Search Results (166)

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

Vibration-assisted water flooding (VA-WF) can improve sweep efficiency. However, unclear macro-scale mechanisms limit its wider adoption in heavy oil reservoirs. This study combines previous sandpack experiments with two-dimensional Volume-of-Fluid (VOF) simulations to show how vibrations reshape permeability fields and, in turn, pressure and production behaviour. Heavy oil sandpacks were water-flooded under conditions of no vibration and 2 Hz and 5 Hz axial excitation. Measured injection pressure histories and oil production were used to calibrate a VOF model in which absolute permeability follows a log-normal distribution with directional anisotropy. Only when axial and radial permeabilities were assigned a negative local correlation did the model reproduce key observations: secondary pressure spikes, irregular viscous-fingering morphologies, delayed production drops, and variability in cumulative recovery. Parameter sweeps quantify the sensitivity of VA-WF performance to the variance and correlation of the permeability field, and multiple runs estimate the variability in outcomes introduced by stochastic heterogeneity. This study proposes a transferable workflow—comprising sample testing, parameter inference, and probabilistic simulation—to screen excitation conditions and forecast VA-WF performance prior to field implementation, enabling operators to optimize vibration frequency based on reservoir-specific permeability characteristics and to anticipate production variability under uncertainty. These results highlight the dominant factors affecting swept volume and oil recovery, supporting data-driven decision making in VA-WF projects. Full article

Dust pollution has emerged as a critical issue in a wide range of industrial applications, creating an urgent demand for effective strategies to mitigate particle deposition. Recent experimental studies have demonstrated that superhydrophobic coatings represent a promising class of self-cleaning materials, primarily attributed to their hierarchical rough structures and intrinsically low surface energy. Nevertheless, the underlying self-cleaning mechanisms of superhydrophobic surfaces have not yet been fully elucidated. This work examines particle deposition on superhydrophobic surfaces featuring stochastic roughness distributions through computational modeling. Surface topographies were generated using Fast Fourier Transform techniques. An integrated lattice Boltzmann–discrete element method (LBM–DEM) framework simulated particle transport in superhydrophobic-coated channels. Particle–fluid coupling was achieved via the immersed moving boundary approach, while particle–surface interactions employed a modified Johnson–Kendall–Roberts (JKR) adhesion model. Parametric studies quantified effects of particle size, interfacial energy, flow Reynolds number, and topographical statistics on deposition dynamics. Experimental validation demonstrates good agreement between numerical predictions and measurements. Smaller particles exhibit a lower tendency to deposit on superhydrophobic surfaces, whereas increasing surface energy significantly enhances particle deposition due to stronger adhesion forces and the suppression of particle resuspension. In addition, higher Reynolds numbers effectively reduce particle deposition. The revealed self-cleaning mechanisms provide theoretical guidance for the design of high-performance self-cleaning coatings, and the identified effects of particle and surface parameters offer practical insights for anti-pollution engineering applications. Full article

The differentiation of human embryonic stem cells (hESCs) into primordial germ cell-like cells (PGC-LCs) provides a robust in vitro model to study human germline specification. Here, we present a simple, reproducible, and cost-effective protocol for generating DEAD-box helicase 4 (DDX4)/VASA and Deleted in Azoospermia-Like (DAZL)-positive PGC-LCs from hESCs using a combination of bone morphogenetic protein 4 (BMP4) and conditioned medium (CM) derived from Stage-Specific Embryonic Antigen-4 (SSEA4)-positive human amniotic fluid stem cells (hAFSC-4). Importantly, unlike conventional protocols that rely on embryoid body formation, our method employs adherent cultures for germ cell differentiation. This approach enhances reproducibility by avoiding the spontaneous and stochastic variability inherent to embryoid body formation. This protocol provides a reproducible and physiologically relevant platform for studying human germ cell development in vitro. Full article

Soft-core Coulomb fluids, exemplified by the two-dimensional Gaussian-charge one-component plasma, serve as fundamental benchmarks for both mathematical theory and computational modeling of coarse-grained dynamics, including stochastic density functional theory, dynamical density functional theory, and dissipative particle dynamics. In these systems, the conventional mean-field description, or the random phase approximation (RPA), is frequently employed due to its analytic simplicity; however, its validity is restricted to weak coupling regimes. Here we demonstrate that Coulomb correlations induce a structural crossover to a strongly correlated liquid where the nearest-neighbor distance saturates rather than decreasing monotonically, a behavior fundamentally incompatible with mean-field predictions. Central to our analysis is the emergence of a universal scaling law: when rescaled by the coupling constant, the short-range direct correlation function (DCF) collapses onto a single curve across the strong coupling regime. Exploiting this universality, we construct a closed-form analytic representation of the DCF using a two-Gaussian basis. This compact form accurately reproduces hypernetted-chain radial distribution functions and structure factors while ensuring exact compliance with thermodynamic sum rules. Beyond theoretical elegance, the proposed kernel offers a computationally efficient alternative to RPA-based approximations, enabling real-space dynamical methods to incorporate strong correlations without modifying long-range smoothed-charge electrostatics. Its analytic transparency bridges rigorous integral equation theory and practical dynamical kernels, additionally providing a physics-informed prior for emerging machine-learning models. Collectively, these results establish a mathematically rigorous testbed for advancing the modeling of strongly correlated soft matter systems. Full article

Traditional Turkish marbling (Ebru) art is an intangible cultural heritage characterized by highly asymmetric, fluid, and non-reproducible patterns, making its long-term preservation and large-scale dissemination challenging. It is highly sensitive to environmental conditions, making it enormously difficult to mass produce while maintaining its original aesthetic qualities. A data-driven generative model is therefore required to create unlimited, high-fidelity digital surrogates that safeguard this UNESCO heritage against physical loss and enable large-scale cultural applications. This study introduces a deep generative modeling framework for the digital reconstruction of traditional Turkish marbling (Ebru) art using a Deep Convolutional Generative Adversarial Network (DCGAN). A dataset of 20,400 image patches, systematically derived from 17 original marbling works, was used to train the proposed model. The framework aims to mathematically capture the asymmetric, fluid, and stochastic nature of Ebru patterns, enabling the reproduction of their aesthetic structure in a digital medium. The generated images were evaluated using multiple quantitative and perceptual metrics, including Fréchet Inception Distance (FID), Kernel Inception Distance (KID), Learned Perceptual Image Patch Similarity (LPIPS), and PRDC-based indicators (Precision, Recall, Density, Coverage). For experimental validation, the proposed DCGAN framework is additionally compared against a Vanilla GAN baseline trained under identical conditions, highlighting the advantages of convolutional architectures for modeling marbling textures. The results show that the DCGAN model achieved a high level of realism and diversity without mode collapse or overfitting, producing images that were perceptually close to authentic marbling works. In addition to the quantitative evaluation, expert qualitative assessment by a traditional Ebru artist confirmed that the model reproduced the organic textures, color dynamics, and compositional asymmetrical characteristic of real marbling art. The proposed approach demonstrates the potential of deep generative models for the digital preservation, dissemination, and reinterpretation of intangible cultural heritage recognized by UNESCO. Full article

We analyze the formal equivalence between the electromagnetic energy conservation law derived from Maxwell’s equations in an optical microcavity and the conservation of a probability fluid associated with the de Broglie–Bohm theory for an effective massive particle describing a photon in this cavity. This work is part of a critical analysis of recent experiments by Sharoglazova et al. carried out with a view to refuting the de Broglie–Bohm theory. Furthermore, the consequences of our analysis for microphotonics go far beyond these experiments. In particular, extensions that take into account photon spin and stochastic aspects associated with radiative or absorption losses are considered. From the point of view of symmetries and probability current, here the effective photon behaves like a spin-1/2 particle. Full article

Wind-induced response poses a significant challenge to the stability of extra-large-span heavy steel box girders during synchronous lifting operations. This study adopted a method combining numerical simulation with on-site monitoring to investigate the aerodynamic characteristics the beam during the overall hoisting process of the Xiaotun Bridge. A high-fidelity finite element model was established using Midas NFX 2024 R1, and fluid–structure interaction (FSI) analysis was conducted, utilizing the RANS k-ε turbulence model to simulate stochastic wind fields. The results show that during the lifting stage from 3 m to 25 m, the maximum horizontal displacement of the steel box girder rapidly increases at wind angles of 90° and 60°, and the peak displacement is reached at 25 m. Under a strong breeze at a 90° wind angle and 25 m lifting height, the maximum lateral displacement was 42.88 mm based on FSI analysis, which is approximately 50% higher than the 28.58 mm obtained from linear static analysis. Subsequently, during the 25 m to 45 m lifting stage, the displacement gradually decreases and exhibits a linear correlation with lifting height. Concurrently, the maximum stress of the lifting lug of the steel box girder increases rapidly in the 3–25 m lifting stage, reaches the maximum at 25 m, and gradually stabilizes in the 25–45 m lifting stage. The lug stress under the same critical condition reached 190.80 MPa in FSI analysis, compared with 123.83 MPa in static analysis, highlighting a significant dynamic amplification. Furthermore, the detrimental coupling effect between mechanical vibrations from the lifting platform and wind loads was quantified; the anti-overturning stability coefficient was reduced by 10.48% under longitudinal vibration compared with lateral vibration, and a further reduction of up to 39.33% was caused by their synergy with wind excitation. Field monitoring validated the numerical model, with stress discrepancies below 9.7%. Based on these findings, a critical on-site wind speed threshold of 9.38 m/s was proposed, and integrated control methods were implemented to ensure construction safety. During on-site lifting, lifting lug stresses were monitored in real time, and if the predefined threshold was exceeded, contingency measures were immediately activated to ensure a controlled termination. Full article

The development of highly sensitive approaches for detecting tumor cells in biological samples remains a critical challenge in laboratory and clinical oncology. In this study, we investigated the structural and magnetic properties of iron oxide nanoparticles incorporated into cellulose microspheres of two size ranges (~100 and ~700 μm) and evaluated their potential for targeted tumor cell isolation. In the smaller microspheres, magnetite-based magnetic nanoparticles (MNPs) were synthesized in situ via co-precipitation, whereas pre-synthesized MNPs were embedded into the larger microspheres. The geometrical characteristics of the resulting magnetic cellulose microspheres (MSCMNs) were assessed by confocal microscopy. Transmission electron microscopy and X-ray diffraction analyses revealed an average magnetic core size of approximately 17 nm. Magnetic properties of the MNPs within MSCMNs were characterized using a highly sensitive nonlinear magnetic response technique, and their dynamic parameters were derived using a formalism based on the stochastic Hilbert–Landau–Lifshitz equation. To evaluate their applicability in cancer diagnostics and treatment monitoring, the MSCMNs were functionalized with a TKD peptide that selectively binds membrane-associated Hsp70 (mHsp70), yielding TKD@MSCMNs. Magnetic separation enabled the isolation of tumor cells from biological fluids. The specificity of TKD-mediated binding was confirmed using Flamma648-labeled Hsp70 and compared with control alloferone-conjugated microspheres (All@MSCMNs). The ability of TKD@MSCMNs to selectively extract mHsp70-positive tumor cells was validated using C6 glioma cells and mHsp70-negative FetMSCs controls. Following co-incubation, the extraction efficiency for C6 cells was 28 ± 14%, significantly higher than that for FetMSC (7 ± 7%, p < 0.05). These findings highlight the potential of TKD-functionalized magnetic cellulose microspheres as a sensitive platform for tumor cell detection and isolation. Full article

This study probabilistically assesses magma ascent by modeling dike propagation as a fully coupled fluid-flow, thermo-mechanical problem, explicitly accounting for the stochastic heterogeneity of the crustal host rock. We study felsic (rhyolite) lava flow and two distinct basaltic feeding regimes that correspond to the conditions necessary to produce the contrasting pāhoehoe and ʻaʻā surface morphologies. Basaltic dikes demonstrate high propagation efficiency to the surface (pāhoehoe-feeding regime 99.5%; ʻaʻā-feeding regime 97.5%), whereas rhyolite dikes have an 89% failure rate, attributed to significant friction. Both regimes represent distinct constructal approaches aimed at maximizing flow persistence. The pāhoehoe-feeding regime is a thermally regulated, stable design characterized by low-velocity, cooling-dominated dynamics. Its slow, persistent flow allows for significant conductive heating of the surrounding rock wall, creating an efficient, pre-heated thermal conduit. In contrast, the ʻaʻā-feeding regime is a mechanically dominated design governed by high-velocity, stochastic dynamics. This morphology is driven by forceful flow, and its thermal budget is supplemented by intense viscous dissipation (internal friction). Rhyolite magma flow fails upon losing constructal viability, driven by a coupled mechanical–thermal cascade. The sequence begins when a mechanical barrier halts the magma velocity, which triggers a freezing event and leads to permanent arrest. Full article

To address the challenge of head stability in a biomimetic robotic dolphin during self-propulsion, this study systematically investigates the passive stabilization mechanism of a bio-inspired caudal keel. A combined experimental and computational fluid dynamics (CFD) approach was employed to evaluate four keel geometries across a tail oscillation frequency range of 0.5–2 Hz. The experimental results demonstrate that the optimal keel configuration reduced the standard deviation of the head pitch angle by 20.9% at 2 Hz. CFD analysis revealed a dual stabilization mechanism: an effective keel not only attenuates the intensity of the primary disturbance moment at the driving frequency but, more critically, also enhances the spectral purity of the signal by suppressing high-frequency harmonics and broadband stochastic noise through the systematic reorganization of caudal vortices. A systematic investigation of keel geometry identified non-dimensional height (h/c) as the dominant parameter, with its stabilizing effect exhibiting diminishing returns beyond an optimal range. Furthermore, a quantifiable design trade-off was established, showing an approximate 9.1% increase in the Cost of Transport (CoT) for the most stable configuration. These findings provide quantitative design principles and a deeper physical insight into the passive stabilization of biomimetic underwater vehicles, highlighting the importance of both disturbance intensity and spectral quality. Full article

This study presents an analysis of transverse vibration behavior of a fluid-conveying pipe mounted on an elastic foundation, incorporating both classical analytical techniques and modern physics-informed neural network (PINN) methodologies. A partial differential equation (PDE) architecture is developed to approximate the solution by embedding the physics PDE, initial, and boundary conditions directly into the loss function of a deep neural network. A one-dimensional fourth-order PDE is employed to model governing transverse displacement derived from Euler–Bernoulli beam theory, with additional terms representing fluid inertia, flow-induced excitation, and stochastic force modelled as Gaussian white noise. The governing PDE is decomposed via separation of variables into spatial and temporal components, and modal analysis is employed to determine the natural frequencies and mode shapes under free–free boundary conditions. The influence of varying flow velocities and excitation frequencies on critical buckling behavior and mode shape deformation is analyzed. The network is trained using the Resilient Backpropagation (RProp) optimizer. A preliminary validation study is presented in which a baseline PINN is benchmarked against analytical modal solutions for a fluid-conveying pipe on an elastic foundation under deterministic excitation. The results demonstrate the capability of PINNs to accurately capture complex vibrational phenomena, offering a robust framework for data-driven modelling of fluid–structure interactions in engineering applications. Full article

This paper presents a comprehensive study of nonlinear wave and oscillator dynamics under both deterministic and stochastic influences. By comparing soliton-like and dispersive waveforms, we employ spectral solvers, Darboux transformations, and nonlinear diagnostics, including Lyapunov exponents, power spectral analysis, and multidimensional phase-space reconstructions, to examine transitions from quasiperiodic motion to chaotic and stochastic regimes. The results highlight the robustness of soliton solutions in preserving energy and structure, in contrast to the degradation observed in dispersive waves under noise and damping. We also show that spectral broadening, entropy growth, and ergodic phase-space patterns are caused by the critical influence of initial conditions and noise intensity on system behavior. Incorporating control strategies such as OGY chaos control, this work provides a flexible framework for analyzing, modeling, and stabilizing nonlinear systems. Applications span nonlinear optics, fluid flows, and electrical lattices, offering insight into the interplay of nonlinearity and noise with implications for both theoretical understanding and practical system design. Full article

This study presents a novel framework for uncertainty propagation in power-law, Bingham, and Casson fluids through rectangular ducts under stochastic viscosity (Case I) and pressure gradient conditions (Case II). Using the computationally efficient Stochastic Finite Difference Method with Homogeneous Chaos (SFDHC), validated via comparison with quasi-Monte Carlo simulations, we demonstrate significantly lower computational costs across varying Coefficients of Variation (COVs). For viscosity uncertainty (Case I), results show a 0.54–2.8% increase in mean maximum velocity with standard deviations reaching 75.3–82.5% of the COV, where the power-law model exhibits the greatest sensitivity (velocity variations spanning 71.2–177.3% of the mean at COV = 20%). Pressure gradient uncertainty (Case II) preserves mean velocities but produces narrower and symmetric distributions. We systematically evaluate the effects of aspect ratio, yield stress, and flow behavior index on the stochastic velocity response of each fluid. Moreover, our analysis pioneers a performance hierarchy: Herschel–Bulkley fluids show the highest mean and standard deviation of maximum velocity, followed by power-law, Robertson–Stiff, Bingham, and Casson models. A key finding is the extreme fluctuation of the Robertson–Stiff model, which exhibits the most drastic deviations, reaching up to 177% of the average velocity. The significance of fluid-specific stochastic analysis in duct system design is underscored by these results. This is especially critical for non-Newtonian flows, where system performance and reliability are greatly impacted by uncertainties in viscosity and pressure gradient, which reflect actual operational variations. Full article

Spiral concentrators are gravity and centrifugal force-based devices designed for mineral concentration. During processing operations, dynamic variations in the slurry’s liquid film thickness can induce hydrodynamic instability, generating roll waves on the free surface that compromise particle separation efficiency. To ensure operational stability and efficacy, this study establishes a theoretical shallow-water flow model for slurry dynamics in spiral concentrators based on hydraulic principles. Through L₂₇(3¹³) orthogonal experiments and real-time ultrasonic film thickness monitoring, the influence of key parameters on roll wave evolution is quantified. Results indicate that roll waves follow an “instability-development-dissipation” sequence. The pitch-to-diameter ratio (P/D) exerts a highly significant effect on roll wave intensity, while particle properties (density and size) exhibit moderate significance. In contrast, feed flow rate and solid concentration show negligible impacts. Roll waves amplify fluid turbulence, triggering stochastic migration of particles (especially low-density grains), which increases the standard deviation of zonal recovery rates (ZRR) and degrades separation precision. This work provides critical insights into particle behavior under roll wave conditions and offers a theoretical foundation for optimizing spiral concentrator design and process control. Full article