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All issues Volume 21 / No 4 (2020) Mechanics & Industry, 21 4 (2020) 410 Full HTML
Open Access
Issue
Mechanics & Industry
Volume 21, Number 4, 2020
Article Number 410
Number of page(s) 31
DOI https://doi.org/10.1051/meca/2020028
Published online 15 June 2020

© AFM, EDP Sciences 2020

1 Introduction

In the last decade of the 20th century, miniaturization of high-tech industries, creating tools and equipment required multifunctional materials. This revolutionary concept is called Multifunctional materials or structures. Engineering sciences introduced a new class of materials that have different properties from their shows. Significant efforts in incorporating the Multifunctional structures concept into materials engineering have led to an entirely new category of materials. Cellular solids are a group of materials with multifunctional attributes, which have tailorable structures to achieve system-level performance as materials that combine mechanical, thermal, electrical, acoustical and possibly other functionalities. Multifunctionality of cellular solids is an interdisciplinary research area that requires a concurrent-engineering approach. One critical application area is ultra-light multifunctional heat exchangers or heat sinks in integrated circuit where cellular solids give off an impression of being more appealing than the ordinary heat dissipation media. As the heat dissipation material, it also required to support large structural loads. In this manner, exceptionally viable and robust thermal management via these cellular solids is crucial. With the prerequisites on the capability of carrying both mechanical and thermal loads in mind, the challenges are to establish relationships between topology and properties to optimize the geometric parameters applicable to various thermo-mechanical applications. Beginning with the spearheading work of Maxwell (1891), heat conduction in completely immersed porous material (e.g., sand, packed beds of cylinders and spheres, fibrous insulations, etc.) has been considered in detail in recent decades.(1)

Figure 1 demonstrates a photo of the metal foam medium. It has an open-celled structure made out of dodecahedron-like cells, which have 12–14 pentagonal or hexagonal countenances. The edges of these cells are made out of the strands, commonly, there is a lumping of material (intersection) at focuses where the strands converge [1,2]. A further issue with these materials is that the repeatability of the morphology is not consistent, notwithstanding when similar assembling conditions are utilized, bringing about an intrinsic disperses in the material properties unless substantial examples are tried [3–5] (Fig. 2).

“When modern man builds large load bearing structures, he uses dense solids: steel, concrete, glass. When nature does the same, she generally uses cellular materials: cork, wood, coral. There must be good reason for it.”

Michael F. Ashby

As a matter of fact, they are particularly interesting for applications requiring multi-functionality. In most of the new fields of applications of metal foams, the knowledge of the thermal transport properties is of essential significance for the dimensioning of the structures. To evaluate the extent of heat conduction, one generally uses the ETC (keff) which implies that the thermal behavior of two-phase materials can be well-matched by a homogeneous conductive medium. The conductive heat transfer is then governed by the simple diffusion equation:

The structure of a porous material is very complex, consisting of different pore sizes and shape factors [6]. A detailed prediction of the ETC of heterogeneous media requires knowledge of the topology, size, location (distribution) and conductivity of each phase in the system together with interaction between particles [7].

thumbnail Fig. 1

Open cell metal foam medium made.
thumbnail Fig. 2

Stochastic structures [Foam as seen through the camera lens of art photographer Michael Boran].

2 Effective parameters in the thermal conductivity open cell metal foams

Thermal conductivity is an intrinsic property of material, which has been influenced by numerous parameters. With the technology advancement, use of composite materials (composed of one or more substances) with cellular structures requires knowledge of their thermos-physical properties. Heat transfer in open-cell metal foams is complex as it takes place in two phases. There is a network of solid ligaments of universally high thermal conductivity and a fluid with lower thermal conductivity [8,9]. In such circumstances, the ETC is no longer a property of a single material but depends on both the solid and fluid material properties, e.g. temperature, pressure, base material and also the topological structure of open-cell metal foams; e.g. its porosity, pore size and shape factor.

2.1 Pore size

Number of pores per inch (PPI) or pore per millimeter or centimeter (PPM-PPC) is a geometric feature of open-cell metal foams that indicate number of pores on a porous surface. However, numbering technique is so elementary and not accurate but it is one of standardization methods among manufacturers and researchers. As more number of pores exist on a surface, smaller cell diameter resulted. Commercial range of pore size commonly varies from 5 PPI to 60 PPI. It is worthy to mention that increasing pore size is followed by decrease of ligament diameter but no significant change in conjunction angle is observed. For heat conduction in high porosity open-cell metal foams, the experimental results on the influence of PPI upon effective thermal conductivity are analyzed below (Figs. 3,4).

Wang et al. [10] numerically analyzed ETC of open cell metal foam by pore scale approaches. They found pore size has no obvious influence on the ETC.

Yang et al. [11] studied the ETCs of aluminum foams with different porosities and pore sizes under various conditions. Pore size (PPI) is found to have little influence upon the ETC of the open-cell metal foams.

thumbnail Fig. 3

Effect of pore size on the ETC.
thumbnail Fig. 4

Effect of pore size on the ETC.

2.2 Porosity

Based on etymology, porosity stems from Greek language and word “pore” meaning “passage” so things with porosity allow something through. Porosity includes different types but generally, it is ratio of void volume to total volume.(2)

Generally, for open-cell metal foams, porosity varies from 50% to 98%. It is notable that porosity is a morphological property of open-cell metal foams that has considerable impact on its structure. Because of wide range of porosity of metal foams in thermal and hydrodynamic application researcher's focus were attracted to it (Fig. 5).

The ETC has been observed to be exceedingly sensitive to the porosity, increasing as the porosity decreases [12–15] (Fig. 6).(3)

thumbnail Fig. 5

Porosity of silver foam.
thumbnail Fig. 6

ETC of open-cell metal foams decreasing with increasing porosity.

2.3 Temperature and pressure

Thermal conductivity is one of critical material properties that defines capability of a material in case of heat transfer. Conductive heat transfer is expressed by Fourier law:

Heat transfer is categorized into various mechanisms: conduction, convection and radiation so, the total material thermal conductivity can be presented as equation (4) (4)

Process of heat transfer conduction is vibration energy by phonons and electrons in solid, liquid and gas medium and convection, radiation is transport of mass and heat electromagnetic energy by photons. Based on Figure 7, total conductivity is dependent on temperature. For temperature under 100 °C conduction is dominant mechanism. Zhao et al. [17] experimentally investigated metal foam ETC under vacuum and atmosphere condition in range of 300K–800K. The results showed that the ETC increased quickly as temperature raised. Also, the ETC in 800 K was reported to be 3 times higher than ETC in ambient temperature (300 K). They proposed an empirical correlation for the effective radiative conductivity as function of temperature.(5)

Sauerhering et al. [18] investigated influence of temperature on ETC of Inconel foams experimentally. They formulated equation (6) (6)

Porosity of samples varied from 55% to 85% and temperature range varies from ambient to 700 °C. Figures 8 and 9 showed ETC variation with temperature and porosity. Using Transient Plane Source Technique (or so called Hot Disk method) in case of temperature above 100 °C ETC significantly increased and above 300 °C radiation and convection were dominant heat transfer mechanisms.

Brendelberger et al. [19] depicted the temperature dependency of the ETC of the two samples of metal foams (Fig. 10). The inherent thermal conductivity of the fluid phase changes by less than 0.2% in the examined pressure range (1 bar–17 bar).

Also for the porous solid network, no influence of the pressure on the ETC in the examined pressure range is expected [20]. Therefore, the dependency of the ETC on the pressure is assumed to be trivial [19]. More studies in field of pressure effects on thermal conductivity of open-cell metal foams need to be done. Moreever its role in determining ETC is not significant.

thumbnail Fig. 7

Effect of temperature on K t , Kcond, Kconv, Krad. [16].
thumbnail Fig. 8

Variation of ETC with temperature [18].
thumbnail Fig. 9

Variation of ETC with porosity [18].
thumbnail Fig. 10

ETC dependency on temperature.
thumbnail Fig. 11

Thermal conductivity of some known metals.

2.4 Base material

A large number of studies demonstrated that base material is an important parameter that affects the ETCs of open-cell metal foams. Aluminum, copper, nickel, iron and other metals and alloys are applied in manufacturing open-cell metal foams. According to conventional thermal conductivity models like parallel and series, one may observe base material is important factor in calculation ETC. Higher the solid thermal conductivity, higher ETC is (Fig. 11).

2.5 Shape factor

Repeatability type is one of recognition features of open-cell metal foams and their shape factor determination. Shape factor is dependent on various parameters including: ligament length, ligament conjunction angels, ligament cross sectional area, ligament diameter, node shape, node diameter, cell diameter, shape of faces (tetrahedral, pentagonal, hexagonal and so on) and etc. Researchers have investigated mentioned parameters and how they may affect ETC to reach an optimize geometry with highest ETC. Generally, increasing the number of pores per inch, decreases the ligament effective diameter. Also, decrease in porosity changed the shape of ligaments from triangular to circular. Many studies have been done conforming that a triangular cross section improved heat transfer for a given pore size.

Markos et al. [21] experimentally studied cross section shape factor and hydraulical parameters of open-cell metal foams by various pore sizes and porosities. They found that by increasing the drag and velocity of flowing through the foam, cross section shape because of increasing porosity deformed from convex ligament to concave ligament. (Fig. 12).

Ozmat et al. [22] estimated an average measure of the ligament dimension of open-cell metal foams. Figure 12 showed ligament cross section shapes by optical microscopic image. They calculated and measured value of ligament dimension by equation below and the results are available in Table 1.(7)

where μ e is the number of edges dodecahedron

μ c is the number of corners of dodecahedron

is the cross sectional area of a ligament

λ1: a linear measure of the nodes

λ2: nodal volume shape factor

ρ: density

S: area

V: volume

According to photographs they observed triangular cross section shape for pore size ranging from 10 PPI to 30 PPI and porosity 80%.

Bodla et al. [23] numerically analyzed open-cell metal foam topology such as effective pore diameter and sphericity, ligament length, ligament area of cross section and node co-ordination number based on X-ray micro tomography and resistance network model (Fig. 13).

Hutter et al. [24] numerically investigated effect of ligament shape pressure drop and turbulent kinetic energy. The results demonstrated the change of ligament shape from sharp edges to rounded edges caused pressure drop reduction to almost 30%.

Yang et al. [25] numerically investigated the effect of ratio of node length to ligament thickness in open-cell metal foams with node scale approach. Results showed increasing the ratio by 4 times caused effective thermal conductivity reduction by 59%, also permeability and volume-average Nusslet number respectively increased by 16% and 27.6%.

Bock and Jacobi [26] analyzed Al open-cell metal foams structures by tomography method in order to earn structured-combinatorial data and characterization. Comprehensively compared ligament length, ligament diameter, ligament density, ligament cross sectional area, pore and face shape of real structure with existing models (Weaire-Phelan, Kelvin cell, P42a [27]). According to porosity range of used metal foam most of the cross sectional areas are triangular rather than circular.

thumbnail Fig. 12

Illustrated different cross section shape of ligaments.
Table 1

Calculated and measured value of open cell metal foam ligament dimensions [22].

3 Theoretical and experimental approaches for characterization thermal conductivity

Transport phenomena in open-cell metal foams have been a requirement of large number of researches because of its different thermal and hydraulic properties. A wide range of analytical, numerical and experimental efforts to predict ETC of open-cell metal foams were reviewed in the literature. Generally, applied methodology in studies is classified as categories below: (1) Asymptotic and Analytical Analysis, (2) Pore scale (Unit cell) approach, (3) Micro tomography-CFD Modeling, (4) Empirical correlation.

3.1 Asymptotic and analytical approach

Numerous efforts have been made by mathematicians and physicists using theoretical methodology to understand the nature of heat transfer features of composite structure materials. Upper and lower bounds were basic asymptotic analyses then many researchers with different approaches and sights upgraded them. Whether thermal resistances of the solid and fluid phases considered Series or Parallel, lower and upper bounds would be made respectively. Maxwell-Eucken using upper and lower bounds modified parallel and series equations as random distribution of small continues and discontinues solid spheres into fluid. Many researchers including Hamilton Crosser [28], Hashin-Shtrikman [29], Hadley [30] and etc. developed correlations taking into account shape and other factors. Open-cell metal foams are composed of two phase systems i.e. a solid structure and fluid. The ETC implies heat transfer through this composite system [31]. Figure 14 illustrated comparison of the ETC predicted by five basic structural models used in open-cell metal foams, including the Series and Parallel models [20], Maxwell–Eucken models (two forms) [28] and Effective Medium Theory (EMT) equation [29]. According to Figure 14 there are large differences among calculated ETCs using above models, so it is clear that the thermal conductivity of open-cell metal foams is influenced by other factors than porosity, like microstructure. Hence, selecting a proper model to predict the thermal conductivity of different types of open-cell metal foams is challenging.

Boomsma and Poulikakos [32] derived a correlation to estimate ETC of open-cell metal foams saturated by fluid based on a cellular structure composed of cells with equal sizes. In their research, term “e” which define dimensionless length of cubic node were calibrated by comparing calculations with existed experimental results of Calmidi and Mahajan [33] (in order to ensure reasonable ratio of node to ligament size). Finally, good agreement was reached comparing experimental results and analytical predictions of their model and it was shown for ε < 0.8 their model predicts: (a) lower value of ETC when and (b) higher value of ETC when than phase-symmetry model. Later, Dai et al. [34] tried to correct some errors in Boomsma and Poulikakos correlations but, results based on analytical predictions were still far from experimental results and reason was hidden in heat transfer direction assumption. When solid phase thermal conductivity is relatively much higher than fluid phase, in solid phase basically heat transfers through ligaments and ligaments orientation is not particularly parallel or perpendicular to heat transfer in fluid phase so by extending Boomsma and Poulikakos model into next level they proposed correlation considering ligaments orientation.

Zehner and Schlunder [35] considering an eighth of a cylinder as representative unit cell and assuming parallel path of heat transfer through fluid and solid phases, derived a correlation to estimate ETC which in addition of porosity, were function of shape factor too. They validated their correlation by comparing results with experimental data for case of porosity of 0.42 and establishing good agreement between them but, Kaviany [1] illustrated this correlation underestimated value of ETC in cases of . Hsu et al. [36] introduced two models by developing Zehner and Schlunder's model to overcome disagreement found by Kaviany. In first one (called Area-Contact model) instead of point contact they consider area contact assumption between spheres and declared term “α” which indicates deformation. Second model (called Phase-Symmetry model) was provided for sponge like mediums with continuous phases but they could not determine second model accuracy because of lack of experimental data at the time. Behrang et al. [37] studied ETC of porous media saturated with two immiscible fluids. An analytical correlation was developed based on a hybrid approach deriving from Maxwell, Parallel and Series models. Using phonon theory, the model was extended to calculate heat transfer in nano-scale and based on it they figured out increase of surface roughness cause reduction of ETC. Kou et al. [38] focused on effective fractal parameters of porous materials and how they may affect dimension less ETC . They formulated a correlation to predict ETC of unsaturated porous media using thermal-electrical and self-similarity method under the assumption of one-dimensional heat transfer. According to results, higher value of fractal dimension for tortuosity and pore diameter, porosity and lower value of saturation degree is followed by lower ETC. This approach led to a correlation consisting no empirical parameter.

Kumar and Topin [39] studied tetrakaidecahedron structures with different ligament cross section shapes including circle, square, hexagon, diamond and star. They investigated relation between geometrical parameters and thermal features of metal foams. Studied cases included porosity range from 0.6 to 0.95 and solid to fluid thermal conductivity ratio varying from 10 to 30 000. Finally, two models for estimating ETC were proposed. They introduced equivalent radius “Req” which is radius of a circle with area equal to ligament cross section area, to simplify and unite the model for all cases of ligament shapes. Main assumption in this model was based on Req and implied that for given equivalent radius, node volume is same and independent from ligament shape. T. Uhlířová and W. Pabst [40] analytically and numerically investigated relative thermal conductivity of porous material with cubic cells and compared quasilaminate solution with the upper Hashin-Shtrikman bounds. Progelhof et al. [41] comprehensively investigated analytical models for effective thermal conductivity of porous media.

3.2 Pore scale (unit cell) approach

Since long time ago, filling space by unit volume cells having least surface area and highest adaptability with real structure of foams has led to new approach in simplifying metal foams structure. Lord Kelvin as one of well-known precursors in 19th century proposed tetrakaidecahedron cell consisting of 14 faces (6 quadrilaterals and 8 hexagonals) and 24 vertices as a polyhedron conforming Plateau's laws and Euler principals (Fig. 15).

Gabbrielli [42] found out Swift–Hohenberg equation can be a proper candidate to Kelvin problem solution (8)

where r is real bifurcation parameter, and N(u) is some smooth nonlinearity. For almost a century Kelvin cell was known as applicable structure till discovery of the Weaire–Phelan model.

Weaire–Phelan model was proposed in 1993 in order to solve kelvin problem with this difference that it consisted of two types of cells with equal volume. As it is shown in Figure 16, first type is a 12-facedpolyhedron with non-plan pentagonal faces and second type is a 14-faced polyhedral with 2 flat hexagonal faces and 12 non-plans pentagonal faces. Two cells of first kind and six cells of second kind composed Weaire-Phelan packed model [43].

Haghighi and Kasiri [44] tried to estimate ETC focusing on geometrical characteristic of open-cell metal foams using unit cell approach. They studied a non-isotropic tetrakaidecahedron unit cell as representative unit cell by dividing and repeating it in y and z directions. They found out with change of dimensionless height or radius of ligaments, dimensionless diameter of nodes and angle of face orientations it is possible to increase ETC to 33%. Edouard [45] focused on establishing an ideal periodic unit cell model and proposed a model consisting 12 cylindrical ligaments and 8 cubes. Each ligament divided between four cubic lattices and each cube divided between eight other cubes, finally he proposed a correlation capable of determining minimum and maximum values of ETC which later Zenner and Edouard [46] revised and derived an improved correlation. Huu et al. [47] investigated open-cell metal foams using pore scale method. They came up with pentagonal dodecahedron model, which was based on material accumulation on nodes and ligaments. It was divided into two categories of flat and slim. Finally, results were compared to experimental data obtained from testing metal foams with porosities from 0.75 to 0.98 and good compromise was found. Researchers [48–50] used Cubic lattice cell model to investigate different features of open-cell metal foams such as heat transfer. In this model, cylindrical ligaments construct cubic and it was assumed that cell size is equal to cubic lattices. Kumar and Topin [39] investigated ETC of open-cell metal foams by unit cell approach. They proposed a model derivation, which were inspired by Singh and Kasana [51], they employed resistor model and applied it on unit cell. For another time importance of ligaments and heat flow orientation was highlighted and were taken into account by defining parameter “F”.

thumbnail Fig. 13

(a) Distribution of ligament length Al foam with porosity 91%, (b) distribution of ligament area Al foam with porosity 91%, (c) distribution of node co-ordination number Al foam with porosity 91%, (d) distribution of effective pore diameter Al foam with porosity 91% [23].
thumbnail Fig. 14

Predicted ETC by five different models .

3.3 Microtomography-CFD modeling approach

Modeling real structure of cellular materials as it was mentioned before has long history of challenging researchers' minds. In 20th century with technological revolution, Computed Tomography (CT) scan using computer process and X-ray measurement from different angles provided a non-destructive approach and realistic 3D virtual image of an object without cutting. Lately, realistic 3D modeling using µ-CT scan imaging has been an innovative approach to understand and analyze metal foam structure. However, µ-CT scan is more expensive method, but it provides a very similar geometry to real structure that captures irregular and randomized cells. Number of researchers using this approach determined ETC of open-cell metal foams. Mendes et al. [52] numerically evaluated ETC of open-cell metal foams using µ-CT scan images to reconstruct precise 3D morphology and results showed a good validation between numerical and experimental data. Wulf et al. [53] experimentally and numerically determined ETC of open-cell metal foams for various samples. They found good agreement between three dimensional Lattice-Boltzmann method results obtained from real foam structure modeling based on 3D CT-scan imaging and experimental data. Ranut et al. [54] simulated 3D structure of the foams by means of high resolution X-ray micro tomography. The results of CFD analyze illustrated ETC is pertinent to morphological features. Bodla et al. [55] numerically studied a 3D complex geometrical nature of metal foams applying µ-CT scan to investigate thermal behavior. They found porosity as an important factor in determining ETC (Fig. 17).

Al-Athel [56] summarized 3D realistic structure of metal foams by µ-CT scan method. As it is shown in Figure 18, after choosing suitable sample and taking photos from 0.7-degree rotation each produced raw model and then by eliminating errors a clean RAW geometry was produced and finally using Solid work software he analyzed metal foam structure.

Amani et al. [57] experimentally obtained realistic 3D stochastic open-cell metal foams structure by X-ray tomography and then numerically calculated ETC using finite element method (Fig. 19).

However, benefits of µ-CT scan method and simulation approach is considerable unfortunately not many studies have been done and requires more efforts to improve simplicity and performance of this method. More studies are needed to improve this approach as a cheap, accessible and fast method to produce a fine 3D geometry image of open-cell metal foams.

thumbnail Fig. 15

Unit cell of Kelvin.
thumbnail Fig. 16

Weaire-Phelan cell: (left): type (a), (middle): type (b), (right) Orientation of Weaire-Phelan bubbles.
thumbnail Fig. 17

Analyzing a realistic open-cell metal foam structure created by µ-CT scan [55].
thumbnail Fig. 18

Steps of µ-CT scans process to produce 3D realistic image to analyze [56].
thumbnail Fig. 19

The 3-D image of the stochastic open cell metal foam obtained by X-ray tomography [57].

3.4 Empirical correlation approach

Empirical method is an experimental process that results formula or curve fitting supported by measured data. In order to describe effective parameters of metal foam thermal conductivity an appropriate analysis can be made using experimental data. Calmidi and Mahajan [33] proposed an empirical correlation based on experimental data. They found thermal conductivity as function of kf , ks and ε. Bhattacharya et al. [13] comprehensively investigated thermo-physical properties of various samples of open-cell metal foams. They found ETC is affected by porosity and cross section ratio and intersection. Finally, a correlation was derived based on measured data from experimental analysis. Singh and Kasana [51], Chaudhary and Bhandari [58] established a correlation based on experimental data to estimate ETC easily. Heat transfer direction may be parallel or perpendicular to matrix orientation, so they introduce term F and weight mean correlation. Term F indicated parallel formation and 1-F indicated perpendicular formation. Finally, they showed F rises linearly with increase of meaning most of heat transfer is parallel. Hamilton and Crosser [59] proposed an empirical correlation to predict ETC focusing on shape of particles by introducing term n as shape factor parameter. This factor indicated sphericity of particles that is ratio of surface area of particle to surface area of a sphere with same volume. Coquard et al. [60] investigated open-cell metal foams in case of fire barrier applications. Radiative heat transfer is not negligible in high temperatures (>1000  °C) so they developed a correlation coupling conductive and radiative heat transfer mechanism based on results of numerical simulations and taking into account physic of structure. Fourie and Du Plessis [61] studied samples with porosity ranging from 0.8 to 0.98 and fluid to solid thermal conductivity ratio between 0.0001 and 0.1 in case of thermal non-equilibrium state. They found temperature distribution by solving 3D conduction equation and results showed insensitivity of ETC to pore size was in agreement with Takegoshi [62] and Peak [3] studies. Based on established data they proposed 4 empirical correlations as function of coupled thermal conductivities and porosity. Krisher [62] belived that since ETC of any sample is limited by values obtained from Parallel and Series models so mechanism structure should be a combination of these two models and based on this idea he proposed correlation introducing an empirical parameter showing share of parallel and perpendicular direction of heat flow to solid orientation. Sadeghi et al. [11] investigated ETCs of open-cell metal foams. They proposed a new equation based on experimental data.

4 Measurement methods of thermal conductivities of metal foams

The ETC of porous structures is one of the important thermophysical parameters that affect heat transfer. Due to large number of analytical and numerical studies made on predicting the ETC an experimental validation is needed to evaluate their correctness. Plenty of researchers have reported various measurement techniques for studying apparent ETC of open-cell metal foams.

The measurement techniques are generally divided into two types: steady state and transient techniques. Each method has its advantages and disadvantages: transient techniques are based on instantaneous measurement of temperature locally and in short time. However, experiment setup is easy to fabricate but data analysis, accuracy and uncertainty analysis require appropriate mathematical tools. In contrast to transient method, in case of steady-state measurement techniques data analysis is not much of challenge but collecting heat transfer data is a long time process in order to be sure of existence of steady state condition. The steady state experiment setup is a complex task and requires a well-engineered process and finally for porous structures ensuring 1D heat transfer, good contact between sample and heat source and accurate measurement could be additional problem. The steady state and transient methods mainly included guarded hot plate, comparative longitudinal, heat flow meter, laser flash, hot wire, hot disk method, temperature wave analysis and etc. Table 2 briefly summarized ETC measurement techniques.

Peak et al. [3] studied thermo-physical properties of Al foams. Employing steady measurement technique while considering one-dimensional heat conduction they determined effect of porosity and cell size on thermal conductivity and permeability. Results demonstrated that decrease of porosity was followed by increase of thermal conductivity but cell size changes with constant porosity simply did not affect it.

Calmidi and Mahajan [33] used similar technique to investigate ETC. They assumed one-dimensional heat conduction in their theoretical studies and developed this assumption to experiments by heating sample from above and cooling it from bottom. Finally based on their experimental data an empirical correlation was derived. Other steady-state methods including comparative longitudinal method and panel test have been used by [17,64].

Table 2

Measurement techniques and their field of application.

5 Conclusions

Effects of different parameters (temperature, pressure, pore size, porosity and shape factor) on ETC of open-cell metal foams were discussed in this work. Additionally, analytical, numerical and experimental approaches were comprehensively reviewed; also, ETC measurement methods were summarized. Increase of pore size caused an ignorable decrease in ETC but decrease of porosity caused higher solid fraction and as a result higher ETC. Generally, higher temperature is followed by higher ETC meanwhile as long as temperature remains below 250 °C conduction would be dominant mechanism of heat transfer but while temperature raises to 500 °C convection and radiation are not ignorable anymore. Even though effect of pressure variation requires more studies but generally, its effect is not significant. Shape factor includes many parameters (such as ligament length, ligament conjunction angels, ligament cross sectional area, ligament diameter, node shape, node diameter, cell diameter and shape of faces (tetrahedral, pentagonal, hexagonal and so on)) even in same circumstances of manufacturing technique, morphological structure would be different and as a result it affects ETC. Analytical predictions showed good agreement with experimental data but absence of an universal model, which includes every effective parameter is noticeable. In last decades, numerical and experimental methods have had promising advancements but developing a cheap, fast and accurate model requires more efforts.

Author contribution statement

The manuscript was written by M.S. and advised by Y.B., S.N., J.K.

Funding

This research received no external funding.

Conflicts of interest

The authors declare no conflict of interest.

Appendix A

Table A1

Summary of existing experimental studies on ETC of open-cell metal foams.

Appendix B

Table B1

Asymptotic models.

Table B2

Analytical models.

Table B3

Empirical correlations.

Table B4

Unit cell models ETCs of open-cell metal foams.

References

  1. M. Kaviany, Principles of Heat Transfer in Porous Media, 2nd ed., Springer, Berlin, 1999 [Google Scholar]
  2. W. Chen, Linear Networks and Systems, World Scientific, Singapore, 1993 pp. 123–135 [Google Scholar]
  3. J. Paek, B. Kang, S. Kim, J. Hyun, Effective thermal conductivity and permeability of aluminum foam materials, Int. J. Thermophys. 21 , 453–464 (2000) [Google Scholar]
  4. E.N.P. Ranut, On the effective thermal conductivity of metal foams, J. Phys. Conf. Ser. 547 , 1 (2014) [Google Scholar]
  5. M. Mendes, P. Goetze, P. Talukdar, E. Werzner, C. Demuth, P. Rössger, R. Wulf, U. Gross, D. Trimis, S. Ray, Measurement and simplified numerical prediction of effective thermal conductivity of open-cell ceramic foams at high temperature, Int. J. Heat Mass Transf. 102 , 396–406 (2016) [Google Scholar]
  6. P. Cheng, C. Hsu, The effective stagnant thermal conductivity of porous media with periodic structures, J. Porous Media 2 , 19–38 (1999) [Google Scholar]
  7. R. Crane, R. Vachon, Prediction of the bounds on the effective thermal conductivity of granular materials, Int. J. Heat Mass Transf. 20 , 711–723 (1977) [Google Scholar]
  8. T. Bauer, A general approach toward the thermal conductivity of porous media, Int. J. Heat Mass Transf. 36 , 4148–4191 (1993) [Google Scholar]
  9. R. Dyga, S. Witczak, Investigation of effective thermal conductivity aluminum foams, Procedia Eng. 42 , 1088–1099 (2012) [Google Scholar]
  10. G. Wang, G. Wei, C. Xu, X. Ju, Y. Yang, X. Du, Numerical simulation of effective thermal conductivity and pore-scale melting process of PCMs in foam metals, Appl. Therm. Eng. 147 , 464–472 (2019) [Google Scholar]
  11. X. Yang, J. Bai, H. Yan, J. Kuang, T. Lu, T. Kim, An analytical unit cell model for the effective thermal conductivity of high porosity open-cell metal foams, Transp. Porous Media 102 , 403–426 (2014) [Google Scholar]
  12. E. Sadeghi, S. Hsieh, M. Bahrami, Thermal contact resistance at a metal foam-solid surface interface, in ASME/JSME 2011 8th Thermal Engineering Joint Conference, Hawaii, USA, 2011 [Google Scholar]
  13. A. Bhattacharya, V. Calmidi, R. Mahajan, Thermophysical properties of high porosity metal foams, Int. J. Heat Mass Transf. 45 , 1017–1031 (2002) [Google Scholar]
  14. R.D. Boer, Theory of porous media − past and present, J. Appl. Math. Mech. 78 , 441–466 (1998) [Google Scholar]
  15. M.S. Phanikumara, R.L. Mahajanb, Non-Darcy natural convection in high porosity metal foams, Int. J. Heat Mass Transf. 45 , 3781–3793 (2002) [Google Scholar]
  16. P.I. Pelissari, R.A. Angélico, V.R. Salvini, D.O. Vivaldini, V.C. Pandolfelli, Analysis and modeling of the pore size effect on the thermal conductivity of alumina foams for high temperature applications, Ceram. Int. 43 , 13356–13363 (2017) [Google Scholar]
  17. C. Zhao, T. Lu, H. Hodson, J. Jackson, The temperature dependence of effective thermal conductivity of open-celled steel alloy foams, Mater. Sci. Eng. A 367 , 123–131 (2004) [CrossRef] [Google Scholar]
  18. J. Sauerhering, O. Reutter, T. Fend, S. Angel, R. Pitz-Paal, Temperature Dependency of the Effective Thermal Conductivity of Nickel Based Metal Foams, in ASME 4th International Conference on Nanochannels, Microchannels, and Minichannels, Parts A and B, Limerick, Ireland, 2006 [Google Scholar]
  19. S. Brendelberger, S. Hötker, T. Fend, R. Pitz-Paal, Macroscopic foam model with effective material properties for high heat load applications, Appl. Therm. Eng. 47 , 34–40 (2012) [Google Scholar]
  20. Y. Asakuma, S. Miyauchi, T. Yamamoto, H. Aoki, T. Miura, Homogenization method for effective thermal conductivity of metal hydride bed, Int. J. Hydrogen Energy 29 , 209–216 (2004) [Google Scholar]
  21. B. Morkos, S.V.S. Dochibhatla, J.D. Summers, Effects of metal foam porosity, pore size and ligament geometry on fluid flow, J. Therm. Sci. Eng. Appl. 10 , 4 (2018) [Google Scholar]
  22. B. Ozmat, B. Leyda, B. Benson, Thermal application of open cell metal foams, Mater. Manuf. Process. 19 , 839–862 (2004) [CrossRef] [Google Scholar]
  23. K. Bodla, J. Murthy, S. Gaeimella, Resistance network-based thermal conductivity model for metal foams, Comput. Mater. Sci. 50 , 622–632 (2010) [Google Scholar]
  24. C. Hutter, A. Zenklusen, S. Kuhn, P.v. Rohr, Large eddy simulation of flow through a streamwise-periodic structure, Chem. Eng. Sci. 66 , 519–529 (2011) [Google Scholar]
  25. X. Yang, Y. Li, L. Zhange, L. Jin, W. Hu, T. Lu, Thermal and fluid transport in micro opencell, J. Heat Transf. 140 , 1 (2018) [Google Scholar]
  26. J. Bock, A. Jacobi, Geometric classification of open-cell metal foams using X-ray, Mater. Character. 75 , 35–43 (2013) [CrossRef] [Google Scholar]
  27. R. Gabbrielli, A new counter-example to Kelvin's conjecture on minimal surfaces, Philos. Mag. Lett. 89 , 483–491 (2009) [Google Scholar]
  28. R. Hamilton, O. Crosser, Thermal conductivity of heterogeneous two-component systems, Ind. Eng. Chem. Fund. 1 , 187–191 (1962) [CrossRef] [Google Scholar]
  29. Z. Hashin, S. Shtrikman, A variational approach to the theory of the effective magnetic permeability of multiphase materials, J. Appl. Phys. 33 , 3125–3131 (1962) [Google Scholar]
  30. G. Hadley, Thermal conductivity of packed metal powders, Int. J. Heat Mass Transf. 29 , 909–920 (1986). [Google Scholar]
  31. B. Nait-Ali, K. Haberko, H. Vesteghem, J. Absi, D. Smith, Thermal conductivity of highly porous zirconia, J. Eur. Ceram. Soc. 26 , 3567–3574 (2006) [Google Scholar]
  32. K. Boomsma, D. Poulikakos, On the effective thermal conductivity of a three-dimensionally structured fluid-saturated metal foam, Int. J. Heat Mass Transf. 44 , 827–836 (2001) [Google Scholar]
  33. V. Calmidi, R. Mahajan, The effective thermal conductivity of high porosity fibrous metal foams, ASME J. Heat Transf. 121 , 466–471 (1999) [CrossRef] [Google Scholar]
  34. Z. Dai, K. Nawaz, Y.G. Park, J. Bockb, M. Jacobi, Correcting and extending the Boomsma–Poulikakos effective thermal conductivity model for three-dimensional, fluid-saturated metal foams, Int. Commun. Heat Mass Transf. 37 , 575–580 (2010) [CrossRef] [Google Scholar]
  35. P. Zehner, E. Schlünder, Wärmeleitfähigkeit von Schüttungen bei mäßigen Temperaturen, Chem. Ing. Tech. 42 , 933–941 (1970) [Google Scholar]
  36. C. Hsu, P. Cheng, K. Wong, Modified zehner-schundler models for stagnant thermal conductivity of porous media, Int. J. Heat Mass Transf. 37 , 2751–2759 (1994) [Google Scholar]
  37. A. Behrang, S. Taheri, A. Kantzas, A hybrid approach on predicting the effective thermal conductivity, Int. J. Heat Mass Transf. 98 , 52–59 (2016) [Google Scholar]
  38. J. Kou, Y. Liu, F. Wu, J. Fan, H. Lu, Y. Xu, Fractal analysis of effective thermal conductivity for three-phase (unsaturated) porous media, J. Appl. Phys. 106 , 5 (2009) [Google Scholar]
  39. P. Kumar, F. Topin, Simultaneous determination of intrinsic solid phase conductivity and effective thermal conductivity of kelvin like foams, App. Therm. Eng. 71 , 536–547 (2014) [CrossRef] [Google Scholar]
  40. T. Uhlířová, W. Pabst, Thermal conductivity and Young's modulus of cubic-cell metamaterials, Ceram. Int. 45 , 954–962 (2019) [Google Scholar]
  41. R. Progelhof, J. Throne, R. Ruetsch, Methods for predicting the thermal conductivity of composite systems: A review, Polym. Eng. Sci. 16 , 615–625 (1976) [Google Scholar]
  42. R. Gabbrielli, A new counter-example to Kelvin's conjecture on minimal surfaces, Philos. Mag. Lett. 89 , 483–491 (2009) [Google Scholar]
  43. A. Kraynik, D. Reinelt, Linear elestic behavior of dry soap foams, J. Colloid. Interface Sci. 181 , 511–520 (1996) [Google Scholar]
  44. M. Haghighi, N. Kasiri, Estimation of effective thermal conductivity enhancement using foam in heat exchangers based on a new analytical model, Braz. J. Chem. Eng. 27 , 127–135 (2010) [CrossRef] [Google Scholar]
  45. D. Edouard, The effective thermal conductivity for “slim” and “fat foams”, AIChE J. 57 , 1646–1651 (2011) [Google Scholar]
  46. A. Zenner, D. Edouard, Revised cubic model for theoretical estimation of effective thermal conductivity of metal foams, Appl. Therm. Eng. 113 , 1313–1318 (2017) [Google Scholar]
  47. T. Huu, M. Lacroix, C. Huu, D. Schweich, D. Edouard, Towards a more realistic modeling of solid foam: Use of the pentagonal dodecahedron geometry, Chem. Eng. Sci. 64 , 5131–5142 (2009) [Google Scholar]
  48. A. Evans, J. Hutchinson, M. Ashby, Cellular metals, Curr. Opin. Solid State Mater. Sci. 3 , 288–303 (1998) [Google Scholar]
  49. L. Giani, G. Groppi, E. Tronconi, Mass-transfer characterization of metallic foams as supports for structured catalysts, Ind. Eng. Chem. Res. 44 , 4993–5002 (2005) [Google Scholar]
  50. M. Lacroix, P. Nguyen, D. Schweich, C. Huu, S. Poncet, D. Edouard, Pressure drop measurements and modeling on SiC foams, Chem. Eng. Sci. 62 , 3259–3267 (2007) [Google Scholar]
  51. R. Singh, H. Kasana, Computational aspects of effective thermal conductivity of highly porous metal foams, Appl. Therm. Eng. 4 , 1841–1849 (2004) [Google Scholar]
  52. M. Mendes, V. Skibina, P. Talukdar, R. Wulf, U. Gross, Experimental validation of simplified conduction–radiation models for evaluation of Effective Thermal Conductivity of open-cell meta foams at high temperatures, Int. J. Heat Mass Transf. 78 , 112–120 (2014) [Google Scholar]
  53. R. Wulf, M. Mendes, V. Skibina, A. Al-Zoubi, D. Trimis, S. Ray, U. Gross, Experimental and numerical determination of effective thermal conductivity of open cell FeCrAl-alloy metal foams, Int. J. Therm. Sci. 86 , 95–103 (2014) [Google Scholar]
  54. P. Ranut, E. Nobile, L. Mancini, High resolution microtomography-based CFD simulation of flow and heat transfer in aluminum metal foams, Appl. Therm. Eng. 69 , 230–240 (2014) [Google Scholar]
  55. K.K. Bodla, J. Murthy, S. Garimella, XMT-based direct simulation of flow and heat transfer through open-cell aluminum foams, in 2010 12th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, Las Vegas, NV, USA, 2010 [Google Scholar]
  56. K. Al-Athel, A computational methodology for assessing the thermal behavior of metal foam heat sinks, Appl. Therm. Eng. 111 , 884–893 (2017) [Google Scholar]
  57. Y. Amani, A. Takahashi, P. Chantrenne, S. Maruyama, S. Dancette, E. Maire, Thermal conductivity of highly porous metal foams: Experimental and image based finite element analysis, Int. J. Heat Mass Transf. 122 , 1–10 (2018) [Google Scholar]
  58. M. Matsushita, M. Monde, Y. Mitsutake, Predictive calculation of the effective thermal conductivity in a metal hydride packed bed, Int. J. Hydrogen Energy. 39 , 9718–9725 (2014) [Google Scholar]
  59. R. Hamilton, O. Crosser, Thermal conductivity of heterogeneous two-component systems, Ind. Eng. Chem. Fundam. 1 , 187–191 (1962) [Google Scholar]
  60. R. Coquard, D. Rochais, D. Baillis, Conductive and radiative heat transfer in ceramic and metal foams at fire temperatures, Fire Technol. 699–732 , 48 (2012) [Google Scholar]
  61. J. Fourie, J.D. Plessis, Effective and coupled thermal conductivities of isotropic open-cellular foams, AIChE J. 50 , 547–556 (2004) [Google Scholar]
  62. E. Takegoshi, Y. Hirasawa, J. Matsuo, K. Okui, A study on the effective thermal conductivity of porous metals, Trans. Jpn. Soc. Mech. Eng. 58 , 879–884 (1992) [CrossRef] [Google Scholar]
  63. O. Krischer, Die wissenschaftlichen Grundlagen der Trocknungstechnik (The Scientific Fundamentals of Drying Technology), Springer-Verlag, New York, 1963 [CrossRef] [Google Scholar]
  64. D.J. Thewsey, Y.Y. Zhao, Thermal conductivity of porous copper manufactured by the lost carbonate sintering process, Phys. Status Solidi 205 , 1126–1131 (2008) [CrossRef] [Google Scholar]
  65. E. Bianchi, T. Heidig, C. Visconti, G.G.H. Freund, E. Tronconi, An appraisal of the heat transfer properties of metallic open-cell foams for strongly exo-/endo-thermic catalytic processes in tubular reactors, Chem. Eng. J. 198–199 , 512–528 (2012) [Google Scholar]
  66. M. Fetoui, F. Albouchi, F. Rigollet, S. Ben Nasrallah, Highly porous metal foams: effective thermal conductivity measurement using a photothermal technique, J. Porous Media, 12 , 939–954 (2009) [CrossRef] [Google Scholar]
  67. E. Schmierer, J. Paquette, A. Razani, K. Kim, Effective Thermal Conductivity of Fully-Saturated High Porosity Metal Foam, in ASME 2004 Heat Transfer/Fluids Engineering Summer Conferenc, Charlotte, North Carolina, USA, 2004 [Google Scholar]
  68. N. Dukhan, K. Chen, Heat transfer measurements in metal foam subjected to constant heat flux, Exp. Therm. Fluid Sci. 32 , 624–631 (2007) [CrossRef] [Google Scholar]
  69. E.N. Schmierer, A. Razani, Self-consistent open-celled metal foam model for thermal applications, J. Heat Transf. 128 , 1194–1203 (2006) [CrossRef] [Google Scholar]
  70. E. Sadeghi, S. Hsieh, M. Bahrami, Thermal conductivity and contact resistance of metal, J. Phys. D: Appl. Phys. 44 , 125406 1–7 (2011) [CrossRef] [Google Scholar]
  71. X. Xiao, P. Zhang, M. Li, Preparation and thermal characterization of paraffin/metal foam composite phase change material, Appl. Energy. 112 , 1357–1366 (2013) [Google Scholar]
  72. X. Xiao, P. Zhang, M. Li, Effective thermal conductivity of open-cell metal foams impregnated with pure paraffin for latent heat storage, Int. J. Therm. Sci. 81 , 94–105 (2014) [Google Scholar]
  73. R. Coquard, D. Rochais, D. Baillis, Experimental investigations of the coupled conductive and radiative heat transfer in metallic/ceramic foams, Int. J. Heat Mass Transf. 52 , 4907–4918 (2009) [Google Scholar]
  74. E. Solórzano, J. Reglero, M. Rodríguez-Pérez, D. Lehmhus, M. Wichmann, J.d. Saja, An experimental study on the thermal conductivity of aluminium foams by using the transient plane source method, Int. J. Heat Mass Transf. 51 , 6259–6267 (2008) [Google Scholar]
  75. P. Chen, X. Gao, Y. Wang, T. Xu, Y. Fang, Z. Zhang, Metal foam embedded inSEBS/paraffin/HDPE form-stable PCMs for thermal energy storage, Sol. Energy Mater. Sol. Cells 149 , 60–65 (2016) [Google Scholar]
  76. D. Wu, C. Huang, Thermal conductivity model of open-cell foam suitable for wide span of porosities, Int. J. Heat Mass Transf. 130 , 1075–1086 (2019) [Google Scholar]
  77. A. Abuserwal, E. Luna, R. Goodall, R. Woolley, The effective thermal conductivity of open cell replicated aluminium metal sponges, Int. J. Heat Mass Transf. 108 , 1439–1448 (2017) [Google Scholar]
  78. C. Wang, T. Lin, N. Li, H. Zheng, Heat transfer enhancement of phase change composite material: Copper foam/paraffin, Renew. Energy 96 , 960–965 (2016) [Google Scholar]
  79. J. Maxwell, A Treatise on Electricity and Magnetism, 3 edn., vol. 1, Dover Publication, INC, New York, 1904, p. 440 [Google Scholar]
  80. D. Bruggeman, Dielectric Constant and Conductivity of Mixtures of Isotropic Materials, Ann. Phys. 24 , 636–679 (1953) [Google Scholar]
  81. G.R. Hadley, Thermal conductivity of packed metal powders, Int. J. Heat Mass Transf. 29 , 909–920 (1986) [Google Scholar]
  82. Y. Yao, H. Wu, Z. Liu, A new prediction model for the effective thermal conductivity of high porosity open-cell metal foams, Int. J. Therm. Sci. 97 , 56–67 (2015) [Google Scholar]
  83. L. Gong, Y. Wang, X.C.R. Zhang, H. Zhang, A novel effective medium theory for modelling the thermal conductivity of porous materials, Int. J. Heat Mass Transf. 68 , 295–298 (2014) [Google Scholar]
  84. A. Bhattacharya, Thermophysical properties and convective transport in metal foam and finned metal foam heat sinks, Ph.D. thesis, University of Colorado, Boulder, CO, 2001 [Google Scholar]
  85. H. Yang, M. Zhao, Z. Gu, L. Jin, J. Chai, A further discussion on the effective thermal conductivity of metal foam: An improved model, Int. J. Heat Mass Transf. 86 , 207–211 (2015) [Google Scholar]
  86. X. Yang, J. Kuang, T. Lu, F. Han, T. Kim, A simplistic analytical unit cell based model for the effective thermal conductivity of high porosity open-cell metal foams, J. Phys. D: Appl. Phys. 46 , 25 (2013) [Google Scholar]
  87. P. Kumar, F. Topin, Simultaneous determination of intrinsic solid phase conductivity and effective thermal conductivity of Kelvin like foams, Appl. Therm. Eng. 71 , 536–547 (2014) [Google Scholar]
  88. G. Dul'nev, V. Novikov, Conductivity of nonuniform systems, J. Eng. Phys. 36 , 601–607 (1979) [CrossRef] [Google Scholar]
  89. C. Hsu, P. Cheng, K. Wong, A lumped-parameter model for stagnant thermal conductivity of spatially periodic porous media, ASME J. Heat Transf. 117 , 264–269 (1995) [CrossRef] [Google Scholar]
  90. J. Wang, J. Carson, J. Willix, M. North, D. Cleland, A symmetric and interconnected skeleton structural (SISS) model for predicting thermal and electrical conductivity and Young's modulus of porous foams, Acta Mater. 56 , 5138–5146 (2008) [Google Scholar]
  91. K. Singh, R. Singh, D. Chaudhary, Heat conduction and a porosity correction term for spherical and cubic particles in a simple cubic packing, J. Phys. D: Appl. Phys. 31 , 1681–1687 (1998) [CrossRef] [Google Scholar]
  92. Jagjiwanram, R. Singh, Effective thermal conductivity of real two-phase systems using resistor model with ellipsoidal inclusions, Bull. Mater. Sci. 27 , 373–381 (2004) [CrossRef] [Google Scholar]
  93. A. Ahern, G. Verbist, D. Waire, R. Phelan, H. Fleurent, The conductivity of foams: a generalisation of the electrical to the thermal case, Colloids Surf. A: Psycochem. Eng. Aspects 263 , 275–279 (2005) [CrossRef] [Google Scholar]
  94. T. Fiedler, E. Solórzano, A. Garcia-Moreno, F. Öchsner, I. Belova, G. Murch, Lattice monte carlo and experimental analyses of the thermal conductivity of random-shaped cellular aluminum, Adv. Eng. Mater. 11 , 843–847 (2009) [Google Scholar]
  95. R. Singh, S. Kumar, R. Beniwal, Bounding of effective thermal conductivity of two-phase materials, Defect Diffus. Forum 336 , 185–193 (2013) [CrossRef] [Google Scholar]
  96. R. Progelhof, J. Throne, R. Ruetsch, Methods for predicting the thermal conductivity of composite systems: a review, Polym. Eng. Sci. 16 , 615–625 (1976) [Google Scholar]
  97. T. Bauer, A general analytical approach toward the thermal conductivity of porous media, Int. J. Heat Mass Transf. 36 , 4181–4191 (1993) [Google Scholar]
  98. J. Stark, R. Prasad, T. Bergman, Experimentally validated analytical expressions for the thermal efficiencies and thermal resistances of porous metal foam-fins, Int. J. Heat Mass Transf. 111 , 1286–1295 (2017) [Google Scholar]
  99. S. Ayatollahi, N. Saber, M. Amani, A. Bitaab, Mathematical investigation of effective thermal conductivity in fractured porous media, J. Porous Media 9 , 625–635 (2006) [Google Scholar]
  100. K. Boomsma, D. Poulikakos, Y. Ventikos, Simulations of flow through open cell metal, Int. J. Heat Fluid Flow, 24 , 825–834 (2003) [CrossRef] [Google Scholar]
  101. B. Dietrich, G. Schell, E. Bucharsky, R. Oberacker, M. Hoffmann, W. Schabel, M. Kind, H. Martin, Determination of the thermal properties of ceramic sponges, Int. J. Heat Mass Tran. 53 , 198–205 (2010) [CrossRef] [Google Scholar]
  102. S. Ackermann, J. Scheffe, J. Duss, A. Steinfeld, Morphological characterization and effective thermal conductivity of dual-scale reticulated porous structures, Materials 7 , 7173–7195 (2014) [CrossRef] [Google Scholar]
  103. Jagjiwanram, R. Singh, Effective thermal conductivity of highly porous two-phase systems, Appl. Therm. Eng. 24 , 2727–2735 (2004) [Google Scholar]
  104. O. Krischer, Die wissenschaftlichen Grundlagen der Trocknungstechnik (The Scientific Fundamentals of Drying Technology), Springer-Verlag, Berlin, 1963 [CrossRef] [Google Scholar]
  105. S. Kumar, R. Bhoopal, P. Sharma, R. Beniwal, R. Singh, Non-linear effect of volume fraction of inclusions on the effective thermal conductivity of composite materials: A modified maxwell model, Open J. Compos. Mater. 1 , 10–18 (2011) [CrossRef] [Google Scholar]
  106. M. Mendes, S. Ray, D. Trimis, A simple and efficient method for the evaluation of effective thermal conductivity of open-cell foam-like structures, Int. J. Heat Mass Tran. 66 , 412–422 (2013) [CrossRef] [Google Scholar]
  107. M. Mendes, S. Ray, D. Trimis, Evaluation of effective thermal conductivity of porous foams in presence of arbitrary working fluid, Int. J. Therm. Sci. 79 , 260–265 (2014) [Google Scholar]
  108. M. Mendes, S. Ray, D. Trimis, An improved model for the effective thermal conductivity of open-cell porous foams, Int. J. Heat Mass Transf. 75 , 224–230 (2014) [Google Scholar]
  109. M. Ashby, The properties of foams and lattices, Phil. Trans. R. Soc. 364 , 15–30 (2006) [CrossRef] [MathSciNet] [Google Scholar]
  110. J.P. Du Plessis, J. Masliyah, Mathematical modelling of flow through consolidated isotropic porous media, Transp. Porous Media 3 , 145–161 (1988) [Google Scholar]
  111. G. Dulnev, Heat transfer through solid disperse systems, J. Eng. 9 , 275–279 (1965) [Google Scholar]
  112. B. Ozmat, B. Leyda, B. Benson, Thermal applications of open-cell metal foams, Mater. Manuf. Process. 19 , 839–862 (2004) [CrossRef] [Google Scholar]
  113. S. Krishnan, S. Garimella, J.Y. Murthy, Simulation of thermal transport in open-cell metal foams: Effects of periodic unit-cell structure, in ASME International Mechanical Engineering Congress and Exposition, Chicago, Illinois, 2006. [Google Scholar]
  114. K. Pietrak, T. Wisniewski, A review of models for effective thermal conductivity of composite materials, J. Power Technol. 95 , 14–24 (2015) [Google Scholar]
  115. P. Talukdar, M. Mendes, R. Parida, D. Trimis, S. Ray, Modelling of conduction–radiation in a porous medium with blocked-off region approach, Int. J. Therm. Sci. 72 , 102–114 (2013) [Google Scholar]
  116. Q. Yu, B. Thompson, A. Straatman, A unit cube-based model for heat transfer and fluid flow in porous carbon foam, ASME J. Heat Transf. 128 , 352–360 (2006) [CrossRef] [Google Scholar]

Cite this article as: M. Saljooghi, A. Raisi, A. Farahbakhsh, Effective actors on thermal conductivity of stochastic structures open cell metal foams, Mechanics & Industry 21, 410 (2020)

All Tables

Table 1

Calculated and measured value of open cell metal foam ligament dimensions [22].

In the text
Table 2

Measurement techniques and their field of application.

In the text
Table A1

Summary of existing experimental studies on ETC of open-cell metal foams.

In the text
Table B1

Asymptotic models.

In the text
Table B2

Analytical models.

In the text
Table B3

Empirical correlations.

In the text
Table B4

Unit cell models ETCs of open-cell metal foams.

In the text

All Figures

thumbnail Fig. 1

Open cell metal foam medium made.
In the text
thumbnail Fig. 2

Stochastic structures [Foam as seen through the camera lens of art photographer Michael Boran].
In the text
thumbnail Fig. 3

Effect of pore size on the ETC.
In the text
thumbnail Fig. 4

Effect of pore size on the ETC.
In the text
thumbnail Fig. 5

Porosity of silver foam.
In the text
thumbnail Fig. 6

ETC of open-cell metal foams decreasing with increasing porosity.
In the text
thumbnail Fig. 7

Effect of temperature on K t , Kcond, Kconv, Krad. [16].
In the text
thumbnail Fig. 8

Variation of ETC with temperature [18].
In the text
thumbnail Fig. 9

Variation of ETC with porosity [18].
In the text
thumbnail Fig. 10

ETC dependency on temperature.
In the text
thumbnail Fig. 11

Thermal conductivity of some known metals.
In the text
thumbnail Fig. 12

Illustrated different cross section shape of ligaments.
In the text
thumbnail Fig. 13

(a) Distribution of ligament length Al foam with porosity 91%, (b) distribution of ligament area Al foam with porosity 91%, (c) distribution of node co-ordination number Al foam with porosity 91%, (d) distribution of effective pore diameter Al foam with porosity 91% [23].
In the text
thumbnail Fig. 14

Predicted ETC by five different models .
In the text
thumbnail Fig. 15

Unit cell of Kelvin.
In the text
thumbnail Fig. 16

Weaire-Phelan cell: (left): type (a), (middle): type (b), (right) Orientation of Weaire-Phelan bubbles.
In the text
thumbnail Fig. 17

Analyzing a realistic open-cell metal foam structure created by µ-CT scan [55].
In the text
thumbnail Fig. 18

Steps of µ-CT scans process to produce 3D realistic image to analyze [56].
In the text
thumbnail Fig. 19

The 3-D image of the stochastic open cell metal foam obtained by X-ray tomography [57].
In the text

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