Search Results (585)

Evaluating grouting effectiveness in deep shafts remains difficult because water-control performance is jointly governed by hydraulic response, seepage-path sealing, grout-body quality, and surrounding rock stability under complex hydrogeological conditions. In this study, an integrated evaluation and seepage analysis framework was developed for the Lianhuashan Phosphate Mine shaft project in Zhongxiang City, Hubei Province, China. Multi-source engineering data from hydrogeological observations, geophysical detection, construction records, and laboratory tests were used to evaluate six representative working faces, and a two-dimensional Darcy flow model was established to interpret the seepage-control mechanism. The evaluation results show differences among the treated sections: the auxiliary shaft at the −29.8 m outlet achieved the highest comprehensive score of 74.79, whereas the main shaft at +13 m showed the weakest performance, with a score of 50.16. Overall, three sections were rated as good, two as moderate, and one as poor. The dominant controls on grouting effectiveness are total shaft inflow, surrounding rock integrity/stability, seepage point number, and sealing-related indices. Numerical simulations further show that grouting reduced total shaft inflow from 6.6080 to 2.0198 m³/h, corresponding to a reduction of 69.43%, and shifted the main hydraulic-gradient concentration from the shaft wall to the outer boundary of the grouted ring. Reducing grouting ring permeability from 5.10 × 10⁻¹³ to 1.00 × 10⁻¹⁴ m² further lowered shaft inflow to 0.2929 m³/h and increased water-control efficiency to 95.57%, whereas increasing ring thickness from 8 to 16 m reduced shaft inflow from 2.7063 to 1.7260 m³/h. In addition, moving the water-rich zone away from the shaft reduced total inflow from 2.5503 m³/h at X_f = 10 m to 2.0079 m³/h at X_f = 26 m. These results indicate that effective shaft grouting depends on the coordinated control of inflow suppression, conductive-path sealing, and structural stabilization. The proposed framework provides a practical basis for grouting evaluation and water hazard control in deep shafts under complex hydrogeological conditions. Full article

Fractured coal in the residual-strength stage is a primary medium for gas migration and drainage in deep mining areas. To investigate the hydro–mechanical seepage response of post-peak fractured coal under constant-pressure-difference conditions, triaxial CO₂ seepage tests were conducted on coal specimens collected from the Xinyuan Coal Mine. A Weibull-based damage constitutive model was established to characterize the confining-pressure-induced hysteresis in the damage-evolution path. The flow-rate evolution and Reynolds number analysis indicated that gas flow remained within the linear Darcy regime. A controlled-variable analysis was used to examine the competing effects governing permeability evolution. Mechanical compaction induced an exponential decrease in permeability, whereas the decrease in permeability with increasing pore pressure was interpreted, within the proposed model framework, as the combined effect of possible adsorption-induced matrix swelling and weakened gas slippage. To address the limitations of conventional constant-slip-factor models, a pressure-dependent slip modulation coefficient was introduced into a composite permeability equation incorporating effective stress, adsorption-related deformation, and dynamic gas slippage. Global nonlinear fitting yielded R² = 0.97 and an RMSE of 0.1909, with the residuals generally distributed around zero, supporting the fitting reliability of the model within the investigated stress–pressure range. Response-surface analysis identified mechanical compaction as the dominant controlling mechanism, while adsorption-related deformation and gas slippage acted as secondary correction mechanisms. The proposed framework provides a quantitative basis for distinguishing the mechanical and fluid-related effects governing permeability evolution in post-peak fractured coal. Full article

Fourier Neural Operators (FNOs) exhibit mode saturation on high-contrast inhomogeneous media, and recent multi-scale extensions (MscaleFNO) further worsen out-of-distribution (OOD) generalization. We introduce the Helmholtz Neural Operator (HNO), a physics-informed, FFT-free branch–trunk operator in the DeepONet family, with a hybrid SIREN+learnable-Fourier trunk and a dual-path rank-32 hypernetwork branch, with bounded multiplicative gating on per-mode coefficients. At a matched parameter count (∼1.05 M, five seeds), HNO achieves a 2.6× lower OOD generalization gap than FNO (19.6% vs. 50.6%, $p=1.7\times {10}^{-3}$, Cohen’s $d=5.1$), 5.1× lower than vanilla DeepONet (19.6% vs. 99.9%, $p=8.2\times {10}^{-3}$), and 6.0× lower than MscaleFNO (19.6% vs. 117.4%, $p=2.4\times {10}^{-6}$); MscaleFNO’s deficit grows at 4.2× more parameters, ruling out capacity starvation. HNO is 4.6×/16.4× faster than FNO/MscaleFNO and 64×–245× faster than multi-threaded FD-PML (MKL PARDISO, 12 cores; 183×–698× vs. single-thread scipy.spsolve), making it suitable as a forward surrogate inside many-query workflows. Absolute accuracy on extreme-contrast (15:1) OOD samples is limited (relative $L^2\approx 1$), so HNO is positioned as a many-query surrogate or warm start for refinement loops, not a stand-alone replacement for direct solvers. A scope limitation is that HNO underperforms FNO on elliptic Darcy Flow, confirming specialization for hyperbolic/wave equations rather than universal operator learning. 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

The present computational work investigates the effects of thermal radiation, activation energy, and diffusion-thermo (Dufour) and thermo-diffusion (Soret) effects on 3D doubly diffusive convective rotational streams across a surface contained in a non-Darcian porous structure. The dominating mathematical system is converted into a group of ODEs (ordinary differential equations) by appropriate similarity transformations. The non-dimensional model is solved using the fourth-order Runge–Kutta method with a shooting procedure numerically. For the fields of concentration, temperature, and velocity, the findings are shown visually. The local heat and mass transport rates are given by computed Sherwood and Nusselt numbers. By growing the values of radiation, activation energy parameters, and Soret number, the local rate of heat transfer increases. Nevertheless, as the Soret and activation energy parameter values increase, the mass transfer decreases. The outcome of the present research can be used to model thermal systems. Full article

The spatiotemporal evolution of seepage fields and the associated hydrodynamic risk of subsequent internal erosion pose a critical threat to the structural integrity of marine and hydraulic infrastructure. To quantify these complex fluid–solid interactions, this study develops a high-fidelity numerical model—coupling the Navier–Stokes equations with the Darcy–Forchheimer resistance model and the Volume of Fluid (VOF) method—to investigate transient hydrodynamics within porous foundations under complex geometric and geological boundary conditions. Parametric analyses reveal that spatial porosity distribution fundamentally dictates the system’s seepage capacity; notably, relocating a highly permeable stratum to the shallow sub-surface eliminates upper hydraulic bottlenecks and significantly escalates total volumetric discharge. Furthermore, the study systematically evaluates the hydrodynamic efficacy of multi-dimensional seepage control structures. Results demonstrate that while increasing the vertical depth of a cutoff wall is highly efficient in restricting bulk volumetric flux, it inadvertently induces intense localized streamline convergence and flow acceleration at the structural tip. Conversely, lateral expansion of the wall base, though yielding only a moderate reduction in total seepage, successfully diffuses this concentrated flow and substantially attenuates peak pore fluid velocities. Ultimately, a combined design paradigm is proposed for practical coastal engineering applications: prioritizing vertical penetration to optimize bulk seepage reduction, concurrently integrated with moderate lateral base expansion to redistribute concentrated hydrodynamic shear stresses, thereby minimizing the hydrodynamic potential for localized piping and ensuring long-term stability against seepage-induced degradation. Full article

The hyporheic zone (HZ) mediates biogeochemical exchanges between rivers and aquifers, yet its spatial and temporal dynamics in large, regulated rivers remain poorly characterized due to limitations of point-based measurements. Here, we combined three time-lapse electrical resistivity tomography (T-ERT) surveys with continuous hydrological and hydrochemical monitoring along a meandering reach of the lower Yellow River, generating a two-dimensional, profile-integrated view of HZ geometry under three hydrodynamic states: low flow (1 December 2020), natural rising stage (1 March 2021), and peak stage during the Xiaolangdi (XLD) water-and-sediment regulation (1 July 2021). Absolute tomograms identified two hydrostratigraphic units: an upper sandy-silt cap (35–170 Ω·m) and an underlying sand aquifer (12–35 Ω·m). Percent-difference tomograms, relative to the low-flow baseline, revealed lateral HZ expansion from ~15 m and vertical growth of 2.5 m at the rising stage to ~36 m and 4.5 m at peak stage, with local resistivity decreases exceeding 38%. In contrast, the deeper mixing zone varied by <10% across surveys. Temperature, rainfall infiltration, and groundwater freshening could not explain the observed patterns. These results were corroborated by three independent lines of evidence: lateral conductivity excursions and in-well temperature records at floodplain well W2, and analytical Darcy–Archie calculations, all consistent with the predicted lateral extent and mixing fraction. River stage, amplified by the XLD release, emerged as the dominant control on two-dimensional HZ geometry. This study provides direct empirical evidence of hyporheic dynamics in a large regulated river and demonstrates that T-ERT, supported by sparse hydrological data, offers a minimally invasive and effective tool for characterizing hyporheic zones. 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

Integrating a heat exchanger (HEX) into the cooling duct of a high-power fuel-cell-based aircraft presents a critical trade-off between thermal performance and aerodynamic penalties. The present study addresses this challenge through the design and system-level analysis of a HEX integrated into the cooling duct. Developed as part of the Clean Aviation project FAME, the design features a rectangular inlet, a circular outlet, and a tilted HEX. The evaluation is performed using high-fidelity Large Eddy Simulations (LESs). The HEX is modeled with a porous media approach based on the Darcy–Forchheimer equation, while the simulations are carried out using a self-adapted version of the pisoFoam solver, termed pisoTempFoam, to account for heat transfer. The study reveals that while component-level design choices, such as a straight inlet and tilted HEX configuration, successfully mitigate local flow separation and duct-induced losses, a critical system-level performance issue emerges. The analysis demonstrates that the cooling duct design, when subjected to realistic operational conditions, generates the high pressure head to overcome the resistance of the HEX. The external aerodynamic analysis also indicates that the HEX resistance is a critical factor, and without overcoming it the system fails to capture the required air mass flow rate, compromising thermal management. The findings highlight the necessity to optimize the design, by an adapted duct shape or an auxiliary fan, to overcome the HEX-induced pressure drop. The porous media approach is thereby validated as an effective tool for rapid system-level design analysis, despite its inherent limitation in capturing detailed downstream turbulence. Full article

This paper introduces a topology optimization framework for steady incompressible Stokes flow based on the non-conforming Virtual Element Method, VEM. The proposed framework combines the geometric flexibility of VEM with an optimality criteria update scheme to minimize viscous and Darcy dissipation under a prescribed volume constraint. The method is applied to the Stokes-flow pipe bend benchmark with parabolic inlet velocity, no-slip wall, and prescribed outlet velocity boundary conditions. By allowing general polygonal elements, including concave and semi-structured polygonal meshes, the method alleviates mesh-related restrictions commonly encountered in conventional finite element discretizations. The methodology is demonstrated through Stokes-flow benchmark problems on different polygonal meshes. The numerical results show that the proposed VEM-based formulation can obtain stable and mesh-insensitive optimized flow channels for Stokes-flow topology optimization. This work offers a systematic approach to obtaining accurate, efficient, and mesh-independent optimal designs for complex fluid systems, providing a stable numerical tool for low-energy-consumption flow channel design in microfluidics, heat exchangers, and biomedical engineering. Extensions to Navier–Stokes and non-Newtonian flow models are left for future work. It should be clarified that the proposed method is only validated for steady Stokes flow and has not been validated for complex fluid models including unsteady Navier–Stokes and non-Newtonian flow models; extensions to these complex models are left for future work. 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

Compressed CO₂ energy storage (CCES) in deep sedimentary basins offers a promising option to integrate carbon management with long-duration energy storage. However, most existing subsurface energy-storage studies focus on salt caverns or generic porous reservoirs, while the potential of evaporite-bounded carbonate reservoirs remains insufficiently explored. This study presents the first application-oriented numerical assessment of CCES in Southern Ontario. It investigates the feasibility of CCES in the Upper Silurian Salina Group beneath offshore Lake Huron, focusing on a porous A-2 carbonate interval vertically confined by B and A-2 halite caprocks. A fully coupled three-dimensional thermo-hydro-mechanical model is developed in COMSOL Multiphysics 6.3 to simulate two-phase (brine-CO₂) Darcy flow, heat transfer, and poroelastic deformation under a realistic Michigan Basin stress, pressure and geothermal regime. After an initial cushion-gas stage at 8 kg/s that establishes a caprock-parallel supercritical CO₂ wedge beneath the B-salt, 24 h injection-production cycles are imposed for two years, followed by a five-month high-resolution window. Three well completion strategies are compared: full-length, upper-only, and split (upper + lower) perforations. Results indicate that in all simulations the CO₂ plume stabilizes as a persistent gas cap beneath the B-salt, far-field pressures remain close to hydrostatic, and reservoir deformations are very small, pointing to a substantial geomechanical safety margin. Among the three completion strategies, the split completion provides the best compromise: it maintains high and relatively stable CO₂ production while avoiding the stronger lower-zone depressurisation seen in the full-length case and the more limited working volume of the upper-only case. These findings suggest that a Salina A-2 carbonate reservoir bounded by B and A-2 salts can accommodate cyclic CCES under realistic basin conditions, and that appropriately designed split completions offer a practical balance between storage utilisation and operational robustness in this setting. 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