Stable-phase method for hierarchical annealing in the reconstruction of porous media images

Chen, Dongdong; Teng, Qizhi; He, Xiaohai; Xu, Zhi; Li, Zhengji

doi:10.1103/physreve.89.013305

Cited by 57 publications

(24 citation statements)

References 27 publications

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“…(23) In contrast to values of permeability and interfacial Nusselt number, effective thermal conductivity values of the reconstructed samples demonstrated a negligible influence of particle or pore size and depended mainly on the porosity. This may be attributed to the limited information contained in the twopoint autocorrelation function S 2 employed for reconstructions.…”

Section: Effective Thermal Conductivitymentioning

confidence: 79%

“…These 3D microstructures are then used for computing transport characteristics such as permeability, effective thermal conductivity and interfacial heat transfer coefficient for single-phase flow through the microstructure. Examples of other recent publications that reconstruct 3D porous media from stochastic information computed from 2D images include Jiang et al [21], Zhang and Du [22], and Chen et al [23].…”

Section: Introductionmentioning

confidence: 99%

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3D reconstruction and design of porous media from thin sections

Bodla

Garimella

Murthy

2014

International Journal of Heat and Mass Transfer

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Characterization and design of fluid-thermal transport through random porous sintered beds is critical for improving the performance of two-phase heat transport devices such as heat pipes. Two-dimensional imaging techniques are quite well developed and commonly employed for microstructure and material characterization. In this study, we employ 2D image data (thin sections) for measuring critical microstructural features of commercial wicks for use in correlation-based prediction of transport properties. We employ a stochastic characterization methodology based on the two-point autocorrelation function, and compare the predicted properties such as particle and pore diameters and permeability with those from our previously published studies, in which 3D x-ray microtomography data was employed for reconstruction. Further, we implement a reconstruction technique for reconstructing a three-dimensional stochastic equivalent structure from the thin sections. These reconstructed domains are employed for predicting effective thermal conductivity, permeability and interfacial heat transfer coefficient in singlephase flow. The current computations are found to compare well with models and correlations from the literature, as well as our previous numerical studies. Finally, we propose a new parametrized model for the design of porous materials based on the nature of the two-point autocorrelation functions. Using this model, we reconstruct sample three-dimensional microstructures, and analyze the influence of various parameters on fluid-thermal properties of interest. With advances in additive manufacturing techniques, such an approach may eventually be employed to design intricate porous structures with properties tailored to specific applications.

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Section: Effective Thermal Conductivitymentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

3D reconstruction and design of porous media from thin sections

Bodla

Garimella

Murthy

2014

International Journal of Heat and Mass Transfer

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“…Correlation functions can be easily calculated and reconstructed for additional phases [ 74 ]. For example, in soil science multiple soil phases could include pores, mineral grains, clays and organic matter, each with their own correlation and cross-correlation functions [ 74 – 76 ].…”

Section: Methodsmentioning

confidence: 99%

Universal Spatial Correlation Functions for Describing and Reconstructing Soil Microstructure

et al. 2015

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Structural features of porous materials such as soil define the majority of its physical properties, including water infiltration and redistribution, multi-phase flow (e.g. simultaneous water/air flow, or gas exchange between biologically active soil root zone and atmosphere) and solute transport. To characterize soil microstructure, conventional soil science uses such metrics as pore size and pore-size distributions and thin section-derived morphological indicators. However, these descriptors provide only limited amount of information about the complex arrangement of soil structure and have limited capability to reconstruct structural features or predict physical properties. We introduce three different spatial correlation functions as a comprehensive tool to characterize soil microstructure: 1) two-point probability functions, 2) linear functions, and 3) two-point cluster functions. This novel approach was tested on thin-sections (2.21×2.21 cm2) representing eight soils with different pore space configurations. The two-point probability and linear correlation functions were subsequently used as a part of simulated annealing optimization procedures to reconstruct soil structure. Comparison of original and reconstructed images was based on morphological characteristics, cluster correlation functions, total number of pores and pore-size distribution. Results showed excellent agreement for soils with isolated pores, but relatively poor correspondence for soils exhibiting dual-porosity features (i.e. superposition of pores and micro-cracks). Insufficient information content in the correlation function sets used for reconstruction may have contributed to the observed discrepancies. Improved reconstructions may be obtained by adding cluster and other correlation functions into reconstruction sets. Correlation functions and the associated stochastic reconstruction algorithms introduced here are universally applicable in soil science, such as for soil classification, pore-scale modelling of soil properties, soil degradation monitoring, and description of spatial dynamics of soil microbial activity.

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“…The autocorrelation function (ACF) was used by the simulated annealing algorithm because it indicates the statistical characteristics of spatial correlation of different phases [31][32][33][34]; hence, it is also used in this paper as the constraint conditions for selecting the edge sampling points. The definition of ACF will be provided in Sec.…”

Section: The Edge Sampling Proceduresmentioning

confidence: 99%

Reconstruction of three-dimensional porous media from a single two-dimensional image using three-step sampling

Gao

Teng

et al. 2015

Phys. Rev. E

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A random three-dimensional (3D) porous medium can be reconstructed from a two-dimensional (2D) image by reconstructing an image from the original 2D image, and then repeatedly using the result to reconstruct the next 2D image. The reconstructed images are then stacked together to generate the entire reconstructed 3D porous medium. To perform this successfully, a very important issue must be addressed, i.e., controlling the continuity and variability among adjacent layers. Continuity and variability, which are consistent with the statistics characteristic of the training image (TI), ensure that the reconstructed result matches the TI. By selecting the number and location of the sampling points in the sampling process, the continuity and variability can be controlled directly, and thus the characteristics of the reconstructed image can be controlled indirectly. In this paper, we propose and develop an original sampling method called three-step sampling. In our sampling method, sampling points are extracted successively from the center of 5×5 and 3×3 sampling templates and the edge area based on a two-point correlation function. The continuity and variability of adjacent layers were considered during the three steps of the sampling process. Our method was tested on a Berea sandstone sample, and the reconstructed result was compared with the original sample, using tests involving porosity distribution, the lineal path function, the autocorrelation function, the pore and throat size distributions, and two-phase flow relative permeabilities. The comparison indicates that many statistical characteristics of the reconstructed result match with the TI and the reference 3D medium perfectly.

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Stable-phase method for hierarchical annealing in the reconstruction of porous media images

Cited by 57 publications

References 27 publications

3D reconstruction and design of porous media from thin sections

3D reconstruction and design of porous media from thin sections

Universal Spatial Correlation Functions for Describing and Reconstructing Soil Microstructure

Reconstruction of three-dimensional porous media from a single two-dimensional image using three-step sampling

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