Thermal Conductivity Of Nanofluids Research Articles

Probing the thermal resistance of solid-liquid interfaces in nanofluids with molecular dynamics.

The significance of interfacial thermal resistance in the thermal conductivity of nanofluids is not well understood, in part because of the absence of measurements of this quantity. Here, we study the interfacial thermal resistance for metal-oil nanofluids of interest as heat transfer fluids for concentrating solar power, using density functional theory and molecular dynamics simulations. Insights on the role of chemical interactions in determining the interfacial thermal resistance are revealed. The results presented here showcase a general picture in which the stronger the chemical interactions between species at the interface, the lower the associated interfacial thermal resistance. The implications toward nanofluid design are discussed. We show that, for this important family of metal-oil nanofluids, the interfacial thermal resistance values are low enough so that it is possible to afford a reduction in particle size, minimizing stability and rheological issues while still offering enhancement in the effective thermal conductivity with respect to the base fluid.

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.

Effect of parameters-dependent viscosity and thermal conductivity on the thermo-hydraulic performance of Al2O3-based nanofluids in a rectangular microchannel

The multi-parameter dependence of nanofluid viscosity and thermal conductivity on the flow and heat transmission properties of Al2O3-H2O nanofluidin microchannels is investigated in this paper using a numerical simulation approach, exploring the effects of various parameters on flow and heat dissipation characteristics, such as flow velocity, pressure drop, temperature, and heat transfer coefficient, under different Reynolds numbers, etc. These parameters include the nanoparticles phericity (0.5~1.0), volume fraction (0.6%~6.0%), and temperature (290K~370K) of the nanofluids, as well as the boundary parameter Reynolds number (200~1000). The results show that the multi-parameter dependence of nanofluid viscosity and thermal conductivity setting has a significant impact on the flow and heat dissipation characteristics of nanofluids, comparing analysis with the nanofluid viscosity and thermal conductivity only related to the volume fraction under the same condition. Under the combined action of sphericity, volume fraction, and temperature, increasing the sphericity increases the pressure drop and decreases the heat transfer coefficient. For example, at a Reynolds number of 1000, the maximum rates of change for pressure drop and heat transfer coefficient are 0.85 and 4.26, respectively. The nanofluid thermo-hydraulic performance is sensitive to temperature, volume fraction and sphericity in turn. Setting up viscosity and thermal conductivity equations with multiple parameter dependencies can provide more accurate results for there search of nanofluids, further deepening the application research of nanofluids.

Prediction of nanofluid thermal conductivity and viscosity with machine learning and molecular dynamics

It is well-known that nanofluids differ significantly from traditional heat transfer fluids in terms of their thermal and transfer characteristics. Two of CO2 transfer characteristics, its thermal conductivity and its viscosity, are crucial to improved oil retrieval methods and industries refrigeration. By combining molecular modelling with various machine learning algorithms, this study predicts the conduction characteristics of iron oxide CO2 nanofluids. It is possible to evaluate the accuracy of these transfer parameter estimates by applying machine learning methods such as decision tree, K-nearest neighbors, and linear regression. Predicting these transfer qualities requires knowing the size, fraction of nanoparticle volume, and temperature. To determine the characteristics, molecular dynamics simulations are run using the large-scale atom Vastly equivalent simulant. An inter- and intra-variable Pearson correlation was established to confirm that the input variables were reliant on m and thermal conductivity. The results were finally confirmed by using statistical coefficients of determination. For a variety of temperature ranges, volume fractions, and nanoparticle sizes, the study found that the decision tree model was the best at predicting the transport parameters of nanofluids. It has a 99% success rate.

Numerical Simulation for Cooling of Integrated Toroidal Octagonal Inductor Using Nanofluid in a Microchannel Heat Sink

This paper presents a comprehensive numerical simulation study focused on the cooling of integrated toroidal octagonal inductor using nanofluids within a microchannel heat sink. The investigation utilizes COMSOL Multiphysics 6.0 integrated with the Fluid Flow and Conjugate Heat Transfer Module. The primary objective is to explore and understand fluid flow and heat transfer characteristics within the integrated inductor. The study involves testing three distinct fluids, water, CuO-water nanofluid, and Al2O3-water nanofluid, under laminar flow conditions within microchannels. The choice of fluid plays a significant role in heat transfer, interacting with the microchannel geometry to optimize performance. Three-dimensional computational fluid dynamics (CFD) models are meticulously developed; focusing on toroidal inductors equipped with micro pin fins heat sinks. The study commences by detailing the geometry of the micro coil and the integrated heat sink. The simulation encompasses a mathematical model that captures the intricate interplay between the governing Navier-Stokes equations for fluid dynamics and the heat transfer equations within the integrated inductor. As φ increases, temperature, viscosity, and pressure decrease. CuO-water and Al2O3-water nanofluids play a significant role in influencing laminar flow and key thermal parameters in the toroidal inductor. These nanofluids, which consist of base fluids (water) with dispersed nanoparticles (CuO or Al2O3), are employed as cooling agents to enhance heat transfer. The presence of nanoparticles in the fluid alters its thermal properties, leading to changes in the flow dynamics and overall heat dissipation within the toroidal inductor.The laminar flow characteristics are affected by the nanofluid's viscosity, density, and thermal conductivity. Additionally, the Nusselt number, Reynolds number, and thermal resistance are key thermal parameters that reflect the performance of the cooling system. The nanofluid's influence on these parameters is crucial for understanding and optimizing the thermal management of the integrated toroidal inductor. The enhancement of heat dissipation in the toroidal inductor is achieved through improved thermal properties of the nanofluid. Higher nanoparticle concentrations result in better heat transfer rates, leading to lower temperatures in the toroidal inductor. This, in turn, improves the overall efficiency and performance of the cooling system. The viscosity of the nanofluid is influenced by the presence of nanoparticles. The pressure within the microchannels is also affected by the nanoparticle concentration. An increase in φ can lead to changes in pressure drop along the microchannels. Understanding these variations is crucial for designing an effective cooling system.

Thermal Conductivity of Nanofluids: Influence of Particle Shape

Thermal Conductivity of Nanofluids: Influence of Particle Shape

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 %.

Experimental and numerical investigation of cooling effectiveness of nano minimum quantity lubrication

The literature on nano-minimum quantity lubrication (NMQL) machining suggests that its effectiveness is due to the increased thermal conductivity of the base oil and the reduced friction at the cutting interface due to the rolling effect of the nanoparticles. However, the relative contribution of the increased nanofluid thermal conductivity and nanofluid lubricity on the performance of NMQL has not been investigated. This study presents an experimental and a numerical investigation of NMQL cooling effectiveness and compares it with that of pure minimum quantity lubrication (MQL). A lumped system analysis was carried out on a flat Inconel workpiece with different heat inputs. Two different heat sources, an aluminum electric resistor and a propane torch, were used for three different cooling strategies: dry, MQL and NMQL. A propane torch was used to replicate the machining operational conditions involving vaporization of the base oil. The experimental results showed minimal improvements in the cooling effectiveness and the heat transfer coefficient of NMQL cooling when compared to MQL. There was less than a 1 % reduction in the surface temperature of the workpiece when using NMQL cooling compared to that of MQL. Additionally, the increase in the heat transfer coefficient for a flow rate of 22 ml/h was 1.07 %, while an increase of 1 % was observed at a flow rate of 44 ml/h. The experimental results suggest that the benefits of nano minimum quantity lubrication observed in the literature could be mostly associated with the lubrication benefits of the nanoparticles. This conclusion was derived from the experimental comparisons of the cooling effectiveness of NMQL over MQL, which yielded only minor improvements. Additionally, this paper presents a novel multiphase numerical model for NMQL. This model considered nanoparticles as distinct discrete particles along with the oil droplets, and it captured the flow conditions, average surface temperatures, and average heat transfer coefficient. This CFD model estimated the surface temperatures with errors below 10 %, while the heat transfer coefficient values had an average error of 12.05 %. This is the first attempt in the open literature to model a nano minimum quantity lubrication and cooling strategy using a two-phase approach.

The effect of number of nanoparticles on atomic behavior and aggregation of CuO/water nanofluid flow in microchannels using molecular dynamics simulation

A nanofluid (NF) is a mixture of metallic or non-metallic nanoparticles (NPs) suspended in a fluid. The high activity and energy of the surface atoms of NPs cause them to stick together and aggregate in the base fluid (BF). This leads to instability, changes in the NF concentration, and fouling and obstruction of the flow path, which reduces the NF's thermal conductivity (TC). In this study, the effect of copper oxide NPs on atomic behavior and the aggregation procedure in a water/CuO NF flow in a microchannel was explained using a Molecular Dynamics (MD) simulation procedure. For this purpose, various physical parameters, such as density, temperature, velocity, aggregation time (AT), and potential energy (PE), were calculated. The resultsshowthat after 1 ns, the temperature and PE converged to 300 K and −553088 eV. By CuO NPs number increasing among H2O molecules, the maximum ratio of density, temperature, and V profiles reached 1.60 g/cm3, 0.0122 Å/ps, and 303 values. The aggregation process in simulated NF was varied by these atomic changes. From a numerical point of view, by CuO NPs number increasing from 2 to 3 NPs, the AT reduced by 1.15 ns to 1.01 ns. Furthermore, this atomic evolution was investigated by PE changed from −652411 to −660258 eV.

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.

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.

Effects of variable thermal conductivity and curvature parameter on the peristalsis of hybrid nanofluid through a curved channel with curvature dependent channel walls

This study focused on the peristaltic motion of hybrid nano-liquid via a curved geometry with curvature-dependent channel walls. The hybrid nanofluid is composed of alumina (Al2O3) nanoparticles mixed with multi-wall carbon nanotubes (MWCNTs) and suspended in thermal oil. This combination is based on an experimental examination conducted by Asadi et al. [58]. Keeping in view the industrial applications of such flows, the hybrid nanofluid thermal conductivity is considered to vary with temperature. Furthermore, heat sink/source is also taken into account in the energy equation. Lubrication approximation is utilized to simplify the flow problem. The resulting model is treated using a numerical scheme NDSolve based on the shooting method (“a built-in command in Mathematica”). The primary focus remains on analyzing the impacts of the curvature parameter, flow rate, and variable thermal conductivity parameter. Outcomes indicate that there is a critical “flow rate” value for such a type of channel. The thermal transfer rate increases with an improvement in the values of the curvature parameter. Furthermore, graphical results reveal that hybrid nanofluid’s temperature decreases for higher values of flow rate, while it rises with heat generation/absorption parameter. It is observed that temperature of the hybrid nanofluid increases as the values of curvature parameter improve. A higher flow rate reduces the rate of heat transfer. It is also noted that velocity profile remains constant at a flow rate value of 7.40. Moreover, the variable thermal conductivity has a remarkable impact on temperature profile.

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.

Significance of Koo-Kleinstreuer-Li model for thermal enhancement in nanofluid under magnetic field and thermal radiation factors using LSM

Investigation of thermal transport in nanofluid flow squeezed inside a channel formed by two sheets with zero slope is common in industrial and engineering applications. The heat transmission could be affected by various physical constraints which reduce the machine efficiency for desired products. Therefore, this attempt clearly focus on the development of new nanofluid thermal transport model using the significance effects of Koo-Kleinstreuer-Li correlation which used for the estimation of nanofluid thermal conductivity, impacts of magnetic field, internal heating species, and thermal radiations. Then, the LSM (Least Square Method) is magnificently implemented and obtained the physical results for multiple ranges of parameters. It is noticed that when the squeezed parameter varied in the ranges of [Formula: see text] to [Formula: see text] and [Formula: see text] to [Formula: see text], the fluid loss their velocity and more reduction is occurred about [Formula: see text]. However, outward movement of the plate lead to quick declines in the velocity. Further, when the Hartmann number increased for [Formula: see text]–[Formula: see text] then the fluid moves slowly and stronger magnetic field resists its motion. Moreover, the Eckert and Radiation numbers boosted the fluid temperature by keeping the feasible nanoparticles concentration in the range of [Formula: see text]–[Formula: see text].

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.

Experimental Investigation on Thermal Conductivity of Palm Oil and Zinc Oxide PFAE-based Nanofluids

Vegetable oil (VO) have been constantly researched as an alternative to the conventional mineral oil (MO) in the application of transformer insulation liquid. VO is deemed as a suitable replacement for MO as they are a renewable source, cheaper in price, and have a high thermal conductivity, high flashpoint, and high breakdown voltage value. In addition, the trending interest in nanofluids has made it possible to further improved the insulating properties of VOs. This paper reports the experimental results of thermal conductivity test of Palm oil-based nanofluids and Palm fatty acid ester (PFAE)-based nanofluids. The nanoparticles used in this work is Zinc Oxide (ZnO) <50nm nano powder and the nanofluid (NF) samples are varied by low, medium and high concentrations. The test was conducted at 9 different temperatures from 25°C to 65°C with 5°C gap. The result shows that a low and medium concentration nanofluid has an improvement in thermal conductivity value, up to 42.6% and 59.5% respectively for palm oil-based nanofluid. Meanwhile, the high concentration palm oil-based nanofluid has lower enhancement in thermal conductivity value at certain temperatures. As for PFAE-based nanofluids, the thermal conductivity value has improved by up to 27% and 14.4% for medium and high concentration respectively. Nanofluids with medium concentration of ZnO, has the highest enhancement in insulating and cooling properties for both palm oil and PFAE-based nanofluids. This observation is supported by the kinematic viscosity value of the mentioned nanofluid.

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.

Enhanced thermal conductivity in Ag-H2O nanofluids by nanoparticles of different shapes: Insights from molecular dynamics simulation

The mechanism of thermal conductivity enhancements and influential factors make a lot of sense in the intensive investigation of nanofluids. While numerous studies have explored the effect of solid–liquid interfacial layers on thermal conductivity improvement in water-based nanofluids with diverse applications, investigations into the intrinsic mechanisms underlying interfacial layers in relation to nanoparticle shape remain limited. The thermal conductivity of water-based Ag-H2O nanofluids was investigated using molecular dynamics simulations of non-equilibrium molecular dynamics (NEMD), with emphasis placed on the shape and volume fraction of nanoparticles. The influence of nanofluid volume fraction on thermal conductivity was revealed through visual analysis of 3D smoothed surface plots, highlighting the positive correlation between thermal properties and increasing volume fraction of nanoparticles. Moreover, the contribution of different shapes of silver nanoparticles to thermal conductivity enhancement demonstrated an increasing trend with the corresponding growth in surface-to-volume ratio (S/V values associated with different particle shapes). For the volume fraction of 1.5%, the minimum thermal conductivity enhancement of 22.75% was observed for the spherical shape, while the maximum thermal conductivity enhancement of 25.51% was achieved with the triple-platelet particles. Importantly, the comprehensive analysis of radial distribution function (RDF) and mean square displacement (MSD) indicated the positive effect of interfacial nanolayers influenced by nanoparticles of various shapes on the thermal conductivity of nanofluids. This study provides valuable insights into the impact of nanoparticle shape on the thermal properties of nanofluids, opening up promising prospects for future molecular dynamics simulations.

Finite element study of nanofluid through porous nonlinear stretching surface under convective boundary conditions

The current model examines the impact of convective boundary condition on the MHD nanofluid flow across a permeable nonlinear stretching surface in a steady, laminar, incompressible boundary layer. Basic equations get transmuted to a nonlinear model after selecting relevant similarity transformations which are explored numerically using validated FEM (finite element method) code. Results explicitly demonstrate traits of pertinent parameters on species concentration, temperature, and velocity functions. The numerical code accuracy is validated by comparing the outcomes with existing data. The study reveals that surge in the nanoparticles enhances nanofluid thermal conductivity. Heat transfer, Skin friction, and Sherwood number are sturdily influenced by the permeability of the sheet. These findings are of practical importance in designing heat transfer systems that employ nano-fluids as their principal heat exchange medium and support applications in futuristic technology which thrives on heat enhancement research like renewable and geothermal energy arena.The current study can be extended to flows considering pressure gradients on arbitrary surfaces and to cases of three-dimensional flows, and non-Newtonian flows.