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An illustration of the effect of voxel size for a µCT scan reconstruction with respectively a (a) 37.5 µm and a (b) 8.5 µm voxel size. Foam samples are made in-house.
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This paper reviews the available methods to study thermal applications with open-cell metal foam. Both experimental and numerical work are discussed. For experimental research, the focus of this review is on the repeatability of the results. This is a major concern, as most studies only report the dependence of thermal properties on porosity and a...
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... and Razani [55] scanned metal foam samples with four different voxel sizes, ranging from 115 down to 58 µm. They found an asymptotically converging surface-to-volume ratio. Another restriction on the voxel size is imposed by the continuum assumption with no-slip boundary conditions, upon which thermal and hydraulic analysis are commonly based (as is the case for this work). Due to the continuum hypothesis to hold and with air as a working fluid, it is not necessary to have a finer spatial discretization than voxel sizes in the order of 5 µm. As a result, the high resolution scan with a voxel size of 8.5 µm of Figure 5b can be considered highly accurate [25]. µCT scans can be used for a full characterization of the foam sample. However, some authors, like De Jaeger et al. [56], use a hybrid model. For this model, both cell diameters (d 1 and d 2 , as indicated in Figure 3a) and the interfacial strut area (A 0 ) are measured with a µCT scan. This interfacial strut area is the average cross-sectional area in the center of the strut (x/l = 0 in Figure 4). The interfacial strut area shows a difference of merely 4% as the voxel size is reduced from 37.5 µm to 8.5 µm and is therefore not strongly influenced by the roughness. An extensive explanation on how this interfacial strut area can be calculated from µCT data can be found in [34]. With these three parameters, the authors were able to make a model of the complete foam structure. Based on that structure, the porosity and the surface-to-volume ratio can be calculated numerically. This allows the continuum scale roughness, which is resolved by the fine µCT scan to be neglected, for it does not contribute to the heat transfer performance. This is a hybrid model that calculates the porosity and surface-to-volume ratio based on d 1 , d 2 and A 0 , instead of using a correlation to obtain the surface-to-volume ratio σ 0 or performing a full characterization of the foam sample through a µCT scan. The surface-to-volume ratio with this hybrid model for the foam studied in Figure 5 is 859 m −1 . This is very close to the value obtained by µCT with a voxel size of 8.5 µm (860 m −1 ). This once again indicated the necessity of using small voxel ...
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... and Razani [55] scanned metal foam samples with four different voxel sizes, ranging from 115 down to 58 µm. They found an asymptotically converging surface-to-volume ratio. Another restriction on the voxel size is imposed by the continuum assumption with no-slip boundary conditions, upon which thermal and hydraulic analysis are commonly based (as is the case for this work). Due to the continuum hypothesis to hold and with air as a working fluid, it is not necessary to have a finer spatial discretization than voxel sizes in the order of 5 µm. As a result, the high resolution scan with a voxel size of 8.5 µm of Figure 5b can be considered highly accurate [25]. µCT scans can be used for a full characterization of the foam sample. However, some authors, like De Jaeger et al. [56], use a hybrid model. For this model, both cell diameters (d 1 and d 2 , as indicated in Figure 3a) and the interfacial strut area (A 0 ) are measured with a µCT scan. This interfacial strut area is the average cross-sectional area in the center of the strut (x/l = 0 in Figure 4). The interfacial strut area shows a difference of merely 4% as the voxel size is reduced from 37.5 µm to 8.5 µm and is therefore not strongly influenced by the roughness. An extensive explanation on how this interfacial strut area can be calculated from µCT data can be found in [34]. With these three parameters, the authors were able to make a model of the complete foam structure. Based on that structure, the porosity and the surface-to-volume ratio can be calculated numerically. This allows the continuum scale roughness, which is resolved by the fine µCT scan to be neglected, for it does not contribute to the heat transfer performance. This is a hybrid model that calculates the porosity and surface-to-volume ratio based on d 1 , d 2 and A 0 , instead of using a correlation to obtain the surface-to-volume ratio σ 0 or performing a full characterization of the foam sample through a µCT scan. The surface-to-volume ratio with this hybrid model for the foam studied in Figure 5 is 859 m −1 . This is very close to the value obtained by µCT with a voxel size of 8.5 µm (860 m −1 ). This once again indicated the necessity of using small voxel ...
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... X-rays used in the scanning equipment interact significantly different with a solid than with a fluid (or vacuum), allowing for a clear distinction between both phases. However, voxels at the solid-fluid interface contain both phases. Therefore, their grey values can span a large range. For further image processing, they need to be binarized, i.e., allocated to either the solid (one) or fluid (zero) phase. This operation is called grey scale segmentation or thresholding [50]. An overview of different segmentation algorithms is given by Linquist [51] and recently by Ohser et al. [52]. In this work, the algorithm is based on a so-called dual threshold, which defines a threshold interval, combined with a labeling operation [53]. This means that neighboring voxels with grey values within the threshold interval are treated as a subset and are all assigned to a phase. The phase assignment is done by comparing grey values with the averaged threshold level of the interval. Grey values smaller than this averaged value are assigned to the fluid phase, while voxels with larger values are considered as solid material. This algorithm is also used in this work. For a more detailed description of the use of this technique to obtain, e.g., surface-to-volume ratios, the reader is referred to the work of De Jaeger et al. [25]. A drawback of these µCT scans is that they are quite expensive and not straightforward to use in comparison with a microscope or the naked eye. The main difficulty lies in the choice of the averaged threshold level to allocate the voxels to either the solid (one) or fluid (zero) phase. A different threshold can yield significantly different allocations of fluid volumes [34] and, thus, a significantly different foam model. Furthermore, the voxel size itself can also significantly influence the results (as shown in Figure 5). Figure 5a is constructed with a voxel size of 37.5 µm, while Figure 5b, which clearly shows more detail, is made through a scan with a voxel size of 8.5 µm. The surface-to-volume ratio of both reconstructions in Figure 5 is respectively 720 and 860 m −1 : a relative difference of 19%. The scan here is done on at least 16 foam cells. The reported values are average ones. This shows that the voxel size, next to thresholding, is an important parameter [25]. The heat transfer performance of a fixed volume of metal foam is determined by the product of the heat transfer coefficient and the surface-to-volume ratio. The heat transfer coefficient is determined based on the measured performance and the determined surface-to-volume ratio. As long as the thermal performance is reconstructed using the same surface-to-volume ratio that was used to determine the heat transfer coefficient, the correct thermal performance will be ...
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... X-rays used in the scanning equipment interact significantly different with a solid than with a fluid (or vacuum), allowing for a clear distinction between both phases. However, voxels at the solid-fluid interface contain both phases. Therefore, their grey values can span a large range. For further image processing, they need to be binarized, i.e., allocated to either the solid (one) or fluid (zero) phase. This operation is called grey scale segmentation or thresholding [50]. An overview of different segmentation algorithms is given by Linquist [51] and recently by Ohser et al. [52]. In this work, the algorithm is based on a so-called dual threshold, which defines a threshold interval, combined with a labeling operation [53]. This means that neighboring voxels with grey values within the threshold interval are treated as a subset and are all assigned to a phase. The phase assignment is done by comparing grey values with the averaged threshold level of the interval. Grey values smaller than this averaged value are assigned to the fluid phase, while voxels with larger values are considered as solid material. This algorithm is also used in this work. For a more detailed description of the use of this technique to obtain, e.g., surface-to-volume ratios, the reader is referred to the work of De Jaeger et al. [25]. A drawback of these µCT scans is that they are quite expensive and not straightforward to use in comparison with a microscope or the naked eye. The main difficulty lies in the choice of the averaged threshold level to allocate the voxels to either the solid (one) or fluid (zero) phase. A different threshold can yield significantly different allocations of fluid volumes [34] and, thus, a significantly different foam model. Furthermore, the voxel size itself can also significantly influence the results (as shown in Figure 5). Figure 5a is constructed with a voxel size of 37.5 µm, while Figure 5b, which clearly shows more detail, is made through a scan with a voxel size of 8.5 µm. The surface-to-volume ratio of both reconstructions in Figure 5 is respectively 720 and 860 m −1 : a relative difference of 19%. The scan here is done on at least 16 foam cells. The reported values are average ones. This shows that the voxel size, next to thresholding, is an important parameter [25]. The heat transfer performance of a fixed volume of metal foam is determined by the product of the heat transfer coefficient and the surface-to-volume ratio. The heat transfer coefficient is determined based on the measured performance and the determined surface-to-volume ratio. As long as the thermal performance is reconstructed using the same surface-to-volume ratio that was used to determine the heat transfer coefficient, the correct thermal performance will be ...
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... X-rays used in the scanning equipment interact significantly different with a solid than with a fluid (or vacuum), allowing for a clear distinction between both phases. However, voxels at the solid-fluid interface contain both phases. Therefore, their grey values can span a large range. For further image processing, they need to be binarized, i.e., allocated to either the solid (one) or fluid (zero) phase. This operation is called grey scale segmentation or thresholding [50]. An overview of different segmentation algorithms is given by Linquist [51] and recently by Ohser et al. [52]. In this work, the algorithm is based on a so-called dual threshold, which defines a threshold interval, combined with a labeling operation [53]. This means that neighboring voxels with grey values within the threshold interval are treated as a subset and are all assigned to a phase. The phase assignment is done by comparing grey values with the averaged threshold level of the interval. Grey values smaller than this averaged value are assigned to the fluid phase, while voxels with larger values are considered as solid material. This algorithm is also used in this work. For a more detailed description of the use of this technique to obtain, e.g., surface-to-volume ratios, the reader is referred to the work of De Jaeger et al. [25]. A drawback of these µCT scans is that they are quite expensive and not straightforward to use in comparison with a microscope or the naked eye. The main difficulty lies in the choice of the averaged threshold level to allocate the voxels to either the solid (one) or fluid (zero) phase. A different threshold can yield significantly different allocations of fluid volumes [34] and, thus, a significantly different foam model. Furthermore, the voxel size itself can also significantly influence the results (as shown in Figure 5). Figure 5a is constructed with a voxel size of 37.5 µm, while Figure 5b, which clearly shows more detail, is made through a scan with a voxel size of 8.5 µm. The surface-to-volume ratio of both reconstructions in Figure 5 is respectively 720 and 860 m −1 : a relative difference of 19%. The scan here is done on at least 16 foam cells. The reported values are average ones. This shows that the voxel size, next to thresholding, is an important parameter [25]. The heat transfer performance of a fixed volume of metal foam is determined by the product of the heat transfer coefficient and the surface-to-volume ratio. The heat transfer coefficient is determined based on the measured performance and the determined surface-to-volume ratio. As long as the thermal performance is reconstructed using the same surface-to-volume ratio that was used to determine the heat transfer coefficient, the correct thermal performance will be ...
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... X-rays used in the scanning equipment interact significantly different with a solid than with a fluid (or vacuum), allowing for a clear distinction between both phases. However, voxels at the solid-fluid interface contain both phases. Therefore, their grey values can span a large range. For further image processing, they need to be binarized, i.e., allocated to either the solid (one) or fluid (zero) phase. This operation is called grey scale segmentation or thresholding [50]. An overview of different segmentation algorithms is given by Linquist [51] and recently by Ohser et al. [52]. In this work, the algorithm is based on a so-called dual threshold, which defines a threshold interval, combined with a labeling operation [53]. This means that neighboring voxels with grey values within the threshold interval are treated as a subset and are all assigned to a phase. The phase assignment is done by comparing grey values with the averaged threshold level of the interval. Grey values smaller than this averaged value are assigned to the fluid phase, while voxels with larger values are considered as solid material. This algorithm is also used in this work. For a more detailed description of the use of this technique to obtain, e.g., surface-to-volume ratios, the reader is referred to the work of De Jaeger et al. [25]. A drawback of these µCT scans is that they are quite expensive and not straightforward to use in comparison with a microscope or the naked eye. The main difficulty lies in the choice of the averaged threshold level to allocate the voxels to either the solid (one) or fluid (zero) phase. A different threshold can yield significantly different allocations of fluid volumes [34] and, thus, a significantly different foam model. Furthermore, the voxel size itself can also significantly influence the results (as shown in Figure 5). Figure 5a is constructed with a voxel size of 37.5 µm, while Figure 5b, which clearly shows more detail, is made through a scan with a voxel size of 8.5 µm. The surface-to-volume ratio of both reconstructions in Figure 5 is respectively 720 and 860 m −1 : a relative difference of 19%. The scan here is done on at least 16 foam cells. The reported values are average ones. This shows that the voxel size, next to thresholding, is an important parameter [25]. The heat transfer performance of a fixed volume of metal foam is determined by the product of the heat transfer coefficient and the surface-to-volume ratio. The heat transfer coefficient is determined based on the measured performance and the determined surface-to-volume ratio. As long as the thermal performance is reconstructed using the same surface-to-volume ratio that was used to determine the heat transfer coefficient, the correct thermal performance will be ...
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... A series of deformable physical objects were fabricated to explore the potentials of multi-material 3D printing based on the analysis components implemented in their software. The applicability of this modeling approach can be extended because voxel models were applied to study the performance of complex materials, such as 3D woven composites [133] or open-cell metal foams [134], in relation to their unique structural and thermal performances. ...
... Golparvar-Fard et al. [74] Peddireddy et al. [110] Wang et al. [112] Yousefian and Tarbu on [111] Kukreja et al. [113] Huang et al. [114] Greminger [115] Chi et al. [116] Van De Walle et al. [124] Maaroufi et al. [125] Vantyghem et al. [126] Hosny et al. [129] Naboni and Kunic [130] Michalatos and Payne [131] Green et al. [133] De Schampheleire et al. [134] Craveiro et al. [138] The first category in Table 7 contains the voxel-model applications related to both architectural and urban design. Nearly all voxel-model applications in the urban context fall into this category, which can be explained by the adoption of diverse 3D-data-acquisition and analysis techniques utilizing voxel-based representations. ...
Although voxel models have been applied to address diverse problems in computer-aided design processes, their role in multi-domain data integration in digital architecture and planning has not been extensively studied. The primary objective of this study is to map the current state of the art and to identify open questions concerning data structuring, integration, and modeling and design of multi-scale objects and systems in architecture. Focus is placed on types of voxel models that are linked with computer-aided design models. This study utilizes a semi-systematic literature review methodology that combines scoping and narrative methodology to examine different types and uses of voxel models. This is done across a range of disciplines, including architecture, spatial planning, computer vision, geomatics, geosciences, manufacturing, and mechanical and civil engineering. Voxel-model applications can be found in studies addressing generative design, geomatics, material science and computational morphogenesis. A targeted convergence of these approaches can lead to integrative, holistic, data-driven design approaches. We present (1) a summary and systematization of the research results reported in the literature in a novel manner, (2) the identification of research gaps concerning voxel-based data structures for multi-domain and trans-scalar data integration in architectural design and urban planning, and (3) any further research questions.
... In order to generate initial training and testing data for ML model, a parameter space (PS) consisting of a wide range of each input features, such as MF porosity = 0.8-0.98, pore density = 5-50 PPI, H = 3-30 mm, and Re = 10 0-170 0 have been selected after a thorough literature survey of MF [6][7][8][9][10][11][12][13] [38] with an intention to cover the entire spectrum of MFHS morphological and geometrical parameters, especially within the laminar flow regime. Since for symmetric MFHS (see Fig. 1 (b)), D h uniquely depends on MF height (H) , for the input PS, H has been considered instead of D h . ...
Metal foam heat sinks (MFHSs) are often used for electronic cooling because of their high specific heat transfer surface area, fluid mixing capability, and lightweight. Thermal-hydraulic performance of MFHS is known to be related to the morphological parameters of the metal foam (MF), physical dimensions of the heat sink, and coolant flow condition. Moreover, the highly random and complex geometrical nature of MFHS makes full-scale numerical simulation and experimental analysis expensive. In this paper, we have utilized machine learning (ML) methods to predict the thermal-hydraulic performance of MFHS, considering MF morphology, heat sink dimension, and coolant flow condition as input features. Considering local thermal non-equilibrium conditions for MFHS, a database of 1000 data points is generated utilizing a validated high-fidelity computational fluid dynamics/heat transfer (CFD/HT) model. The data sets consist of two non-dimensional descriptors, i.e., Nusselt number (Nu) and friction factor (f), along with the four input features such as MF morphological parameters, i.e., porosity and pore density, heat sink geometrical dimensions, i.e., hydraulic diameter (Dh) and flow condition, i.e., Reynolds number (Re). Five different ML-based regression models, namely k-nearest neighbor (KNN), random forest (RF), extreme gradient boosting (XGBoost), support vector regressor (SVR), and artificial neural networks (ANN), have been developed. Hyper-parameters of each ML model have been systematically optimized, and optimized model's performance have been compared using statistical score metrics and considering the initialization effect. Additionally, in order to demonstrate the robustness of the developed ML models, individual model performance has been evaluated by independent datasets. Results indicate that for testing datasets, all ML algorithms, except KNN, can predict the thermal-hydraulic performance of MFHS reasonably well, with a mean absolute percent error (MAPE) of about 4.59% and a 1.24 standard deviation (SD). However, SVR and ANN outperform other models for independent datasets with a MAPE of less than 3.1%.
... Their internal structure results from the liquefaction, foaming, and solidification of the metal structure. According to the terminology adopted by de Schampheleire et al. [2], several characteristic components can be identified in the foam skeleton. The skeleton of the material is made of fine struts. ...
The paper reports the results of experimental tests and numerical simulations related to the pressure drop during two-phase air-water mixture flow through a pipe containing metal foam packing. Aluminium foam with 40 PPI open cells was used in the tests. A horizontal pipe with an internal diameter of 10 mm was used, and the foam only occupied a section of the pipe length equal to 240 mm. In the section of the pipe upwards of the foam, stratified flow pattern was generated, i.e., the most characteristic type for the gas-liquid flow. The results of the experimental research were compared with the values derived on the basis of the empirical method, which was developed for several different metal foams and two-phase systems. The values derived from measurements and calculations were subsequently applied to validate one numerical simulation method that is known to be particularly applicable for two-phase gas-liquid flow through metal foams. As a final result, the phenomena resulting from the presence of foam in the stratified flow through a gas-liquid system, the deficiencies of the methods applied in calculating pressure drops and modeling their values in accordance with the adopted numerical procedure were indicated. All research and modelling were carried out with the purpose of testing the potential of metal foam use in pipes dedicated to heat exchanger design, particularly ones intended to improve energy efficiency.
... In recent years there has been a great deal of interest in the use of material to increase heat exchange: one of them is metal foam which can be used in many applications such as compact heat exchangers [1][2][3][4][5][6], solar thermal components and systems [7][8][9][10][11][12], heat sinks in electronic cooling [13], thermal energy storage [14][15][16] and geothermal operations [17]. ...
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Latent heat thermal energy storage (LHTES) has engrossed augmenting consideration to eliminate the mismatch between energy supply and demand. Latent Heat Thermal Energy Storage has the benefit of greater high-energy densities at nearly constant temperatures among the three thermal energy storage systems. These benefits are assigned to phase change material use; however, those materials possess low thermal conductivity that degrades their thermal performance in latent heat thermal energy storage systems. This paper aims to make a practical guide on recent numerical and experimental investigations of solid-liquid phase-change heat transfer using some heat transfer enhancement techniques in order to understand clearly the current state of these techniques and the investigated parameters. Furthermore, this study will be useful to researchers in validating their numerical studies. Overviews of numerical methods and experimental setups of recent studies have been represented. Outcomes show that hybrid heat transfer enhancement techniques can be considered as the most effective enhancement technique in terms of thermal performance improvement, and further research must be done to find an optimal combination. A summary of different studies dealing with heat storage using phase change materials in Morocco has also been presented. Moreover, a latent heat storage system based on a honeycomb metal matrix was suggested for a concentrated solar power plant application, and results have shown its ability to reduce the overall melting time by 50 %.
... Metal foam is a new type of material used to improve heat exchange in many appliances and systems. Its main applications are in heat sinks in electronic cooling [1], geothermal operations [2], solar thermal components and systems [3][4][5][6][7][8], thermal energy storage [9][10][11] and compact heat exchangers [12][13][14][15]. The related behavior is influenced by the structure of the foam, and by parameters such as diameter of cells, struts, pore density, shape of struts and the porosity. ...
A numerical investigation on foams in forced convection with Kelvin cell structure is carried out on water/Al2O3 nanofluids. The three-dimensional governing equations are written considering the single-phase model for the nanofluid. The analyzed foams have a porosity of ɛ = 0.95 and four different pore density values, with cells per inch (CPI) equal to 5, 10, 20 and 40, with assigned heat flux on the surface. The nanofluid velocity is in the range 0.003–0.1 m/s and the Reynolds number ranges from 0.6 to 5 × 10². It is observed that thermal development occurs first for the lowest velocities and for highest CPI values whereas the maximum temperature is obtained for the 5 CPI configuration and v = 0.003 m/s. Results show that the highest local entropy generation values are recorded near the points at higher temperature in correspondence with the inlet section and close to the geometric variation of the cells. The local entropy generation density reaches the maximum value at 40 CPI for the maximum velocity considered of 0.1 m/s. Global dimensionless entropy generation decreases for Reynolds number increases and the opposite is related to the dimensionless viscous entropy generation. Thermal dimensionless entropy generation is significantly greater than the dimensionless viscous entropy generation.
... In order to generate initial training and testing data for ML model, a parameter space (PS) consisting of a wide range of each input features, such as MF porosity = 0.8-0.98, pore density = 5-50 PPI, H = 3-30 mm, and Re = 10 0-170 0 have been selected after a thorough literature survey of MF [6][7][8][9][10][11][12][13] [38] with an intention to cover the entire spectrum of MFHS morphological and geometrical parameters, especially within the laminar flow regime. Since for symmetric MFHS (see Fig. 1 (b)), D h uniquely depends on MF height (H) , for the input PS, H has been considered instead of D h . ...
... The metal foam enhanced PCM can be modelled on pore scale [69], however this is computationally expensive [70]. Therefore, volume-averaging theory (VAT) has been used to successfully model metal foam enhanced PCM [14,[71][72][73][74]. Volume-averaging theory (VAT) averages the metal foam and fluid in representative fluid volumes [75] which require less elements to achieve grid convergence. ...
The performance of latent thermal energy storage (LTES) heat exchangers is related to the stored energy (i.e. state of charge) during the (dis)charging of the energy storage system. Therefore, measuring the stored energy is crucial to understand the behavior of LTES systems. However, technical considerations often oppose the measurability of the stored energy. The present article presents a case where only measurements at the in- and outlet of the heat transfer fluid and on the outer surface of the heat exchanger were possible. Based on this sparse set of measurements, estimators for the stored energy are developed. To test these estimators, a finite volume model of the LTES heat exchanger is made. The finite volume model is fitted by comparing the predicted heat transfer fluid outlet temperature for 36 experiments. The energy estimators are compared to simulation results. The estimators for the stored energy in the heat transfer fluid, metal and phase change material obtain an average deviation of respectively 0.5 %, 3.9 % and 0.6 % with the stored energy predicted by the finite volume model. The estimator for the stored energy in the heat transfer fluid should be applicable to all LTES heat exchangers while the applicability of the estimators for stored energy in the metal and phase change material depend on small temperature differences between surface and interior of the metal and negligible heat losses.
... To solve the resulting averaged equations for a full REV, the number of numerical elements will be too large. For example, to analyze an REV of 10-ppi foam with a porosity of 93.2%, the number of elements would be in the order of 200 million [31]. The volume-average theory, conceived for tight porous media, has produced some unacceptable results when applied to highly-porous metal foam. ...
A new geometric modeling of isotropic highly-porous cellular media, e.g., open-cell metal, ceramic, and graphite foams, is developed. The modelling is valid strictly for macroscopically two-dimensional heat transfer due to the fluid flow in highly-porous media. Unlike the current geometrical modelling of such media, the current model employs simple geometry, and is derived from equivalency conditions that are imposed on the model’s geometry a priori, in order to ensure that the model produces the same pressure drop and heat transfer as the porous medium it represents. The model embodies the internal structure of the highly-porous media, e.g., metal foam, using equivalent parallel strands (EPS), which are rods arranged in a spatially periodic two-dimensional pattern. The dimensions of these strands and their arrangement are derived from equivalency conditions, ensuring that the porosity and the surface area density of the model and of the foam are indeed equal. In order to obtain the pressure drop and heat transfer results, the governing equations are solved on the geometrically-simple EPS model, instead of the complex structure of the foam. By virtue of the simple geometry of parallel strands, huge savings on computational time and cost are realized. The application of the modeling approach to metal foam is provided. It shows how an EPS model is obtained from an actual metal foam with known morphology. Predictions of the model are compared to experimental data on metal foam from the literature. The predicted local temperatures of the model are found to be in very good agreement with their experimental counterparts, with a maximum error of less than 11%. The pressure drop in the model follows the Forchheimer equation.
... For example, the struct diameter can be measured via scanning electron microscopy. Alternately, using microtomography (lCT), all three-dimensional foam structure parameters can be obtained via image reconstruction [125]. However, lCT is an expensive technique and is not commonly used. ...
High porosity metal foams offer large surface area per unit volume and have been considered as effective candidates for convection heat transfer enhancement, with applications as heat sinks in electronics cooling. In this paper, the research progress in thermo-hydraulic performance characterization of metal foams and their application as heat sinks for electronics cooling are reviewed. We focus on natural convection, forced convection, flow boiling, and solid/liquid phase change using phase change materials (PCMs). Under these heat transfer conditions, the effects of various parameters influencing the performance of metal foam heat sink are discussed. It is concluded that metal foams demonstrate promising capability for heat transfer augmentation, but some key issues still need to be investigated regarding the fundamental mechanisms of heat transfer to enable the development of more efficient and compact heat sinks.
