Transport in Porous Media最新文献

Understanding the permeability of granular systems is essential for flow-related studies, but it is complex due to the influence of grain morphology. This paper proposes new explicit interpretable permeability and pore models for granular materials using the multivariate adaptive regression splines (MARS) algorithm. The models use a dataset generated in a previous study and establish connections between particle morphology descriptors and permeability parameters, as well as pore indexes. Within this framework, correlation of permeability and pore characteristics is also investigated. Sensitivity analyses are then performed on the developed hydraulic conductivity model. The proposed explicit models are simple in form and have fewer parameters. The models were trained using fivefold cross-validation on 80% of the randomly selected data from the database, and then tested on the remaining 20% of the data. The effectiveness of the models is quantitatively evaluated through statistical indicators. Results show high (R^{2}) values (> 0.73) and low RMSE (close to zero), demonstrating the effectiveness of the proposed models. This work provides valuable insights into the impact of particle morphology on permeability and contributes to the development of explicit permeability models for granular materials.

The prediction of nanoparticle adsorption in porous media plays a crucial role in both scientific research and engineering applications. A novel pore network model is developed to account for the effects of nanoparticle adsorption on porous media structure. In the proposed model, nanoparticle transport is described by the convective diffusion equation and irreversible kinetic equations to calculate adsorption efficiency. Based on the principle of equal hydraulic conductance, an equivalent contraction coefficient is derived for throat diameter. This coefficient characterizes throat contraction due to nanoparticle adsorption and facilitates accurate simulation of the interplay between adsorption processes and fluid flow. An efficient solution is achieved by estimating and correcting transport conductance coefficients. Using the proposed model, the effects of inlet boundary type, injection velocity, and nanoparticle size on the flow field in porous media are investigated. The results indicate that the developed pore network model can accurately reflect the transport and adsorption of nanoparticles. The destruction of porous media by nanoparticle adsorption can be divided into two stages: deep adsorption and surface adsorption. Different driving forces of flow lead to variations in nanoparticle concentration and pressure distribution within the porous media.

The spatial arrangement of catalytic particles is crucial for the optimization of packed-bed microchannel reactors for efficient direct synthesis of hydrogen peroxide, as it largely affects the multiphase flow behaviors within the catalytic particle pattern. In this work, a lattice Boltzmann study is conducted to unravel the effects of packed-bed column structure, porosity, Reynolds number, and gas bubble size on bubble residence time in and contact area with catalytic particles. Various hierarchy column structures are designed and then examined. We find that reducing the porosity or the Reynolds number can prolong the bubble residence time, while increasing the bubble size can shorten the residence time. Double-layer ordered structure design can increase the contact area by 12% and shorten the residence time by 6%, while a random structure leads to a significant reduction in the contact area by 18.79%–32.66%. The three-level structure design further reduces the residence time by 12% and increases the contact area by 8.7%. Notably, the Coarse-Fine-Random structure shows the longest bubble residence time and Fine-Coarse pattern achieves a maximum contact area. The design of hierarchical pore structures can optimize the residence time and contact area of gas and liquid phases and provide a helpful strategy for the optimization of direct hydrogen peroxide synthesis.

The performance of chemical vapor deposition (CVD) systems is critically governed by reactor geometry, flow behavior, and thermal gradients, particularly in vertical configurations tailored for high-aspect-ratio thin-film fabrication. This study presents a comprehensive, multi-scale optimization framework that integrates high-fidelity computational fluid dynamics (CFD) simulations, porous media augmentation, and surrogate modeling to enhance deposition rate and film uniformity. A vertical CVD reactor was first modeled and rigorously validated against benchmark data to ensure accuracy in capturing transport and surface reaction phenomena. To mitigate recirculation zones and improve gas-phase distribution, porous media were strategically embedded within the reactor. This modification resulted in improved thermal and species uniformity, directly influencing film quality. Following reactor enhancement, a systematic parametric study was conducted by varying susceptor temperature and inlet gas velocity. The resulting dataset was used to construct a second-order polynomial regression model, serving as a surrogate for rapid response surface analysis and optimization. To identify the optimal operating conditions, gray relational analysis (GRA) was employed as a robust multi-response optimization technique, effectively reducing the experimental burden while capturing key process interactions. The model demonstrated excellent predictive accuracy and provided valuable insights into process sensitivities. This integrated CFD–surrogate modeling–GRA approach offers a scalable and computationally efficient pathway for optimizing complex CVD systems. The findings underscore the potential of porous medium engineering and data-driven optimization in advancing next-generation thin-film deposition technologies.

This work investigates the influence of particle shape and asperity height on the flow behavior of porous granular media using computational fluid dynamics (CFD). The developed framework includes the generation of random structured particle beds with LIGGGHTS using the Discrete Element Method (DEM) and the subsequent analysis of the pore space in terms of porosity and specific surface area. CFD is then applied to analyze the flow through the pore space at a Reynolds number of (Re=1). In the post-processing, the permeability of the granular porous media is derived and a significant influence of the particle shape and asperity height on the permeability and porosity can be seen. In the end, a comparative analysis of simulations results and analytical models based on Ergun and Carman-Kozeny is conducted. The study reveals that the Carman-Kozeny approach exhibits a remarkable capacity to replicate the influence of particle shape and surface asperity, while the Ergun approach demonstrates a more limited suitability.

The present study investigates the thermal performance of a fully wetted cylindrical porous fin under the combined influence of convection and radiation, considering the temperature-dependent nature of surface emissivity, thermal conductivity and heat transfer coefficient. Despite extensive studies on cylindrical porous fins, the simultaneous variation of these temperature-dependent parameters under natural convection and radiation has not been adequately addressed. The governing energy equation is formulated based on Darcy’s model, incorporating the solid–fluid interaction within the porous medium, and transformed into a nonlinear ordinary differential equation. The equation is solved numerically using the Runge–Kutta–Fehlberg fourth–fifth-order (RKF-45) method. The results reveal that as concective parameter increases, the temperature of the fin decreases by 38.134%. An increase in the radiation parameter and the wet porous parameter results in a reduced temperature profile by 28.011 and 17.895%, thereby promoting fin cooling. Also as the emissivity parameter increases, the temperature of the fin decreases by 1.786%. As the thermal conductivity parameter increases, the temperature increases by 2.879%. The present analysis has wide number of applications in the field of thermal management in electronics, solar collectors, aerospace, gas turbines, nuclear power plants, air conditioners, refrigeration and so on.

This study focuses on performing a linear instability analysis to explore the onset of convective instability arising from the combined effects of vertical throughflow and thermal dispersion in a fluid-saturated porous layer governed by the Darcy–Brinkman model. The considered system is subjected to a temperature gradient, with both walls maintained at distinct constant temperatures. This temperature difference generates buoyancy forces, which act as a driving mechanism for the onset of convection within the system. The stability characteristics of the flow are influenced by several dimensionless parameters, including the Darcy number (Da), the thermal dispersion coefficient (Di), and the Péclet number (Pe). We employed the Chebyshev-tau method, combined with the QZ-algorithm, to numerically solve the generalized eigenvalue problem. The investigation is exemplified through results that highlight scenarios where advection dominates diffusion as well as cases where diffusion prevails over advection. We found that the behavior of thermal convective instability is highly dependent on the Péclet number (Pe). For values of (Pe le 2.5), the system exhibits destabilizing behavior, indicating a greater likelihood of instability. However, for Péclet numbers exceeding 2.5, the system transitions to a stabilizing regime, where the onset of instability is suppressed. This demonstrates a clear threshold at (Pe = 2.5), distinguishing two distinct dynamic behaviors within the system. Additionally, our results indicate that both the Darcy number (Da) and the thermal dispersion coefficient (Di) contribute to stabilizing thermal convective instability. These factors act to reduce the growth rate of disturbances, thereby enhancing the stability of the system under thermal gradients.

Accurately determining the absolute permeability of heterogeneous and anisotropic porous materials, such as sedimentary deposits, is a critical step in pore-scale studies. In recent years, various machine learning models, particularly Convolutional Neural Networks (CNNs), have been employed to predict absolute permeability from X-ray images of porous media. However, these efforts have largely focused on predicting scalar permeability in a single direction, without addressing the full permeability tensor. The CNNs often struggle to capture image orientation, posing challenges for the prediction of tensorial properties such as permeability. To address this limitation, we have developed 3D Capsule Network (CapsNet) regression models to predict permeability tensors from 3D grayscale and binary X-ray images of porous media. We compiled a dataset comprising 3D images from six sandstone types. Corresponding permeability tensors were computed using the Lattice Boltzmann Method (LBM). Subsequently, we customized the CapsNet for a 3D regression problem and trained the model using the generated dataset. Our comparative analysis revealed that CapsNet outperformed CNN, achieving an overall R² score of 0.91 compared to CNN’s 0.86. Importantly, CapsNet demonstrated greater consistency across various rock types and flow directions, whereas CNNs exhibited more variability and generally underperformed. To the best of our knowledge, this study represents the first application of Capsule Networks in the context of porous media analysis. Our findings highlight the superior predictive capability of CapsNets over CNNs, suggesting their potential as a robust alternative for characterizing porous materials in a wide range of applications, including carbon capture and storage, enhanced oil recovery, membrane design, and biomedical studies.

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We present a history matching (HM) workflow applied to the International FluidFlower benchmark study dataset, which features high-resolution images of CO(_2) storage in a meter-scale, geologically complex reservoir. The dataset provides dense spatial and temporal observations of fluid displacement, offering a rare opportunity to validate and enhance HM techniques for geological carbon storage (GCS). The combination of detailed experimental data and direct visual observation of flow behavior at this scale is novel and valuable. This study explores the potential and limitations of using experimental data to calibrate standard models for GCS simulation. By leveraging high-resolution images and resulting interpretations of fluid phase distributions, we adjust uncertain parameters and reduce the mismatch between simulation results and observed data. Simulations are performed using the open-source OPM Flow simulator, while the open-source Everest decision-making tool is employed to conduct the HM. After the HM process, the final simulation results show good agreement with the experimental CO(_2) storage data. This suggests that the system can be effectively described using standard flow equations, conventional saturation functions, and typical PVT properties for CO(_2)–brine mixtures. Our results demonstrate that the Wasserstein distance is a particularly effective metric for matching multi-phase, multi-component flow data. The entire workflow is implemented in a Python package named pofff (Python OPM Flow FluidFlower), which organizes all functionality through a single input file. This design ensures reproducibility and facilitates future extensions of the study.

Foam injection in porous media is a promising technique for enhanced recovery and other industrial applications, but its modeling is complicated by instabilities in the computed foam apparent viscosity. This study investigates oscillations observed during foam displacement simulations. Mesh refinement demonstrates that increasing discretization reduces the amplitude of global oscillations in the average apparent viscosity; however, sharp local peaks persist at shock fronts and may intensify over time. These instabilities are not solely numerical artifacts but are linked to the mathematical structure of the foam model, particularly the presence of steep saturation fronts. We show that numerical diffusion, unavoidable in simulation frameworks, can amplify such effects. To address this issue, we introduce a filtering technique that reconstructs the water saturation profile in the vicinity of the shock without affecting convergence. The method effectively suppresses viscosity oscillations while maintaining physical accuracy near discontinuities.