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Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis.

A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN.

The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R²) is 0.93 for the validation set, and the R² for the test set A and test set B are 0.93 and 0.94, respectively.

This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications.

Lattice Boltzmann method (LBM) is employed to explore friction factor of single-phase fluid flow through porous media and the effects of local porous structure including geometry of grains in porous media and specific surface of porous media on two-phase flow dynamic behavior, phase distribution and relative permeability. The paper aims to discuss this issue.

The 3D single-phase LBM model and the 2D multi-component multi-phase Shan-Chen LBM model (S-C model) are developed for fluid flow through porous media. For the solid site, the bounce back scheme is used with non-slip boundary condition.

The predicted friction factor for single-phase fluid flow agrees well with experimental data and the well-known correlation. Compared with porous media with square grains, the two-phase fluids in porous media with circle grains are more connected and continuous, and consequently the relative permeability is higher. As for the factor of specific porous media surface, the relative permeability of wetting fluids varies a little in two systems with different specific surface areas. In addition, the relative permeability of non-wetting fluid decreases with the increasing of specific surface of porous media due to the large flow resistance.

Fluid-fluid interaction and fluid-solid interaction in the SC LBM model are presented, and schemes to obtain immiscible two-phase flow and different contact angles are discussed. Two-off mechanisms acting on the wetting fluids is proposed to illustrate the relative permeability of wetting fluids varies a little in two systems with different specific surface.

Natural convection from a semi‐infinite vertical plate embedded in a fluid saturated porous medium is studied both analytically and numerically. The plate is assumed to be heated isothermally or by a constant heat flux. The porous medium, modeled according to Darcy’s law, is anisotropic in permeability with its principal axes oriented in a direction that is oblique to the gravity vector. In the large Rayleigh number limit, the governing boundary‐layer equations are solved in closed form, using a similarity transformation. Comparisons between the numerical solution of the full equations and analytical solutions are presented for a wide range of the governing parameters. The effects of the anisotropic permeability ratio K^*, of the orientation angle of the principal axes θ, and of the Rayleigh number R_H on the flow and heat transfer are investigated. Results indicate that the anisotropic properties of the porous medium considerably modify the heat transfer, velocity and temperature profiles from that expected under isotropic conditions.

The aim of this study is to numerically simulate the density-driven convection in heterogeneous porous media associated with anisotropic permeability field, which is important to the safe and stable long term CO₂ storage in laminar saline aquifers.

The study uses compact finite difference and the pseudospectral method to solve Darcy’s law.

The presence of heterogeneous anisotropy may result in non-monotonic trend of the breakthrough time and quantity of CO₂ dissolved in the porous medium, which are important to the CO₂ underground storage.

The manuscript numerically study the convective phenomena of mixture contained CO₂ and brine. The phenomena are important to the process of CO₂ enhanced oil recovery. Interesting qualitative patterns and quantitative trends are revealed in the manuscript.

The objective of this paper is to provide a better understanding of the effect of irregular columnar jointed structure on the permeability and flow characteristics of rock masses.

An efficient numerical procedure is proposed to investigate the permeability and fluid flow in columnar jointed rock masses (CJRMs), of which the columnar jointed networks are generated by a modified constrained centroid Voronoi algorithm according to the field statistical results. The fractures are represented explicitly by using the lower-dimensional zero thickness elements. And the modeling scheme is validated by a benchmark test for flow in fractured porous media. The effective permeability and representative elementary volume (REV) size of CJRMs are estimated using finite element method (FEM). The influences of joint density and variation coefficient of columnar joint structure on the permeability of the rock mass are discussed.

The simulation results indicate that the permeability is scale-dependent and tends to be stable with increase of model size. The hydraulic REV size is determined as 3.5 m for CJRMs in the present study. Moreover, the joint density is a dominant factor affecting the permeability of CJRMs. The average permeability of columnar jointed structures increases linearly with the joint density under the same REV size, while the influence from the coefficient of variation can be neglected.

The present paper investigates the REV size of the CJRMs and the effect of joint parameters on the permeability. The proposed method and the results obtained are useful on understanding the hydraulic characteristic of the irregular CJRMs in engineering projects.

The purpose of this paper is to form a numerical simulation method for permeability coefficient that can consider the characteristics of gravel gradation and further explore the effects of indoor test factors and gradation characteristics on the permeability coefficient of gravel.

The random point method is used to establish the polyhedral gravel particle model, the discrete element method (DEM) is used to construct the gravel permeability test sample with gradation characteristics and the finite element method is used to calculate the permeability coefficient to form a DEM-computational fluid dynamics combined method to simulate the gravel seepage characteristics. Then, verified by the indoor test results. Based on this method, the influence of sample size, treatment method of oversize particles and the content of fine particles on the permeability coefficient of gravel is studied.

For the gravel containing large particles, the larger size permeameter should be used as far as possible. When the permeameter size is limited, the equal weight substitution method is recommended for the treatment method of oversized particles. Compared with the porosity, the pore connectivity has a higher correlation with the permeability coefficient of the sample.

Insufficient consideration of the movement of gravel particles in the seepage process is also an issue for further study.

The simulation method described in this paper is helpful for qualitative analysis, quantitative expression of pore size and makes up for the defect that the seepage characteristics in pores cannot be observed in laboratory tests.

The purpose of this paper is to present a fully automated numerical tool for computing the effective permeability of porous media from digital images which come from the modern imagery technique.

The permeability is obtained by the homogenization process applied to a periodic rigid solid in which the fluid flow is described by the Stokes equations. The unit cell problem is solved by using the Fast Fourier Transform (FFT) algorithm, well adapted for the microstructures defined by voxels.

Various 3-D examples are considered to show the capacity of the method. First, the case of flow through regular arrays of aligned cylinders or spheres are considered as benchmark problems. Next, the method is applied to some more complex and realistic porous solids obtained with assemblies of overlapping spherical pores having identical or different radii, regularly or randomly distributed within the unit cell.

The use of FFT allows the resolution of high-dimension problems and open various possibilities for computing the permeability of porous microstructures coming from X-ray microtomography.

Porous medium has always been introduced as an environment for increasing heat transfer in cooling systems. However, increase in heat transfer and resolving pressure drop in the fluid flow have been focused on by researchers.The purpose of this paper is to study the effects of creating porous micro-channels inside porous macro-blocks to optimize system performance in channels.

To simulate flow field, a developed numerical code that solves Navier–Stokes equations by finite volume method and semi-implicit method for pressure linked equations (SIMPLE) algorithm will be used together with bi-disperse porous medium (BDPM) method. Working fluid is air with Pr = 0.7 in laminar state. Influence of permeability changes by creation of micro-channels containing porous medium in vertical, horizontal and cross-shape patterns will be investigated.

By creating porous micro-channels inside macro-blocks, not only does the heat transfer increase significantly but the pressure also drops remarkably. Increase in performance evaluation criteria (PEC) is more evident in lower Reynolds numbers that can increase the PEC to 75 per cent by creating cross-shape micro-channels. By changing the permeability of micro-channels, PEC will increase by reducing the pressure drop but it has minor changes in Nu.

The current work is applicable to optimizing system performance by decreasing the pressure drop and increasing the heat transfer.

The developed patterns are useful in increasing the system performance including the increase in heat transfer and decrease in pressure drop in systems such as air coolers required in electrical circuits.

Development and optimization of system performance by new patterns using BDPM in comparison to the previous patterns.

The purpose of the paper is to conduct a numerical and experimental investigation into the properties of nanofluids containing spherical nanoparticles of random sizes flowing through a porous medium. The study aims to understand how the thermophysical properties of the nanofluid are affected by factors such as nanoparticle volume fraction, permeability of the porous medium, and pore size. The paper provides insights into the behavior of nanofluids in complex environments and explores the impact of varying conditions on key properties such as thermal conductivity, density, viscosity, and specific heat. Ultimately, the research contributes to the broader understanding of nanofluid dynamics and has potential implications for engineering and industrial applications in porous media.

This paper investigates nanofluids with spherical nanoparticles in a porous medium, exploring thermal conductivity, density, specific heat, and dynamic viscosity. Studying three compositions, the analysis employs the classical Maxwell model and Koo and Kleinstreuer’s approach for thermal conductivity, considering particle shape and temperature effects. Density and specific heat are defined based on mass and volume ratios. Dynamic viscosity models, including Brinkman’s and Gherasim et al.'s, are discussed. Numerical simulations, implemented in Python using the Langevin model, yield results processed in Origin Pro. This research enhances understanding of nanofluid behavior, contributing valuable insights to porous media applications.

This study involves a numerical examination of nanofluid properties, featuring spherical nanoparticles of varying sizes suspended in a base fluid with known density, flowing through a porous medium. Experimental findings reveal a notable increase in thermal conductivity, density, and viscosity as the volume fraction of particles rises. Conversely, specific heat experiences a decrease with higher particle volume concentration.xD; xA; The influence of permeability and pore size on particle volume fraction variation is a key focus. Interestingly, while the permeability of the medium has a significant effect, it is observed that it increases with permeability. This underscores the role of the medium’s nature in altering the thermophysical properties of nanofluids.

This paper presents a novel numerical study on nanofluids with randomly sized spherical nanoparticles flowing in a porous medium. It explores the impact of porous medium properties on nanofluid thermophysical characteristics, emphasizing the significance of permeability and pore size. The inclusion of random nanoparticle sizes adds practical relevance. Contrasting trends are observed, where thermal conductivity, density, and viscosity increase with particle volume fraction, while specific heat decreases. These findings offer valuable insights for engineering applications, providing a deeper understanding of nanofluid behavior in porous environments and guiding the design of efficient systems in various industrial contexts.

The focus is set on the development and evaluation of a numerical mgodel describing the impregnation stage of a method to manufacture fibre reinforced polymer composites, namely the vacuum infusion process. Examples of items made with this process are hulls to sailing yachts and containers for the transportation industry. The impregnation is characterised by a full 3D flow in a porous medium having an anisotropic, spatial‐ and time‐dependent permeability. The numerical model has been implemented in a general and commercial computational fluid dynamic software through custom written subroutines that: couple the flow equations to the equations describing the stiffness of the fibre reinforcement; modify the momentum equations to account for the porous medium flow; remesh the computational domain in each time step to account for the deformation by pressure change. The verification of the code showed excellent agreement with analytical solutions and very good agreement with experiments. The numerical model can easily be extended to more complex geometry and to other constitutive equations for the permeability and the compressibility of the reinforcement.