Search Results (224)

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

Proton exchange membrane water electrolysis is a promising technology for large-scale green hydrogen production due to its high efficiency, compact design, and rapid dynamic response. However, its commercialization is strictly limited by high material costs, durability issues, and complex multiphysics coupling within the membrane electrode assembly. This work provides a comprehensive and critical review of key physicochemical processes and advanced predictive modeling approaches for PEMWEs. To capture recent paradigm shifts, we introduce an innovative multi-dimensional classification framework—incorporating spatial resolution, temporal dynamics, and methodological paradigms—to critically evaluate lumped-parameter, continuum, microscale, and multiscale models, explicitly defining their applicability bounds and inherent limitations. The fundamental mechanisms governing electrode kinetics, membrane water transport, and gas–liquid two-phase flow are analyzed, establishing state-of-the-art quantitative benchmarks for microstructural parameters and advanced 3D flow field topologies under high-current-density and high-pressure regimes. Furthermore, we systematically examine model validation rigor, typical prediction errors, and the critical failure of static models in capturing dynamic property shifts during extreme bubble breakthrough. Recent breakthroughs integrating in situ diagnostics, pore-scale simulations, density functional theory, and Physics-Informed Neural Networks are extensively discussed. Future efforts must prioritize mechanical–electrochemical–thermal coupling, transient degradation prognostics, and machine learning-driven predictive digital twin technologies to overcome current empirical limitations and accelerate the gigawatt-scale deployment of PEMWE systems. Full article

Polysaccharide-based hydrogels for gastric retention face the inherent challenge of achieving effective retention through swelling while avoiding mechanical failure. Here, we introduce a strategy by incorporating curdlan into chitosan/sodium polyacrylate interpenetrating networks to reinforce the hydrogel and regulate swelling-induced transport behavior. Curdlan-reinforced chitosan/polyacrylate (CS/CUR/PAAS) hydrogels with varying curdlan content (0–4 wt.%) were synthesized and characterized. Optimal reinforcement was achieved with 2 wt.% curdlan, yielding an indentation hardness of ~80 kPa and an elastic modulus of ~63 kPa without compromising swelling capacity. Under acidic conditions (pH 1.2), the hydrogel swelled rapidly (~50-fold at 3 h; ~140-fold at 8 h) while maintaining structural integrity. Using a dynamic in vitro human stomach simulator (DHSI-IV), the optimized hydrogel demonstrated gastric retention for up to 5 h, with ~60% of the initial mass retained at 6 h. Metformin hydrochloride release followed diffusion-controlled kinetics (~69% over 8 h), governed primarily by pH with secondary shear modulation. Microstructural and rheological analyses revealed that acidic conditions regulated network expansion, viscoelastic relaxation, and pore formation, which in turn controlled transport pathways and drug release. The findings highlight that curdlan reinforcement stabilizes swelling behavior under acidic conditions, offering a robust and pH-responsive strategy for designing mechanically stable, gastric-retentive hydrogels. Full article

Granite residual soil slopes are highly water-sensitive and prone to rapid collapse, strength degradation, and rainfall-induced erosion. This study investigates the improvement effects and underlying mechanisms of carboxymethyl cellulose (CMC) on the water stability, mechanical properties, and rainfall erosion resistance of granite residual soil from Fuzhou, Fujian Province, China. Laboratory tests, including unconfined compressive strength (UCS) tests, direct shear tests, disintegration tests, slope rainfall scouring model experiments, X-ray diffraction test (XRD) and scanning electron microscopy (SEM) observations, were conducted to evaluate the performance and microstructural behavior of CMC-stabilized soils. The results indicate that the addition of CMC significantly enhances soil resistance to disintegration: the 24 h disintegration ratio decreased to 0.5% at 0.5% CMC content. The incorporation of CMC can significantly enhance the unconfined compressive strength (UCS) of the soil and lead to an increase in cohesion, while its effect on the internal friction angle is limited. Under simulated rainfall conditions (30° slope, 120 mm·h⁻¹ rainfall intensity, 60 min duration), slopes stabilized with 0.5% CMC exhibited suppressed rill formation and a 47.5% reduction in sediment yield, accompanied by delayed moisture increase at different depths and reduced infiltration rates. Microstructural analyses reveal that CMC hydration forms gel-like films and filamentous bridges, promoting particle aggregation and pore filling, thereby constructing a denser particle network without generating new chemical compounds. This microstructure collectively enhances soil disintegration resistance, mechanical strength, and slope erosion resistance. Full article

Robust, lightweight, and thermally insulating building materials, developed according to the In Situ Resource Utilization (ISRU) paradigm, are essential for enabling Moon settlements. With this aim, we have investigated the formulation and characterization of porous geopolymeric materials based on a lunar regolith simulant, focusing on the influence of surfactants and rheology-modifying additives on pore structure and final material performance. As an optimized procedure, a pre-formed TTAB foam was, in fact, incorporated into the geopolymeric precursor slurries to achieve a suitable porosity. Then, the effects of three thickeners (xanthan gum, bentonite, and Actigel-208) were evaluated in view of the possible utilization for the production of building blocks by 3D printing. Observations of the pore structure after the geopolymeric consolidation of the slurries showed predominantly closed-cell networks across all formulations, with a pore morphology strongly dependent on the thickener used. Xanthan gum promoted high porosity but reduced mechanical integrity, whereas bentonite produced denser structures with higher thermal conductivity. Actigel-208 provided the most balanced performance, combining adequate porosity with improved strength. These findings demonstrate the potential of producing thermally insulating, structurally stable solid foams from lunar regolith simulants via a geopolymerization route. Full article

Managed pressure drilling (MPD) is the core technology for developing formations with high pressure and narrow density windows. It precisely maintains the bottomhole pressure (BHP) within the safe operating window defined by formation pore pressure and fracture pressure by actively regulating the wellbore pressure profile. If pressure control becomes unstable, it can easily trigger gas kicks or lost circulation, posing a severe threat to operational safety. However, existing model predictive control (MPC) schemes have significant limitations: pure data-driven models exhibit poor generalization under complex conditions, while control algorithms based on traditional mechanistic models struggle to meet the stringent real-time requirements of field control cycles due to high-complexity numerical iteration processes. To balance control precision and real-time performance, this paper proposes a physics-informed model predictive control framework (PINC-MPC). During the training phase, physical prior knowledge such as the law of mass conservation is embedded into the neural network as constraints to construct a physically consistent deep surrogate model, enabling it to characterize complex wellbore characteristics. In the control phase, this surrogate model replaces the time-consuming numerical solving process of the mechanistic model within the MPC loop, achieving near-real-time state prediction and rolling optimization while ensuring physical fidelity. Experimental results indicate that PINC-MPC demonstrates superior control performance. Its median single-step solving time is only 16.81 ms, achieving an 11.1-fold acceleration compared to the mechanistic model-based scheme (187.3 ms). In a 5000 s full-cycle closed-loop control experiment, the total time required for the former is only 1.68 s, while the latter reaches 18.73 s, representing an efficiency improvement of approximately 91%. In terms of control accuracy, the integrated absolute error (IAE), reflecting the total deviation of the control process, significantly decreased from 63.40 MPa·s for the industrial successive linearization MPC (SLMPC) to 12.90 MPa·s, an improvement of 79.7%. Especially in extreme dynamic conditions such as simulated pump shutdowns for pipe connections and sudden gas kicks, the framework demonstrates excellent predictive ability and response efficiency. It can proactively trigger compensation actions to keep BHP fluctuations within 0.30 MPa, significantly outperforming the traditional SLMPC method. The research results prove that PINC-MPC provides an efficient, precise, and robust nonlinear control strategy for MPD systems, offering important engineering reference value for enhancing the automation level of intelligent drilling systems. Full article

Modeling the dynamic response of saturated marine soils is crucial yet computationally challenging for traditional methods. Meanwhile, purely data-driven models suffer from sparse data and lack of physical interpretability. To overcome these limitations, this study proposes an intelligent engineering framework based on a frequency-domain physics-informed neural network (FD-PINN) for the forward simulation and inverse parameter identification of saturated seabed soils. Constrained directly by physical laws during the learning process, FD-PINN remains highly reliable even when training data is sparse. By formulating the governing equations in the frequency domain, it directly predicts complex-valued displacement and pore-pressure phasors. Multiscale Fourier feature mappings mitigate spectral bias and capture boundary layers and high-frequency effects. For inverse problems, a phase-sensitive lock-in extraction strategy transforms time-domain measurements into robust frequency-domain targets, enabling the accurate and noise-tolerant identification of poroelastic parameters with clear physical meaning (nondimensional storage parameter S and permeability parameter $\Gamma$). Numerical experiments show that FD-PINN substantially outperforms conventional time-domain PINN, achieving relative $L^2$ errors of ${10}^{-2}\sim {10}^{-3}$ for single- and multi-frequency excitations typical of wave-induced loadings. In particular, $\Gamma$ is consistently recovered with sub-percent relative error, while S can be reliably identified with multi-frequency data. The framework offers a data-efficient, noise-robust approach for high-fidelity modeling and robust parameter inversion, which is particularly valuable in offshore environments where high-quality data is scarce. Full article

Calcium–silicate–hydrate (C-S-H), the primary binding phase governing cement paste cohesion, undergoes progressive physicochemical transformation upon carbonation—a process that critically dictates concrete durability in atmospheric environments. When CO₂ penetrates the porous cement matrix, it triggers a cascade of degradation mechanisms: calcium leaching decalcifies the C-S-H structure, inducing polymerization of silicate chains from dimeric to longer-chain configurations, while concurrent precipitation of calcium carbonate and amorphous silica gel fundamentally reconstitutes the nanoscale architecture. These nanoscale alterations propagate to macroscopic property evolution, manifesting as initial strength and stiffness gains due to pore-filling carbonation products followed by eventual deterioration as the cohesive binding network deteriorates. This review synthesizes current understanding of carbonation-induced structural evolution, examining the coupled influences of environmental parameters—CO₂ concentration, relative humidity, and temperature—alongside C-S-H intrinsic chemistry (Ca/Si ratio, aluminum substitution, and alkali content) on reaction kinetics and material performance. However, significant knowledge gaps persist: predictive models for in-service carbonation rates remain elusive due to the disconnect between idealized laboratory conditions and the heterogeneous, cracked reality of field concrete; the causal linkage between nanoscale C-S-H alteration and macroscale cracking patterns along with physical performance is poorly resolved, and most mechanistic studies rely on synthetic C-S-H, neglecting the compositional complexity of real Portland cement systems. We further propose emerging protection strategies, including surface barrier coatings and low-carbon alternative binders (geopolymers, calcium sulfoaluminate cements, carbon-negative materials such as recycled cement), which demonstrate enhanced carbonation resistance. Future research priorities include developing effective coating barriers for carbonation protection, developing operando characterization techniques for real-time reaction monitoring, deploying machine learning algorithms to bridge atomistic simulations with structural-scale predictions, and establishing long-term field performance databases to validate laboratory-derived degradation models. Full article

Granular deposition and grading strongly influence pore-space topology and hence hydraulic conductivity in natural and engineered porous media, yet quantitative links between deposition sequence, particle-scale morphology, pore-network descriptors, and permeability anisotropy remain incomplete. Here, we develop a process-based digital porous-media framework that couples discrete element method (DEM) deposition with pore-network characterization and Darcy-scale permeability evaluation. Two deposition sequences—normal grading (coarse-to-fine) and reverse grading (fine-to-coarse)—are simulated using bi-disperse particle sets with controlled size ratios. To further isolate the role of particle morphology, particle irregularity is parameterized by a Perlin-noise-based shape perturbation factor and incorporated into the DEM-generated packings. For each packing, pore networks are extracted and quantified in terms of pore/throat size distributions and connectivity, while pore-space complexity is measured via box-counting fractal dimension. Single-phase flow is solved under imposed pressure gradient, and intrinsic permeability is computed along three orthogonal directions to evaluate anisotropy. Results show that increasing size contrast reduces porosity, shifts pore and throat distributions toward smaller characteristic radii, increases pore-space fractal dimension, and yields a monotonic permeability reduction. For identical size ratios, reverse grading consistently yields higher permeability than normal grading, suggesting that deposition sequence exerts a strong control on the continuity and efficiency of effective flow pathways at the sample scale. Increasing particle irregularity decreases permeability and systematically modifies permeability anisotropy, transitioning from weak horizontal anisotropy toward near-isotropy and, at strong irregularity, toward preferential vertical permeability. The proposed framework provides a reproducible route to relate depositional history and particle morphology to pore-network structure and directional permeability, offering implications for filtration, packed-bed design, and sedimentary reservoir characterization. Full article

This study investigates the effects of three group IV two-dimensional (2D) materials (graphene, silicene, and germanene) on the sintering process and tensile properties of copper nanoparticle pastes for electronic packaging. Using atomic-scale simulations, we constructed models of pure copper and composite pastes, tracking particle rearrangement, neck formation, and pore closure under identical sintering conditions, followed by uniaxial tensile testing. All composites formed continuous copper networks, with densification rates increasing in the order: graphene < silicene < germanene. The yield strength of the pure copper paste was 2.41 GPa and increased to 2.96, 4.39, and 5.46 GPa with graphene, silicene, and germanene, respectively, corresponding to gains of about 23%, 82%, and 127% relative to pure copper. Increasing the sintering temperature led to a monotonic increase in the tensile strength of the germanene composite, with the highest value being obtained at 650 K. Dislocation and stress field analyses revealed that silicene and germanene strengthen the material by promoting pronounced plastic accommodation in neck regions, whereas graphene mainly redistributes strain along the interfaces and produces a more moderate increase in strength. These findings demonstrate that the strength and deformation mode of copper nano-solder joints can be effectively tuned by selecting the type of 2D filler and optimizing the sintering temperature. Full article

This study introduces a computational method for designing realistic, geometrically controlled three-dimensional (3-D) micromodels of porous media to investigate fluid flow in hydrocarbon reservoirs. The methodology utilizes a virtual framework of cubes where an arbitrary, continuous 3-D pore network is generated via two-dimensional (2-D) sketches. A key strength of this deterministic, cube-by-cube approach is the ability to independently control porosity and permeability by adjusting channel size and connectivity, facilitating the systematic study of spatial heterogeneity. Six digital models were developed with porosities ranging from 18.4% to 44.4%. Unlike traditional stochastic algorithms, this explicit geometric control enabled the accurate extraction of pore volume distributions and the establishment of a robust power-law relationship between localized porosity and specific surface area. Statistical analysis confirmed a linear correlation between porosity and pore dimensions. While focusing on design and validation, these models are 3-D printable and provide exact boundary conditions for CFD simulations. Single-phase simulations confirmed the capability to decouple absolute permeability from porosity. Consequently, this framework bridges the gap between numerical simulations and physical laboratory experiments to optimize Enhanced Oil Recovery (EOR) processes. Full article

Deep shale gas reservoirs in the southern Sichuan Basin (Weiyuan area) exhibit strong heterogeneity and complex pore-fracture networks. Traditional reservoir evaluation methods struggle to accurately capture their microscale pore characteristics and fracability, thereby restricting efficient development and precise sweet spot prediction. Therefore, integrating digital core technology with geological analysis is essential to systematically quantify key reservoir parameters, including microscale pore structure, mineral composition, and brittleness characteristics. To clarify the controlling factors of high-quality deep shale gas reservoirs in the Weiyuan area and assess their exploration and development potential, we performed digital core analysis at micron to nanometer scales. Three-dimensional digital core models of representative deep shale gas wells were constructed. Integrating mineral composition, geochemical characteristics, and pore space features, we discuss the geological conditions for deep shale gas accumulation and the fracability of horizontal wells, and we delineate favorable shale reservoir zones. The results show that digital core technology enables quantitative and visual characterization of each sublayer of the Longmaxi Formation shale reservoir, including mineral types, laminae types, pore-throat structures, and organic matter distribution. From the Long 11-1 sublayer to the Long 11-4 sublayer, the pore-throat radius, total pore volume, total throat volume, connected pore-throat percentage, and coordination number all gradually decrease. In the eastern Weiyuan area, the siliceous components in deep shale gas reservoirs at the base of the Longmaxi Formation are primarily of both biogenic and terrigenous origin. Due to local variations in the sedimentary environment, terrigenous input contributes significantly to the total siliceous content in this region. Although the Long 11-1 sublayer of the Longmaxi Formation is lithologically classified as mud shale, its particle size and mineral composition more closely resemble those of clayey siltstone or argillaceous sandstone, suggesting considerable potential for reservoir space development. Typical wells in the eastern Weiyuan area exhibit distinct lithological characteristics, including coarser grain sizes, stronger hydrodynamic conditions during deposition, and abundant terrigenous clastic supply. The rigid framework formed by silt- to sand-sized particles effectively mitigates compaction, thereby facilitating the preservation of intergranular pores and microfractures. High organic matter abundance, appropriate thermal maturity, and a considerable thickness of high-quality shale ensured sufficient hydrocarbon supply. The main types of natural fractures are intergranular and grain-edge fractures formed by differences in sedimentary grain size, and bedding-parallel fractures generated by hydrocarbon generation overpressure. Based on reservoir mineral composition, pore characteristics, areal porosity, and pore size distribution identified via digital core analysis, the bottom 0–3 m of the Long 11-1 sublayer is determined to be the optimal target interval. By delineating the microscopic characteristics of the shale reservoir and predicting rock mechanical parameters, a fracability evaluation index was established from digital core simulations. This guides the selection of target layers in deep shale gas reservoirs and optimizes hydraulic fracturing design. Full article

Rational design of high-performance viscosifying polymers is critical for enhancing supercritical CO₂ flooding efficiency in enhanced oil recovery (EOR). Traditional experimental and simulation approaches are limited in exploring the vast design space of polymer architecture, flexibility, and intermolecular interactions. This work presents an integrated machine learning (ML) and mesoscopic simulation framework using Dissipative Particle Dynamics (DPD) to accelerate the development of tailored polymeric thickeners. We systematically investigate synergistic effects of linear and branched polymer blends on solvent viscosity under Poiseuille flow, representative of flow in micro-fractures and pore throats. Key molecular descriptors are varied to generate a comprehensive rheological database. This data trains a deep neural network (DNN) surrogate model linking molecular parameters to macroscopic viscosity. The DNN is coupled with gradient ascent optimization for inverse design, enabling rapid virtual screening of thousands of formulations. A focused case study demonstrates that the star-like architectures with associative cores and semi-flexible backbones outperform linear analogs for supercritical CO₂ viscosity enhancement. The optimal candidate—a four-arm star polymer with linear side chains—was validated by DPD simulation. This multiscale “simulation-to-surrogate” methodology bridges molecular design with continuum-scale flow behavior, offering a transformative tool for formulating cost-effective, efficient, and sustainable next-generation EOR chemicals. Full article

Accurate and efficient analysis of embankment seepage is of vital importance for scientific assessment of embankment safety. Conventional numerical simulation techniques for embankment seepage analysis suffer from high computational cost and low efficiency. To address this issue, this paper proposes an embankment seepage response analysis method based on physical information neural network (PINN). Initially, this method considering the fluid–solid coupling and spatial variability of soil parameters of the embankment. Consequently, a numerical simulation method was developed using the finite difference method to analyze the seepage response. On this basis, a neural network loss function for the surrogate model is introduced by integrating the governing equations for fluid–solid coupling of embankments with boundary conditions. This integration incorporates physical restrictions into the seepage analysis, hence improving its interpretability. Furthermore, a feature sequence is derived from the soil parameter field via a Variational Autoencoder (VAE) to diminish input dimensionality and improve training accuracy. The feature sequence and hydraulic loading function as the model input, while the output is the piezometric head obtained from the pore water pressure. The PINN model is trained by numerical simulation results to establish the surrogate model for seepage responses analysis. A case study on the practical embankment engineering is employed to confirm the feasibility and efficacy of the proposed strategy. Comparative tests demonstrate that the PINN surrogate model markedly enhances computational accuracy relative to conventional baseline models. Overall, this approach offers a trustworthy and effective method for rapid and accurate assessment of embankment seepage characteristics. Full article

Rising atmospheric CO₂ levels have increased the demand for robust, scalable adsorbents for practical CO₂ capture and separation. Porous organic polymers (POPs) are attractive candidates because their pore architecture and binding site properties can be precisely tuned via building blocks and linkage formation. This review summarizes experimental and computational studies of azo-linked POPs and, more broadly, nitrogen–nitrogen (N–N) linked systems, emphasizing how synthetic routes, building blocks, and framework topology govern CO₂ uptake. We highlight key synthetic strategies and representative systems, including porphyrin–azo networks, and discuss the relatively sparse experimental literature on alternative N–N linked POPs incorporating azoxy and azodioxy motifs. Emphasis is placed on reversible nitroso/azodioxide chemistry as a potential pathway to ordered porous organic materials. Computational studies provide a practical route to connect structure with adsorption behavior in largely amorphous or partially ordered networks. We review hierarchical workflows combining periodic DFT and electrostatic potential properties, grand canonical Monte Carlo (GCMC) simulations, and binding energy calculations to rationalize trends and identify favorable binding environments. Computational findings demonstrate that pore accessibility and stacking models can strongly influence predicted CO₂ adsorption. This review provides guidelines for designing POPs with enhanced CO₂ adsorption, offering an outlook and discussing challenges for future studies. Full article