Properties Of Porous Media Research Articles

Modeling and Analysis of Droplet Evaporation at the Interface of a Coupled Free-Flow–Porous Medium System

AbstractEvaporation of droplets formed at the interface of a coupled free-flow–porous medium system enormously affects the exchange of mass, momentum, and energy between the two domains. In this work, we develop a model to describe multiple droplets’ evaporation at the interface, in which new sets of coupling conditions including the evaporating droplets are developed to describe the interactions between the free flow and the porous medium. Employing pore-network modeling to describe the porous medium, we take the exchanges occurring on the droplet–pore and droplet–free-flow interfaces into account. In this model, we describe the droplet evaporation as a diffusion-driven process, where vapor from the droplet surface diffuses into the surrounding free flow due to the concentration gradient. To validate the model, we compare the simulation results for the evaporation of a single droplet in a channel with experimental data, demonstrating that our model accurately describes the evaporation process. Then, we examine the impact of free-flow and porous medium properties on droplet evaporation. The results show that, among other factors, velocity and relative humidity in the free-flow domain, as well as pore temperature in the porous medium, play key roles in the droplet evaporation process.

Utilizing numerical techniques for modeling radiating Casson nanofluid flow with thermophoretic phenomenon on a heated stretching surface

This work aims to use mathematical modeling to investigate the mechanics of heat and mass transport in dissipative Casson nanofluid flows over a linear rough sheet. This study considers various elements such as thermal radiation, magnetic fields, heat generation, the varied properties of porous media, and the thermophoretic impact. Besides that, it looks into the variations in viscosity, diffusivity, and thermal conductivity, as well as the energy dissipation from viscous internal friction and fluid temperature modifications. The method involves coming up with and changing the boundary layer equations into a group of linked nonlinear ordinary differential equations that use variables that do not have any dimensions. The foundation of the solution strategy for these equations is the Hermite collocation method (HCM), which is renowned for its precision and adaptability. It offers an organized approach to solving the complex differential equations, allowing for accurate numerical solutions. The use of graphical representations ensures thorough data analysis and clarity, while also providing insightful information about the computed outcomes. The code validation method uses numerical comparisons with recent research to confirm the algorithm’s accuracy and dependability, as well as its resilience in comparison to the existing literature. Important conclusions from the study show that thermal boundary layers and nanofluid velocity decrease with increases in the porosity parameter, slip parameter, and magnetic field parameter.

Sound absorbers from walnut shell waste: measurement and models

Waste walnut shells have been cleaned, dried, chopped, glued, pressed, and molded to form porous cylinders for measurement of normal incidence sound absorption coefficient spectra in an impedance tube. Porosities and flow resistivities have been measured non-acoustically. Field Emission Scanning Electron Microscopy images, used to obtain fragment size for determining shell density, reveal that the fragment surfaces are rough, which may influence acoustical performance. Measured absorption spectra have been compared with predictions of two analytical models for acoustical properties of rigid porous media, which assume microstructures of identical uniform parallel slanted slits (SS) and non-uniform cylindrical pores with a log normal radius distribution (NUPSD) respectively. Measured values of flow resistivity and porosity are used in the models but tortuosity is adjusted to give best agreement with the quarter wavelength resonance in the measured absorption coefficient spectra. Predictions agree best with data for samples made from the smallest shell fragments. These samples have the highest values of tortuosity, flow resistivity and offer good absorption at speech frequencies which suggests that recyclable and sustainable sound absorbers made from walnut shell wastes could be useful for controlling indoor reverberation..

Understanding the role of flux chamber configuration in the measurement of VOC emissions from porous media

The accurate quantification of volatile organic compound (VOC) emission rates from porous media to the air is a challenging problem, as measurements are affected by the chemical and physical characteristics of the porous media, and the operating parameters of the sampling device itself. The main objective of this study is to investigate how flux chamber (the most commonly used sampling device) configurations influence emission rate measurement from three selected porous media. Various parameters were studied, including sweep air flow rate, presence of a mixing fan, headspace volume and thickness of media. Controlled experiments focused on the behaviour of two VOCs commonly found in area sources: acetic acid and 1-butanol. Sweep gas flow rate emerged as the most influential factor, inducing turbulence and dilution over porous media surfaces and impacting emission rate measurements more significantly than headspace volume and fan installation. Variations in porous media properties also affected mass transfer, with emissions from coco coir showing higher mass transfer as its porosity and particle size facilitated gas transportation. While behaviour of acetic acid emission through the media supported the diffusion theory, emission of 1-butanol was affected by a combination of factors, highlighting the role of both diffusive and advective transport mechanisms. Understanding how flux chamber setups and porous media properties influence emission rates is crucial for accurately interpreting data. This knowledge also guides the design of studies, especially when investigating complex sources like biosolids and organic-amended soil.

Permeability monitoring of underground concrete structures using elastic wave characteristics with modified Biot’s model

This study aims to develop a theoretical model for predicting the permeability of concrete in underground structures using compressive elastic waves. This research is motivated by the necessity of monitoring the permeability of concrete used in critical underground infrastructure, such as tunnels and radioactive waste disposal sites, to ensure their long-term safety. Increased permeability owing to crack generation can lead to groundwater inflow, undermining the structural integrity of these facilities. Traditional methods for permeability monitoring face challenges at depths of 500 m–1 km owing to high temperatures, high pressures, and limited space conditions. To address these issues, Biot’s model, which correlates the P-wave characteristics with the properties of porous media, was applied in this study. The P-wave velocity and attenuation were studied according to the permeability of concrete based on Biot’s model. Subsequently, concrete specimens were prepared to measure the permeability, P-wave velocity, and attenuation. The permeability results from the experiment were compared with those obtained from the model for validation. The findings indicate that the modified Biot’s model can effectively monitor permeability through elastic wave characteristics, offering a non-destructive and reliable method for assessing the condition of concrete structures in underground environments. This approach is expected to enhance the safety of underground infrastructure through accurate permeability monitoring.

Fractal permeability model for power-law fluids in embedded tree-like branching networks based on the fractional-derivative theory

The investigation of permeability in tree-like branching networks has attracted widespread attention. However, most studies about fractal models for predicting permeability in tree-like branching networks include empirical constants. This paper investigates the flow characteristics of power-law fluids in the dual porosity model of porous media in embedded tree-like branching networks. Considering the inherent properties of power-law fluids, non-Newtonian behavior effects, and fractal properties of porous media, a power-law fluids rheological equation is introduced based on the fractional-derivative theory and fractal theory. Then, an analytical formula for predicting the effective permeability of power-law fluids in dual porous media is derived. This analytical formula indicates the influences of fractal dimensions and structural parameters on permeability. With increasing length ratio, bifurcation series, and bifurcation angle, as well as decreasing power-law exponent and diameter ratio, the effective permeability decreases to varying degrees. The derived analytical model does not include empirical constants and is consistent with the non-Newtonian properties of power-law fluids, indicating that the model is an effective method for describing the flow process of complex non-Newtonian fluids in porous media in natural systems and engineering. Therefore, this study is of great significance to derive analytical solutions for the permeability of power-law fluids in embedded tree-like bifurcation networks.

Atomistic-to-continuum modeling of carbon foam: A new approach to finite element simulation

Carbon foam materials exhibit useful characteristics for unique and diverse applications. However, accurately modeling three-dimensional carbon foam remains a significant challenge. This paper introduces a novel technique for transitioning atomistic-scale models of porous carbon, obtained via molecular dynamics simulation, to continuum-scale carbon foam models suited for finite element simulations. We present our method using the fractal properties of porous media, specifically carbon foams. The resulting models demonstrate testable properties consistent with those derived from computed tomography (CT) scans and experimental data. Stress–strain curves obtained from finite element analysis (FEA) calculations of our models correlate well with experimental measurements from dogbone testing samples of carbon foam sourced from CONSOL Innovations LLC, WV, USA, as well as CT scan models derived from the carbon foams. Our approach presents a computationally efficient alternative and encourages innovation in modeling porous media, particularly in extracting physical properties from an ensemble of models.

Phase and group speeds of airborne surface waves over porous layers and periodically rough hard surfaces.

Sound fields above porous layers or rough acoustically hard boundaries may include airborne surface waves. The surface wave properties depend on the effective surface admittance. Analytical expressions for surface wave speeds are derived from models for the acoustical properties of rigid porous media. Surface wave effects on measurements of level difference spectra over porous asphalt are investigated and predictions of phase, group speeds, and vertical attenuation of the surface waves over externally reacting hard backed layers corresponding to a porous asphalt are compared. Predictions of surface wave characteristics above an identical vertical slit medium are compared with data obtained over arrays of parallel aluminum strips on an acoustically hard surface. Group speeds of surface waves over lattices, parallel regularly spaced strips, and snow obtained by numerical differentiation of the phase speed spectra corresponding to admittance spectra deduced from complex excess attenuation are found to compare well with those estimated from time domain data. An effective admittance, deduced from a boundary element method simulation of the excess attenuation spectrum over regularly spaced ribs so that the frequency of the peak corresponds with that in the measured spectrum, is used to estimate the group speed of the associated surface wave.

Numerical investigation on the influence of CO2-induced mineral dissolution on hydrogeological and mechanical properties of sandstone using coupled lattice Boltzmann and finite element model

The impact of CO2-induced mineral reactions, specifically the CO2-water-rock interactions, has been widely recognized for its potential to compromise the hydrogeological and mechanical properties of porous media, thereby deteriorating their integrity and mechanical characteristics. This raises concerns regarding the safety of numerous projects such as CO2 geological storage. However, the mechanism underlying the influence of CO2-induced mineral reactions on these parameters is still not fully understood, thereby introducing uncertainty into the reliability of predicted outcomes. Direct pore-scale simulation plays a crucial role in deepening the understanding of CO2 -water-rock interaction and its associated impacts. The present study proposes a novel simulation model that integrates the lattice Boltzmann and finite element methods to simulate the dissolution of calcite in a 3D sandstone sample invaded by saturated CO2 solution, aiming to investigate the resulting evolution of hydrogeological parameters (i.e., porosity and permeability) and mechanical properties (i.e., bulk and shear moduli). The injection rates of reactive fluid (i.e., CO2 solution) were varied in the model to simulate different operational conditions of CO2 geological storage project. The simulation results demonstrate that the decrease in injection velocity inhibits the dissolution of calcite in the middle and downstream regions, concentrating it near the inlet. These distinct modes of calcite dissolution result in differentiated permeability and mechanical properties within rocks possessing identical porosity levels. In general, the increase in fluid injection velocity leads to a more pronounced enhancement in permeability and a greater attenuation in bulk and shear moduli under the same porosity. Thus, it is confirmed that the utilization of porosity is inadequate in accurately characterizing the evolution of rock permeability and mechanical properties resulting from mineral reactions. We ascertain that the application of fractal parameters, i.e., succolarity and fractal dimension, can more precisely depict the progression of permeability and bulk/shear modulus, 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.

Multiobjective optimization of porous medium for efficient transpiration cooling of hypersonic vehicles using genetic algorithm

Transpiration cooling has been proven an effective method for reducing heat flux on the surfaces of high-speed vehicles. This study investigates the effects of porous medium properties, employing a black-box optimization method to determine the optimal length, thickness, and porosity for a porous medium in a transpiration cooling system on a flat plate under hypersonic laminar flow. The objectives include optimizing thermal effectiveness, coolant consumption, and the weight and cost of the porous material. A multiobjective genetic optimization algorithm is directly integrated with a Computational Fluid Dynamics (CFD) solver, and 1562 CFD simulations were conducted to identify the optimal configuration. The results demonstrate that the length and porosity of the porous medium more significantly impact thermal effectiveness than the thickness. Furthermore, the optimization identified a configuration for the porous medium that, when compared to the original case, shows reductions in length, thickness, and porosity of 3.5%, 11%, and 29%, respectively. Additionally, there were average improvements in thermal effectiveness and coolant consumption of 4.56% and 3.9%, respectively, while the weight and cost of the porous material increased by 3.73% and 3.65%, respectively.

Machine Learning Assisted Prediction of Porosity and Related Properties Using Digital Rock Images.

Accurately estimating reservoir rock properties is paramount for modeling the storage and flow of fluids (hydrocarbon, carbon dioxide, and groundwater) in porous media. However, existing laboratory techniques to measure rock properties are usually time-consuming, expensive, and computationally intensive. This work proposes an efficient workflow that uses the machine learning algorithm, based on the convolutional neural network (CNN) framework, to predict rock properties from microcomputed tomography (micro-CT) X-ray images. The workflow involves data preprocessing, label extraction, training, and prediction using the segmented images of the rock to predict porosity, throat area, and pore surface area, which are essential for pore-scale modeling. The model was trained and validated on the Bentheimer sandstone, which was then used to predict properties of other sandstones (Castlegate and Leopard) with different pore structures and flow properties. The model yielded a good prediction for the throat and pore surface area but a significant error for porosity. Subsequently, a new complex model was trained and validated using diverse images from Bentheimer and an additional rock Castlegate, which was then used to predict the properties of Leopard sandstone. The new model improved the prediction of each property, resulting in mean absolute percentage error (MAPE) values of 2.19%, 3.04%, and 6.08% for porosity, pore surface area, and throat area with the binary images, respectively. In addition, we present a novel data-driven method using a simple regression model to predict the absolute permeability of a digital rock sample using the pore network parameters as predictors. The extreme gradient boost (XGBoost), which performed the best among several machine algorithms, was trained and validated using digital rock images from Bentheimer and Castlegate sandstone. The generated model was then used to predict the absolute permeability of the Leopard sandstone with an R 2 of 0.813, which was a significant improvement over the model generated solely by using either the Bentheimer or the Castlegate sandstone images. Furthermore, our analysis showed that the tortuosity had the most significant effect on the absolute permeability prediction of the rock sample. This study showed that we can reliably predict the morphological properties of porous media using computationally efficient models generated from digital rock images, which can be used to build a regression model to predict the crucial petrophysical properties needed to model the flow of fluids in porous media.

Research on the Migration and Adsorption Mechanism Applied to Microplastics in Porous Media: A Review.

In recent years, microplastics (MPs) have emerged as a significant environmental pollutant, garnering substantial attention for their migration and transformation behaviors in natural environments. MPs frequently infiltrate natural porous media such as soil, sediment, and rock through various pathways, posing potential threats to ecological systems and human health. Consequently, the migration and adsorption mechanisms applied to MPs in porous media have been extensively studied. This paper aims to elucidate the migration mechanisms of MPs in porous media and their influencing factors through a systematic review. The review encompasses the characteristics of MPs, the physical properties of porous media, and hydrodynamic factors. Additionally, the paper further clarifies the adsorption mechanisms of MPs in porous media to provide theoretical support for understanding their environmental behavior and fate. Furthermore, the current mainstream detection techniques for MPs are reviewed, with an analysis of the advantages, disadvantages, and applications of each technique. Finally, the paper identifies the limitations and shortcomings of current research and envisions future research directions.

A deep learning-based surrogate model for trans-dimensional inversion of discrete fracture networks

Fractures and their geometrical patterns are usually required to analyze the mechanical and flow properties of porous media in the subsurface. Fracture characterization is therefore regarded of crucial importance for optimizing production management or achieving maximum storage capacity. In this research, we propose to invert the fracture networks under the Bayesian framework for the uncertainty quantification. In particular, the number of fractures in the modelling system is treated as unknown, leading to a trans-dimensional inverse problem, and the reversible jump Markov chain Monte Carlo algorithm is applied to sample the model space with possible model moves proposed in the sampling process. A deep learning network is further applied as a surrogate model in the sampling process for increasing the computational efficiency, instead of using the physical forward simulator. We apply the proposed methodology to estimate the spatial distribution of fracture networks based on the head measurements from the steady-state flow simulation. The prior distributions of fracture parameters such as position, orientation and length are described using the discrete fracture networks approach that is deeply rooted in stochastic modelling. Due to the high non-uniqueness, the correct spatial distribution of fracture patterns cannot be successfully recovered in this case study, even a good match between observed and simulated head data is reached. More analysis could be performed in the future with the production historical data or more informative priors.

A Novel Finite Element-Based Method for Predicting the Permeability of Heterogeneous and Anisotropic Porous Microstructures.

Permeability is a fundamental property of porous media. It quantifies the ease with which a fluid can flow under the effect of a pressure gradient in a network of connected pores. Porous materials can be natural, such as soil and rocks, or synthetic, such as a densified network of fibres or open-cell foams. The measurement of permeability is difficult and time-consuming in heterogeneous and anisotropic porous media; thus, a numerical approach based on the calculation of the tensor components on a 3D image of the material can be very advantageous. For this type of microstructure, it is important to perform calculations on large samples using boundary conditions that do not suppress the transverse flows that occur when flow is forced out of the principal directions. Since these are not necessarily known in complex media, the permeability determination method must not introduce bias by generating non-physical flows. A new finite element-based method proposed in this study allows us to solve very high-dimensional flow problems while limiting the biases associated with boundary conditions and the small size of the numerical samples addressed. This method includes a new boundary condition, full permeability tensor identification based on the multiscale homogenization approach, and an optimized solver to handle flow problems with a large number of degrees of freedom. The method is first validated against academic test cases and against the results of a recent permeability benchmark exercise. The results underline the suitability of the proposed approach for heterogeneous and anisotropic microstructures.

Electrokinetics of Erosive Seepage through Deformable Porous Media.

Channelization and branching patterns frequently appear in porous structures as a result of fluid-flow-mediated erosion, which causes spatiotemporal changes in the medium. However, most studies on electrokinetic effects in porous media focus on the overall impact of the electric field on electrical double-layer formation in micropores and its influence on ionic transport, without addressing the spatiotemporal erosive characteristics and resulting porosity distribution. In this study, we explore the interplay between flow-induced shear stress and an external electric field on the dynamic evolution of porosity in deformable porous media using semi-analytical modeling. Our numerical simulations accurately predict the differences in porosity and erosive development when the electric field aligns with or opposes the flow, highlighting the importance of the direction of the external stimulus and not just its magnitude. Our findings establish a foundation for electric-field-mediated control of porous media properties and explain electrokinetic transport by considering dynamic porosity variations as a result of erosive deformation, an aspect previously unaddressed.

Learning a general model of single phase flow in complex 3D porous media

Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics—in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning (ML) models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our ML model exhibits strong performance (R 2 = 0.9) when tested on extremely diverse real domains at multiple scales.

Introducing the area under stress–velocity curve: Theory, measurement and association with rock properties

AbstractSince many years ago, ultrasonic velocity has been used to investigate the physical and mechanical behaviour of rocks, thereby playing an important role in reservoir characterization and seismic interpretation. In order to develop the knowledge of ultrasonic tools, we performed a noble analysis on the ultrasonic behaviour of rocks under confining stress and evaluated a distinctive property of porous media that is measured as the area under the stress–velocity curve (here defined as S*). We further investigated its relationship with elastic and mechanical behaviours of rock. To validate the theoretical framework developed in this work, 20 core plugs from various rock units with complex microstructures were subjected to triaxial compressional tests to calculate their area under the curve. Calculations were made for crack‐closing, elastic and post‐elastic stages (e.g. pore collapse) along the ultrasonic velocity–stress curve. Moreover, the selected samples had their microstructure investigated by thin‐section studies to quantify their porosity and pore type. The results were analysed to check for the effect of pore type on S* in different stages of the stress–velocity curve. Based on the outputs of the analysis of variance and Pearson's correlation coefficient analysis, the curve had its shape and underlying area closely related to the porosity and pore geometry. Indeed, the results showed that the shale and sandstone with micro cracks and carbonate with stiff pores correspond to smaller and larger areas under the curve in crack‐closing and inelastic stages, respectively. Cross‐correlating the results to compressibility (inverse of bulk modulus), it was figured out that the calculated area under curve was well consistent with the compressibility. In addition, S* represents both static and dynamic behaviours of the rock, and the results revealed that the shape and curvature of the stress–velocity curve give valuable information about the rock microstructure. Another finding was the fact that the type of fluid and wave velocity seemingly affect the S*. Our findings can help interpret wave velocity behaviour in reservoir rocks and other stressful porous media.

Sensitivity analysis of natural convection in a porous cavity filled with nanofluid and equipped with horizontal fins using various optimization methods and MRT-LB

In the present study, natural convection heat transfer is investigated in a porous cavity filled with Cu/water nanofluid and equipped with horizontal fins. Optimization and sensitivity analysis of the fin’s geometry, porous medium and nanofluid properties to maximize heat transfer rate is the aim of this work. To achieve this purpose, a design space is created by input parameters which include length, number of fins, distance between fins, porosity, Darcy number and volumetric fraction of the nanoparticles. Several tools have been used to implement optimization methods including the Taguchi method (TM) for design points generation, sensitivity analysis of design variables by using signal-to-noise ratio (SNR) and analysis of variance (ANOVA), response surface method (RSM) for interpolation and regression by using nonparametric regression, and genetic algorithm (GA) for finding optimum design point. The double multi-relaxation time lattice Boltzmann method (MRT-LBM) is used to analyze and simulate the flow field and heat transfer in each design point. The results show that the optimal configuration leads to an average Nusselt number of 5.56. This optimal configuration is at the length of fins L/2, the number of fins 2, the distance between fins L/12, porosity 0.8, Darcy number 0.1, and the volumetric fraction of the nanoparticles 0.02. By using the SNR results, the Darcy number and the number of fins have the most and the least effect in maximizing the average Nusselt number, respectively. The ANOVA results and global sensitivity analysis (GSA) findings further validated this conclusion.

Turbulent Properties of Stationary Flows in Porous Media.

In this study, we investigate the flow dynamics in a fixed bed of hydrogel beads using particle tracking velocimetry to compute the velocity field in the middle of the bed for moderate Reynolds numbers (Re=[124,169,203,211]). We discover that even though the flow is stationary at the larger scales, it exhibits complex multiscale spatial dynamics reminiscent of those observed in classical turbulence. We find evidence of the presence of an inertial range and a direct energy cascade, and are able to obtain a value for a "porous" Kolmogorov constant of C_{2}=3.1±0.3. This analogy with turbulence opens up new possibilities for understanding mixing and global transport properties in porous media.