Thermal Conductivity Of Nanoparticles Research Articles

Thermal characteristics of hybrid Nanofluid (Cu-Al2O3) flow through Darcy porous medium with chemical effects via numerical successive over relaxation technique

The flow of fluids through porous media is commonly described using the Darcy model, therefore investigating hybrid nanofluids in this setting is rather new. The present work offers insightful information on how the hybrid nanofluids behave and function in porous medium. The study's conclusions may have an impact on a lot of different engineering applications like filtration systems, chemical reactors, and environmental engineering. The study concentrates on a hybrid nanofluid which consists of Cu and Al₂O₃ nanoparticles. The metallic nanoparticles such as copper have high thermal conductivity and non-metallic nanoparticles such as aluminum oxide are chemically stable and has high thermal resistance. This is the reason that the combination Cu-Al₂O₃ is believed to give better heat transfer composite than using individual nanofluids. By employing proper similarity transformation, the governing PDEs are turned into ODEs. To discretize these ODEs, the central finite difference method is used first. Then the successive over relaxation technique is utilized to numerically solve the nonlinear equations. The findings are summarized in a graphical and tabular format. The impacts of several controlling parameters such as porosity, suction, Schmidt number and volume fraction on flow pattern, thermal properties, and concentration are investigated and discussed. The streamwise and normal velocity profiles fall and those of concentration and temperature rise with increase in the values of the porosity parameter.

Comparative analysis of varied thermal conductivity and viscosity models in a hybrid nanofluid flow - a non-similar solution

ABSTRACT We aim to investigate the flow of an ethylene glycol-based hybrid nanofluid over a horizontal surface with a free stream velocity. To assess the viscosity and thermal conductivity of nanoparticles with various shapes, we employed the Brinkman, Timofeeva, and Yamada Ota unit cell models. The study explores heat transfer in the flow system considering the effects of viscous and ohmic dissipations, linear radiative heat flux, and heat generation under convective boundary conditions. A non-similar system is developed using appropriate non-similarity transformations up to the third-level truncation and numerically solved using the bvp4c algorithm. The novelty of the proposed model lies in obtaining the non-similar solution and incorporating the Brinkman, Timofeeva, and Yamada Ota unit cell models. The results indicated that the Brinkman viscosity model resulted in an increase in the wall heat transfer rate with a rise in particle volume fraction. It is also inferred that platelet-shaped nanoparticles exhibit a significantly higher heat transfer rate compared to spherical-shaped nanoparticles.

Intelligence modeling of the flow boiling heat transfer of nanorefrigerant for integrated energy system

To promote the application of nanorefrigerant in Organic Rankine Cycle and Integrated Energy System a reliable model with simple structure and favorable accuracy for predicting the flow boiling heat transfer coefficient (HTC) of nanorefrigerant is essential. In this work, four intelligence models—the radial basis function (RBF), multilayer perceptron (MLP), least squares support vector machine (LSSVM), and adaptive neuro fuzzy inference system (ANFIS)—were developed to predict the flow boiling heat transfer coefficient using nanorefrigerants, based on 765 experimental samples. The performances of these artificial intelligence models were comprehensively evaluated through accuracy analysis, variation trend analysis, and sensitivity analysis. Results indicated that the comprehensive performance of the RBF model was superior than those of other intelligence models and the existing empirical models. The RBF model accurately captured the variation trend of the output as the input variables were varied. Meanwhile, the impact degrees of all input variables in decreasing order were nanoparticle concentration (φ), mass flux (G), thermal conductivity of nanoparticle (kp), and vapor quality (x).

Expanded graphite nanoparticles-based eutectic phase change materials for enhancement of thermal efficiency of pin–fin heat sink arrangement

This experimental work foresights the synthesis and characterization of the expanded graphite (EG) enhanced organic-organic eutectic PCM (EGePCM) with different weight percentages of the EG (ϕ = 1–5 %). In particular, the expanded graphite nanoparticles were exfoliated from the graphite flakes by acid protonation followed by extraction from the traditional co-precipitation synthesis. A series of EGePCM nanocomposites were fabricated through the homogeneous suspension of EG nanoparticles in the eutectic mixture (1:1) of organic PCM. The surface texture, physical morphology and chemical interaction of individual elements present inside the nanocomposites were thoroughly examined by FTIR, XRD, SEM, and FESEM. Conversely, the physical attributes, thermal stability, thermal degradation, and charging-discharging performance were also investigated. The EGePCM nanocomposite with 3 wt% of EG nanoparticles expresses a favourable increase of thermal conductivity by 232.24 %, with a nominal reduction in the latent heat storage capacity (176.5 J/g) of the PCM. In conjunction with the thermal attribute intensification, the incorporation of EG in nanocomposite reduces the supercooling degree to 9.16 °C with a phase change enthalpy of 174.8 J/g in cooling and 176.5 J/g in heating stage for 3 wt% of EG. A comprehensive experimental methodology was employed to investigate the thermal enactment features of different EGePCM nanocomposites inside a regular pin–fin arrangement imperilled with steady-state heat transfer. The effectiveness of the cooling rate was also encompassed under a variable range of heat flux (1.6–3.2 KW/m2) and EGePCM mixture (ϕ = 1–5 %). The heat transfer effectiveness was optimal at a mass fraction of 5 wt%, resulting in a 30 °C base temperature drop of the heat sink and a decrease in the charging-discharging duration of the nano PCM composite. This observation suggests an improvement in the thermal conductivity of the system with an incorporation of high thermal conductive EG nanoparticles. The enhanced thermal performance and notable thermal, physical and chemical stability facilitate the phase transition progression and render it highly advantageous for electronic or micro-electronic thermal cooling applications.

A Review on the Nanofluids-PCMs Integrated Solutions for Solar Thermal Heat Transfer Enhancement Purposes

The current review offers a critical survey on published studies concerning the simultaneous use of PCMs and nanofluids for solar thermal energy storage and conversion processes. Also, the main thermophysical properties of PCMs and nanofluids are discussed in detail. On one hand, the properties of these types of nanofluids are analyzed, as well as those of the general types of nanofluids, like the thermal conductivity and latent heat capacity. On the other hand, there are specific characteristics of PCMs like, for instance, the phase-change duration and the phase-change temperature. Moreover, the main improvement techniques in order for PCMs and nanofluids to be used in solar thermal applications are described in detail, including the inclusion of highly thermal conductive nanoparticles and other nanostructures in nano-enhanced PCMs and PCMs with extended surfaces, among others. Regarding those improvement techniques, it was found that, for instance, nanofluids can enhance the thermal conductivity of the base fluids by up to 100%. In addition, it was also reported that the simultaneous use of PCMs and nanofluids enhances the overall, thermal, and electrical efficiencies of solar thermal energy storage systems and photovoltaic-nano-enhanced PCM systems. Finally, the main limitations and guidelines are summarized for future research in the technological and research fields of nanofluids and PCMs.

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.

A comprehensive review electronic cooling: A nanomaterial perspective

In the 21st-century era of electronics and the digital world, electronic gadgets are handy worldwide, and routine life without utilizing these gadgets is difficult. Compact size and lightweight are the most predominant factors in the development of chronic devices. Compacting the size of the electronic device faces the biggest challenges of heat transfer caused though maximum collapse. A study area that combines the advantages of nanofluids with mini channels has emerged as one of the most intriguing options as the heat generated by new electronic components keeps increasing. In this review paper, paramount information in the form of a summary of on-going research and completed research work of alternate methods are included. This article reviews current studies on the application of nanofluids for cooling electronics. This article also presents several fascinating aspects on the utilization of nanofluids and methods to be used for cooling electrical components. Additionally, this article provides and discusses the potential directions for future study and the current issues in this field. In the not-too-distant future, it has been shown that using nanofluids as innovative cooling medium said as novel coolants and heat pipes can considerably improve the cooling technology for electronic equipment. The traditional cooling methods are not efficient for thermal management, so the new devices developed through carbon nanotubes (CNT) with high thermal conductive nanoparticles, nanofluids, and hybrid materials enhance heat transfer through electronic devices. The advancement of such technology may provide viable outcomes by reducing the dimensions of electronic gadgets and depicting an improvement in their efficiency.

Analysis of thermophysical properties and performance of nanorefrigerants and nanolubricant-refrigerant mixtures in refrigeration systems

Nanorefrigerants and nanolubricants have improved refrigeration system productivity. This research theoretically investigates Multi-Walled Carbon nanotubes (MWCNTs) and Copper Oxide (CuO) nanoparticles at volume fractions of 0.5, 1, and 2% in R152a and R134a refrigerants with Polyester (POE) lubricant. Thermophysical characteristics and COP will be examined. As volume concentration increases, thermal conductivity, density, and viscosity enhance, while specific heat capacity diminishes. However, after the nanoparticle volume concentration surpasses its optimal value, the specific heat capacity declines, potentially resulting in a reduction in cooling capacity. This inverse link suggests that in order to perform at one's best, a balance must be achieved. Despite this, nanorefrigerants and nanolubricant-refrigerants have improved COPs. Nanoparticle thermal conductivity is the main reason. R152a-based nanolubricant-refrigerants have greater COP values than R134a-based ones. R152a-MWCNTs-based nanorefrigerant had a maximum COP increase of 27.63% over R152a.In conclusion, nanorefrigerants made of R152a and Polyester (POE) lubricant are a good refrigeration option. Due to their high performance and environmental friendliness, these materials improve efficiency and energy consumption more than R134a.

Nanofluids: New Generation Fluids on Industrialization Path

In the last Editorial, we presented the evolution of the journal "Energy and Thermofluids Engineering, ETE" (Mahbubul 2021) and mentioned that the Journal focuses on three main areas: renewable energy, conventional energy, and thermofluids engineering. The current Editorial is about nanofluids as one of the most promising thermofluids. Nanofluids are complex mixtures of nanoscale solid particles (1-100 nm) with heat transfer fluids. Conventional heat transfer fluids (e.g., water, glycols, refrigerants, oils, etc.) have very low thermal conductivities. This restricts the thermal phenomena, resulting in poor thermal efficiencies and exergetic outputs. When nanofluids are designed to have high thermal conductivity nanoparticles, they exhibit higher thermal conductivities than those of conventional heat transfer fluids.

Heat generation/absorption effects in thermally radiative mixed convective flow of [formula omitted] hybrid nanofluid

Investigation on hybrid nanofluids has increased significantly, and findings indicate that these fluids are effective heat transfer fluids for industrial applications. The thermal conductivity of nanoparticles, particle volume fractions, and mass flow rates play a major role in the enhancement of heat transfer of nanofluids. The enhancement in thermal transport only depends on the thermal conductivity of the nanoparticles when particle volume fractions and flow rates are constant. Therefore, the purpose of this article is to analyze the flow of Zn−TiO2/H2O hybrid nanofluid past an inclined shrinking surface. The PDEs of the flow model are converted into ODEs by using similarity conversions. The mathematical problem is tackled numerically by employing the bvp4c solver in Matlab software. The impacts of controlling parameters on fluid velocity, temperature distribution, skin friction coefficient, and local Nusselt number are studied and graphically depicted. The obtained results reveal that the mixed convection parameter is found to enhance friction drag and heat transport rate for the upper branch while it reduces the lower branch. Moreover, the kinetic energy is inclined as the heat sink/source, Eckert number, and temperature ratio parameters increase, causing the thermal distribution to rise for both branches.

Photothermal and thermoelectric performance of PCMs doped with nanoparticles and metal foam

Combination of solar thermal power generation and Phase Change Materials (PCMs) helps solve the disadvantages of solar instability. In order to improve the photothermal performance of PCMs, PCMs doped with high thermal conductivity metal foam and nanoparticles were applied. The influence of nanoparticle mass fractions (ω = 0%, 0.5%, 0.8%, 1.0%), pore densities of metal foam (PPI = 5, 10, 15), light intensities (I = 600–1000 mw/cm2) was discussed. Results showed that an increase in pore densities of metal foam is beneficial to improving the thermal conductivity and increasing the temperature difference, the metal foam PPI = 15 can bring a maximum temperature rise of 7.9%, and can increase the temperature difference growth rate by 24.82%. The addition of nanoparticles increases the maximum voltage and power by up to 8.53% and 8.0%, respectively. This research has certain reference and guiding value for the application of thermoelectric power generation.

Radiative and exponentially space-based thermal generation effects on an inclined hydromagnetic aqueous nanofluid flow past thermal slippage saturated porous media

Advances in nanoscience and technology acquired the significance of the nanofluid in novel functional polymers like fibre insulation, geothermal system and chemical catalytic reactors. Inspired by the above applications, an innovative mathematical model is established for radiative nanoliquid flow and is engendered due to stretching sheet with inclined magnetic field which is immersed with nanoparticles. Joule dissipation and exponentially-based heat source/sink effects are employed in the present phenomenon under the heat constraints. The governing equations, which describe the flowing nanofluid, are transformed into invariant dimensionless equations with suitable similarity quantities. With the adoption of a shooting scheme with Runge–Kutta-45, the resultant equations are numerically simplified. The impact of several converted dimensionless elements on physically interesting values is depicted visually. The current analysis is validated through comparison with some selected related literature, which shows a positive correlation. The nanoparticle thermal conductivity is raised for an increased value of the thermal radiation, thermal viscosity and heat source to propel temperature profiles. The heat flux gradient significantly affects the heat propagation all over the flow regime.

Simulation and Property Characterization of Nanoparticle Thermal Conductivity for a Microscale Selective Laser Sintering System

Abstract Current additive manufacturing (AM) technologies are typically limited by the minimum feature sizes of the parts they can produce. This issue is addressed by the microscale selective laser sintering system (μ-SLS), which is capable of building parts with single micrometer resolutions. Despite the resolution of the system, the minimum feature sizes producible using the μ-SLS tool are limited by unwanted heat dissipation through the particle bed during the sintering process. To address this unwanted heat flow, a particle scale thermal model is needed to characterize the thermal conductivity of the nanoparticle bed during sintering and facilitate the prediction of heat affected zones. This would allow for the optimization of process parameters and a reduction in error for the final part. This paper presents a method for the determination of the effective thermal conductivity of copper nanoparticle beds in a μ-SLS system using finite element simulations performed in ansys. A phase field model (PFM) is used to track the geometric evolution of the particle groups within the particle bed during sintering. Computer aided design (CAD) models are extracted from the PFM output data at various time-steps, and steady-state thermal simulations are performed on each particle group. The full simulation developed in this work is scalable to particle groups with variable sizes and geometric arrangements. The particle thermal model results from this work are used to calculate the thermal conductivity of the copper nanoparticles as a function of the density of the particle group.

Unsteady Water-Based Ternary Hybrid Nanofluids on Wedges by Bioconvection and Wall Stretching Velocity: Thermal Analysis and Scrutinization of Small and Larger Magnitudes of the Thermal Conductivity of Nanoparticles

The uniqueness of nanofluids in the field of thermal analysis and engineering is associated with their thermal conductivity and thermodynamics. The dynamics of water made up of (i) single-walled carbon nanotubes with larger magnitudes of thermal conductivity of different shapes (i.e., platelet, cylindrical, and spherical) and (ii) moderately small magnitudes of thermal conductivity (i.e., platelet magnesium oxide, cylindrical aluminum oxide, spherical silicon dioxide) were explored in order to address some scientific questions. In continuation of the exploration and usefulness of ternary hybrid nanofluid in hydrodynamics and geothermal systems, nothing is known on the comparative analysis between the two dynamics outlined above due to the bioconvection of static wedges and wedges with stretching at the wall. Reliable and valid numerical solutions of the governing equation that models the transport phenomena mentioned above are presented in this report. The heat transfer through the wall increased with the wall stretching velocity at a smaller rate of 0.52 and a higher rate of 0.59 when the larger and smaller thermal conductivity of nanoparticles were used, respectively. Larger or smaller magnitudes of the thermal conductivity of nanoparticles were used; the wall stretching velocity had no significant effects on the mass transfer rate but the distribution of the gyrotactic microorganism was strongly affected. Increasing the stretching at the wedge’s wall in the same direction as the transport phenomenon is suitable for decreasing the distribution of temperature owing to the higher velocity of ternary hybrid nanofluids either parallel or perpendicular to the wedge.

Application of Nanofluids in Improving the Performance of Double-Pipe Heat Exchangers-A Critical Review.

Nanofluids can be employed as one of the two fluids needed to improve heat exchanger performance due to their improved thermal and rheological properties. In this review, the impact of nanoparticles on nanofluid properties is discussed by analyzing factors such as the concentration, size, and shape of nanoparticles. Nanofluid thermophysical properties and flow rate directly influence the heat transfer coefficient and pressure drop. High thermal conductivity nanoparticles improve the heat transfer coefficient; in particular, metallic oxide (such as MgO, TiO2, and ZnO) nanoparticles show greater enhancement of this property by up to 30% compared to the base fluid. Nanoparticle size and shape are other factors to consider as well, e.g., a significant difference in thermal conductivity enhancement from 6.41% to 9.73% could be achieved by decreasing the Al2O3 nanoparticle size from 90 to 10 nm, affecting nanofluid viscosity and density. In addition, equations to determine the heat transfer rate and the pressure drop in a double-pipe heat exchanger are presented. It was established that the main factor that directly influences the heat transfer coefficient is the nanofluid thermal conductivity, and nanofluid viscosity affects the pressure drop.

Thermal operation by nanofluids with various aspects: a comprehensive numerical appraisal

This research is a good understanding of unsteady convective transport of nanofluids inside a skewed cavity considering MHD, different combinations of base fluids and nanoparticles, shapes, sizes (1–50 nm), volume fractions, and with/without Brownian effects. The right and left sidewalls of the enclosure are heated at low and high temperature, respectively, whereas the bottom and top walls are insulated. The finite element technique with Galerkin’s residual has been implemented for solving non-linear PDEs that govern the flow for the current problem. The numerical simulations have been presented using streamlines, isotherms, and temperature transport rates for different parameters, non-dimensional time, skewed angles, base fluids, nanoparticles, shapes, sizes, and volume fractions. The outcomes show that heat transport rate augments about 12.55% with an additional 2.5% nanoparticles into the base fluid for Cu-H2O nanofluid. An optimal thermal transport rate is observed for kerosene-based nanofluid, blade shape, and 1 nm size of nanoparticles. Magnetite nanoparticles show a greater thermal performance of 4.72% than higher thermal conductivity nanoparticles (copper and cobalt). Thermal transport enhances about 110.78% with Brownian motion for 5% concentrated blade-shaped kerosene-Fe3O4 nanofluid than without Brownian effects. In addition, a new linear regression equation with multiple variables has been derived from the obtained results.

Experimental Investigation into Heat Transfer Enhancement of Phase Change Material

It is known fact that thermal energy storage system is very promising technique used for storing energy. The present work investigates the performance of Latent heat storage system (LHS) using phase change material (PCM) i.e paraffin wax during charging and discharging. There are number of ways to improve thermal performance of energy storage systems. Thermal conductivity of PCM can be improved by addition of high thermal conductive nano particles. In this work, Latent heat storage experimental set up has been developed and series of experiments have been carried out. An appropriate geometry in the form of a concentric double pipe heat storage unit is chosen. Graphene Nanoparticles (GNP) are added to improve the thermal conductivity of PCM and its effect has been investigated. Charging and discharging performances have been evaluated in terms of contours of temperature and liquid fraction variation for both plain PCM and PCM with 3% GNP for process parameters such as Stephen number (St) and Reynolds number (Re). The obtained contours help in predicting and drawing concluding remarks for the effect of addition of GNP on charging and discharging performances of PCM..

Using molecular dynamics simulation to investigate the number of wall layers and pyramidal surface roughness on atomic behavior and boiling characteristics of water/Fe nanofluid flow

In today’s heating and cooling industry, all investigators assume environmentally friendly and economic procedures because of environmental and energy crisis issues. Boiling heat transfer (BHT) is one of the impressive heat transfer (HT) procedures known in various engineering applications. The thermal conductivity of nanoparticles (NPs) is several times that of the base fluid. One way to amend a fluid’s thermophysical attributes is to utilize nanofluids (NFs) as boiling fluid. In this research, Fe NPs have been used in the water-based fluid. Fe NPs to the base fluid for reasons like enhancement of the HT surface, enhancement of the heat capacity of the fluid, enhancement of the effective thermal conductivity, and monotony of the temperature gradient in the fluid cause a substantial enhancement in HT coefficient. So far, the effect of phase change time and the effects of pyramidal surface height on the Fe/water NF have been rarely done numerically using molecular dynamics simulation (MDS). The main purpose of the current modeling is to improve the atomic behavior and pool boiling heat transfer (PBHT) of water/Fe NF. This paper investigates the effect of atomic layers and atomic pyramidal surface roughness (SR) with different heights on atomic behavior and PBHT by the MDS. The outcomes display that increasing the number of layers increases the maximum density value from 0.029 to 0.033 atom/Å3 and reduces the maximum temperature from 789 K to 653 K. The increase in the number of atomic layers in the microchannel (MC) wall corresponds to the decrease in heat flux (HF) in the MC. With addition of pyramidal SR, the HF in the MC increases.

A Smart Model for the Prediction of Heat Transfer Coefficient during Flow Boiling of Nanofluids in Horizontal Tube

The goal of this study is to improve the accuracy and the validity of the prediction of the heat transfer coefficient (HTC) throughout flow boiling of different water-based nanofluids in a horizontal tube by developing an artificial neural network model using Ag/water, Cu/water, CuO/water, Al2O3/water, and TiO2/water nanofluids. The multiple layer perceptron (MLP) neural network was designed and trained by 354 experimental data points that were collected from the literature. Thermal conductivity of nanoparticle, mass flux, volumetric concentration, and heat flux were used to serve as input variables of the model. The heat transfer coefficient (HTC) was used as the output variable. Via the method of the trial-and error, MLP with 8 neurons in the hidden layer was attained as the optimal artificial neural network structure. This developed smart model is more accordant with the experimental data than the correlations of the literature. The accuracy of the developed smart model was validated by the value of mean squared error (MSE=0.042) and the value of determination coefficient (R2= 0.9992 ) for all data.

Free Convection Boundary Layer Flow from a Vertical Truncated Cone in a Hybrid Nanofluid

The present study investigates the mathematical model of free convection boundary layer flow from a vertical truncated cone immersed in Cu/water nanofluid and Al2O3-Cu/water hybrid nanofluid. The governing non-linear equations are first transformed to a more convenient set of partial differential equations before being solved numerically using the Keller-box method. The numerical values for the reduced Nusselt number and the reduced skin friction coefficient are obtained and illustrated graphically as well as temperature profiles and velocity profiles. Effects of the alumina Al2O3 and copper Cu nanoparticle volume fraction for hybrid nanofluid are analyzed and discussed. It is found that the high-density and highly thermal conductivity nanoparticles like copper contributed more in skin friction and convective heat transfer capabilities. The appropriate nanoparticles combination in hybrid nanofluid may reduce the friction between fluid and surface but yet still gave the heat transfer capabilities comparable to metal nanofluid.