Search Results (291)

Hydrological processes in dryland urban lakes are jointly shaped by landscape pattern dynamics and water resource scarcity, yet the spatial correspondence between landscape fragmentation and lake ecological water demand remains poorly understood. This study took Yuehai Lake, a typical dryland urban lake in Northwest China, as a case study. Landscape pattern analysis was integrated with a water balance model to quantify ecological water demand and its spatial correspondence with landscape metrics. The model coupled the Penman–Monteith equation, a depth-modified evaporation model, and a Darcy’s Law-based zonal seepage calculation. Results showed that: (1) the landscape structure remained highly stable over 2014–2022, with the Aggregation Index ranging from 95.07% to 95.28% and the Largest Patch Index from 90.20% to 90.70%; (2) the annual ecological water demand for maintaining ecosystem integrity was estimated at 2036.97 × 10⁴ m³, comprising inherent lake water volume of 1138.02 × 10⁴ m³ (55.9%), evapotranspiration of 659.72 × 10⁴ m³ (32.4%), and lakebed seepage of 239.23 × 10⁴ m³ (11.7%); and (3) evapotranspiration was concentrated between May and August, accounting for 80.5% of annual losses, with water surface evaporation dominating the flux at 91.5%. These findings suggest a spatial correspondence between landscape metrics and ecological water demand components, providing quantitative support for differentiated water supplementation strategies in dryland urban lakes. Full article

In this work, we develop a coupled multiphysics model that integrates polymer carriers exhibiting time-dependent thixotropic structural recovery with Darcy flow, linear Biot poroelasticity and advection–diffusion transport in a spherically symmetric, isotropic and homogeneous tissue domain. The formulation explicitly links rheological evolution to pressure-driven flow, interstitial deformation and solute transport through a unified framework, enabling systematic prediction of post-injection behavior. Unlike conventional approaches that assume constant carrier properties, the present model incorporates a time-dependent viscosity evolution, capturing the transition from an initially shear-thinned state to a recovered, highly viscous structure. Numerical simulations using hydroxypropyl methylcellulose and methotrexate parameters as representative components demonstrate that rapid post-injection viscosity recovery suppresses pressure-driven transport and diffusion, thereby enhancing local drug retention near the injection site. A systematic sensitivity analysis identifies the equilibrium viscosity as the dominant parameter controlling spatial localization, whereas tissue mechanical properties exert a comparatively minor influence. An effectiveness metric based on the Kullback–Leibler divergence reveals a tumor-size-dependent trade-off between spatial coverage and retention. The proposed framework thus introduces a predictive tool for analyzing coupled rheological-transport interactions and for the rational design and optimization of thixotropy-enhanced local chemotherapy strategies. Full article

This paper presents a comprehensive numerical modeling of nonlinear groundwater flow in a synthetic heterogeneous four-layer porous medium. Multilayered aquifer systems present significant modeling challenges due to nonlinear filtration and interlayer exchange processes. The mathematical model consists of four coupled nonlinear parabolic partial differential equations, where the nonlinearity arises from the dependence of hydraulic conductivity on hydraulic head. Vertical exchange between layers is described by Darcy’s law through separating aquicludes. The system is solved using a fully implicit finite-difference scheme by employing an alternating-direction implicit approach, resulting in a block-tridiagonal system of equations. The model is verified using analytical solutions and mass conservation tests. Application to a synthetic aquifer system demonstrates the model’s ability to reproduce complex transient behavior, including delayed response of upper layers to pumping and asymmetry of water-level drawdown cones due to nonlinear conductivity. The model’s greatest sensitivity is observed to the conductivity of the pumped layer and the vertical conductivity of the separating layers. The proposed approach represents a robust tool for groundwater management in structurally complex geological settings. Full article

Long-term monitoring of surface-water dynamics in hyper-arid reservoir systems requires consistent remote-sensing methods that can distinguish open water from bright desert surfaces, shallow water, wet sand, and mixed shoreline pixels. This study evaluates Landsat-derived spectral water indices for delineating surface water in Lake Nasser and the adjacent Tushka Lakes, generates a multi-decadal record of surface-water extent using Google Earth Engine, and places the resulting surface-water patterns in the context of available hydrogeological observations. Landsat TM and OLI surface reflectance imagery was used to compare seven commonly applied water indices (NDWI, EWI, NDX, WRI, AWEI_nsh, TCW, and NWI) based on mapped water area, relative area differences, and classification accuracy metrics derived from 1000 stratified reference samples. Among the tested indices, NDWI provided stable water–land separation (overall accuracy ≈ 93.6%; κ ≈ 0.898) and was selected for long-term mapping. The NDWI-based workflow was implemented in Google Earth Engine to generate quarterly composites of surface-water extent for the period 1987–2026. The resulting time series reveals stable, persistent surface water in the central and southern sectors of Lake Nasser, in contrast to pronounced seasonal and interannual variability in the shallow, intermittently connected Tushka basins. Total mapped water area increased from 2631 km² in 1987 to 8923 km² in early 2026, with Lake Nasser ranging from 2411 to 6060.7 km² and the Tushka Lakes expanding from no mapped water before 1998 to more than 3300 km² during 2025. To assess possible surface–subsurface interaction, daily lake-stage records (1965–2014) and monthly groundwater levels from 44 observation wells were used to estimate potential seepage losses from Lake Nasser to the Nubian Sandstone Aquifer System using Darcy’s law. Annual seepage estimates ranged from 15.58 × 10⁶ to 36.68 × 10⁶ m³/year, suggesting spatial variability in potential lake–aquifer seepage along the western lake margin. The combined remote-sensing and hydrogeologic results provide complementary, non-causal evidence for interpreting where surface-water persistence and estimated seepage may co-occur. Because spatial correlation analysis, calibrated ground-water modeling, full water-budget analysis, and independent field validation were not performed, the inferred seepage–surface-water relation should be regarded as a cautious hypothesis rather than proof of causality. Full article

Graphite foil is widely used as a sealing material in flange joints in the form of gaskets or gasket components. Predicting gasket permeability during stress relaxation remains challenging because both the compression state and the gas pressure affect leakage. No unified semi-empirical model based on the Darcy–Klinkenberg framework with compression pressure as a direct input has been available for use in flange-joint numerical simulations. Graphite foil gaskets with a density of about 1.0 g/cm³ and a thickness of ~1.5 mm were tested under compression pressures from 5 to 100 MPa. Helium leakage was measured at helium pressures from 0.5 to 8 MPa. Leakage and deformation during loading and unloading were recorded using EN 13555-based procedures. The results were analyzed using a Darcy–Klinkenberg formulation and equivalent slit- and capillary-based representations of the leakage channels. The second-order model reproduced the pressure-dependent leakage more accurately than the first-order Darcy approximation (R² ≥ 0.9985 vs. 0.916–0.992), particularly where slip-flow effects were significant. Exponential dependences of the intrinsic permeability and the Klinkenberg coefficient on deformation and power-law relations with compression pressure are proposed to model leakage during unloading. The proposed semi-empirical model allows estimation of graphite-foil permeability under stress relaxation with the use of EN 13555 test procedures and its subsequent implementation in numerical simulations of flange joints. Limits of the model’s applicability, including loading regime, ranges of compression pressure, gas pressure and anisotropic nature of permeability, are discussed. Full article

Slug test interpretation can fail when measured recovery follows a time scale that differs from the classical Cooper–Bredehoeft–Papadopulos (CBP) finite-diameter well solution. This study derives a conformable slug test formulation by showing that a local weighted derivative converts the governing problem into the classical solution evaluated in transformed time. The formulation therefore does not introduce a nonlocal memory kernel; instead, it provides a reproducible diagnostic with one fitted exponent for testing power law time scaling while retaining the finite-diameter wellbore storage boundary condition. The solution is evaluated using double-precision Stehfest numerical inversion with 12 terms and is verified by the exact classical limit and by sensitivity tests on the number of inversion terms. Type curves, Morris sensitivity indices, objective function slices, synthetic benchmarks, and measured slug test data from the Minnelusa and Madison aquifer system near Spearfish, South Dakota, are used to evaluate the added exponent. A benchmark with an exponent above one recovered fitted exponents of 1.397 without noise and 1.417 under Gaussian noise with a standard deviation of 0.01. Field fitting over exponents from 0.5 to 2.0 reduces root mean square error and information criteria relative to the classical model for the analyzed datasets, especially the LA-88B pressure tests. However, exponents above one are interpreted only as accelerated transformed time behavior, not as conventional fractional orders or unique physical mechanisms. Comparison with a published semi-analytical slug test model that represents near-well formation damage and non-Darcy flow for the same field dataset supports using the conformable exponent as a diagnostic indicator of time-scale mismatch alongside mechanistic slug test models. Full article

Mass transfer is central to food processing but remains difficult to quantify because food materials are heterogeneous, multiphase, porous, biologically structured, and dynamically changing. Under these conditions, experiments alone cannot fully capture the spatiotemporal complexity of transport behavior, making modeling and simulation essential for mechanism interpretation, process prediction, and engineering optimization. Existing reviews mainly address specific operations or numerical methods, with limited synthesis of governing equations, simulation workflows, application implementation, and practical applicability. This review examines food mass transfer by linking coupled momentum, heat, and mass transfer laws with governing equation selection, simulation workflow, and representative food processing applications. Governing formulations for Fickian diffusion, conservation-based transport, heat–mass coupling, multicomponent transfer, Darcy-type porous-medium flow, and related model extensions are summarized, together with their assumptions, geometric applicability, and dimensionless criteria. A unified simulation workflow is then organized, covering transport type identification, governing equation and physical model selection, geometric representation, parameter determination, initial and boundary condition specifications, numerical method and simulation tool selection, numerical implementation, validation, and transferability assessment. Representative applications are discussed for drying, heat–mass coupled processes, multicomponent transfer, transport in porous foods, and redistribution in multi-ingredient or multilayer foods. Overall, future progress requires more integrated, structure-aware, experimentally validated, transferable, and application-oriented simulation frameworks. Full article

Quantitative characterization of permeability in porous media represents a central problem in filtration theory, geosciences, and materials engineering. Standard numerical approaches, including finite element methods and Lattice Boltzmann simulations, typically require extensive domain-specific expertise together with specialized computational software. This motivates the development of computationally simpler and more accessible geometric approaches applicable directly to binary volumetric data. We introduce a novel algorithmic framework for the analysis of porous structures that reformulates permeability-related characterization in terms of discrete geometry and graph-based computation. The method combines parallel raster-grid and graph representations of a binarized three-dimensional CT image. The principal transport-limiting feature of the pore network, interpreted as the minimal constriction governing connectivity, is identified through iterative morphological dilation coupled with a three-dimensional scanline seed-fill procedure. In addition, a dichotomous bisection strategy is proposed to accelerate the determination of the critical bottleneck scale. The proposed methodology was evaluated on five volumetric datasets of size 100 × 100 × 100 voxels obtained from CT-derived porous structures. Experimental results demonstrate that dilation- and erosion-based formulations yield equivalent estimates of the bottleneck parameter in four of the five investigated samples. Furthermore, incorporation of the bisection optimization reduces computational time in three-dimensional experiments by approximately 50% relative to sequential iteration. The presented approach provides a computationally efficient and fully open-source alternative to conventional physics-based permeability solvers for binary porous media. The resulting bottleneck parameter b should be interpreted as a discrete geometric invariant characterizing the pore-network connectivity and minimal transport cross-section. It is not intended to replace the absolute permeability coefficient K appearing in Darcy’s law, but rather to serve as an independent structural descriptor suitable for comparative and topological analysis of porous systems. Full article

Capillary imbibition is the process by which liquids are absorbed into porous materials as a result of capillary pressure differences at the pore scale. Accurate characterization of imbibition dynamics, particularly in the presence of gravitational potential, is essential for understanding fluid transport in diverse systems such as soil, fractured rocks, filtration media, and plant roots. This study presents systematic imbibition experiments using filter papers with pore sizes of 2.5 µm, 11 µm, and 20 µm, each inclined at 80° to quantify the influence of gravitational potential on imbibition behavior. For horizontally positioned samples, the imbibition front propagated radially and symmetrically, exhibiting a power law dependence on time. The measured temporal exponents ranged from 0.386 to 0.403, consistently lower than the theoretical value of 1/2 predicted by the Lucas–Washburn law. With increasing permeability, the temporal exponent approached the Washburn limit, indicating a marked dependence of imbibition dynamics on pore structure. For the inclined configuration at an 80° angle, the imbibition fronts remained nearly circular but exhibited a pronounced displacement of the front center toward gravity. This displacement increased with permeability, from approximately 0.497 cm for the 11 µm filter paper to 3545 cm for the 20 µm filter paper, highlighting the combined effects of permeability and gravitational potential on fluid movement. Furthermore, the advance of the imbibition front was significantly slower in the smallest pores (2.5 µm) compared to the larger ones. Experimental results were evaluated against a theoretical model proposed by Medina, demonstrating moderate quantitative agreement at early times, when gravitational potential effects are less significant. These findings confirm that both the temporal scaling exponent and the spatial evolution of the imbibition front are governed by the porous medium’s permeability and inclination angle, providing experimental evidence of deviations from ideal Washburn behavior in real porous systems. Full article

Fluid flow in fractured-vuggy carbonate reservoirs is characterized by extreme multiscale heterogeneity, where the coexistence of tight matrix rock and macroscopic cave challenges traditional Darcy-based continuum models. This paper presents a semi-analytical solution for two-phase immiscible displacement in a one-dimensional composite porous–cavernous–porous (PCP) system. The main feature of the model is that the cave region is treated separately from the porous domains: classical Darcy flow is used in the surrounding matrix, whereas an idealized free-flow representation is introduced for open caves based on a simplified one-dimensional treatment of the cave momentum balance. To elucidate the impact of distinct flow regimes on displacement dynamics, three physical models are compared for the cave region: (1) an open-cave model represented by a simplified free-flow formulation; (2) a filled-cave non-Darcy model governed by the Forchheimer equation using the Ergun correlation; and (3) a creeping-flow model governed by Darcy’s law. A piecewise semi-analytical solution procedure is established to enforce flux continuity, characterize interfacial state remapping, and determine the downstream front under global water-balance closure. The results show that both cave geometry and internal cave-flow mechanism critically control water-front advancement. While the open-cave model exhibits piston-like displacement behavior with high local displacement efficiency but stronger preferential flow, the Forchheimer model shows that inertial resistance can modify the saturation profile and delay breakthrough relative to the Darcy prediction. The proposed framework provides an idealized theoretical reference for benchmarking numerical simulators and for interpreting waterflooding behavior in complex vuggy reservoirs under one-dimensional, incompressible, gravity-free, and capillarity-free conditions. Full article

This paper probes into the propagation characteristics of Rayleigh waves in a partially saturated, porous, thermo-viscoelastic half-space, with full consideration of the fractional viscoelastic effect and thermal coupling effect. A fractional Zener model is introduced to depict the thermo-viscoelastic mechanical behavior of the solid skeleton by constructing a complete set of governing equations that include mass balance, generalized Darcy’s law, momentum balance, and generalized heat conduction. Field equations are derived by means of Helmholtz vector decomposition, and the dispersion equation, and the phase velocity expression of Rayleigh waves are obtained by combining the traction-free and adiabatic boundary conditions of the medium. The impacts of key material properties, such as medium saturation, intrinsic permeability, medium viscoelasticity, and thermal expansion coefficient, on the dispersion feature of Rayleigh waves are discussed in detail. Numerical analysis results show that an increase in the thermal expansion coefficient will lead to a rise in Rayleigh wave phase velocity, in which the increase in P1 compressional wave velocity plays a dominant role among the velocities of various types of waves. Meanwhile, the attenuation coefficient of Rayleigh waves presents a decreasing trend and gradually tends to be stable with the growth of the thermal expansion coefficient. Similarly, the phase velocity of Rayleigh waves also increases with the rise in fractional order index, which is jointly dominated by the velocity enhancement of P1 waves and S waves. In addition, the attenuation coefficient of Rayleigh waves increases first and then decreases with the increase in fractional order index and reaches the peak value when the fractional order index is about 0.4. The research results reveal the influence of laws of thermal expansion characteristics and viscoelasticity on Rayleigh wave propagation and provide theoretical support for the analysis of wave propagation characteristics in porous media in relevant engineering applications. Full article

Dominant flow channels formed during the late stages of waterflooding can severely reduce sweep efficiency and intensify ineffective interwell circulation. Conventional identification approaches, including tracer testing, well testing, and numerical simulation, often suffer from high operational cost, long execution time, or limited adaptability to heterogeneous interwell connectivity. Although ant colony optimization (ACO) is suitable for path-search problems in reservoir networks, its performance depends strongly on hyperparameter settings, and sample-by-sample parameter tuning introduces substantial online computational overhead. This study proposes a structure-informed GIN–ACO framework for adaptive dominant flow channel identification in connection-element reservoir graphs. A physics-constrained benchmark model is first established using Darcy’s law and the connection element method to provide reference flow paths. A geometry-based surrogate model is then developed to approximate flow splitting coefficients efficiently while preserving the main physical trends. Based on graph topology and geometric descriptors, a graph isomorphism network is trained to predict task-specific ACO parameters, replacing iterative online search with direct parameter inference. Experiments on 1000 synthetic reservoir graphs show that the proposed method achieves a 100% success rate with an average online computation time of 143.5 ms, outperforming fixed-parameter ACO, PSO-ACO, and BO-ACO. On 20 semi-realistic SPE10 reservoir models, GIN–ACO achieves a success rate of 92 ± 1% with an average runtime of 160.3 ± 5 ms. Ablation studies further confirm that graph-structure learning, combined topology–geometry features, and GIN-based parameter prediction are essential for robust performance. The proposed framework provides a promising and computationally efficient route for structure-aware dominant channel identification in connection-element reservoir models. Full article

Magmatic–hydrothermal systems transport heat through coupled conduction and buoyancy-driven fluid flow in porous rock, behavior conventionally modeled with grid-based finite-difference simulators such as HYDROTHERM. We demonstrate that a physics-informed neural network (PINN), built on the NVIDIA PhysicsNeMo framework using automatic differentiation and mesh-free collocation, can produce a stable two-dimensional time-dependent solution for a magma-chamber configuration based on the Rio Pisco pluton in the Peruvian Coastal Batholith. Boundary conditions and material parameters are taken from a prior HYDROTHERM study of the same pluton, and 28 temperature samples digitized from that study are used as a supervised constraint. The PINN couples Fourier conduction, advective heat transport, Darcy flow with a temperature-dependent permeability law, and a mass-conservation formulation; the mass-conservation equation is written in two-phase form, but in the regime studied here, the simulation remains below the boiling curve, so the steam-phase saturation stays at zero and the formulation reduces to its single-phase liquid–water limit. The network reproduces the conductive temperature gradient and a directionally consistent buoyancy-driven flow field, with weaker and less organized circulation than the reference simulation, and a cooling time of approximately $1.6\times {10}^5$ years, comparable to the ∼175,000 years reported for the matching $k={10}^{-16}{\mathrm{m}}^2$ HYDROTHERM reference scenario from which the supervised training data was digitized. We discuss the conditions under which the mesh-free, automatically differentiable PINN approach offers a useful alternative to grid-based solvers. Full article

Purely data-driven machine-learning methods are currently limited by weak physical interpretability; meanwhile, the sparsity of well-site data in oil and gas fields further degrades the prediction performance of deep learning models for reservoir seepage simulation. To overcome this bottleneck, this study embeds Darcy’s law-based seepage equations as physical constraints into the loss function of a deep learning framework, thereby constructing a physics-informed neural network (PINN) for seepage flow in porous media of oil and gas reservoirs. Numerical simulations are performed in heterogeneous porous media to compare the predictive performance of the proposed PINN against conventional purely data-driven approaches, via evaluation metrics including the coefficient of determination (R²) and root mean square error (RMSE). The results show that both models achieve comparable predictive accuracy with sufficient training samples. In contrast, the PINN retains high predictive accuracy even with a reduced number of samples, and it delivers prominent superiority under conditions of sparse well data and strong reservoir heterogeneity. This study clarifies the applicable scenarios of the two aforementioned methods (physics-informed neural networks and purely data-driven machine-learning models) for fluid flow simulation in porous media and provides a solid theoretical and technical foundation for the accurate prediction of reservoir seepage fields and the optimization of oil and gas reservoir development. This work also offers a validated physics-constrained deep learning framework to guide the deployment of intelligent algorithms in practical subsurface flow engineering. Full article

Counter-current flow in channels separated by a membrane has been studied by several scientists and researchers. The current study aims to analytically simulate and describe the distribution of pressure, volumetric flow rate, and velocity in square channels separated by a membrane. Consequently, the study was conducted using one-dimensional (1D) analytical solutions to achieve several objectives: avoiding the execution of experimental tests, reducing the effort required for expensive and time-consuming module design, and enabling easy observation of variations in pressure, volumetric flow rate, and velocity. The 1D analytical solution directly simulates flow in square channels separated by a membrane by solving the continuity equation and Darcy’s law, through which pressure, volumetric flow rate, and velocity are calculated. Experimental results were used to validate the 1D analytical solutions. The results of the current study indicate that pressure decreases from the inlet to the outlet of the channel, while the horizontal velocity decreases from the inlet to the midpoint of the channel length and then increases toward the outlet. The 1D analytical solutions show acceptable accuracy when compared with experimental results. Consequently, these solutions can be used to explore and illustrate the distributions of pressure, volumetric flow rate, and velocity in square channels separated by a membrane, enabling the evaluation of hemodialysis prototype module performance and efficiency prior to fabrication. Full article