Particle Thermal Conductivity Research Articles

Numerical Simulation Study on Heat Transfer Characteristics of Particle‐Loaded Flow in Microchannels

AbstractParticle suspension is considered powerful and perspective in enhancing heat transfer, but the unclear nature of particle‐loaded flow limits further understanding of particle suspension. Therefore, in this paper, orthogonal experiments and the arbitrary Lagrangian‐Eulerian method were used to investigate the heat transfer characteristics of particle‐loaded flow. Results indicate that when the particle is at the inlet of the microchannel, the factors arranged in important order are the Reynolds number, blockage ratio, particle initial position, specific heat capacity, and thermal conductivity. When the particle is far from the microchannel inlet, the factors arranged in important order are the Reynolds number, blockage ratio, particle initial position, thermal conductivity, and specific heat capacity.

Experimental and numerical investigation of bimodal particle distributions for enhanced thermal conductivity in particle based concentrating solar power applications

Ceramic-based solid particles have recently attracted substantial interest as a thermal transport medium in high-temperature energy storage and thermal energy conversion systems due to their ability to operate at high temperatures (>1000 °C). This is especially useful in the concentrating solar power (CSP) industry where solid particles are utilized as heat transfer and energy storage media. Thermal conductivity of particles in CSP is critically important to the overall heat transfer that occurs within a heat exchanger. A low-cost and effective method to increase the thermal conductivity of a particle bed is by reducing its porosity by employing 2 differently sized particles. This is done in this work by examining several large particle volume fractions for the binary particle mixture of HSP 16/30–40/70. The thermal conductivity can be increased further by applying a load to the particles. At lower temperatures (20 to 300 °C), previous work has demonstrated a binary particle distribution has superior thermal conductivity. In this paper, the thermal conductivities of binary particle distributions under pressure loading are explored at ambient temperatures revealing enhanced thermal conductivity. Furthermore, high temperature (from 300 to 700 °C) analysis of binary particle distributions are also explored with results being that monodispersed distributions yield higher thermal conductivities due to enhanced surface radiation in larger particles. A bimodal distribution increases packed bed thermal conductivity only up to around ∼ 375 °C at which monodisperse distributions with larger particles yield the highest thermal conductivities. These results are compared to existing models (such as the widely used ZBS model) to demonstrate that the current models don’t adequately predict the impact of porosity on thermal conductivity, particularly at higher temperatures where radiation becomes the dominant heat transfer mechanism.

Particle-particle contact heat transfer models in thermal DEM: A model comparison and experimental validation

Models for particle-particle heat transfer are essential in DEM of thermal processes. A recently proposed model that easily calculates particle-particle heat transfer coefficients from effective packed bed thermal conductivity has for the first time been implemented in DEM for the thermal equilibration of particles with initially different temperatures in a rotating flat 2D drum and in a stirred 3D mixer. Experiments have been performed with low thermal conductivity polyoxymethylene and highly thermal conducting aluminum spheres. Comparisons show that it is mandatory to consider heat conduction in both involved phases, particles and gas, when the particle thermal conductivity is low. Derivation of particle-particle heat transfer from bed conductivity works well for both particle types, which is also true for classical, more complicated mixed conduction models. Contrary, the often used model of Batchelor for conduction only through the contact area between particles fails to describe heat transfer at low particle thermal conductivity.

Studies on Evaluation of the Thermal Conductivity of Alumina Titania Hybrid Suspension Nanofluids for Enhanced Heat Transfer Applications.

Extensive investigations were made and empirical relations were proposed for the thermal conductivity of mono-nanofluids. The effect of concentration, diameter, and thermal properties of participating nanoparticles is missing in the majority of existing thermal conductivity models. An attempt is made to propose a model that considers the influence of such missing parameters on the thermal conductivity of hybrid nanofluids. Al2O3-TiO2 hybrid nanofluids have a 0.1% particle volume concentration prepared with distinct particle volume ratios (k - 1:6 - k, k = 1 to 6) in DI water. The samples were characterized, and the size and shape of the nanoparticles were verified. Also, the influence of varying particle volume ratios and the fluid temperature (varying from 283 to 308 K) were examined. 2.4 and 2.1% enhancements were observed in the thermal conductivity of alumina (5:0) and titania (0:5) nanofluids (having 0.1% volume concentration), respectively. Due to the low thermal conductivity of titania nanoparticles, the conductivity of the hybrid solution is above that of titania and below that of alumina nanofluids. An empirical relation for the thermal conductivity of hybrid nanofluids is established and validated considering the individual particle size, volume ratio, and thermal conductivity of particles.

Particle-resolved simulations for nanofluid thermal enhancement in channel flows

Nanofluids have been studied extensively for improved heat transfer performances in various thermal systems. In this article, we adopt the particle-resolved method to study the thermal enhancement of nanofluids in channel flows under constant wall temperature (CWT) and constant heat flux (CHF) conditions. The smoothed profile method is employed to describe the nanoparticles suspending in the base fluid and the lattice Boltzmann method is utilized to solve the flow and temperature fields. Unlike the continuum representation of nanofluids, this particle-resolved approach can incorporate the thermophysical properties of the base fluid and nanoparticles directly in simulation, and flow and temperature distributions around nanoparticles are available. Our simulations reveal detailed information on the nanoparticle influence on the wall thermal distributions, and illustrate the enhancement mechanism for heat transfer between the boundary wall and fluid flow: The nanoparticles near the wall increase the local heat flux under CWT and decreases the local wall temperature under CHF condition. The heat transfer coefficient is utilized to characterize the thermal performance for nanofluids, and it increases 6–16% for the nanofluid systems considered in our simulations. The effects of key system parameters, such as the nanoparticle volume fraction, thermal conductivity, and Reynolds number, are also investigated. Our results show that, for both CWT and CHF systems, the particle volume fraction and thermal conductivity can both increase the heat transfer coefficient; however, the particle conductivity effect becomes less significant or even negligible when it is greatly larger than the base fluid conductivity. For the limited range and relatively low values of Reynolds numbers considered in this work, it appears that the Reynolds number has no obvious influence on the system thermal performance. These finding could be beneficial for a better understanding of the complexity of nanofluid systems and for future nanofluid development and applications.

A novel fractal model for effective thermal conductivity in granular porous media

Quantitative prediction of effective thermal conductivity in porous media is very important in various industrial applications and scientific researches. As the topology and geometry characteristics of pore-solid space is extremely complicated, an accurately theoretical calculation in porous media is still a great challenge. In this study, a novel theoretical model of effective thermal conductivity is derived in granular porous media originated from the Laplace's Equation with a new boundary condition. The proposed model considers the size of solid particles following the fractal distribution characteristics, which is expressed as a function of porosity, fractal dimension of solid particle, maximum particle radius, representative length, and thermal conductivity of both solid particle and pore phase. The predictions of proposed model show a good agreement with the existing models and various published experimental data, which validates its accuracy and reliability. The effect of different geometrical parameters for proposed model is analyzed and discussed. This novel proposed model of effective thermal conductivity may reveal a better insight for thermophysical mechanisms in granular porous media than conventional models.

Effect of particle characteristics, kinetics and transport phenomena on the prediction of particle mass loss and products yields during biomass fast pyrolysis

A low computational cost model for the pyrolysis of biomass anisotropic particles was used to study the effect of thermal conductivity of the particle, CRECK kinetic scheme implementation, intraparticle secondary reactions, heats of reaction, and advection on particle conversion prediction. The model considers a shrinking anisotropic cylindrical particle with biomass pseudo-components, the appearance of liquid intermediates, intra-particle mass and heat transport and secondary reactions of volatiles. The model was validated against oak and birchwood single particle experiments. More accurate predictions for mass loss evolution are found when considering the change of the particle thermal conductivity anisotropy with conversion and the temperature dependency of the thermal conductivities of the biomass and the gas phase, compared to ignoring these phenomena. The CRECK biomass pyrolysis reaction scheme provides insights into the effect of biomass composition on the evolution of the liquid intermediate phase allowing future studies about the presence of heavy compounds in the bio-oil and other phenomena such as aerosol ejection. Secondary intraparticle vapor phase reactions showed a negligible effect in volatiles conversion while heterogeneous reactions slightly overestimate its conversion. Heats of reaction and product advection are important for accurate prediction of particle mass loss.

Effects of carbon black on epoxidized natural rubber composites: Rheological, abrasion, and mechanical study

A detailed analysis of the rubber composites is necessary for the designing and fabrication of advanced rubber products. In this study, composite materials are synthesized by adding different nanofillers of carbon black nanoparticles into epoxidized natural rubber (ENR). The specific characteristics of carbon black (CB) affect the crosslinking density between CB and polymeric chains in ENR. The effects of particle size, structure, thermal conductivity, and functionality of CB on rheology and mechanical properties of filled reinforced ENR are examined. The composite materials are thoroughly characterized for their abrasion, rheology, tensile, hardness, elongation, resilience, and compression behavior. The results revealed that various properties of composite materials depend on the characteristics of the reinforcing CB fillers. It was found that lower particle size and ordered structure provide good tensile strength, tear strength, abrasion, and aging resistance. In contrast, larger particle size and a lower ordered structure provide high flow rate, low hardness, excellent re-bounce, and good compression set characteristics.

Thermophoretic force on a sphere of arbitrary thermal conductivity in a rarefied gas

The thermophoretic force on a sphere with arbitrary thermal conductivity immersed in a rarefied gas subject to a uniform temperature gradient is calculated in the framework of kinetic theory of gases in a range of the Knudsen number which covers the free molecular, transition and continuum regimes of the gas flow around the sphere. The condition of continuity of the radial heat flux at the gas–solid interface is used to calculate the thermophoretic force as a superposition of the thermophoretic force on a sphere with an uniform temperature and the radiometric force on a sphere due to its thermal polarization. Both solutions were previously obtained from the Shakhov model to the linearized Boltzmann equation and the Cercignani–Lampis model of gas–surface interaction. To analyze the influence of the gas and particle thermal conductivities on the thermophoretic force, spheres of PolyStyrene Latex (PSL), glass and nickel in helium and argon gases are considered. The force is calculated for several values of tangential momentum accommodation coefficient and normal energy accommodation coefficient as introduced in the Cercignani–Lampis scattering kernel. A comparison with experimental data available in the literature allowed to extract the values of the accommodation coefficients for helium interaction with the particle surface.

Joint Analysis and Reliability Test of Epoxy-Based Nano Silver Paste Under Different Pressure-Less Sintering Processes

Abstract Recent years, the sintered silver paste was introduced and further developed for power electronics packaging due to low processing temperature and high working temperature. The pressure-less sintering technology reduces the stress damage caused by the pressure to the chip, improves reliability, and is widely applied in manufacturing. Currently, most existed studies are focused on alcohol-based sintered silver pastes while resins have been demonstrated to improve the bonding properties of solder joints. Hence, the performance and sintering mechanisms with epoxy-based silver paste need to be further explored. In this work, a methodology for multifactor investigation is settled on the epoxy-based silver paste to reveal the relationship between the strength and the different influence factors. We first analyzed the characteristics of commercialized epoxy-based silver paste samples, including silver content, silver particle size, organic composition, sample viscosity, and thermal conductivity. Samples were then prepared for shear tests and microstructure analysis under different pressure-less sintering temperatures, holding time, substrate surface, and chip size. Full factor analysis results were further discussed in detail for correlation. The influence factors were ranked from strong to weak as follows: sintering temperature, substrate surface, chip size, and holding time. Finally, a thermal cycling test was carried out for reliability analysis. Epoxy residues are one of the possible reasons, which result in shear strength decreasing exponentially.

Production of Particleboard Using Various Particle Size Hemp Shives as Filler

Research was performed into the use of hemp shive as a fast-growing and carbon-storing agricultural waste material in the production of particleboard for the construction industry. Hemp shives were acquired and prepared for board production with the use of milling and sieving to reach two target groups with 0.5 mm to 2 mm and 2 mm to 5.6 mm particle size ranges. The cold pressing method was used to produce hemp boards with Kleiberit urea formaldehyde resin as a binder. The boards were made as 19 mm thick single-layer parts with a density range of 300 ± 30 kg/m3, which qualifies them as low-density boards. Exploratory samples were made using milled hemp fibers with higher density. Additional components such as color pigments and wood finishes were added to test improved features over raw board samples. Tests were performed to determine moisture contents, density range, structural properties, and water absorption amounts. Produced board bending strength reached 2.4 MPa for the coarser particle group and thermal conductivity of 0.057 ± 0.002 W/(mK). The results were compared with existing materials used in the industry or in the development stage to indicate options of developed board applications as indoor insulation material in the construction industry.

Modeling thermal conductivity of nanofluids using advanced correlative approaches: Group method of data handling and gene expression programming

Accurate estimation of thermal conductivity (TC) of nanofluids is essential in thermophysical studies of nanofluids. In this study, more than 3200 data of thermal conductivity of nanofluids were collected from literature to develop simple-to-use and accurate correlations for predicting relative thermal conductivity of nanofluids. The dataset includes 13 different nanofluids with temperature from −30.00 to 149.15 °C, particle size from 5.00 to 150.00 nm, particle thermal conductivity from 1.20 to 1000.00 W/mk, particle volume fractions from 0.01 to11.22%, and base fluid thermal conductivity from 0.11 to 0.69 W/mk. Group method of data handling (GMDH) and gene expression programming (GEP) as two white-box powerful models were used for modeling. The results of proposed models were compared to 23 well-known theoretical and empirical models. The statistical and graphical results showed that the proposed models are more precise and reliable than the existing ones in literature. The GMDH model showed a better performance compared to GEP, and could predict all data with an average absolute relative error of 2.27% in training and 2.44% in testing data set. In addition, it was found that the proposed models could capture the physically expected trends with variation of temperature, size of nanoparticles, and volume fraction. The sensitivity analysis illustrated that temperature has the highest effect on the relative TC followed by base fluid TC and particle volume fraction

Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen

Frost heave action is a major issue in permafrost regions that can give rise to various geotechnical engineering problems. To analyze and predict this phenomenon at a specimen scale, this study conducted a fully coupled thermal-hydro-mechanical analysis and evaluated the frost heave behavior of frozen soil considering geotechnical parameters. Furthermore, a parametric study was performed to quantitatively analyze the effects of major geotechnical properties on frost heave behavior. According to the results of the parametric study, the amount of heave tended to decrease as the particle thermal conductivity increased, whereas the frost heave ratio tended to increase as the initial hydraulic conductivity increased. After evaluating the sensitivity of each parameter to frost heave behavior through statistical analyses, an artificial neural network model was developed to practically predict frost heave behavior. According to the verification results of the neural network model, the trained network model demonstrated a reliable accuracy (R2 = 0.893) in predicting frost heave ratio, even when the model used test datasets that were not part of the training datasets.

Preparation, characterisation and thermal property study of micro/nanocellulose crystals for vacuum insulation panel application

In this work, bleached and unbleached micro/nanocellulose crystals (MNCC) were successfully extracted from raw banana fibre through a mechanochemical cellulose extraction method and integrated with fumed silica-based Vacuum Insulation Panel (VIP) core as a filler material. Both types of extracted MNCC were characterised for structural changes, chemical composition and thermal properties associated with insulation purpose. Thermal stability, specific heat capacity, thermal diffusivity and thermal conductivity were measured for both MNCC and compared with the commercially available Microcrystalline Cellulose (MCC). The effect of both MNCC on thermal conductivity of VIP was compared. The characterisation results concluded that the effect of the bleaching process in MNCC extraction was observed in particle size, crystallinity index, thermal diffusivity and thermal conductivity. VIP made with bleached MNCC showed lower thermal conductivity than unbleached MNCC. It was concluded that bleached MNCC extract would be comparatively a better filler material for VIP.

In-Pile Performance of TRISO Particle Used Stainless Steel Foams as Buffer Layer

Thermal conductivity of TRISO particle deteriorated during in-pile operation due to the shrinkage of the low density carbon buffer layer and the gap appeared. In order to solve this problem, stainless steel foam was used to replace the low density carbon buffer layer as buffer layer in TRISO particle. The in-pile performance of the new type TRISO particle was simulated, and the result indicated that the stainless steel foam can avoid the gap between the buffer and the IPyC layer which can increase the thermal conductivity and decrease the kernel temperature during operation process; at the same time, regardless of stainless steel foam or low density carbon buffer layer used as buffer layer, the stress on IPyC and OPyC layers all exceeded the strength of the two layers. Hoop stress on SiC layer decreased with the elasticity modulus of metal foam, and the stress can be controlled by decreasing the elasticity modulus. To sum up, metal foam with high porosity and low elasticity modulus can be selected as the buffer layer to improve the thermal conductivity and assure the integrity of the coated layer and increase the life time. This study offered the optimization direction of TRISO particle for engineering application.

Influence of Thermal Annealing on the Sinterability of Different Grades of Polylactide Microspheres Dedicated for Laser Sintering.

We present a comparison of the influence of the conditioning temperature of microspheres made of medical grade poly(L-lactide) (PLLA) and polylactide with 4 wt % of D-lactide content (PLA) on the thermal and structural properties. The microspheres were fabricated using the solid-in-oil-in-water method for applications in additive manufacturing. The microspheres were annealed below the glass transition temperature (Tg), above Tg but below the onset of cold crystallization, and at two temperatures selected from the range of cold crystallization corresponding to the crystallization of the α’ and α form of poly(L-lactide), i.e., at 40, 70, 90, and 120 °C, in order to verify the influence of the conditioning temperature on the sinterability of the microspheres set as the sintering window (SW). Based on differential scanning calorimetry measurements, the SWs of the microspheres were evaluated with consideration of the existence of cold crystallization and reorganization of crystal polymorphs. The results indicated that the conditioning temperature influenced the availability and range of the SWs depending on the D-lactide presence. We postulate the need for an individual approach for polylactide powders in determining the SW as a temperature range free of any thermal events. We also characterized other core powder characteristics, such as the residual solvent content, morphology, particle size distribution, powder flowability, and thermal conductivity, as key properties for successful laser sintering. The microspheres were close to spheres, and the size of the microspheres was below 100 µm. The residual solvent content decreased with the increase of the annealing temperature. The thermal conductivities were 0.073 and 0.064 W/mK for PLA and PLLA microspheres, respectively, and this depended on the spherical shape of the microspheres. The wide angle X-ray diffraction (WAXD) studies proved that an increase in the conditioning temperature caused a slight increase in the crystallinity degree for PLLA microspheres and a clear increase in crystallization for the PLA microspheres.

A general effective thermal conductivity model for composites reinforced by non-contact spherical particles

Particle reinforced materials are widely used in many areas, including energy storage, energy saving, thermal protection, and thermal management. These applications tend to require an accurate foreknowledge of the effective thermal conductivity of these materials. Although there exist many different effective thermal conductivity models for this kind of materials, a general and accurate model for the composites reinforced by different types of spherical particles, including mono-particles, hybrid particles, and core-shell particles, is still lacking. This paper firstly collected some commonly used effective thermal conductivity models and tested their scope of use by numerical results. Then, a general and easy-to-use effective thermal conductivity model was provided by extending the model of Meredith and Tobias. This extended Meredith and Tobias's model was proven to be able to accurately predict the effective thermal conductivity of multi-phase composites reinforced by different types of spherical particles with large ranges of particle volume fraction and thermal conductivity ratio of particles to the matrix.

Prediction of mechanical and thermal properties of particle reinforced hydrogel composites using the structural genome approach

In this paper, the structural genome approach is used for multiscale analyses to predict the mechanical and thermal properties of particle reinforced hydrogel composites. First, the structure genome model of particle reinforced hydrogel composites is created by the random sequential adsorption algorithm. Then the mechanical properties and equivalent thermal conductivity of hydrogel composites are numerically studied by the structural genome approach. The effects of particles with different volume fractions and material properties on their mechanical and thermal properties are investigated. From the simulation results, it can be found that within a certain range of volume fraction, the mechanical properties and equivalent thermal conductivity of hydrogel composites are positively correlated with the volume fractions of particles. We also find that with the increase of the mechanical properties and thermal conductivity of particles, the properties of hydrogel can be improved and eventually reach stabilization. The structural genome approach shows excellent efficiency in multiscale structure analysis. It is a convenient method for the simulation of complex soft material composites.

Predicting effective thermal conductivity in sands using an artificial neural network with multiscale microstructural parameters

Accurate and efficient prediction of thermal conductivity of sands is challenging due to the variations in particle size, shape, connectivity and mineral compositions, and external conditions. Artificial Neural Networks (ANN) models have been used to predict the effective thermal conductivity but they have not considered variables related to particle connectivity. This work uses computed tomography (CT) scanned images of four dry sands and network analysis to redress this significant shortcoming. Here sands are represented as networks of nodes (grains) and edges (interparticle contacts or/and small gaps between neighbouring particles) to extract network features that characterise interparticle connectivity. A network feature – weighted coordination number (WCN) capturing both particle connectivity and contact area – was found to be a good predictor of effective thermal conductivity in dry materials. Roundness, sphericity, solid particle thermal conductivity and porosity are other input parameters rigorously selected for an ANN model that predicts well the effective thermal conductivity of sands.

Effects of solid particle thermal conductivity on heat storage performance of heat storage bed

The thermal properties of particles are important parameters that directly affect the heat storage performance of the solid particle heat storage bed. This paper reports the effects of solid particle thermal conductivity on heat storage performance of heat storage bed. A three-dimensional steady-state heat transfer model of heat storage bed with a single vacancy was established using ANSYS 19.0. The effects of thermal conductivity of particles were studied. With the increase in thermal conductivity of particles, the effect of the vacancy on heat storage particle bed heat transfer performance increases, the affected area size increases, so the heat storage and release performance of heat storage bed decreases. With the increase of thermal conductivity of particles from 0.5 W/ (m·K) to 50 W/ (m·K), the size of the affected region increased from 0.61 times of particle size to 1.82 times of particle size in the direction perpendicular to the heat transfer, and the size of the affected region increased from 1.61 times of particle size to 3.91 times of particle size in upstream of the direction parallel to the heat transfer. At the same time, the size of the affected region increased from 1.69 times of particle size to 3.93 times of particle size in downstream of the direction parallel to the heat transfer. It can provide a basis for improving the thermal resistance theory.