Search Results (5,913)

Today, microfluidics has become a revolutionary field of engineering due to its wide range of applications, including lab-on-a-chip devices, microscale biochemical reactors, drug delivery systems, and disease diagnostics. Efficient fluid mixing has been a significant challenge in these systems due to the dominance of laminar flow and low-Reynolds number conditions, where mixing relies primarily on slow molecular diffusion. It is very difficult to achieve rapid mixing and homogeneous mixing within a limited length. In this study, a bioinspired passive micromixer is developed based on the cristae architecture of mitochondria, which is known for maximizing surface area and transport efficiency in biological systems. The micromixer incorporates cristae-like microstructures within a straight microchannel to produce continuous flow deflection, stretching, and folding, thereby promoting chaotic advection without relying on external energy sources. It also includes mitochondrial granules, such as micropillars, within the channel to disrupt streamline flow. Thus, a numerical investigation was conducted to design four different micromixer geometries: conventional T-channel, and T-channels with a single, double and triple matrix of cristae. The analysis was performed in COMSOL Multiphysics, in which “Laminar flow” and “Transport of diluted species” physics were used, and a stationary study was executed. Simulations were conducted at different Reynolds numbers (Re = 0.1–100) to observe the feasibility of the proposed designs. For analysis, the mixing index and concentration profiles at the outlet and along the length were also examined. The results showed that the high cristae density channel performed well, achieving a mixing index of 95.85% at Re = 0.1 and 85.84% at Re = 100, proving that the proposed mitochondria-inspired cristae Mito-mixer delivers efficient mixing over a broad Reynolds-number range while maintaining a compact, length-efficient design. Full article

We introduce a minimal relational network model in which superconducting-like behavior emerges as a collective phase of constrained connectivity and phase coherence, without assuming microscopic electrons, phonons, or material-specific interactions. The model is formulated as a concrete instantiation of a previously introduced axiomatic relational–informational framework for emergent geometry and effective spacetime, in which geometry and effective forces arise from constrained information flow rather than from a background manifold. Mathematically, this construction is realized on a finite weighted graph with binary edge-activation variables and compact vertex phase variables, sampled through a Gibbs ensemble generated by an additive informational action. The system is represented as a finite weighted graph with weighted edges encoding transport or informational costs, augmented by dynamically activated low-cost channels and compact phase degrees of freedom defined at vertices. The effective edge costs induce a weighted shortest-path metric, providing an operational notion of emergent relational geometry. Using Monte Carlo simulations on two-dimensional periodic lattices, we show that the same informational action supports three distinct emergent regimes: a normal resistive phase, a fragile low-temperature–like superconducting phase characterized by noise-sensitive coherence, and a noise-robust high-temperature–like superconducting phase in which global phase coherence persists under substantial fluctuations. These regimes are identified using purely relational observables with direct graph-theoretic and statistical-mechanical interpretation, including percolation of low-cost channels, phase correlation functions, an operational phase stiffness (helicity modulus), and a geometric diagnostic based on relational ball growth. In particular, we extract an effective geometric dimension from the scaling of low-cost accessibility balls, using a ball-growth relation of the form $⟨B\left(r\right)⟩~r^{d_{eff}}$, revealing a clear monotonic hierarchy between normal, fragile superconducting, and noise-robust superconducting—like regimes. This demonstrates that superconducting-like behaviour in the present framework corresponds not only to percolation and phase alignment, but also to a qualitative reorganization of relational geometry. Robustness is tested via finite-size comparison between 8 × 8, 12 × 12 and 16 × 16 lattice realizations. Within this framework, normal and superconducting-like behavior arise from the same underlying relational mechanism and differ only in the structural stability of connectivity, coherence, and geometric accessibility under fluctuations. The aim of this work is structural rather than material-specific: we do not reproduce detailed experimental phase diagrams or microscopic pairing mechanisms, but identify minimal relational conditions under which low-dissipation, phase-coherent transport can emerge as a generic organizational regime of constrained relational systems. Full article

Electromagnetic hydrodynamic (EMHD) mixed convective flow of tetra hybrid nanofluid (TeHNF) in a Darcy-Forchheimer porous medium in a vertical channel with thermal radiation is considered in the paper. The electric and magnetic fields are homogeneous, magnetic perpendicular to the walls of the channel, and electric perpendicular to the plane formed by the directions of the magnetic field and the basic current. The channel walls are impermeable, and they are at constant but different temperatures. The basic equations that describe this problem are ordinary nonlinear differential equations (ODEs), and they are transformed into dimensionless ODEs by introducing dimensionless quantities, which are analytically solved using the homotopy perturbation method (HPM). The relations for velocity and temperature distributions, Nusselt numbers and shear stresses on the channel walls were determined. These relations are functions of introduced physical parameters that characterize the observed problem. For TeHNF, where the base fluid is water and the nanoparticles are made of aluminum oxide, titanium dioxide, magnesium oxide and magnetite, a part of the obtained results is given. Velocity and temperature plots are presented in the form of graphs, and Nusselt numbers and shear stresses are presented in the form of tables. Based on the analysis of the obtained results, appropriate conclusions were drawn. It was concluded that an increase in the Hartmann number as well as an increase in the porosity factor decrease the fluid velocity and shear stress, and increase the fluid temperature and Nusselt numbers. Higher values of the Forchheimer factor and higher heat radiation correspond to lower fluid velocities, lower temperatures, lower values of shear stresses and Nusselt numbers. By increasing the value of the Grashof number, the velocity of the fluid increases, and so do the shear stresses. TeHNF shows advantages over simpler hybrid nanofluids and commercial fluids. Full article

Airfoil flow-field prediction is important for aerodynamic design, but wind-tunnel testing and computational fluid dynamics (CFD) remain costly and time-consuming. Deep learning enables fast inference, yet many existing models still rely on fixed grid representations, which may lead to insufficient learning in high-gradient regions and larger local errors. This study proposes Spatial Multilayer Perceptron (Spatial MLP) together with an Error-Gradient-Guided Data Sampling (EGDS) strategy for airfoil flow-field prediction. Spatial MLP adopts a coordinate-based point-wise prediction framework. A spatial decoder is introduced as an auxiliary branch to enhance global flow consistency during pretraining, while channel-wise multi-head attention is incorporated to improve cross-variable feature coupling. EGDS prioritizes physically informative points according to relative prediction error and gradient magnitude, while retaining random samples to preserve data diversity. Experiments on an independent test set show that Spatial MLP reduces the mean relative error (averaged over the velocity components u, v, and pressure p) by 15.2% relative to the MLP baseline. With EGDS, the overall mean relative error is further reduced by 34.5% relative to the MLP baseline. These results demonstrate that combining global consistency constraints with targeted sampling effectively improves both global prediction accuracy and local reconstruction quality in high-gradient flow regions. Full article

Extreme hydrological events fundamentally alter sediment transport dynamics across grain, reach, and watershed scales, rendering classical equilibrium-based transport formulations inadequate. This review synthesizes recent advances in multiscale sediment transport modeling under highly unsteady and high-magnitude forcing conditions. At the grain scale, particle-resolved simulations demonstrate that sediment entrainment is governed by turbulence intermittency and transient force exceedance rather than mean bed shear stress thresholds, particularly when the hydrograph rise timescale (T_h) becomes comparable to particle response times (T_p). At the reach scale, non-equilibrium transport emerges when the unsteadiness ratio T_h/T_a∼O(1), where T_a is the sediment adaptation timescale representing the time required for sediment flux to adjust toward transport capacity. Under these conditions, pronounced hysteresis between discharge and sediment flux is observed, requiring relaxation-based transport formulations instead of instantaneous equilibrium laws. At the watershed scale, the sediment delivery ratio (SDR), defined as the ratio of sediment yield at the basin outlet to total hillslope erosion, becomes highly time-dependent. Extreme precipitation events can activate hillslope-channel connectivity, increasing SDR by orders of magnitude relative to baseline conditions. A unified dimensionless scaling framework is presented based on mobility intensity (θ/θ_c, where θ is the Shields parameter and θ_c is its critical value for incipient motion), unsteadiness ratio (T_h/T_a), and morphodynamic coupling (T_f/T_m, where T_f is the hydraulic advection timescale and T_m is the morphodynamic adjustment timescale). This framework enables classification of sediment transport regimes ranging from quasi-equilibrium to cascade-dominated states. The synthesis demonstrates that predictive uncertainty increases nonlinearly across scales due to timescale compression, threshold activation, and feedback between flow hydraulics and evolving morphology. Recent developments in hybrid physics-AI approaches show promise in improving predictive capability by enabling dynamic transport closures, surrogate modeling of computationally expensive microscale processes, and data assimilation for real-time forecasting. However, these approaches remain limited by extrapolation uncertainty and the need to enforce physical constraints. Overall, this review concludes that regime-aware multiscale coupling, combined with uncertainty quantification and adaptive modeling strategies, is essential for robust sediment hazard prediction and climate-resilient infrastructure design under intensifying hydrological extremes. Full article

The increasing deployment of dense offshore wind turbine monopile foundations pose significant challenges for accurately simulating tidal-flow modification and energy transport at the array scale. Balancing physical realism with computational efficiency remains a key challenge in hydrodynamic modelling of offshore wind farms. In this study, an established drag-based source-term approach is implemented through a dedicated module developed within the TELEMAC-3D framework to represent the momentum-blocking effects of offshore wind-farm arrays. A representative dense 8 × 10 wind turbine monopile array configuration is constructed in a typical tidal channel to systematically examine array-induced tidal-flow responses. The results indicate that the drag-based source-term approach preserves the regional-scale tidal flow structure while effectively capturing array-induced local velocity adjustments and pronounced downstream wake attenuation and recovery. Detailed analyses further reveal distinct spatial and temporal characteristics of the velocity response, including the decay and recovery of velocity deviations downstream of the array. In addition, the monopile array induces a clear modulation of flow kinetic energy, characterized by enhanced energy dissipation and a finite array-scale redistribution of kinetic energy. These findings demonstrate that this approach efficiently simulates the array-scale hydrodynamic and energetic impacts of large offshore wind farms and contribute to a better understanding of array-induced tidal flow modification and energy redistribution. Full article

While check dams are crucial for debris flow mitigation, they face increasing failure risks under extreme weather and seismic activities. Their collapse can severely amplify debris flow magnitude, yet quantitative understanding of this amplification mechanism remains limited. Based on field investigations in southern Gansu, China, and a total of 12 flume experiments (comprising 11 distinct scenarios and 1 representative repeatability test), this study quantitatively assesses the amplification effect of dam breaches under varying channel slopes, check dam types, and bed conditions. Results indicate that dam-breach debris flow evolution comprises three stages: material initiation and deposition, breaching and material release, and recession. Crucially, dam breaching shifts the initiation mode from progressive retrogressive erosion to a near-instantaneous release of mass and potential energy. Compared to no-dam scenarios, breaches amplified peak discharge, erosion rate, and downstream inundated area by factors of 1.65–3.04, 1.44–1.55, and 2.14–2.77, respectively. This amplification is driven by the rapid initial release of material and energy, compounded by erosional entrainment during the transport phase. Furthermore, check dam type and channel slope act as key controlling factors. By revealing how check dams transition from protective structures to hazard sources, this study provides quantitative experimental evidence for optimizing dam design and advancing resilient disaster risk reduction strategies in mountainous regions. Full article

This work numerically reveals a novel strategy to modulate two ionic conductance state in a nanochannel via pressure-dependent fluidic motion inside the channel. Steady and transient simulations based on Poisson–Nernst–Planck–Stokes equations demonstrate that the two states with distinct ionic conductance and ion selectivity can be reversibly switched by external pressure, with a characteristic time of ~100 μs. Furthermore, the two conductance states are found to depend on the transversal electric field, which gives rise to two distinct intrachannel fluidic flow patterns, namely laminar flow and vortex flow, respectively. This finding suggests the potential of pressure-controlled ionic conductance switching for applications in nanofluidic ionic circuits, flow-regulated sensing, and integrated micro/nanoscale devices. It also provides insights into nonlinear ionic current–voltage behaviors. Full article

Carbonate reservoirs exhibit complex combinations of pores, fractures, and vugs, and their strong heterogeneity makes pore-scale displacement mechanisms and recovery enhancement difficult to predict. In this study, six microfluidic glass-etched models representative of pore-type, vuggy, and fracture-pore carbonate reservoirs were designed from cast thin sections of the S oilfield. Experiments were conducted to investigate the effects of different factors on microscopic displacement behavior and residual-oil distribution. The results show that microscopic residual oil in carbonate reservoirs mainly occurs as film flow, droplet flow, columnar flow, multi-pore flow, and cluster flow, with cluster flow dominating the late stage of development in all model types. Under waterflooding, pore-type reservoirs exhibit the most uniform sweep and the highest recovery factor (44.26%), whereas vuggy reservoirs readily develop preferential flow channels and show the lowest recovery factor (41.58%). For fracture-pore reservoirs, injection perpendicular to the fracture provides the best performance, and wider or denser fractures improve displacement efficiency. Compared with gas flooding, waterflooding increases recovery by 10.48% in pore-type reservoirs and by 16.44% in fracture-type reservoirs. High-rate waterflooding and mid-stage flow diversion further improve recovery by 9.05–10.87% and 17.12–19.63%, respectively. These results provide pore-scale evidence for optimizing development strategies for carbonate reservoirs. Full article

Fishways play a critical role in restoring river connectivity and conserving fishery resources, yet their efficiency is often limited by mismatches between hydraulic conditions and species-specific behavioral traits. To quantify the upstream migration behavior of fish under the combined influence of flow heterogeneity and schooling effects, this study examined the endangered species L. elongata in the Yangtze River Basin. Volitional swimming behavior was tested in an open-channel flume under three spatially heterogeneous flow regimes (I: Low–Moderate–High; II: High–Moderate–Low; III: Moderate–High–Low). A video monitoring system recorded the upstream movement of solitary fish and three-individual schools. Swimming trajectories, upstream migration time, preferred flow velocities, and schooling metrics—including nearest neighbor distance (NND) and mean pairwise distance (MPD)—were analyzed. Linear mixed-effects models were employed to account for repeated measures and individual variability. Results showed that schooling behavior significantly enhanced upstream migration efficiency: schooling fish arrived at the target area on average 8.93 s earlier than solitary individuals (p < 0.01), while flow condition alone had no detectable effect on arrival time. L. elongata consistently preferred low-velocity zones (0.20–0.50 m/s) and avoided high-velocity regions (0.75–1.25 m/s), with meandering upstream trajectories predominating. NND showed no significant differences across flow conditions (p > 0.05), indicating stable schooling cohesion. However, MPD increased significantly under Flow III compared to Flows I and II (p < 0.01), suggesting that higher flow heterogeneity leads to more dispersed group spacing while overall cohesion is maintained. Distinct movement strategies were observed: solitary fish predominantly utilized boundary regions as hydraulic refuges (wall-following: 63.8–80.5%), whereas schools exhibited greater spatial exploration and reduced wall-following. These findings demonstrate that schooling enhances migration efficiency while preserving a cohesive group structure and that flow heterogeneity influences within-group spatial organization. To optimize fishway performance for L. elongata, we recommend maintaining flow velocities within 0.20–0.50 m/s. This study provides scientific guidance for hydraulic regulation in fishway design and habitat restoration, emphasizing the combined effects of flow heterogeneity and schooling behavior on migration performance. Full article

The European Union’s Carbon Border Adjustment Mechanism (CBAM), the world’s first system to impose tariffs on the carbon emissions of imported products, commenced its transition period in October 2023 and is scheduled for full implementation in January 2026. This mechanism exerts a profound impact on the global trade landscape and corporate environmental management practices. Taking the CSI All Share Index constituent companies as a research sample, this paper empirically evaluates the impact of the CBAM transition period on the environmental scores of Chinese export enterprises utilizing the Propensity Score Matching Difference-in-Differences (PSM-DID) method. The results indicate that the CBAM transition period significantly inhibits the short-term environmental performance of regulated enterprises. Mechanism analysis reveals that increased financing constraints serve as a core mediating channel, wherein escalated compliance costs and compressed cash flows crowd out resources for low-carbon investments. Furthermore, heterogeneity analysis demonstrates that the negative impact is more pronounced among state-owned enterprises, firms with lower audit quality, and firms with a higher proportion of female executives. Accordingly, the study recommends establishing targeted green transition financing mechanisms, accelerating domestic carbon market reforms, and strengthening international technical harmonization to build corporate resilience against global climate governance shocks and promote sustainable growth. Full article

Two-phase displacement between cement slurry and drilling fluid in wellbore systems is inherently nonlinear, interface-dominated, and strongly affected by geometric confinement, posing substantial challenges to efficient and stable numerical simulation. Conventional CFD approaches rely on mesh discretization and explicit interface tracking, which become computationally demanding and sensitive to grid quality in complex geometries and convection-dominated regimes. To address these limitations, this study develops a unified physics-informed neural network (PINN) framework for directly solving the coupled incompressible Navier–Stokes and Volume of Fluid (VOF) equations governing pressure-driven displacement. The framework is first validated against canonical transient flows and then applied to two-phase displacement in parallel-plate channels, semicircular bends, and a casing–annulus geometry representative of well cementing operations. The predicted velocity, pressure, and volume fraction fields exhibit strong agreement with ANSYS Fluent (2024R1) results, with relative errors generally around 5%, thereby demonstrating physical consistency and numerical stability without mesh generation or pressure–velocity splitting, while also showing favorable computational efficiency for the cases considered. Sensitivity analyses demonstrate that a smoother casing-shoe geometry significantly enhances PINN convergence, while higher Péclet numbers deteriorate training stability by increasing convection-dominated stiffness and optimization difficulty. The results demonstrate that the proposed PINN framework, with its mesh-free and geometrically flexible characteristics, is a promising approach for modeling multiphase displacement in cementing applications. Full article

This research develops a new low-cost method for continuous flow monitoring in open channels. Flow is calculated using a standard 1D hydraulic model that integrates surveyed cross-sections and water level measurements at the boundaries of a studied reach, from which the name Boundary Water Level Method (BWLM) is derived. By implementing low-cost ultrasonic sensors for water level measurement, the method gains advantage for application on smaller channels, which are often not included in national hydrological monitoring networks due to limited budgets. New and innovative monitoring methods in hydrology are a necessary alternative to increasing the monitoring budgets, especially for continuous, real-time flow monitoring. Like any novel method, it requires validation under the intended environmental conditions, especially when designed primarily for ungauged channels. Validation was conducted on two test-sites by comparing the BWLM discharge and the discharge from official hydrological stations, with an error of up to 15%. BWLM provides reliable discharges using estimated hydraulic roughness values based on the literature and experience. Sensitivity analysis of the estimated hydraulic roughness coefficient demonstrated a substantial influence on the resulting discharge values. This has to be considered when implementing the method in unstudied basins. Full article

High-temperature proton exchange membrane fuel cells (HT-PEMFCs) coupled with methanol reforming hold promise for distributed energy systems, yet channel hydrodynamics and geometry optimization remain underexplored. This study develops a 3D multiphysics model to investigate coupled behaviors in HT-PEMFCs fueled by methanol reformate. Results reveal bifurcation-induced Dean vortices have dual effects: they cause flow maldistribution (15–18% velocity deviation) and contribute 50% of inlet pressure loss, while generating a lateral pumping effect that enhances local mass transfer. A continuous parametric sweep of channel widths (0.9–1.9 mm) identifies a voltage-dependent performance crossover—narrower channels (1.3 mm) excel at high voltages by improving electronic conduction, whereas wider channels (1.5 mm) perform better at low voltages by mitigating mass transfer limitations. These findings provide quantitative design criteria for optimizing flow field geometry in HT-PEMFC stacks. Full article

Previous approaches to flexible-lined channel design typically isolate least-cost cross-section optimization from parameter uncertainty, or restrict reliability analysis to specific cases, limited failure modes, and proprietary codes. This paper presents VegyRap, an open-source QGIS-based plugin with an intuitive graphical user interface that unites these traditionally disjointed, sequential tasks into a single computational framework. The tool guides designers sequentially through: (i) terrain-driven longitudinal profile optimization using dynamic programming; (ii) least-cost cross-sectional optimization for riprap and vegetated linings; and (iii) multi-mode probabilistic reliability analysis coupled with dual risk–cost Pareto optimization. To seamlessly handle the stochastic behavior of uncertain variables, the framework features built-in statistical distributions and allows users to flexibly evaluate up to four distinct failure modes: overtopping, erosion, sedimentation, and near-critical flow oscillation. The framework’s capabilities are demonstrated through nine diverse design examples, incorporating benchmark validations against published studies and a comprehensive real-world case study in Wadi Al-Arja, Saudi Arabia. Results highlight that for vegetated channels, a hierarchical two-phase design logic is essential to satisfy both establishment-phase stability (Class E) and long-term conveyance (Class B). While benchmark comparisons show VegyRap achieves consistent cost reductions of 10–15% over traditional methods, the case study demonstrates that deterministic least-cost solutions can carry non-negligible failure probabilities. By utilizing marginal efficiency analysis to identify cost-effective enhancements, the integrated Pareto-based dual optimization produces transparent trade-off surfaces, empowering practitioners to transition from a single least-cost solution to a defensible, risk-calibrated preferred alternative. Full article