scholarly journals Thermal Conductivity Calculations for Nanoparticles Embedded in a Base Fluid

2021 ◽  
Vol 11 (4) ◽  
pp. 1459
Author(s):  
Soran M. Mamand
Keyword(s):  
Thermal Conductivity ◽  
Base Fluid ◽  
Theoretical Models ◽  
Simple Method ◽  
Size Dependent ◽  
Nanolayer Thickness ◽  
Embedded Nanoparticles ◽  
Comparison With Experimental Data ◽  
Good Agreement

The Prasher analytical model was used for calculating the thermal conductivity of the embedded nanoparticles of Al2O3, CuO, ZnO, and SiO2 in conventional fluids, such as water and ethylene glycol. The values that were obtained were used in the nanofluid theoretical models for comparison with experimental data, where good agreement was obtained. Liang and Li’s theoretical model was also used to calculate the thermal conductivity of these nanoparticles, where the results agreed with those obtained using the Prasher model. The effect of the liquid nanolayer thickness around the nanoparticles that was used to enhance the effective thermal conductivity of nanofluids was explained. The role of the nanoparticles’ surface specularity parameter, which was size-dependent, was clarified. This theoretical trend provides a simple method for estimating the thermal conductivity of nanoparticles and nanofluids.

Dual Role of Nanoparticles in the Thermal Conductivity Enhancement of Nanoparticle Suspensions

2005 ◽  
Author(s):  
Calvin H. Li ◽  
G. P. Peterson
Keyword(s):  
Experimental Data ◽  
Thermal Conductivity ◽  
Brownian Motion ◽  
Theoretical Models ◽  
Conductivity Enhancement ◽  
Nanoparticle Suspensions ◽  
The Difference ◽  
Physical Phenomena ◽  
Good Agreement

Experimental evidence exists that the addition of a small quantity of nanoparticles to a base fluid, can have a significant impact on the effective thermal conductivity of the resulting suspension. The causes for this are currently thought to be due to a combination of two distinct mechanisms. The first is due to the change in the thermophysical properties of the suspension, resulting from the difference in the thermal conductivity of the fluid and the particles, and the second is thought to be due to the transport of thermal energy by the particles, due to the Brownian motion of the particles. In order to better understand these phenomena, a theoretical model has been developed that examines the effect of the Brownian motion. In this model, the well-known approach first presented by Maxwell, is combined with a new expression that incorporates the effect of the Brownian motion and describes the physical phenomena that occurs because of it. The results indicate that the enhanced thermal conductivity may not in fact be due to the transport of energy by the particles, but rather, due to the stirring motion caused by the movement of the nanoparticles which enhances the heat transfer within the fluid. The resulting model shows good agreement when compared with the existing experimental data and perhaps more importantly helps to explain the trends observed from a fundamental physical perspective. In addition, it provides a possible explanation for the differences that have been observed between the previously obtained experimental data, the predictions obtained from Maxwell’s equation and the theoretical models developed by other investigators.

Role of Interfacial Surfactant Layers in the Thermal Conductivity of Nanofluids

2012 ◽  
Vol 531-532 ◽  
pp. 535-538
Author(s):  
Liu Yang ◽  
Kai Du ◽  
Yun Long Wu ◽  
Shuai Yang Bao
Keyword(s):  
Thermal Conductivity ◽  
Interfacial Layer ◽  
Langmuir Adsorption ◽  
Adsorption Theory ◽  
Proposed Model ◽  
Classical Models ◽  
Interfacial Surfactant ◽  
S Model ◽  
Good Agreement

Various mechanisms and correlations have been developed for prediction of thermal conductivity of nano-suspensions. However, seldom theoretical researches on thermal conductivity of nanofluids containing surfactant are found. In this work, a thermal conductivity prediction-model of nanofluid containing surfactants is proposed based on Leong et al.’s model and Langmuir adsorption theory by considering the interfacial surfactant layers. The thickness of the interfacial layer is defined by Langmuir adsorption theory. Compared with the experimental data available in the literature on thermal conductivity of nanofluid containing surfactants, the calculated values on the proposed model have been verified that the proposed models show reasonably good agreement with the experimental results and give better predictions for the effective thermal conductivity of nanofluids compared to existing classical models.

Thermal Conductivity Characterization and Modeling of P-Type Metal/Semiconductor Nanocomposites

2010 ◽  
Author(s):  
Dongyan Xu ◽  
Joseph P. Feser ◽  
Yang Zhao ◽  
Hong Lu ◽  
Peter Burke ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Boltzmann Transport ◽  
Omega Method ◽  
Thermoelectric Energy ◽  
Semiconductor Matrix ◽  
Semiconductor Nanocomposites ◽  
Embedded Nanoparticles ◽  
P Type

Semiconductor alloys with epitaxially embedded nanoparticles have been shown to be very promising materials for thermoelectric energy conversion applications. In this work, we report on thermal conductivity characterization of two classes of p-type nanoparticle-in-alloy composite materials: compensated InGaAs semiconductor matrix with randomly distributed ErAs nanoparticles, and GaSb and its alloys with embedded ErSb nanoparticles. The three omega method is used to measure thermal conductivity of all materials. It is shown that thermal conductivity of compensated p-type ErAs:InGaAs is comparable to the n-type ErAs:InGaAs and it reduces with the increase in the erbium concentration. ErSb:GaSb nanocomposites are intrinsically p-type and show a thermal conductivity substantially lower than the pure GaSb compound. By comparing nanostructured samples from alloyed (InGaSb) and unalloyed (GaSb) matrix materials, we show that alloying is complimentary to the role of the nanostructure in reducing thermal conductivity. We also discuss Boltzmann transport modeling that indicates an optimum nanocrystal size, and the prospects for further reductions in the lattice thermal conductivity.

Molecular Dynamics Simulation of the Thermal Conductivity of Ar-Al Nanofluid Using Simplified Model

2009 ◽  
Author(s):  
Jie Liu ◽  
Wen-Qiang Lu
Keyword(s):  
Thermal Conductivity ◽  
Molecular Dynamics ◽  
Brownian Dynamics ◽  
Base Fluid ◽  
Numerical Results ◽  
Solid Particles ◽  
Dynamics Simulation ◽  
Simplified Model ◽  
Heat Current

Nanofluid is a colloidal solution of nano-sized solid particles in liquids. Ar-Al nanofluid is a promising heat transport fluid in the fields of low-temperature engineering. A simplified model based on the equilibrium molecular dynamics (EMD) simulation is constructed to calculate the thermal conductivity of argon suspension containing aluminum nanoparticles. The numerical method is verified by comparing the numerical results with the existing numerical results and the experimental data of the base fluid. The influence of various nanoparticle loadings is obtained and the results show that the thermal conductivity with 1% nanoparticle loading enhances up to 31% compared with the base fluid. The heat current autocorrelation functions converge well for the basefluid and nanofluid. Furthermore, interesting distinct oscillations are obtained especially at higher nanoparticle loading. The significant role of the interaction between the fluid atoms and the solid nanoparticle rather than Brownian dynamics motion of the nanoparticle in yielding the high thermal conductivity of nanofluid is numerically revealed.

scholarly journals Nanofluid properties for forced convection heat transfer :a review

2015 ◽  
Vol 3 (1) ◽  
pp. 145 ◽  
Author(s):  
Mohsen Darabi ◽  
Reza Naeimi ◽  
Hamid Mohammadiun ◽  
Saeed Mortazavi
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Base Fluid ◽  
Convection Heat Transfer ◽  
Theoretical Models ◽  
Transfer Coefficients ◽  
Practical Applications ◽  
Heat Transfer Coefficients ◽  
The Stability

<p>The thermal conductivity of nanofluids depends on various parameters, such as concentration, temperature, particle size, pH, shape, material, and possibly on the manufacturing process of the nanoparticles. Data on the viscosity of nanofluids, available in the literature, are very limited. Theoretical models for the determination of the thermal conductivity and viscosity of nanofluids have been pursued. Experiments with nanofluids indicate that they higher heat transfer coefficients than the base fluid. No significant increase in a pressure drop is reported with nanofluids, compared with values with the base fluid. However, the stability of nanofluids with regard to settlement/agglomeration, especially at higher concentrations, is still a problem for practical applications.</p>

Boundary Conditions and Evolution of Ballistic Heat Transport

2009 ◽  
Vol 132 (1) ◽  
Author(s):  
F. X. Alvarez ◽  
D. Jou
Keyword(s):  
Thermal Conductivity ◽  
Boundary Conditions ◽  
Heat Transport ◽  
Ballistic Transport ◽  
Theoretical Models ◽  
Effective Size ◽  
Size Dependent ◽  
Fourier Equation

We analyze the effects of boundary conditions on the evolution of ballistic heat transport in four theoretical models and propose that a Fourier equation with an effective size-dependent thermal conductivity is a good candidate for the description of ballistic transport when boundary conditions are suitably imposed.

scholarly journals Assessment of Morphological, Physical, Thermal, and Thermal Conductivity Properties of Polypropylene/Lignosulfonate Blends

Materials ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 543
Author(s):  
Mariane Schneider ◽  
Noriê Finimundi ◽  
Maria Podzorova ◽  
Petr Pantyukhov ◽  
Matheus Poletto
Keyword(s):  
Thermal Conductivity ◽  
Thermal Stability ◽  
Flame Retardant ◽  
Thermal Conductivity Coefficient ◽  
Theoretical Models ◽  
Sodium Lignosulfonate ◽  
Reactive Component ◽  
Experimental Values ◽  
Potential Use ◽  
Good Agreement

Lignosulfonate is a cheap material available in large quantities obtained as a byproduct of paper and cellulose. In this work, blends of polypropylene (PP) and sodium lignosulfonate (LGNa) were developed to evaluate the potential use of lignosulfonate as a lightweight, thermal insulation and flame retardant material. The blends were obtained by mixing in a torque rheometer and molded after compression. The blend proprieties were evaluated by physical, morphological, thermal, thermal conductivity, and flammability tests. The measured values were compared with theoretical models. The results indicated that a heterogeneous blend with a higher number of separated domains is formed when the LGNa content increases from 10 to 40 wt%. In addition, the density and thermal conductivity coefficient of the blends studied are not affected by the addition of LGNa. However, when the LGNa content in the blend exceeds 20 wt% the thermal stability and flame retardant proprieties are considerably reduced. The theoretical models based on the rule of mixtures showed a good agreement with the experimental values obtained from blend density, thermal conductivity, and thermal stability. In general, lignosulfonate tested in this work shows potential to be used as a reactive component in polymer blends.

Prediction of effective thermal conductivity of nanofluids by modifying renovated Maxwell model

2016 ◽  
Vol 30 (3) ◽  
pp. 289-301 ◽  
Author(s):  
Deepti Chauhan ◽  
Nilima Singhvi
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Base Fluid ◽  
Volume Fraction ◽  
Maxwell Model ◽  
Theoretical Models ◽  
Solid Particles ◽  
Nanoparticle Volume Fraction ◽  
Suspended Nanoparticles

Nanofluids, which are formed by suspending nanoparticles into conventional fluids, exhibit anomalously high thermal conductivity. Renovated Maxwell model was developed by Choi in which the presence of very thin nanolayer surrounding the solid particles was considered, which can measurably increase the effective thermal conductivity of nanofluids. A new model is proposed by introducing a fitting parameter χ in the renovated Maxwell model, which accounts for nanolayer, nonuniform sizes of filler nanoparticles together with aggregation. The model shows that the effective thermal conductivity of nanofluids is a function of the thickness of the nanolayer, the nanoparticle size, the nanoparticle volume fraction and the thermal conductivities of suspended nanoparticles, nanolayer and base fluid. The validation of the model is done by applying the results obtained by the experiments on nanofluids, other theoretical models, and artificial neural network technique. The uncertainty of the present measurements is estimated to be within 5% for the effective thermal conductivity.

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