Thermal Conductivity Of Nanofluids Research Articles

Enhanced heat transfer and thermal storage performance of molten K2CO3 by ZnO nanoparticles: A molecular dynamics study

Molten salt serves as a popular medium for thermal storage and transfer in concentrated solar power (CSP) systems. The existing experimental studies have found the capacity of enhancing the heat storage property of the molten carbonate by doping ZnO nanoparticles. However, it has been rarely reported to conduct simulations of ZnO/molten carbonate nanofluids, and the underlying mechanism of how ZnO nanoparticles enhance the heat transfer and storage capacity has less been explored. In this study, molecular dynamics (MD) simulation has been used to investigate the heat transfer and storage properties of a molten K2CO3 nanofluid containing ZnO nanoparticles. It was found that as the nanoparticle mass ratio in molten salt based nanofluids rises, their specific heat capacity initially increases but subsequently diminishes. The specific heat capacity peaks when the ZnO mass ratio is at 1 wt%, resulting in an increase of 17.83 % compared with the base salt. The thermal conductivity in nanofluids based on molten salt increases with the increasing of ZnO mass ratio. Multi-perspective analysis of the mechanism how ZnO nanoparticles enhance the thermal energy storage performance of molten salt, including the microstructural analysis, number density, vibrational density of state, and heat flow decomposition, is conducted. In addition, results of the number density illustrates that a compressed layer of K+ is formed surrounding the ZnO nanoparticles, and the change in the potential energy leads to an increasing heat capacity. Analysis of the heat flow decomposition suggests that the enhanced nonbonded interaction is the main reason for the improvement of thermal conductive performance in the nanofluids based on molten salt. The findings in the present study provide a new perspective of how ZnO nanoparticles enhance the thermophysical properties of molten salts.

Thermal conductivity of carbon-based nanofluids; a theoretical modeling using nano-quantitative structure–property relationships

Heat transfer processes account for a significant portion of the world's total energy consumption, making it crucial to enhance their efficiency. Nanofluids have garnered considerable attention in this domain as a means to achieve this objective. This study focuses on investigating the impact of key variables, including temperature, concentration, and specific surface area of graphene nanoplatelets (GNPs) dispersed in distilled water, on the thermal conductivities of these nanofluids. The quantitative structure–property relationship (QSPR) methodology is employed for this analysis. The experimental conditions in preparing GNP-based nanofluid are encoded using the quasi-SMILES notation. The calculated optimal descriptors were acquired using the Monte Carlo method in CORAL software. The results of the best model indicated that the R2m values for all splits are more than 0.5 and ΔR2m less than 0.2. The predictive prowess and reliability of the models were substantiated by the statistical characteristics. For the best model (split 2), these values are R2m= 0.9290 and ΔR2m= 0.0301, which reveals the quality of the model. The created models had a clearly defined application domain, and correlation weights were calculated for all splits. The analysis of calculated correlation weights for the developed model are used to identify the main effects of experimental variables. The obtained results indicated that high concentrations, high temperatures, and high surface areas positively influenced the thermal conductivity ratio of the investigated carbon-based nanofluids. Conversely, low temperatures and low surface areas had a negative impact on the target property.

Peristaltic transport of diethylene glycol-based magnesium aluminate nanofluid under the effects of viscous dissipation and Hall current

ABSTRACT The fundamental importance of peristaltic phenomena in numerous biological systems, including dialysis, heart-lung machines, urine transportation, invertebrate locomotion, and the passage of gallbladder bile into the intestines, persuaded scientists to investigate the different aspects of it over the past few years. In addition, peristaltic pumping is an important consideration in industrial processes including, roller pumps used for the transport of sanitary materials, finger pumps, cell separation, and endoscopes. Keeping such inspirational applications in mind, the present investigation is dedicated to exploring the peristaltic transport of a Cross-magneto nanofluid through a curved geometry in the presence of heat transfer. The inspection of the heat transfer is conducted by utilizing variable viscosity and ohmic heating aspects. This study also retains the characteristics of viscous dissipation, heat generation/absorption, and thermal conductivity of nanofluids. Modeling of the 2D, incompressible Cross nanofluid with peristaltic movement is carried out using a curvilinear coordinate system. The basic equations of the problem are mPodeled in the light of physical laws and then simplified by adopting the lubrication theory. The solutions of the resulting nonlinear system are computed numerically. The outcomes of the related flow parameters on the nanofluid’s temperature, pressure gradient, velocity distribution, heat transfer, streamlines pattern, and stresses at the wall are discussed through graphs. The graphical results depict that the rates of heat transfer are augmented by increasing the curvature parameter, Hartmann number, and nanoparticles’ volume fraction while decreasing the Hall parameter and temperature-dependent viscosity parameter. Moreover, the nanofluid’s temperature increases by improving the values of the heat generation parameter, whereas it decreases for the Hall parameter. Furthermore, a reduction in the axial velocity occurs near the center of the channel, when Hartmann number attains higher values.

Why Carbon Nanotubes Improve Aqueous Nanofluid Thermal Conductivity: A Qualitative Model Critical Review

Media thermal conductivity is important in various heat-transfer processes. Many conventional fluid conductors suffered low conductivity and environmental issues. Therefore, research was active in finding out alternative systems, mostly relying on aqueous liquids that are low-cost and ecofriendly. After the emergence of carbon nanotubes (CNTs), with their many special structural, electrical and thermal properties, they have been examined for many applications, including heat-transfer processes. Adding CNTs to water yields CNT aqueous nanofluids that have been widely investigated as heat-transfer media. The literature shows that CNT addition improves water thermal conductivity and other water properties, such as viscosity, surface tension, freezing point and boiling point. The literature also shows that nanofluid thermal conductivity improvement is affected by CNT type and concentration, in addition to other factors such as surfactant addition. All these subjects were widely described in literature, focusing on experimental, modelling and theoretical accounts. Despite the wide literature, there exist inconsistencies and discrepancies between reports that need to be justified. In addition to technical papers, many reviews were published on various aspects of the subject including experimental results and mathematical modeling. However, the very basic question here is as follows: Why does adding CNT to water affect its thermal conductivity? In spite of the wide published literature, this issue was not targeted in a simple qualitative approach. This review provides a clear understanding of how CNTs improve thermal conductivity of aqueous nanofluids. A qualitative model is presented to explain mechanisms behind improvement as presented in the literature. CNT type effects are discussed with other factors such as aspect ratio, Reynold number, dispersion quality, composition, temperature and additives. CNT functionalization is described. Relations to estimate nanofluid thermal conductivity are discussed. The model will help specialists to tailor CNT aqueous nanofluid characteristics as desired by varying types and concentrations of CNT and surfactant, and other factors.

Combined conjugate free convection and thermal radiation in a porous inclined enclosure occupied with Cu-Al2O3 hybrid nanofluid

This study deals numerically with a steady laminar combined conjugate natural convection and thermal radiation heat transfer within an inclined porous enclosure occupied with Cu-Al2O3 /water hybrid nanofluid; the thermal conductivity and nanofluid viscosity are correlated experimentally. The centered heat sink and source are attached respectively to the upper and lower enclosure walls at a set distance of 0.5 from the sidewall and were parameterized with length values of 0.2, 0.4, 0.6, 0.8, and 1. This analysis is performed in wide ranges of enclosure inclination angles (0≤ α ≤ 180°), thermal radiation parameters (0≤ Rd ≤ 200), hybrid nanofluid volume fractions (0.1, 0.33, 0.75, 1, 2, 3, 4, and 5%), Rayleigh numbers (103≤ Ra ≤ 106), Darcy numbers (10−5≤ Da ≤ 10−2), medium porosities (0.1≤ ε ≤ 0.9), and thermal conductivity ratios (1≤ γ ≤ 10). The Nusselt number increases as the heat sink/source length decreases. This trend is even more pronounced when the length of the sink/source is reduced. Furthermore, the average Nusselt numbers for the fluid and solid matrix gradually increase as the inclination angle of the enclosure varies from 0° to 45°. However, beyond 45°, both Nusselt numbers exhibit a noticeable decrease with increasing inclination angle of the enclosure. The average Nusselt number of the fluid significantly increases with varying thermal radiation parameters. When the thermal radiation parameter ranges from 0 to 2 and 0 to 200, the average Nusselt number of the fluid increases by 192% and 7343%, respectively. The Nusselt numbers significantly improve when the porosity decreases and the thermal conductivity ratio increases. For a porosity of 0.1 and a porous thermal conductivity ratio ranging from 1 to 10, the average Nusselt number of the fluid increases by approximately 129% and 141% when the Darcy number increases from 10−5 to 10−2.

Analysis of the heat transfer enhancement in water-based micropolar hybrid nanofluid flow over a vertical flat surface

Abstract This article presented micropolar hybrid nanofluid flow comprising copper and alumina nanoparticles over a flat sheet. The mixed convection phenomenon is studied under the effect of gravity. Some additional forces such as magnetic field, thermal radiation, Eckert number, heat source, and thermal slip condition are adopted in this analysis. The leading equations are transformed into dimensionless format by employing appropriate variables and then evaluated by homotopy analysis method (HAM). The obtained results are compared with published results and found a good agreement with those published results. Also, the results of HAM are compared with those of numerical method and found a good agreement as well. The fluctuations within the flow profiles are showcased utilizing figures and tables, followed by an in-depth discussion and analysis. The outcomes of this work show that the higher volume fractions of copper and alumina nanoparticles improved the hybrid nanofluid viscosity, which results in the augmenting variation in the velocity profiles. The higher volume fractions of copper and alumina nanoparticles improved the hybrid nanofluid thermal conductivity, which results in the augmenting variation in thermal distribution. The growing mixed convection factor amplifies the buoyancy force toward the stagnation point flow, which enlarges the velocity panel. The effects of hybrid nanoparticles (Cu-Al2O3/water) at the surface are smaller on friction force and larger in case of thermal flow rate when compared to the nanofluids (Cu/water and Al2O3/water).

Thermophysical Properties of SWCNT Nanofluid

AbstractThe current paper has the objective of exploring the thermophysical properties (TPPs) of single walled carbon nanotube (SWCNT) nanofluids (NFs) for heat transfer applications. The effect of dispersing the nanotubes in conventional heat transfer fluids (HTFs) has been investigated. Carbon nanotubes (CNTs) have exceptionally high thermal conductivities. Using the effective medium theory, density, specific heat, thermal conductivity, and viscosity has been evaluated as functions of volume fractions. The base fluids considered in this work are water, ethylene glycol, engine oil, and kerosene oil as these are the commonly used HTFs for industrial applications. Theoretical predictions from this study show an increase in density, thermal conductivity, and viscosity with volume fraction while there is a decrease in specific heat with volume fraction. The effect of the aspect ratio (AR) of CNTs on the thermal conductivity of NF is also investigated. For low volume fractions of SWCNT, the thermal conductivity has been found to increase with AR. Further, this study has been extended for a real‐world application of heat transfer using SWCNT. In this paper, a numerical investigation of heat transfer in a square enclosure with differentially heated vertical walls, filled with SWCNT‐water NF under natural convection is carried out. Due to low concentrations of SWCNT in water, single phase flow is assumed. Velocity and temperature distributions are obtained as solutions to the energy and Navier–Stoke's equations. A comparison of the temperature and velocity profile has been represented by simulating the system over a range of Rayleigh numbers.

Thermal Conduction in Hybrid Nanofluids and Aggregates

Hybrid nanofluids contain more than one type of nanoparticle and have shown improved thermofluidic properties compared to more conventional ones that contain a single nanocomponent. Such hybrid systems have been introduced to improve further the thermal and mass transport properties of nanoparticulate systems that affect a multitude of applications. The impact of a second particle type on the effective thermal conductivity of nanofluids is investigated here using the reconstruction of particle configurations and prediction of thermal efficiency with meshless methods, placing emphasis on the role of particle aggregation. An algorithm to obtain particle clusters of the core–shell type is presented as an alternative to random mixing. The method offers rapid, controlled reconstruction of clustered systems with tailored properties, such as the fractal dimension, the average number of particles per aggregate, and the distribution of distinct particle types within the aggregates. The nanoparticle dispersion conditions are found to have a major impact on the thermal properties of hybrid nanofluids. Specifically, the spatial distribution of the two particle types within the aggregates and the shape of the aggregates, as described by their fractal dimension, are shown to affect strongly the conductivity of the nanofluid even at low volume fractions. Cluster configurations made up of a high-conducting core and a low-conducting shell were found to be advantageous for conduction. Low fractal dimension aggregates favored the creation of long continuous pathways across the nanofluid and increased conductivity.

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.