent on the thermal conductivity of nanofluids were compared with linear polyelectrolytes. Further, the experimental values were compared to the existing classical models based on the reasonable aggrement, the prepared nanofluids were employed as a working medium. The conventional screen mesh heat pipe and the temperature distribution to the thermal resistance of the heat pipe was investigated experimentally. The result shows, optimum concentration of dispersants on nanoparticles exhibits an enhanced heat efficiency as compared with the base fluids. Further, the thermal resistance and temperature distribution show decreased behaviour by increasing the particle volume fraction and dispersant concentration.

Stability, optimum ultrasonication, and thermal and electrical conductivity estimation in low concentrations of Al12Mg17 nanofluid by dynamic light scattering and beam displacement method

The thermal conductivity and stability of nanofluids pose challenges for their use as coolants in thermal applications. The present study investigates the heat transfer coefficient (HTC) of an Al12Mg17 nanofluid through the utilization of a novel beam displacement method. The study also examines the nanofluid's stability, particle size distribution (PSD), TEM micrograph, and electrical conductivity. From three distinct categories of surfactants, a particular surfactant (CTAB) was chosen to disperse Al12Mg17 nanoparticles in DI water, and subsequently, a two-step method was employed to generate the nanofluid. Dispersion stability is visually monitored and quantified with a zeta potential test. HTC and PSD are measured using optical setups. To evaluate the results, the HTC obtained from the beam displacement method is compared with that of the KD2 Pro apparatus, and the PSD findings are analyzed through TEM micrographs. The results show that a 0.16 vol.% CTAB is the maximum stability for 0.025 vol.% Al12Mg17 nanofluid properly. The optimum ultrasonication period is 2 h, yielding a peak PSD of 154 nm. Increasing nanoparticle concentration enhances HTC up to 40% compared to the base fluid at 0.05 vol.%. Electrical conductivity increases linearly from 155 to 188 μ{rm S}/mathrm{cm} with nanoparticle concentration. Optical methods for measuring HTC in nanofluids offer the advantage of early results, prior to bulk motion. Thus, the application of nanofluids in thermal systems necessitates the development of optical techniques to improve accuracy.

Microscopic thermal characteristics of parallelly arranged nanowires in a liquid: the role of interface thermal resistance, solid-like liquid layer, and the restricted phonon transport

Nanofluids based on extended nanostructures, such as nanowires, have been demonstrated improved thermal conductivities (κ). However, the lack of a complete understanding at the microscopic level hinders the development of such nanofluids towards practical applications. We aim to provide it by investigating how the interface thermal resistance (R b ), ballistic phonon transport, and the solid-like liquid layer affect the heat conduction in nanowire-based nanofluids. By employing Non-Equilibrium Molecular Dynamics (NEMD), it is found that the heat conduction in the parallelly arranged liquid and the nanowires exhibit a coupled thermal behavior owing to the R b . This contradicts the predictions of the classical parallel heat conduction model, therefore, a novel model is proposed taking this coupled behavior into account. Using this model, it is shown that the high κ of the solid phase has a limited contribution to the effective κ of nanofluids having short nanowires due to the dominant R b effect. For the case of long nanowires, however, the individual nanowire κ becomes a vital parameter defining the effective κ. Further, NEMD calculations reveal that the κ of suspended nanowires in a liquid is markedly reduced, questioning the validity of classical effective medium theories which use the bulk parameters. This reduction is attributed to surface atoms’ restricted vibrational freedom and the nanowire’s phonon-boundary scattering. By substituting this reduced κ of the solid phase into the new mathematical model, the theoretical predictions align closely with the NEMD calculations, exhibiting deviations below 10%. The sole contribution from the solid-like liquid layer to the κ enhancement lies between 20%–30% in the nanofluids presently considered. Therefore, the findings of this study highlight the important roles play by the identified microscopic thermal characteristics in defining the effective κ of nanofluids based on nanowires.