Analysis Of Porous Media Research Articles

Three-dimensional reconstruction of porous media by fusing multi-grid image features based on extended feature pyramid network

Microstructural analysis of porous media has significant research value in the study of their macroscopic properties. An accurate reconstruction of the digital microstructure model is a prerequisite for understanding their physical properties and transport properties. Deep learning can accurately extract features from training images for a fast and precise reconstruction of porous media. The reconstruction methods based on deep learning have yielded important achievements in the reconstruction of porous media. Aiming at the complex morphology and multi-scale image information of porous media, this paper proposes a generic end-to-end deep learning-based encoder–decoder generative method for the reconstruction of porous media from a single 2D image. This paper designs an extended feature pyramid network encoder to extract image features of different resolutions and different scales, and a generator network incorporating multi-scale image information. Through the training process of the network, the final three-dimensional images that meets certain requirements are generated by means of confrontation. By introducing the loss function of the pattern density function and the loss function of Wasserstein distance with gradient penalty, we constrain the reconstructed 3D structures. Finally, the reconstructed 3D structures and the target 3D structures are visualized to compare their similar morphologies. The morphological parameter indicators of the reconstructed 3D structures were line with those of the target 3D structure. The microstructure parameters of the 3D structures were also compared and analyzed. For complex porous media images, our method can achieve diverse, accurate, and stable 3D reconstruction.

The modified physics-informed neural network (PINN) method for the thermoelastic wave propagation analysis based on the Moore-Gibson-Thompson theory in porous materials

This paper presents novel contributions to both theory and solution methodology in AI-based analysis of solid mechanics. The physics-informed neural network (PINN) method is developed for thermoelastic wave propagation and Moore-Gibson-Thompson (MGT) coupled thermoelasticity analysis of porous media, a first in the field. The coupled thermoelasticity governing equations, based on the MGT heat conduction model, are derived for a porous half-space, with the thermal relaxation coefficient and strain relaxation factor being considered. Mechanical and thermal shock loading boundary conditions are imposed. The behavior of a magnesium-made porous body is analyzed using the PINN method, with highly accurate results being achieved for the system of coupled PDEs. An adaptive hyperparameter tuning approach, integrating a generalized subset design (GSD) and Bayesian optimization algorithm, is used to automatically select the optimal structure based on the L2 relative error. This hybrid methodology eliminates manual adjustment concerns. The proposed method is verified through a thorough comparison with the Lord-Shulman theory of coupled thermoelasticity. The strength of the methodology lies in its ability to operate without domain data, with only boundary and initial points being required. Four example sets are examined to demonstrate the capabilities of the modified PINN, and high-quality predictions of dimensionless fields’ variables over an extended time interval are obtained, confirming its extrapolation abilities.

The effective thermal conductivity of random isotropic porous media analysis and prediction

Effective thermal conductivity of porous media is a crucial parameter for heat transfer within them. Many studies have characterized various porous media by adjusting the control parameters generated through the Quartet Structure Generation Set method. The porous media effective thermal conductivity is then determined through Computational Fluid Dynamics calculations, which, however, necessitate significant computational resources and time. Thus, following an exploration of the influence of control parameters (i.e., porosity, core growth probability, and their coupling) on the effective thermal conductivity of isotropic porous media generated by the Quartet Structure Generation Set method, this study developed a multi-layer perceptron prediction model. The aim was to establish a prediction model from the porous media control parameters to effective thermal conductivity, thereby reducing the time spent on iterative calculations. The findings indicate that the effective thermal conductivity does not uniformly increase with the growth of core probability, and instead fluctuates after a certain threshold. Notably, the trained model exhibits a high prediction accuracy, with average deviations of 0.0887 and 0.0748 for the training and testing datasets, respectively.

Enhancing Computational Efficiency in Porous Media Analysis: Integrating Machine Learning with Monte Carlo Ray Tracing

Abstract Monte Carlo ray tracing (MCRT) has been a widely implemented and reliable computational method for calculating light-matter interaction in porous media, the computational modeling of porous media and performing MCRT becomes significantly expensive when dealing with intricate structures and numerous dependent variables. Hence, Machine Learning (ML) models have been utilized to overcome computational burdens. In this study, we investigate two distinct frameworks for characterizing radiative properties in porous media for pack-free and packing-based methods. We employ two different regression tools for each case, namely Gaussian process regressions for pack-free MCRT and Convolutional Neural Network (CNN) models for pack-based MCRT to predict the radiative properties. Our study highlights the importance of selecting the appropriate regression method based on the physical model, which can lead to significant computational efficiency improvement. Our results show that both models can predict the radiative properties with high accuracy (>90%). Furthermore, we demonstrate that combining MCRT with ML inference not only enhances predictive accuracy but also reduces the computational cost of simulation by more than 96% using the GP model and 99% for the CNN model.

Improvement of macroscopic turbulence model for subchannel analysis in the rod bundle array

The PRIUS program was established to generate an experimental database for the 6 × 4 and 12 × 6 rod bundle geometry. The database will be used to address the subchannel and CFD code analysis required for modeling and validation. This is necessary because Small Break Loss of Coolant Accident (SBLOCA) and Intermediate Break Loss of Coolant Accident (IBLOCA) present three-dimensional phenomena in the core due to the radial power profile, crossflow, and diffusion-dispersion. Therefore, specific experimental programs are required, especially during core reflooding, to investigate the large-scale three-dimensional effects. However, validating each sensitive model of the code separately in the presence of 3D effects is not possible due to the inability to implement instrumentation at high pressure and temperature steam-water flow conditions. The PRIUS test program uses a single-phase flow test to simulate a non-homogeneous velocity distribution and provide information on crossflow with radial mixing effects between subchannels. The CUPID code, which uses a macroscopic turbulence model, has been validated using the PRIUS-II experimental database. Existing macroscopic turbulence models were also validated for their prediction capabilities with different inlet flow conditions. However, the validation revealed significant errors in the shear region between subchannels. An improved macroscopic turbulence model showed promising results in predicting turbulence kinetic energy in porous media analysis.

Advanced Support Structures in Metal Additive Manufacturing: A Comparative Analysis of Porous Media for Enhanced Part Quality and Removal Efficiency

In laser powder bed fusion additive manufacturing support, structures are crucial for constructing overhang features and maintaining dimensional accuracy. However, traditional support structures exhibit inherent drawbacks, including compromised surface quality, prolonged post‐processing times, and other challenging requirements. This study investigates innovative approaches to address these issues by analyzing two types of specimens—cuboid and lap shear. The cuboid samples explore the manipulation of process parameters to adjust energy densities, transitioning from powder to porous support structures, with porosity measurement assessment. Subsequently, a shear test specimen objectively evaluates porous support parameters alongside traditional block‐type support, analyzing their impact on removal force, distortion, and surface quality. Results reveal that optimized porous support structures surpass traditional counterparts, showcasing superior surface quality, reduced removal forces, and minimized part distortion. Specifically, the shear test demonstrates a 94.25% reduction in removal forces and a 33.67% decrease in upper part distortion. Moreover, the maximum surface roughness (Rz) improves by 22.8% with the implementation of porous supports. By introducing a methodology for comparative analyses of measurable performance indicators, this research enhances understanding of support structure dynamics in additive manufacturing, facilitating advancements in efficiency and quality within the field.This article is protected by copyright. All rights reserved.

Evaluation of Recovery of Tight Sandstone Gas Reservoirs Based on a Seepage Steady-State Model

As an important indicator for measuring the effectiveness and level of oil and gas field development, recovery rate has always been a focus in the research of oil and gas fields. Reservoirs of tight sandstone gas formations have significant characteristics of low porosity, high permeability, and high water content, which leads to greater difficulty in their development and makes it challenging to evaluate the recovery rate. Newtonian mechanics, as an important component of the mechanical system, is an innovative application of classical mechanics in the field of seepage mechanics when applied to the two-phase flow of gas and water. Firstly, starting from the perspective of mechanics analysis, we derive a steady-state model for gas–water two-phase infiltration and obtain the productivity equation based on this model. Then, according to the steady-state model, we establish a method to calculate the effective control radius of gas reservoirs under different production conditions and reservoir physical properties. Finally, using Matlab 2018a programming based on the productivity equation, we calculate the gas recovery under different conditions during constant pressure drop production and constant production rate production. The results indicate that the effective control radius of the reservoir decreases with an increase in the economic ultimate daily gas production, increases with an increase in production pressure difference, slightly decreases with an increase in startup pressure gradient, and correspondingly increases with an increase in microtube radius and quantity. Regardless of whether it is production with a fixed pressure drop or production with a fixed production rate, the gas recovery decreases as the production pressure drop and bottomhole abandonment pressure increase, but it increases as the proportion of the single-well control radius increases. In production with a fixed pressure drop, the gas recovery remains consistent across different reservoir quality indices. However, in production with a fixed production rate, the gas recovery initially increases rapidly and then gradually slows down as the reservoir quality index increases, and there is an obvious critical permeability (0.1 mD). The research findings are based on the mechanical analysis of porous media, delving into the laws governing fluid flow during infiltration. The derived infiltration model can be used to calculate the effective control radius and evaluate recovery rates, providing practical guidance for reservoir development.

Preliminary conceptual design with neutronics and thermal-hydraulics assessments of FuSTAR

The fluoride-salt-cooled high-temperature advanced reactor (FuSTAR) is an advanced small modular reactor, which aims to the electric supplement and clean heat production to western China. In order to have good economy and safety, FuSTAR needs to have the characteristics of small size, light weight, deep burnup, and inherent safety. A conceptual design of FuSTAR is proposed in this study with neutronics and thermal-hydraulics assessments. In this study, its concept is established with two-region fuel to flatten core power, and the neutron spectrum of the core is in the thermal spectrum to improve the utilization of thermal neutrons and reduce fuel inventory. Neutronics and thermal-hydraulics analyses are also carried out, and established the core layout and material ratio under the near-critical condition. Burnup depth of the fuel, power distribution, neutron flux distribution, flow and heat transfer in rod-bundles channel, and 1/6 core porous media analysis are also investigated. The parameters of the designed core are evaluated quantitatively from various aspects. The results show that the core design in this study has sufficient shutdown depth, the equilibrium cycle is 3.24 years, and the discharge burnup depth is 106 GWd/MtU. The helical cruciform fuel (HCF) adopted by this reactor has a better flow and heat transfer capacity than cylindrical-type fuel at the inner channel. The analysis of 1/6 core porous media shows that the peak fuel temperature under normal operating condition is 1009.3 K, which is far below the limit of 1573 K. Relevant design and analysis results can provide the reference for the subsequent small modular FHR design and construction.

Homogenization and numerical algorithms for two-scale modeling of porous media with self-contact in micropores

The paper presents two-scale numerical algorithms for stress–strain analysis of porous media featured by self-contact at pore level. The porosity is constituted as a periodic lattice generated by a representative cell consisting of elastic skeleton and a void pore. Unilateral frictionless contact is considered between opposing surfaces of the pore. For the homogenized model derived in our previous work, we justify incremental formulations and propose several variants of two-scale algorithms which commute iteratively solving of the micro- and the macro-level contact subproblems. A dual formulation which takes advantage of the assumed microstructure periodicity and a small deformation framework, is derived for the contact problems at the micro-level. This enables to apply the semi-smooth Newton method. For the global, macrolevel step two alternatives are tested; one relying on a frozen contact identified at the microlevel, the other based on a reduced contact associated with boundaries of contact sets. Numerical examples of 2D deforming structures are presented as a proof of the concept.

High-fidelity neutronics and thermal-hydraulics analysis of helical cruciform fuel based on small modular FHR

Fluorine High-temperature Reactor (FHR) is a novel reactor type without water-cooling, but with a high outlet temperature. Small Modular FHR is convenient for modular assembly and transportation. Therefore, it is suitable for remote and water shortage areas to provide efficient and clean nuclear energy. The neutronics and heat transfer of the fuel are the key issues when the small modular FHR has high thermal power output. This research proposes the fuel – TRISO (TRi-ISOtropic) particles dispersed in Helical Cruciform Fuel (HCF). HCF can be self-supported in radial direction, no additional grid-spacers are required, which can decrease the pumping power and improve the efficiency of the reactor system. For small modular FHR, it can be disassembled, transported, and assembled better without grid-spacers and simplifying the core structure. The heat transfer capacity of the coolant also could be further improved because of the special structure of HCF. Based on the Monte-Carlo particle transport code MCNP, the neutronic characteristic from the assembly to the whole core of FHR is studied. By obtaining the fission power distribution in near-critical condition, the thermal-hydraulics of the single most heated HCF and 7 bundle HCF in the heated assembly are calculated. In order to preliminary analyze the thermal-hydraulics of the whole core scale, the thermal-hydraulics analysis of porous media is carried out for 1/6 core. The isotropic resistance tensor and thermal equilibrium porous model have been introduced with reasonable approximation, and porous resistance relation has been carried out. The radial power peak factor is 1.185 and the axial power peak factor is 1.435 in near-critical condition, which proves the arrangement of the HCF and the control rods can flatten the power distribution effectively. The average temperature of the coolant outlet in the active region of the core is 974.44 K, and the average temperature of the upper plenum outlet is 972.85 K. High coolant outlet temperature can ensure effective supply of high-temperature process heat. And the highest temperature of the fuel is 1271.49 K, which is lower than the limitation 1573 K. The parameters of this research can supply the neutronics and thermal-hydraulics references for further FHR design.

Determination of thermal conductivity, thermal diffusivity and specific heat capacity of porous silicon thin films using the 3ω method

Here the thermal transport properties of low thermal conductivity porous silicon thin films attached to high thermal conductivity silicon substrates are studied using the 3ω method implemented over the 100 Hz to 33 kHz frequency range. The thermal conductivity and thermal diffusivity of the films are extracted using temperature impedance monitoring of electrical contacts deposited on films, combined with an extended-frequency, multi-layer thermal model. From the extracted thermal conductivity and diffusivity of the films, the heat capacity could be determined. Validation of the approach is performed using the known properties of thick substrate glass and SU-8 layers spun on silicon substrates, the latter ranging in thickness from 1.35 to 12.5 µm. The technique was then applied to porous silicon films with porosities ranging from 45% to 77%. The extracted thermal properties for as-fabricated films show a reduction of thermal conductivity and diffusivity from 1.7 to 0.15 W/mK and 1.9 to 0.2 mm2/s, respectively as the porosity increases. After passivation by annealing in nitrogen and at 600 °C, the same films exhibited higher values of thermal conductivity and diffusivity ranging from 2.7 to 0.7 W/mK and 2.5 to 0.65 mm2/s. The ability to extract both thermal conductivity and thermal diffusivity for these films removes the need to make assumptions around specific heat capacity, commonly made during analysis of porous media. These results show for the first time a monotonic increase in specific heat capacity of porous silicon films as a function of porosity.

Scattering problems for a rectangular crack in a saturated porous material: application of the Chebyshev's functions

Analytic solutions are derived for the problem of scattering of obliquely incident plane waves from a rectangular crack in a fluid-saturated porous solid. The propagation direction and polarization direction of the incident waves can be arbitrary. The solutions are based on expanding the crack opening displacements in terms of Chebyshev functions. Explicit expressions for the frequency-dependent stress intensity factors and scattering far fields are obtained. The poroelastic effects play a key role in determining the crack opening mode by changing the effective normal stress applied on the crack surfaces. Consequently, the dynamic responses of the crack can be quite different from those of conventional dry crack in an elastic solid. The effects of scattered slow-P waves as well as wave-induced crack fluid pressure on the dynamic stress intensity factors are clarified, and a corner effect is observed in the scattering patterns for moderately high frequencies. The comparisons of synthetic seismograms with the elastic model show the slow-P waves can cause significant energy loss and waveform change. This study can be used for fracture analysis of porous media and nondestructive testing for individual hydraulic fractures in reservoirs.

Anisotropic non-linear time-fractional diffusion equation with a source term: Classification via Lie point symmetries, analytic solutions and numerical simulation

The non-linear anomalous diffusion in anisotropic media is using in various fields, e.g. in molecular dynamics, hydrology, financial systems, porous media analysis, etc. The Lie group method is developed to study the time-fractional diffusion equation with a source term in anisotropic media. As an application, the complete Lie group classification is performed up to the equivalence transformations for all special cases of the coefficients. Some similarity reductions are obtained for implicit cases. The analytical invariant subspace method is used in order to find some exact solutions. The work is concluded by the fractional Chebyshev pseudo-spectral (FCPS) method for constructing a numerical simulation for some of the reduced equations in the symmetry analysis section.

Evaluation of image segmentation techniques for image-based rock property estimation

Accurate characterization of rock and fluid properties in porous media using X-ray imaging techniques depends on reliable identification and segmentation of the involved phases. Segmentation is critical for the estimation of porosity, fluid saturations, fluid and rock topology, and pore connectivity, among other pore-scale properties. Therefore, the purpose of this study is to compare the effectiveness of different image segmentation techniques when applied to image data analysis in porous media. Two machine learning based segmentation techniques – a supervised ML technique called Fast Random Forest, and an unsupervised method combining k-means and fuzzy c-means clustering algorithms – were compared using an experimental data set. Comparisons are also presented against traditional thresholding segmentation. In addition, we discuss the potential and limitations of applying deep learning based segmentation algorithms. The performance of the segmentation techniques were compared on estimates of porosity, saturation, and surface area, as well as pore-scale estimates such as fluid-fluid interfacial areas, and Euler characteristic. X-ray micro-computed tomography images for a sintered glass frit, saturated with two-phases (air and brine), were acquired at two different voxel resolutions. The high-resolution images (6 μm) were used as the benchmark case, while the low-resolution images (18 μm) were segmented by three segmentation techniques: Fast Random Forest, clustering, and thresholding. The results for porosity and phase saturation from thresholding and from the supervised ML method (i.e. Fast Random Forest) were found to be close to the benchmark case. Segmentation results from the unsupervised ML method (i.e. clustering) were largely unsatisfactory, except for total surface area measurements. The supervised ML segmentation results provided better measurements for air-brine interfacial areas by capturing three-phase interfacial regions. Also, all segmentation techniques resulted in similar measurements for air-phase Euler characteristic confirming poor connectivity of the trapped air phase, although the closest results were obtained by the supervised ML method. Finally, despite the supervised ML segmentation technique being more computationally intensive, it was found to require less user intervention and its implementation was more straightforward. In summary, this work provides insights into different segmentation techniques, their implementation, as well as advantages and limitations with regards to quantitative analysis of pore-scale properties in saturated porous media.

Importance of surface roughness on perfluorooctanoic acid (PFOA) transport in unsaturated porous media

Understanding the subsurface transport of perfluorooctanoic acid (PFOA) is of considerable interest for evaluating its potential risks to humans and ecosystems. In this study, packed-column experiments were conducted to examine the influence of surface roughness on PFOA transport in unsaturated glass beads, quartz sand and limestone porous media. Results showed decreasing moisture content significantly increased the air-water interfacial adsorption of PFOA and led to greater retardation in all three types of porous media. Particularly, rougher surface (limestone > quartz sand > glass beads) and smaller grain size (i.e. a larger solid specific surface area, SSSA) significantly enhanced PFOA retardation under unsaturated conditions. These results were further supported by bubble column experiments and SSSA analysis of porous media, which demonstrate that except for the factors affecting PFOA transport in solid-water interface (e.g. surface charge and chemical heterogeneity), the greater retardation of PFOA during transport is attributed to the larger air-water interfacial areas associated with rougher surface and smaller grain size and hence greater interfacial adsorption of PFOA. Our results indicated the importance of surface roughness on the retention and transport of PFOA in the unsaturated zone.

An equivalent pipe network model for free surface flow in porous media

Free surface flow analysis in porous media is challenging in many practical applications with strong non-linearity. An equivalent pipe network model is proposed for the simulation and evaluation of free surface flow in porous media. On the basis of representative elementary volume with homogeneous pore-scale patterns, the pore space of the homogeneous isotropic porous media is conceptualized as a collection of capillary tubes. According to Hagen-Poiseulle's law and flux equivalence principle, equivalent hydraulic parameters and unified governing formulations for the pipe network model are deduced. The two-dimensional free surface flow problem is reduced to a one-dimensional problem of pipe networks and a one-dimensional procedure based on the finite element method is then developed by introducing a continuous penalized Heaviside function. The proposed equivalent pipe network model is verified with results from numerical solutions and laboratory-measured data available in the literature, and good agreements are obtained. The proposed equivalent pipe network model is shown to be effective in analyzing the free surface flow in porous media. The numerical results also indicate that the proposed equivalent pipe network model has weak sensitivity of the mesh size and penalty parameters.

Droplet Impact on Suspended Metallic Meshes: Effects of Wettability, Reynolds and Weber Numbers

Liquid penetration analysis in porous media is of great importance in a wide range of applications such as ink jet printing technology, painting and textile design. This article presents an investigation of droplet impingement onto metallic meshes, aiming to provide insights by identifying and quantifying impact characteristics that are difficult to measure experimentally. For this purpose, an enhanced Volume-Of-Fluid (VOF) numerical simulation framework is utilised, previously developed in the general context of the OpenFOAM CFD Toolbox. Droplet impacts on metallic meshes are performed both experimentally and numerically with satisfactory degree of agreement. From the experimental investigation three main outcomes are observed—deposition, partial imbibition, and penetration. The penetration into suspended meshes leads to spectacular multiple jetting below the mesh. A higher amount of liquid penetration is linked to higher impact velocity, lower viscosity and larger pore size dimension. An estimation of the liquid penetration is given in order to evaluate the impregnation properties of the meshes. From the parametric analysis it is shown that liquid viscosity affects the adhesion characteristics of the drops significantly, whereas droplet break-up after the impact is mostly controlled by surface tension. Additionally, wettability characteristics are found to play an important role in both liquid penetration and droplet break-up below the mesh.

Permeability Prediction using multivariant structural regression

A novel method for permeability prediction is presented using multivariant structural regression. A machine learning based model is trained using a large number (2,190, extrapolated to 219,000) of synthetic datasets constructed using a variety of object-based techniques. Permeability, calculated on each of these networks using traditional digital rock approaches, was used as a target function for a multivariant description of the pore network structure, created from the statistics of a discrete description of grains, pores and throats, generated through image analysis. A regression model was created using an Extra-Trees method with an error of <4% on the target set. This model was then validated using a composite series of data created both from proprietary datasets of carbonate and sandstone samples and open source data available from the Digital Rocks Portal (www.digitalrocksporta.org) with a Root Mean Square Fractional Error of <25%. Such an approach has wide applicability to problems of heterogeneity and scale in pore scale analysis of porous media, particularly as it has the potential of being applicable on 2D as well as 3D data.

Dynamic Analysis of Porous Media using Generalized Plasticity Model and Non-Darcy Flow Rule

Dynamic Analysis of Porous Media using Generalized Plasticity Model and Non-Darcy Flow Rule

The use of AFM in quantitative analysis of pore characteristics in coal and coal-bearing shale

Quantitative characterization of nanopore structure is of great importance for both accurate assessment of gas reservoir capacity and gas migration behaviors. In this work, atomic force microscopy (AFM) experiments were performed on coal and coal-bearing shale samples, attempting to gain clearer insights into the nanopore characteristics and surface roughness. Scanning electron microscopy (SEM) and low pressure N 2 gas adsorption (LP-N 2 GA) were used for qualitative and quantitative comparative analysis, respectively. SEM results are found in good accordance with AFM surface topographies. The values of R a are 18.04, 7.24 and 14.17 nm for Coal-1, Coal-2 and shale, respectively. The obtained similar nanopore distribution curves via LP-N 2 GA and AFM indicate that the nanoporosity determined by AFM is reliable. However, the differences between these two methods can not be neglected. The percentage of nanopores with diameter <4 nm generated from LP-N 2 GA are higher than that rendered from AFM for both coal and shale samples, due to the gas adsorption-induced swelling, but it is reverse for macropore (>200 nm). AFM is a high-resolution tool for nanopore characterization, and it is expected to be widely used for quantitative analysis of porous media. • The surface 3-D topographies of coal and shale were provided by AFM. • Both qualitative and quantitative comparative analysis were made for nanopores. • SEM results are in good accordance with AFM surface topographies. • The similar pore distributions indicate that the nanoporosity by AFM is reliable.