Colloid Transport and Retention in Constricted Tube Pore Spaces With Diverse Geometries and Orientations

Colloidal contaminants infiltrate and can be attached onto grain surfaces of soils and aquifers, where they may persist. In this study, Lagrangian particle tracking is used to investigate particle trajectories and attachment in pore and fracture spaces modeled as three-dimensional constricted tubes with diverse geometries and orientations relative to gravity. A comprehensive force balance arising from hydrodynamic drag and lift, gravitational settling, Brownian motion, and attractive DLVO interactions is simulated. Results show that the collection efficiency η is primarily governed by the dimensionless settling number S, representing the relative dominance of gravitational over hydrodynamic forces experienced by the particles. High-S scenarios have larger η and are more sensitive to pore orientation, while low-S scenarios are more sensitive to pore geometry. For all scenarios but especially low-S scenarios, the majority of colloid attachment occurs near pore extremities, where fluid velocities are low, such that mechanical remobilization of particles attached is improbable. In low-S scenarios, particles may spread and become immobilized at greater distances from the contamination source owing to lower η, are harder to mechanically remobilize as they attach more disproportionately at pore extremities, and have trajectories more sensitive to minor forces, rendering their environmental fates complex. Characterizing the collection efficiency and deposition morphology for various pore space geometries and orientations is crucial in understanding particle fate and developing continuum-scale models of colloid transport in real soils, where pore spaces are heterogeneous and advection paths are tortuous

PoreSkel: Skeletonization of grayscale micro-CT images of porous media using deep learning techniques

Skeletonization, a crucial step in pore network modeling, traditionally involves the extraction of skeleton pixels from binarized, segmented X-ray images of porous materials. However, this conventional approach often suffers from user bias during segmentation, potentially leading to the loss of essential image details. This study addresses this limitation by developing deep learning model, called PoreSkel, designed to directly perform skeletonization and distance map extraction from unprocessed grayscale images, thus eliminating the need for additional image processing steps. The model was trained, validated, and tested using an expansive databank of micro-CT images from 20 distinct sandstones, carbonates, and sand pack samples, a total of 10,240 images, with each sample represented by a cube of size 5123. A fifth of these images, specifically 15.6 % from sixteen sandstone and sand pack samples, were used for training, while the remainder served for model validation (4.4 %) and extensive testing (80 %). PoreSkel showed an excellent performance, achieving a mean f1-score of 0.964 for skeletonization and an RMSE of 0.057 for distance map extraction during the testing phase. Our assessments revealed that the model is robust to bias toward the majority class, namely the background pixels. Furthermore, the model showed high generality, maintaining its performance when tested using unseen images from three carbonates and an additional sandstone. Notably, PoreSkel effectively handles disruptions caused often by the presence of minerals in pore spaces and perturbations on pore boundaries - a common challenge for the medial axis technique - resulting in fewer nodes (i.e., pore junctions) and pore coordination numbers, but a higher number of connected skeletons. Therefore, PoreSkel provided a more precise and representative pore structures of porous material that is needed for accurate pore network generation and modeling

Model predictive controller of voltage dosage for safe and effective electrochemical treatment of tumors

Electrochemical treatment is an emerging technology that employs direct current to treat cancerous tumors. A significant limitation of this method is the lack of a standardized protocol for voltage application. Our study addresses this by aiming to develop an optimal treatment strategy through the integration of mathematical modeling, numerical simulations, and controller design. We introduce a mathematical model that merges transport equations with electrode kinetics to represent the electrochemical treatment of tumor tissue accurately. The COMSOL software is then utilized to simulate this model, serving as a basis for controller design. Generalized model predictive control is applied to adjust the pH by manipulating the voltage applied to the electrodes. Additionally, we derive a second-order model to predictively characterize the system's behavior. Our designed controller successfully maintains the pH in the electrode's vicinity at a desired level (pH = 2), showcasing robust performance in counteracting disturbances and uncertainties. Analysis of the system's dynamic response reveals an effective ablation zone for the tumor located 5 mm from the anode. By controlling the hydrogen concentration near the anode (up to 5 mm), we ensure the optimal current dosage for efficient tumor ablation, thus minimizing potential harm to adjacent healthy tissues. Our findings offer critical insights into devising an optimal strategy for electrochemical cancer therapy, suggesting significant enhancements in the treatment's efficacy and safety. This proposed method holds promise for broader clinical adoption, potentially revolutionizing electrochemical treatment modalities for cancer

Assessment of a Multi-Layer Aquifer Vulnerability Using a Multi-Parameter Decision-Making Method in Mosha Plain, Iran

In recent decades, there has been a growing emphasis on assessing aquifer vulnerability. Given the availability of spatial data and the GIS advantages, mapping the groundwater vulnerability has become a common tool for protecting and managing groundwater resources. Here, we applied the GIS indexing and an overlay method to explore a combination of the potential contamination factors needed to assess groundwater vulnerability in the Mosha aquifer. The data from a borehole data logger and chemical analysis of spring water show groundwater responses to the surface contaminating sources. To assess the aquifer vulnerability, the potential contaminating sources were classified into three groups, namely (1) geological characteristics such as lithology and structural geology features; (2) the infrastructures induced by human activities such as roads, water wells, and pit latrines; and (3) land use. By considering these components, the risk maps were produced. Our findings indicate that the aquifer is very responsive to the anthropogenic contaminants that may leak into the aquifer from urbanized areas. Additionally, roads and pit latrines can significantly release pollutants into the environment that may eventually leak into the aquifer and contaminate the underlying groundwater resources

Small Object Detection and Tracking: A Comprehensive Review

Object detection and tracking are vital in computer vision and visual surveillance, allowing for the detection, recognition, and subsequent tracking of objects within images or video sequences. These tasks underpin surveillance systems, facilitating automatic video annotation, identification of significant events, and detection of abnormal activities. However, detecting and tracking small objects introduce significant challenges within computer vision due to their subtle appearance and limited distinguishing features, which results in a scarcity of crucial information. This deficit complicates the tracking process, often leading to diminished efficiency and accuracy. To shed light on the intricacies of small object detection and tracking, we undertook a comprehensive review of the existing methods in this area, categorizing them from various perspectives. We also presented an overview of available datasets specifically curated for small object detection and tracking, aiming to inform and benefit future research in this domain. We further delineated the most widely used evaluation metrics for assessing the performance of small object detection and tracking techniques. Finally, we examined the present challenges within this field and discussed prospective future trends. By tackling these issues and leveraging upcoming trends, we aim to push forward the boundaries in small object detection and tracking, thereby augmenting the functionality of surveillance systems and broadening their real-world applicability

Assessing Rheology Effects and Pore Space Complexity in Polymer Flow Through Porous Media: A Pore-Scale Simulation Study

Non-Newtonian fluid flow within porous media, exemplified by polymer remediation of contaminated groundwater/aquifer systems, presents complex challenges due to the fluids' complex rheological behavior within 3D tortuous pore structures. This paper introduces a pore-scale flow simulator based on the OpenFOAM open-source library, designed to model shear-thinning flow within porous media. Leveraging this developed solver, extensive pore-scale flow simulations were conducted on μ-CT images of various real and synthetic porous media with varying complexity for both power-law and Cross-fluid models. We focused on the macroscale-averaged deviation between bulk viscosity and the in-situ viscosity, commonly denoted by a shift factor. We provided an in-depth evaluation of the shift factor's dependency on the fluid's rheological attributes and the rock's pore space complexity. The least-squares fitted values of the shift factor fell in the range of 1.6–9.5. Notably, the most pronounced shift factor emerged for extreme flow behavior indices. Our findings highlight not just the critical role of rheological parameters, but also demonstrate how the shift factor fluctuates based on tortuosity, characteristic pore length, and the cementation exponent. In particular, less porous/permeable systems with smaller characteristic pore lengths exhibited larger shift factors due to higher variations of shear rate and local viscosity in narrower flow paths. Additionally, the shift factor increased as rock became more tortuous and heterogeneous. The introduced pore-scale simulation proves instrumental in determining the macroscopic averaged shift factor. This, in consequence, is vital for precise computations of viscosity and pressure drop when dealing with non-Newtonian fluid flow in porous media

The impact of pore-throat shape evolution during dissolution on carbonate rock permeability: pore network modelling and experiments

Pore network model simulation (PNM) is an important method to simulate reactive transport processes in porous media and to investigate constitutive relationships between permeability and porosity that can be implemented in continuum-scale reactive-transport modeling. The existing reactive transport pore network models (rtPNMs) assume that the initially cylindrical pore throats maintain their shape and pore throat conductance is updated using a form of Hagen-Poiseuille relation. However, in the context of calcite dissolution, earlier studies have shown that during dissolution, pore throats can attain a spectrum of shapes, depending upon the imposed reactive-flow conditions (Agrawal et al., 2020). In the current study, we derived new constitutive relations for the calculation of conductance as a function of pore throat volume and shape evolution for a range of imposed flow and reaction conditions. These relations were used to build animproved new reactive pore network model (nrtPNM). Using the new model, the porosity-permeability changes were simulated and compared against the existing pore network models. In order to validate the reactive transport pore network model, we conducted two sets of flow-through experiments on two Ketton limestone samples. Acidic solutions (pH 3.0) were injected at two Darcy velocities i.e., 7.3 x 10(-4) and 1.5 x 10(-4) m.s(-1) while performing X-ray micro-CT scanning. Experimental values of the changes in sample permeability were estimated in two independent ways: through PNM flow simulation and through Direct Numerical Simulation. Both approaches used images of the samples from the beginning and the end of experiments. Extracted pore networks, obtained from the micro-CT images of the sample from the beginning of the experiment, were used for reactive transport PNMs (rtPNM and nrtPNM). We observed that for the experimental conditions, most of the pore throats maintained the initially prescribed cylindrical shape such that both rtPNM and nrtPNM showed a similar evolution of porosity and permeability. This was found to be in reasonable agreement with the porosity and permeability changes observed in the experiment. Next, we have applied a range of flow and reaction regimes to compare permeability evolutions between rtPNM and nrtPNM. We found that for certain dissolution regimes, neglecting the evolution of the pore throat shape in the pore network can lead to an overestimation of up to 27% in the predicted permeability values and an overestimation of over 50% in the fitted exponent for the porosity-permeability relations. In summary, this study showed that while under high flow rate conditions the rtPNM model is accurate enough, it overestimates permeability under lower flow rates

A computationally efficient modeling of flow in complex porous media by coupling multiscale digital rock physics and deep learning: Improving the tradeoff between resolution and field-of-view

Digital rock physics is at the forefront of characterizing porous media, leveraging advanced tomographic imaging and numerical simulations to extract key rock properties like permeability. However, fully capturing the heterogeneity of natural rocks necessitates imaging increasingly larger sample volumes, presenting a significant challenge. Direct numerical simulations at these scales become either prohibitively expensive or computationally unfeasible due to limitations in resolution and field of view (FOV). This issue is particularly pronounced in carbonate rocks, known for their complex, multiscale pore structures, which exacerbate the resolution-FOV tradeoff. To address this, we introduce a machine learning strategy that merges multiscale imaging data from various resolutions with a 3D convolutional neural network (CNN) model. This approach is innovative in its ability to identify cross-scale correlations, thereby enabling the estimation of transport properties in larger volumes—properties that are difficult to simulate directly—using trainable proxies. The integration of multiscale imaging with deep learning allows for accurate permeability predictions at scales beyond those feasible with traditional direct simulation methods. By employing transfer learning across different scales during the training phase, our multiscale machine learning model achieves robust performance, with an R² exceeding 0.96 when evaluated on diverse lower-resolution domains with larger FOVs. Notably, this method significantly enhances computational efficiency, reducing the computational time by orders of magnitude. Originally developed for the intricate pore structures of carbonate rocks, our approach shows promise for application to a wide range of multiscale porous media, offering a viable solution to the longstanding tradeoff between imaging resolution and FOV in digital rock physics

Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin

A water supply is vital for preserving usual human living standards, industrial development, and agricultural growth. Scarce water supplies and unplanned urbanization are the primary impediments to results in dry environments. Locating suitable sites for artificial groundwater recharge (AGR) could be a strategic priority for countries to recharge groundwater. Recent advances in machine learning (ML) techniques provide valuable tools for producing an AGR site suitability map (AGRSSM). This research developed an ML algorithm to identify the most appropriate location for AGR in Iranshahr, one of the major districts in the East of Iran characterized by severe drought and excessive groundwater consumption. The area’s undue reliance on groundwater resources has resulted in aquifer depletion and socioeconomic problems. Nine digitized and georeferenced data layers have been considered for preparing the AGRSSM, including precipitation, slope, geology, unsaturated zone thickness, land use, distance from the main rivers, precipitation, water quality, and transmissivity of soil. The developed AGRSSM was trained and validated using 1000 randomly selected points across the study area with an accuracy of 97%. By comparing the results of the proposed sites with those of other methods, it was discovered that the artificial intelligence method could accurately determine artificial recharge sites. In summary, this study uses a novel approach to identify optimal AGR sites using machine learning algorithms. Our findings have practical implications for policymakers and water resource managers looking to address the problem of groundwater depletion in Iranshahr and other regions facing similar challenges. Future research in this area could explore the applicability of our approach to other regions and examine the potential economic benefits of using AGR to recharge groundwater