Thermal Conductivity Model Research Articles

Thermal insulation of phenolic resin modified fly ash geopolymer

Phenolic resin has been widely used owing to its excellent thermal insulation performance. However, the application of phenolic resin in the field of geopolymer remains unexplored. Here, the foamed fly ash geopolymer samples with high porosity were prepared using phenolic resin for the purpose of foaming materials. The thermal insulation properties of these samples were evaluated by Transient Plane Source Method, and the effect of phenolic resin on pore characteristics was investigated by Mercury Intrusion Porosimetry (MIP), aim to explore how phenolic resin affects the thermal conductivity by changing the pore characteristics of geopolymer. In addition, considering that the internal gas thermal conductivity of pores with different pore sizes is also different (Knudsen effect), in order to predict the thermal conductivity more accurately, this research proposes an iterative calculation method to establish a thermal conductivity model. The results show that phenolic resin can improve the thermal insulation effect of geopolymer, and the minimum thermal conductivity of the sample is 0.17 W/m·K. In addition, according to the test results of MIP, it can be found that phenolic resin mainly promotes the porosity of micron pores (>1 μm) whereas there is slight inhibition on nano pores (<1 μm), but the beneficial effect of the increase of micron-sized pores (>1 μm) on the thermal insulation performance of geopolymer is greater than the negative influence effect of the disappearance of nano pores. Moreover, according to the results of MIP, phenolic resin can narrow the throat in the pore of the ink bottle, limit the thermal convection of the gas between the pores, and reduce the thermal conductivity. Finally, the newly modified Russell model considering the Knudsen effect can effectively predict the thermal conductivity by using the experimental data. This study provides a new method to improve thermal insulation performance of geopolymers and offers a new building insulation product.

Thermal analysis of magnetized [formula omitted] nanolubricant flow in a Darcy-Forchheimer porous medium with nonlinear heat generation

The nanolubricant ZnO−SAE50 is widely used in automobiles and heat exchange systems to minimize friction and resist corrosion and scrape between moving parts. It also improves system's performance and durability and cuts fuel consumption. In this work, the flow of magnetized ZnO−SAE50 nanolubricant is analyzed over a flat plate with variable temperature immersed in a Darcy-Forchheimer medium. The thermal analysis is carried out in the occurrence of nonlinear heat generation and thermal radiation. Furthermore, the influences of viscous dissipation and Joule heating have also been deliberated. A micro-nano convection model named as Patel model of thermal conductivity has been used in view of rise in thermal conductivity. The governing PDEs are altered to ODEs with the incorporation of similarity transformations and then numerically treated by using an eminent MATLAB's built-in bvp4c method. The influences of different physical parameters on velocity, temperature and Nusselt number are discussed and analyzed graphically and in tabular form. The velocity profile diminishes under the impacts of inertia coefficient and magnetic field. The temperature and exponential dependent heat generation parameters considerably enhance temperature of the nanolubricant ZnO−SAE50. The temperature power coefficient tends to reduce the temperature profile. The enhancing strength of thermal radiations and nonlinear heat generation significantly enhance the value of Nusselt number. Furthermore, an increase in the value of temperature dependent heat generation parameter from 0.2 to 0.6 tends 12.37% increment in the value of Nusselt number in conventional SAE50oil, whereas the nanolubricant ZnO−SAE50 experiences a more significant 26.72% increment in the value of Nusselt number. Thus, better heat transmission rate can be achieved by using ZnO−SAE50 nanolubricant rather than conventional SAE50 oil.

Track-scale Anisotropic Thermal Material Model as a Viable Substitution In Selective Laser Melting

High temperature gradients and resulting residual stresses are the main sources of defects such as cracks, distortion and fatigue failure is selective laser melting. In this paper, a new material model utilizing anisotropic thermal conduction model and track-scale heat input model is used to predict the melt pool geometry, material state and thermal history during the SLM process of SS316L in a large range of laser parameters. The model takes into consideration both the material state and the issue of phase transition during the track-scale simulation.. The simulated melt pools in the beam-scale and track-scale simulations are compared with experimental measurements in different laser parameters. It is found that the proposed material model is able to maintain accuracy between the beam-scale and track-scale simulations at an average of 5μm regarding melt pool dimensions. Furthermore, It can be inferred that both track-scale and beam-scale models exhibit the capability to provide precise predictions of melt pool geometries when compared to experimental measurements while the former being about 100 times faster than the latter. An average error of 10% was concluded for the material state comparison of the two models while the track-scale model was able to capture the temperature profile and cooling rate accurately in comparison with the beam-scale model.

Prediction of the thermal conductivity of freezing soils using the soil freezing characteristic curve

The thermal conductivity of freezing soils is a critical parameter in cold engineering regions. Measuring the thermal conductivity of freezing soils, especially in the field, is difficult. Therefore, we attempt to predict the thermal conductivity. By analyzing the advantage of the geometric mean model, four different soil freezing characteristic curve (SFCC) estimation models and five quartz content estimation models were introduced, and an estimated SFCC-based geometric mean model for soil thermal conductivity was proposed. The results showed that the Xin et al. SFCC model performed the best. The four SFCC-based thermal conductivity models with quartz content = sand content performed the best. Among the four SFCC-based thermal conductivity models, the Xin et al-based thermal conductivity model provided the best estimation of the thermal conductivity. It is demonstrated that the estimated SFCC-based thermal conductivity model is easy to use and can be used for predicting the relationship between thermal conductivity and temperature of freezing soils by using easy-to-obtain soil parameters.

Inversion of the Lunar Subsurface Rock Abundance Using CE-2 Microwave Brightness Temperature Data

The rock strongly affects the surface and subsurface temperature due to its different thermophysical properties compared to the lunar regolith. The brightness temperature (TB) data observed by Chang’E-1 (CE-1) and Chang’E-2 (CE-2) microwave radiometers (MRM) give us a chance to retrieve the lunar subsurface rock abundance (RA). In this paper, a thermal conductivity model with an undetermined parameter β of the mixture has been employed to estimate the physical temperature profile of the mixed layer (rock and regolith). Parameter β and the physical temperature profile of the mixed layer are constrained by the Diviner Channel 7 observations. Then, the subsurface RA on the 16 large (Diameter &gt; 20 km) Copernican-age craters of the Moon is extracted from the average nighttime TB of the CE-2 37 GHz channel based on our previous rocky TB model. Two conclusions can be derived from the results: (1) the subsurface RA values are usually greater than the surface RA values retrieved from Diviner observations of the studied craters; (2) the spatial distribution of subsurface RA extracted from CE-2 MRM data is not necessarily consistent with the surface RA detected by Diviner data. For example, there are similar RA spatial distributions on both the surface and subsurface in Giordano Bruno, Necho, and Aristarchus craters. However, the distribution of subsurface RA is obviously different from that of surface RA for Copernicus, Ohm, Sharonov, and Tycho craters.

Excluding quartz content from the estimation of saturated soil thermal conductivity: Combined use of differential effective medium theory and geometric mean method

Saturated soil thermal conductivity (λsat) is the maximum soil thermal conductivity value of a given soil. Although it can be determined accurately with a heat pulse sensor, there are challenges to prepare fully saturated soil samples. Numerous models have been developed to estimate λsat, and among these, the geometric mean method (GMM) generally performs well. The GMM requires soil mineral composition or quartz content information, which is unavailable for most soils. Earlier studies commonly used assumed that quartz content (fquartz) was equal to sand content (fsand) or to 0.5 × fsand, which led to significant λsat estimation errors especially on coarse-textured soils. We derived a novel method to estimate λsat from soil porosity (ϕ) based on a combination of the GMM and differential effective medium theory (DEM). The new DEM-GMM approach has a single parameter, cementation exponent (m). Using a calibration dataset of 43 soils, we determined best fit m values for soils in three groups: 1.66 for Group I (fsand < 0.4), 1.62 for Group II (0.4 <= fsand < 1) and m = -1.34ϕ+1.70 for Group III (fsand = 1). Using best fit m values for different groups, the new model can estimate λsat values from ϕ. Independent validation results on another 46 soils showed that the new model outperformed the GMM method with the assumption that fquartz = fsand or fquartz = 0.5 × fsand. The mean RMSE, Bias and R2 values of the DEM-GMM approach were 0.202 W m−1 K−1, 0.013 W m−1 K−1 and 0.89, respectively, and corresponding values of the GMM with the two assumptions were 0.295 and 0.476 W m−1 K−1, 0.056 and -0.28 W m−1 K−1, 0.80 and 0.82, respectively. The robust performance of the DEM-GMM approach suggests that it can be incorporated into thermal conductivity models to accurately estimate the thermal conductivity of unsaturated soils.

Fractal Pore-Scale Model for Effective Thermal Conductivity of Multiscale Unsaturated Porous Media

Fractal Pore-Scale Model for Effective Thermal Conductivity of Multiscale Unsaturated Porous Media

Assembling feather-like reentrant structures with multiple mechanical properties fabricated by laser powder bed fusion

A new assembly design method for structural components is proposed. Inspired by the feathers of the grey crane, which have an energy dissipation effect, novel re-entrant scapus component and novel re-entrant double-crank component are proposed. Novel re-entrant structures are formed by assembling honeycomb structure (HS), poisson structure (PS), and chiral structure (CS). Laser powder bed fusion technology (LPBF) is utilized for the manufacturing of bio-inspired structures. And the impact of structural assembly on mechanical performance is comprehensively analyzed through experimental, numerical, and theoretical approaches. Euler theory is employed to predict the influence of bionic components on structural load trends, while explaining the relationship between compression load and deformation. Specifically, the average load capacity of bio-inspired scapus re-entrant structures and double-crank re-entrant structures exceeds that of their original designs by at least 55% and 13.2%, respectively. Moreover, specific energy absorption increases by a minimum of 8.6% and 3.4%, accompanied by improved heat dissipation performance in these structures. Finally, battery pack impact resistance models as well as thermal conductivity models for electric-powered vehicles are presented as examples to validate the potential application prospects of this novel bio-inspired re-entrant structure in engineering.

Pore scale analysis for flow and thermal characteristics in metallic woven mesh

Metallic woven mesh screens are representative porous media with periodic structures, which have great application prospects in emerging technology and industrial equipment. However, the effect of their pore structure on macroscopic properties is not clear, which limits their large-scale engineering application. In this paper, to investigate the influence mechanism of stacking patterns and wire contact conditions on flow and heat conduction from the pore scale, a representative elementary volume model of the square-shaped plain copper micromesh was reconstructed according to the geometric parameters obtained by the optical microscope and porosity test experiment. Furthermore, based on the understanding of microscopic mechanism, the theoretical calculation model for macroscopic characteristics was proposed. The results show that the flow characteristics in meshes are mainly affected by stacked patterns. When ReK is 0.08 and 0.28, the flow in single layer and staggered stacked woven meshes transitions from Darcy flow to Forchheimer flow. In Forchheimer flow regime, neglecting the inertia force causes a maximum 50 % error for permeability calculation. The permeability empirical correlations for Darcy and Forchheimer flow are proposed according to Kozeny-Carman and Kozeny-like model. The z-direction effective thermal conductivity of copper woven meshes is significantly affected by wire contact conditions, the maximum value is from 2.5 to 13.5 W/(m·K). The z-direction effective thermal conductivity of tangent and gap contact conditions can be described by the ZBS model, while the random model is suitable for that of overlapped contact conditions. The x-direction thermal conductivity is little affected by contact conditions, the maximum value is 50.3 W/(m·K), but the tortuosity effect of copper wires should be considered for calculation. The calculation model of anisotropic thermal conductivity for single-layer woven meshes is established according to Kirchhoff's law, which is also extended to the staggered stacked woven meshes by introducing a compactness factor.

Shape factor analysis in stagnation point flow of Casson nanofluid over a stretching/shrinking sheet using Cattaneo–Christov model

In this article, the stagnation point flow of an incompressible Au–blood fluid over a stretching sheet in porous medium is examined with suction/injection and Cattaneo–Christov effects. The transformed ODEs are solved numerically by the shooting-based bvp4c method with MATLAB software. The flow phenomenon is influenced by thermal relaxation with slip boundary conditions. The shape of gold (Au) nanoparticle is considered as: cylindrical, platelet, and blade, and the Hamilton–Crosser model of thermal conductivity is taken. The Casson parameter increases the nanofluid velocity and decreases the fluid temperature. The increment in the thermal relaxation parameter increases the fluid temperature profiles. The particle shape can also enhance the temperature. The nanoparticles of blade shape have huge influences on temperature rise due to greater thermal properties. A comparative analysis is made in accordance with previous works and is in good agreement.

Monitoring heat transfer for seepage in earth-rock dams with clay cores considering damaged areas: Laboratory experiments and numerical modeling

This study conducted a laboratory test and two-dimensional numerical simulation on seepage heat monitoring of a core rockfill dam. A fully automatic laboratory test system for dam seepage monitoring has been developed, which can achieve automatic control of water flow and temperature, as well as automatic data collection. A flow-heat coupling model based on [Johansen, O., (1975). Thermal conductivity of soils. P. D. thesis. University of Trondheim, Norway, Trondheim] empirical thermal conductivity model was established through secondary development using COMSOL Multiphysics finite element software, and its accuracy was verified. The influence of the location of damaged leakage in the core wall of the dam on flow and heat migration was analyzed. Among the investigated conditions, a damaged channel with a curved cross-section causes the formation of the prominent arc in the saturation line of the dam body downstream, induces the largest increase in the average seepage discharge, and makes the dam body most unstable. The effects of damage on the temperature and seepage fields are positively correlated for different damage types. The presence of a damaged area induces sudden changes in the pressure, velocity, and temperature at each monitoring point. The pressure, velocity and temperature are correlated, and there is a lag in temperature transfer. This study provides a reference for the application of distributed fiber optic temperature measurement technology in core rockfill dam engineering, especially in terms of fiber optic placement position. The established numerical model can provide an accurate and effective reference method for identifying abnormal temperature data and locating dam leakage. Our next step will be to study the flow and heat transfer characteristics throughout the entire process from undamaged to dam failure.

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.

Study on thermal properties of bio-char prepared by photo-thermal pyrolysis

Photo-thermal pyrolysis provides a new method for the preparation of bio-char through the energy absorption of the intermittent solar energy into the carbon materials to achieve the energy storage and negative carbon emissions. The evolution of thermal properties of different chars prepared by photo-thermal pyrolysis with different final-reaction temperatures and heating rates was studied. The study of specific heat capacity showed that the final-reaction temperature had a stronger impact than the heating rate. At a final-reaction temperature of 800 °C, the maximum specific heat capacity of rice straw and pine char reached 1595 J·kg−1·K−1 and 2070 J·kg−1·K−1 respectively. Measurements of thermal conductivity showed that the higher final-reaction temperature and the higher heating rate both increased the thermal conductivity of char. Rice straw char had a maximum value of 0.2203 W·m−1·K−1 at 800 °C and 500 °C/min, which was almost twice the thermal conductivity of char at 400 °C and 50 °C/min. Further correlation analysis showed the physical structure of the char, especially its density and pore size, had a greater impact on thermal conductivity. The multiple linear model of thermal conductivity was established, which not only had a good fitting effect but also well predicted the thermal conductivity of another kind of char, and the error was within 10%. This study demonstrates the potential of photo-thermal pyrolysis char as a heat carrier and light absorption medium during photo-thermal pyrolysis.

A Thermal Conductivity Model for Grey Iron

Thermal conductivity is an important property for many iron cast components, and the lack of widely accepted thermal conductivity model for cast iron, especially grey cast iron, motivates the efforts in this research area. The present study contributes to understanding the effects alloy microstructure has on thermal conductivity. A thermal conductivity model for a pearlitic cast iron has been proposed, based on the as-cast alloy composition and microstructural parameters obtained at different solidification rates. According to the model, available parallel heat transfer paths formed by connected graphite flakes across eutectic cells are determined by the space between dendrite arms. The uncertainties both for model inputs and for validation measurements have been estimated. Sensitivity analysis has been conducted to result in better understanding of the model behaviour. The agreement between modelled and measured thermal conductivities has been achieved within 5% on the average for the investigated samples.

Prediction of effective thermal conductivity of packed beds of polyhedral particles

In packed beds, particle shape influences the heat transfer considering different particle-particle contacts and pore morphology. This paper extends a predictive model for effective thermal conductivity, the Zehner, Bauer and Schlünder (ZBS) model for various shapes. Thus, real structures were studied parallel with numerically generated packings and thermal simulations were performed in FEM solver. ZBS model parameters, porosity, shape factor and interparticle contact area ratio were derived. FEM and ZBS results were compared over a wide range of particle-to-gas conductivity ratios. ZBS model predicted well for lower conductivity particles with the newly introduced sphericity term. In the mid-range of particle conductivity, the results are shape factor sensitive, model predicts well with experimental validation. New correlations were built for ZBS model parameters, extending the model for irregular, non-spherical particles. Prospectively, particle-particle heat transfer coefficients for the discrete element method (DEM) can be derived from the effective thermal conductivity of such particles.

Temperature field of multi-barrier with gap layer in nuclear waste repository

A three-dimensional four-layer thermal conduction model including an air layer was developed to analyze temperature evolution in nuclear waste repository. Laplace-domain temperature solutions for each layer were obtained using finite Fourier transform and Laplace transform. The Laplace-domain solutions were transformed into time-domain semi-analytical solutions by numerical inversion. The surface temperature of central canister was obtained by superimposing the temperature increments of air and buffer layers on rock wall temperature caused by every canister in one disposal panel. The effects of the geometrical parameters, thermal parameters and nuclear waste parameters on surface temperature of canister were studied based on the obtained semi-analytical solution. The results indicate that the presence of air layer has little effect on temperature distribution in bentonite while its effect on temperature at surface of canister is significant. Thickness of air layer has a greater effect on temperature than that of its thermal conductivity. The burn-up value and cooling time also have significant influence on the temperature. Finally, the influences of repository’s geometric size on temperature in this model were analyzed.

Predictive modelling of thermal conductivity in single-material nanofluids: a novel approach

BackgroundThis research introduces a novel approach for modelling single-material nanofluids, considering the constituents and characteristics of the fluids under investigation. The primary focus of this study was to develop models for predicting the thermal conductivity of nanofluids using a range of machine learning algorithms, including ensembles, trees, neural networks, linear regression, Gaussian process regressors, and support vector machines.The main body of the abstractTo identify the most relevant features for accurate thermal conductivity prediction, the study compared the performance of established feature selection algorithms, such as minimum redundancy maximum relevance, Ftest, and RReliefF, a newly proposed feature selection algorithm. The novel algorithm eliminated features lacking direct implications for fluid thermal conductivity. The selected features included temperature as a thermal property of the fluid itself, multiphase features such as volume fraction and particle size, and material features including nanoparticle material and base fluid material, which could be fixed based on any two intensive properties. Statistical methods were employed to select the features accordingly.ResultsThe results demonstrated that the novel feature selection algorithm outperformed the established approaches in predicting the thermal conductivity of nanofluids. The models were evaluated using fivefold cross-validation, and the best model was the model based on the proposed feature selection algorithm that exhibited a root-mean-squared error of validation of 1.83 and an R-squared value of 0.94 on validation set. The model achieved a root-mean-squared error of 1.46 and an R-squared value of 0.97 for the test set.ConclusionsThe developed predictive model holds practical significance by enabling nanofluids' numerical study and optimisation before their creation. This model facilitates the customisation of conventional fluids to attain desired fluid properties, particularly their thermal properties. Additionally, the model permits the exploration of numerous nanofluid variations based on permutations of their features. Consequently, this research contributes valuable insights to the design and optimisation of nanofluid systems, advancing our understanding and application of thermal conductivity in nanofluids and introducing a novel and methodological approach for feature selection in machine learning.

Experimental research on the thermal conductivity of unsaturated rocks in geothermal engineering

The thermal conductivity of rock is an essential thermophysical parameter in geothermal engineering. Experiments have shown that when rock changes from an unsaturated to a saturated state, thermal conductivity can be increased by 50% at most. In this paper, the variation of thermal conductivity with saturation of 101 samples of six different rock types from Songliao Basin, Gonghe Basin, and Ordos Basin in China was measured in the laboratory. Correlations between saturation, porosity, internal structural characteristics of rocks and thermal conductivity were investigated. The results show that the effect of saturation on the thermal conductivity of rock increases exponentially with porosity. Based on three classical models and the relationship between porosity, saturation, and thermal conductivity, improved models and a fitted model of thermal conductivity are proposed. By comparing the improved models to the fitted model, the applicable scope of each model is obtained. The errors of each model are analyzed in detail. The average MAE value of the improved models is 0.405 and the average RMSE value is 1.207 over the applicable model scopes. The R2 of the fitted model is 0.94. Compared with experimental data from literature, MAE values of the improved models and the fitted model are 0.040 and 0.003 respectively. Unsaturated rock with a single fracture heat transfer model is established by simulation. A thermal-Hydrological coupling model is used to simulate seepage heat transfer through fracture. The effect of variable thermal conductivity simulation is the best with the experimental value, and the MAPE value was 8.3%. This study fills a gap in available models of unsaturated thermal conductivity of rock, and provide theoretical support for the accurate calculation of heat transfer efficiencies in related geothermal engineering applications.

Lattice Boltzmann modeling of the effective thermal conductivity in plant fiber porous media generated by Quartet Structure Generation Set

This study proposes a novel approach that combines a Quartet Structure Generation Set (QSGS) algorithm with the Lattice Boltzmann Method (LBM) to investigate effective thermal conductivity in plant fiber materials. The QSGS algorithm is utilized to construct a two-dimensional digital structure of the porous media, enabling the determination of effective thermal conductivity through LBM simulations. The simulation results demonstrate good agreement with experimental data, exhibiting a maximum relative error of only 1.7%. The study examines the influence of key factors on effective thermal conductivity, including temperature, porosity, fiber distribution, and fractal dimension. The effective thermal conductivity increased by approximately 6.1% for each 10 °C increase in temperature between 10 °C and 50 °C. Moreover, the effective thermal conductivity showed an average decrease of 16.3% with each 10% increment in porosity. The increase in thermal conductivity becomes more significant with increasing temperature and decreasing porosity. Conversely, an increased fractal dimension correlates with a decline in effective thermal conductivity. The effective thermal conductivity is influenced synergistically by the directional growth probability of the solid phase and the direction of heat flow. The findings offer valuable insights into thermal conductivity behavior and heat transfer mechanisms in plant fiber porous media.

Analytical Fractal Model for Effective Thermal Conductivity of Open-Cell Metallic Foams With Coated Hollow Ligaments

Abstract Coating the hollow ligaments of open-cell (fluid-through) metallic foams (MFs) fabricated via the sintering route with a thin layer of graphene can improve the effective thermal conductivity (ETC) of the foam without significantly increasing its flow resistance, potentially important for thermal storage applications. However, the Euclidean geometry cannot accurately depict the random distribution of pores within MFs. To this end, the present study aims to analyze how such thin coatings affect the ETC of MF by employing the fractal theory to depict the random distribution of its open pores. Subsequently, a cubic representative structure is chosen for self-similar pores in the fractal to establish a correlation between the geometric parameters of MF and its fractal dimension. Upon determining the thermal resistance provided a representative structure of the foam having coated hollow ligaments, its ETC is derived as a function of fractal dimension, dimensionless parameters of pore size, porosity, and thermal conductivity of relevant materials (e.g., ligaments, coatings, and filling medium). For validation, existing experimental data are used to compare with analytical predictions, with good agreement achieved. It is demonstrated that the ligament hollowness weakens the thermal conduction of MFs. In addition, when the coating has a thermal conductivity greater than that of ligament, the coating enhances the ability of the foam to conduct heat. Although the ligament hollowness and coating thickness are imperative factors affecting the ETC, the material makes of ligament and coating plays a decisive role in the ETC.