Properties Of Porous Media Research Articles

Study of Gas–Liquid Two-Phase Flow Characteristics at the Pore Scale Based on the VOF Model

To study the effects of liquid properties and interface parameters on gas–liquid two-phase flow in porous media. The volume flow model of gas–liquid two-phase flow in porous media was established, and the interface of the two-phase flow was reconstructed by tracing the phase fraction. The microscopic imbibition flow model was established, and the accuracy of the model was verified by comparing the simulation results with the classical capillary imbibition model. The flow characteristics in the fracturing process and backflow process were analyzed. The influence of flow parameters and interface parameters on gas flow was studied using the single-factor variable method. The results show that more than 90% of the flowing channels are invaded by fracturing fluid, and only about 50% of the fluid is displaced in the flowback process. Changes in flow velocity and wetting angle significantly affect Newtonian flow behavior, while variations in surface tension have a pronounced effect on non-Newtonian fluid flow. The relative position of gas breakthrough in porous media is an inherent property of porous media, which does not change with fluid properties and flow parameters.

Effects of Congestion in Human Lung Investigated Using Dual-Scale Porous Medium Models.

Chronic obstructive pulmonary disease (COPD) is a primary chronic respiratory disease associated with pulmonary congestion that restricts airflow and thereby affects the exchange of gases between the alveoli and the blood capillaries in the lungs. Dual scale-global and local-porous medium models have been developed and reported in this work, to study the effects of air-side congestion on the blood-oxygen content in the alveolar region of the human lung. The human lung is model as a global, equivalent, heterogeneous porous medium comprising three zones with distinct permeabilities related to their progressively complex branching structure. Airflow for each breathing cycle is determined by solving mass and momentum transfer equations across the three porous medium zones. The congestion is introduced by appropriate modification of the porous medium properties of the zones considered. The congestion-affected air velocity reaching Zone 3 is given as input to a separate "local model" employed at several locations of the alveoli of Zone 3. The local model determines the oxygen content in the blood flow in the capillaries of the alveoli by solving suitable mass, momentum and species transport equations. The transient simulation results performed for a long duration of multiple breathing cycles, demonstrate that a normal, healthy human lung is functional for up to 40% volume congestion or when 50% of the lung is congested to about 23.5%. Increasing congestion beyond this value, quickly-within a few hours-depletes the oxygen exchange in the blood flow of the alveolar region (of Zone 3), leading to hypoxemia. The effects of congestion progression on oxygen exchange dynamics determined through the dual-scale porous medium modelling approach provide researchers and medical professionals with in silico predictive estimates to generate treatment strategies for chronic respiratory diseases.

Magnetically Controlled Transport of Nanoparticles in Solid Tumor Tissues and Porous Media Using a Tumor-on-a-Chip Format.

This study addresses issues in developing spatially controlled magnetic fields for particle guidance, synthesizing biocompatible and chemically stable MNPs and enhancing their specificity to pathological cells through chemical modifications, developing personalized adjustments, and highlighting the potential of tumor-on-a-chip systems, which can simulate tissue environments and assess drug efficacy and dosage in a controlled setting. The research focused on two MNP types, uncoated magnetite nanoparticles (mMNPs) and carboxymethyl dextran coated superparamagnetic nanoparticles (CD-SPIONs), and evaluated their transport properties in microfluidic systems and porous media. The original uncoated mMNPs of bimodal size distribution and the narrow size distribution of the fractions (23 nm and 106 nm by radii) were demonstrated to agglomerate in magnetically driven microfluidic flow, forming a stable stationary web consisting of magnetic fibers within 30 min. CD-SPIONs were demonstrated to migrate in agar gel with the mean pore size equal to or slightly higher than the particle size. The migration velocity was inversely proportional to the size of particles. No compression of the gel was observed under the magnetic field gradient of 40 T/m. In the brain tissue, particles of sizes 220, 350, 820 nm were not penetrating the tissue, while the compression of tissue was observed. The particles of 95 nm size penetrated the tissue at the edge of the sample, and no compression was observed. For all particles, movement through capillary vessels was observed.

Experimental Study of a Bionic Porous Media Evaporative Radiator Inspired by Leaf Transpiration: Exploring Energy Change Processes

The performance of photovoltaic (PV) cells is significantly influenced by their operating temperature. While conventional active cooling methods are limited by economic feasibility, passive cooling strategies often face challenges related to insufficient heat dissipation capacity. This study presents a bio-inspired evaporative heat sink, modeled on the transpiration and water transport mechanisms of plant leaves, which leverages porous media flow and heat transfer. The device uses capillary pressure, generated through the evaporation of the cooling medium under sunlight, to maintain continuous coolant flow, thereby achieving effective cooling. An experimental setup was developed to validate the device’s performance under a heat flux density of 1200 W/m2, resulting in a maximum temperature reduction of 5 °C. This study also investigated the effects of porous medium thickness and porosity on thermal performance. The results showed that increasing the thickness of the porous medium reduces cooling efficiency due to reduced fluid flow. In contrast, the effect of porosity was temperature-dependent: at evaporation temperatures below 67 °C, a porosity of 0.4 provided better cooling, while at higher temperatures, a porosity of 0.6 was more effective. These findings confirm the feasibility of the proposed device and provide valuable insights into optimizing porous media properties to enhance the passive cooling of photovoltaic cells.

Gas Diffusivity of PEM Fuel Cells Gas Diffusion Layer: Experimental Measurement and Modeling

The rate of gas transport through the PEMFC gas diffusion layers (GDL) is a function of GDL morphology, such as thickness, porosity, fibre diameters, tortuosity, etc.. Numerical methods can capture the intricacies of the GDL pore structure. However, the results obtained are specific to the analyzed structure and cannot be generalized to other GDLs due to inherent structural variations among different GDL types. By using a simplified representation of the complex pore structure, analytical methods offer a systematic approach to isolating the impact of each GDL structural property on GDL diffusivity. This understanding can be leveraged to adjust GDL design levers for an optimal GDL design. One of the key GDL structural properties is tortuosity which has a crucial impact on the transport properties of porous media. The literature [1,2,3], however, is unclear on how to relate tortuosity to gas diffusivity, and confusion arises when the two parameters, tortuosity, and tortuosity factor (defined as tortuosity squared) are used interchangeably. Besides tortuosity, varying cross-section areas along the path of gas transport affect the diffusivity of porous media. This parameter is referred to as the constrictivity factor [4].In this study, an analytical method is employed to demonstrate the relationship between diffusivity and tortuosity, resolving ambiguity on this matter. Additionally, a model is proposed to determine the constrictivity factor of GDLs. With the constrictivity factor established, the relative diffusivity of the GDL can be estimated through a closed-form analytical solution. The developed model is validated with data collected using our symmetrical modified Loschmidt cell. The gas diffusivity of several GDL samples has been measured, including TGP-060 with different PTFE contents (5, 10, and 20 wt%) which had relative diffusivities of 0.31±0.02, 0.29±0.02 and 0.26±0.01. In addition, experimental data available in the literature on TGP-060, TGP-090 and TGP-120 are also used for the validation of the developed model. The results show that the model estimates diffusivity of the sample with a maximum relative difference of 15%.[1] Flückiger, Reto, Stefan A. Freunberger, Denis Kramer, Alexander Wokaun, Günther G. Scherer, and Felix N. Büchi. "Anisotropic, effective diffusivity of porous gas diffusion layer materials for PEFC." Electrochimica Acta 54, no. 2 (2008): 551-559.[2] Inoue, Gen, Takashi Yoshimoto, Yosuke Matsukuma, and Masaki Minemoto. "Development of simulated gas diffusion layer of polymer electrolyte fuel cells and evaluation of its structure." Journal of Power Sources 175, no. 1 (2008): 145-158.[3] Tjaden, Bernhard, Dan JL Brett, and Paul R. Shearing. "Tortuosity in electrochemical devices: a review of calculation approaches." International Materials Reviews 63, no. 2 (2018): 47-67.[4] Van Brakel, Jaap, and P. M. Heertjes. "Analysis of diffusion in macroporous media in terms of a porosity, a tortuosity and a constrictivity factor." International Journal of Heat and Mass Transfer 17, no. 9 (1974): 1093-1103. Figure 1

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

Evaporation 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..

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.

Multi‐Scale Soil Salinization Dynamics From Global to Pore Scale: A Review

AbstractSoil salinization refers to the accumulation of water‐soluble salts in the upper part of the soil profile. Excessive levels of soil salinity affects crop production, soil health, and ecosystem functioning. This phenomenon threatens agriculture, food security, soil stability, and fertility leading to land degradation and loss of essential soil ecosystem services that are fundamental to sustaining life. In this review, we synthesize recent advances in soil salinization at various spatial and temporal scales, ranging from global to core, pore, and molecular scales, offering new insights and presenting our perspective on potential future research directions to address key challenges and open questions related to soil salinization. Globally, we identify significant challenges in understanding soil salinity, which are (a) the considerable uncertainty in estimating the total area of salt‐affected soils, (b) geographical bias in ground‐based measurements of soil salinity, and (c) lack of information and data detailing secondary salinization processes, both in dry‐ and wetlands, particularly concerning responses to climate change. At the core scale, the impact of salt precipitation with evolving porous structure on the evaporative fluxes from porous media is not fully understood. This knowledge is crucial for accurately predicting soil water loss due to evaporation. Additionally, the effects of transport properties of porous media, such as mixed wettability conditions, on the saline water evaporation and the resulting salt precipitation patterns remain unclear. Furthermore, effective continuum equations must be developed to accurately represent experimental data and pore‐scale numerical simulations.

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.

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.