Search Results (619)

Research on atomized drag-reducing agent (DRA) delivery in hydrogen-blended natural gas (HBNG) pipelines is limited, although inlet-section droplet transport and wall interaction are of practical importance. A three-dimensional CFD–DPM model of a $15\mathrm{m}$ straight pipe equipped with a pressure–swirl hollow-cone atomizer was developed to evaluate the effects of atomization pressure drop, injection mass flow rate, spray cone angle, nozzle orifice diameter, hydrogen blending ratio, and operating pressure. Numerical robustness was assessed through mesh and parcel-number independence tests, turbulence-model sensitivity analysis, representative simulations at $\mathrm{MPa}$-level operating pressures, and qualitative comparison with published pressure–swirl spray experiments. Increasing atomization pressure drop from $0.5$ to $5\mathrm{MPa}$ reduced the Sauter mean diameter from $120.07$ to $32.85\mathsf{\mu }\mathrm{m}$ and the numerical wall-trapping ratio from 32.96% to 13.68%, with diminishing changes above approximately $2\mathrm{MPa}$. Higher injection mass flow rates increased droplet size and numerical wall trapping, whereas larger cone angles intensified radial migration and caused severe inlet-localized trapping at ${80}^{\circ }$. Nozzle orifice diameter and hydrogen blending ratio showed weaker effects. Increasing operating pressure from $101,325\mathrm{Pa}$ to $2\mathrm{MPa}$ reduced the SMD from $54.60$ to $9.91\mathsf{\mu }\mathrm{m}$ and the numerical wall-trapping ratio from 15.88% to 5.96%. These model-dependent trends require high-pressure spray, flow-loop, or field validation before engineering application. Full article

Doppler Light Detection and Ranging (LIDAR) systems and a mesoscale meteorological model coupled with computational fluid dynamics (CFD) for the monitoring of low-level wind and turbulence have been extensively applied for the Hong Kong International Airport. This study represents the first application in Hong Kong to apply such techniques for the exploration of providing meteorological support for the operation of Unmanned Aircraft Systems (UASs) in a sandbox project in Hong Kong. The flight route under consideration is between the western coast of Hong Kong Island and an outlying island called Lamma Island, with a sea channel in between. Based on the LIDAR observations in three different prevailing wind directions, low-level turbulence may arise from wind flow disruptions by natural terrain and human-made buildings. Simulations of the wind and turbulence are attempted using the GPU-based FastEddy, with the turbulent kinetic energy equation being used to output the eddy dissipation rate (EDR). Comparisons between observed and simulated fields showed broadly consistent patterns across wind speed, wind direction, and EDR. Quantitative validation yielded RMSE of 1.35 m/s for wind speed, 28.4° for wind direction, and 0.032 m²/s² for EDR, with corresponding R² values of 0.72, 0.48, and 0.07, respectively. However, point-to-point comparison as in the scatter plot of the two datasets is still challenging, due to low correlation for EDR. Nonetheless, FastEddy is found to shed preliminary insights to generate reasonable simulations of low-level winds and turbulence to support the operation of UASs for the cases under study. These findings should be considered preliminary and exploratory given the limited number of case studies analyzed. More cases would need to be studied to find out the performance of FastEddy in other meteorological conditions. Full article

The generation of realistic wind speed time histories is essential for wind engineering analysis but remains challenging due to the scarcity of high-quality measured data and the non-stationary, stochastic nature of atmospheric turbulence. While existing artificial intelligence-based data-driven methods in wind engineering mainly focus on conditional forecasting tasks, the unconditional generation of independent wind speed time histories has received limited attention. To address this gap, a novel spectrogram-based generative framework, called DiffWind, is proposed for wind speed time history generation based on denoising diffusion probabilistic models (DDPM). Wind speed time histories are transformed into magnitude spectrograms using the short-time Fourier transform (STFT), modeled in the spectral domain using a U-Net-based diffusion model, and reconstructed through the Griffin–Lim algorithm (GLA). Field-measured wind speed records were employed to tune the STFT-GLA hyperparameters and train the DDPM. The effectiveness and accuracy of the STFT-GLA combination were validated through numerical experiments, while the diffusion-based spectrogram generation was evaluated using quantitative metrics. The results indicate that the proposed framework can generate high-fidelity wind speed time histories that reproduce key statistical, temporal, and spectral characteristics of measured wind data while demonstrating the capability to generate longer-duration records, highlighting its potential for wind engineering applications. Full article

Can the distress of a cryptocurrency be predicted from its market behavior, and is that predictability reliable when it matters most? Using daily data for 609 USD-quoted coins traded on Kraken between 2013 and 2025, we built a survivorship-inclusive coin-quarter panel and model the onset of severe, sustained price distress—a deep, non-recovering drawdown relative to a trailing peak. A panel logit confirmed that realized volatility, illiquidity, weak momentum, and asset youth predict distress, with a coin-stratified cross-validated out-of-sample AUC of about 0.68. Our central contribution was to show that this predictability is regime-dependent. Interactions between coin-level signals and contemporaneous market-wide volatility are jointly significant (likelihood-ratio p < 0.001), and a rolling-origin evaluation reveals prospective accuracy swinging from no better than chance (AUC 0.43) to strong (0.79) across years. This regime-dependence is robust across alternative distress thresholds, regime proxies, data frequencies, cluster-bootstrap inference, and replication on a second exchange (Binance), though the individual signal channels are not. Testing the most natural mechanism—rising cross-asset co-movement in turbulent markets—we find no support. Microstructure-based early-warning signals for crypto distress are thus conditionally reliable: informative in calm markets but unreliable in the turbulent conditions where warning is most valuable. Full article

Methanol is highly soluble and with spreads quickly in natural water bodies, which could bring about serious environmental risks if leaked. In the present work, the transport and diffusion behavior of methanol in an open-channel flume under controlled hydraulic conditions is investigated experimentally. A closed-loop experimental system was designed to mimic the pipeline leakage scenarios and image-based reconstruction methods were applied to quantify the spatiotemporal evolution of the methanol concentration fields. Systematic analysis was performed on the effects of flow velocity, water depth, leakage rate and leakage location. The results indicate that flow velocity is the dominant factor controlling the downstream advective transport, with increasing velocity significantly reducing the downstream extent of high-concentration zones. Water depth affects vertical mixing and dilution capacity, with deeper flows maintaining more persistent plume structures. Higher leak rates result in higher local concentrations and larger near-field contaminated regions. The position of the leakage is also very important for the plume morphology: the boundary effects lead to a limited and asymmetric dispersion when the leakage is close to the boundary, while the dispersion is more symmetric when the leakage is in the middle of the domain. The study highlights the combined roles of advection, turbulent mixing and boundary confinement in governing methanol plume evolution. The results provide experimental evidence for the understanding of soluble pollutant transport mechanisms in open-channel flows under simplified hydraulic conditions. Full article

This study experimentally investigated the wave transmission and setup characteristics of biomimetic submerged structures as Nature-based Solutions (NbSs) for coastal protection. A non-porous monolithic pillar and a highly porous, multi-branched staghorn coral replica were tested in a 2D flume featuring a 1:20 foreshore slope representative of Kuta Beach, Bali. The results revealed a highly divergent, period-dependent hydrodynamic response. Under short-period waves ($T=0.8 \mathrm{s}$), attenuation was density dependent; the porous replica gradually dissipates energy through canopy micro-turbulence, yielding transmission coefficients ($K_t$) ranging from 0.25 to 1.18. Conversely, under long-period waves ($T=1.6 \mathrm{s}$), the attenuation mechanism shifted to density-independent, depth-induced breaking. This establishes a critical hydrodynamic trade-off: higher wave attenuation (lower $K_t$) inherently generates a higher coastal wave setup due to momentum transfer. Crucially, while both structures reduced transmission, the internal porosity of the multi-branched replica facilitated sub-surface return flow, effectively capping the maximum normalized wave setup at 0.09. This represents an 18% reduction in setup-induced coastal hazards compared to the monolithic baseline. To facilitate practical engineering design, new empirical equations ($R^2\approx 0.80$) for predicting $K_t$ were derived, integrating the frontal area index (${\lambda }_f$). Ultimately, these findings demonstrate that multi-branched biomimetic structures provide an optimal NbS design, balancing effective wave energy attenuation with the mitigation of secondary setup hazards for vulnerable coastal regions. Full article

Entrepreneurial marketing (EM) has emerged as a critical strategic orientation for small and medium-sized enterprises (SMEs) navigating volatile markets, yet the mechanisms through which EM is associated with environmental performance of SMEs remain theoretically underdeveloped. Drawing on entrepreneurial marketing theory, dynamic capabilities theory, the resource-based view, the natural resource-based view, and contingency theory, this study proposes and tests a two-stage capability model in which EM is linked to market agility and customer agility as parallel dynamic mechanisms that subsequently build marketing capability, which in turn is associated with environmental performance of SMEs. Market turbulence is examined as a boundary condition moderating the agility-to-capability pathways. Data were collected from 402 SME owners and managers across manufacturing and service sectors in the United Arab Emirates and analyzed using partial least squares structural equation modeling (PLS-SEM) in SmartPLS 4. Results confirm that EM is positively associated with market agility, customer agility, and marketing capability, and that both agility constructs partially mediate the EM-to-marketing capability relationship. Marketing capability shows a strong positive association with environmental performance of SMEs. Market turbulence significantly strengthens the market agility-to-marketing capability and customer agility-to-marketing capability relationships, while its moderating role in the direct EM-to-marketing capability path is not significant. These findings contribute to the entrepreneurial marketing and dynamic capability literature by specifying the organizational mechanisms linking EM to environmental performance and by identifying market turbulence as a selective boundary condition that strengthens agility-driven, but not orientation-driven, capability development. Practical implications for SME managers in emerging market contexts are discussed. Full article

Kadomtsev–Petviashvili (KP)-type nonlinear dispersive wave equations play a fundamental role in the description of weakly nonlinear waves in plasmas, fluids, and nonlinear optical systems. In strongly turbulent or heterogeneous media, however, transport processes often become nonlocal and exhibit anomalous scaling behavior. Such phenomena are naturally described using fractional differential operators. Recent work has significantly advanced the mathematical understanding of fractional Kadomtsev–Petviashvili models by proving the existence of periodically modulated solitary waves and lump solutions, and by analyzing instability and other significant properties. The present paper complements this line of research by combining a plasma-oriented modeling motivation with a variational existence framework, structural properties of traveling profiles, and a Fourier spectral computational pipeline for profile construction and dynamical validation. The mathematical properties of the resulting equation are investigated. In particular, we prove the existence of traveling-wave solutions using a variational formulation and concentration-compactness arguments. To compute these coherent structures numerically, we develop a Fourier pseudospectral Petviashvili iteration scheme adapted to the fractional KP operator. The computed profiles are validated through direct time integration of the governing equation using an exponential time-differencing spectral method. The results demonstrate that fractional dispersive effects (e.g., the dependence on $\alpha$) significantly modify the structure of nonlinear plasma waves and provide a natural framework for describing wave dynamics in turbulent plasma environments. The numerical results include a detailed verification of the predicted algebraic decay law, a convergence study of the Petviashvili iteration, validation against the exact KP soliton, and dynamical stability tests over long time intervals. A quantitative comparison with existing results in the literature is also provided. Full article

To improve the unsteady aerodynamic response of the IAR 330 PUMA rotor, the present analysis provides a two-dimensional (2D) CFD-based framework for rotor blade sections integrated with trailing-edge flaps (TEFs). From a biomimetic perspective, the TEF is treated as an engineering abstraction of the adaptive aft-chord and camber variation observed in natural flyers, providing a controlled morphing envelope for aerodynamic-load regulation. The scientific contribution consists of an integrated assessment of the NACA 13112 section over an extended TEF deflection range, the comparison of several relative TEF chord lengths, and the transfer of the section-level framework to a four-section representation of the IAR 330 PUMA rotor. First, the effect of TEF deflection on the trajectory and strength of the dynamic stall vortex (DSV) is examined for a pitching NACA 13112 airfoil with a chord length of $c=0.6 \mathrm{m}$ and a pitching axis located at $x/c=0.25$. The pitching motion was prescribed in ANSYS Fluent through a user-defined function (UDF), imposing a hysteresis variation of the angle of attack (AoA) from $\alpha =-3°$ to $\alpha =23°$, while flap deflection angle ($\beta$) varied from $\beta =-20°$ to $\beta =8°$, corresponding to upward and downward TEF deflection, respectively. The second part of this study extends the same pitching law to real-scale rotor blade sections under hovering flight conditions. For the rotor simulations, the Multiple Reference Frame (MRF) model was used for the steady-state analysis, whereas a Sliding Mesh interface was adopted for the transient computations. A 2D pressure-based solver was employed, together with the SST k-ω turbulence model, the Unsteady Reynolds-Averaged Navier–Stokes (URANS) formulation, and a coupled pressure–velocity scheme. The rotational speed was set to $\omega =265 \mathrm{RPM}$, corresponding to a local tangential velocity of approximately $U=145 \mathrm{m}/\mathrm{s}$ at the analysed radius of $r=5.225 \mathrm{m}$ and to a local Mach number of $M\approx 0.43$. The ideal-gas assumption and energy equation were employed to account for compressibility effects. Among the investigated IAR 330 PUMA rotor-section configurations, the TEF with a chord length of $c_f=0.25c$ TEF provided the most balanced aerodynamic response, reducing the peak pitching-moment coefficient by approximately $32\%$ relative to the baseline airfoil. Full article

To investigate the coupled evolution of cavity morphology, hydrodynamic characteristics, and motion behavior during the wide-speed-range acceleration of an underwater hypervelocity vehicle, a numerical framework for supercavitating multiphase flow was established by coupling the Improved Delayed Detached Eddy Simulation (IDDES) turbulence model, the Schnerr–Sauer cavitation model, and the Volume of Fluid (VOF) method. Combined with the overset mesh technique and the DFBI six-degree-of-freedom model, the multiphase flow and motion characteristics during acceleration were systematically studied. The results show that the ventilated cavity strongly compresses the natural cavity, leading to a complex gas–vapor–liquid three-phase coexistence structure in the mid-body conical section and stern region, with the ventilated cavity eventually becoming dominant. The drag coefficient exhibits a three-stage evolution associated with cavity development over the conical section, cylindrical section, and the final formation of a supercavity. Once the vehicle is enveloped by the supercavity, pressure drag becomes dominant. Ventilation timing significantly affects supercavity formation and flow stability. Low-speed ventilation reduces drag earlier but prolongs the three-phase coexistence period and cavity formation process, whereas high-speed ventilation promotes the rapid formation of a stable supercavity. The supercavity formation time reaches 0.5 s under ventilation at 30 m/s, which is more than twice the value for ventilation at 70 m/s. Full article

The Vortex Ring State (VRS) is a critical aerodynamic hazard for rotorcraft, characterized by highly unsteady fluid–structure interactions and severe low-frequency vibrations. While data-driven deep learning models have shown promise in aviation state monitoring, their inherent “black-box” nature fundamentally contradicts the stringent interpretability requirements of airworthiness certification. To address this, we propose an “AI for Science” paradigm, investigating whether advanced Vision Transformers (ViT) can autonomously discover underlying aerodynamic mechanisms without human physical priors. First, to ensure absolute data fidelity, flight test datasets of a coaxial unmanned aerial vehicle were rigorously labeled using cross-validation from high-fidelity Computational Fluid Dynamics (CFD) simulations and wind tunnel tests. One-dimensional vibration signals were then transformed into two-dimensional Continuous Wavelet Transform (CWT) spectrograms. By employing Target-Layer Gradient Adaptation (Grad-CAM) techniques, we conducted a systematic comparison between traditional Convolutional Neural Networks (ResNet50) and ViT. The results demonstrate that while CNNs suffer from diffuse attention caused by high-frequency noise, the frozen-backbone ViT model achieves a physically interpretable accuracy of $93.24\%$, while autonomously locking its global attention onto a perfectly horizontal feature band centered at $41.7$ Hz. Crucially, this autonomously discovered feature precisely aligns with the theoretically derived once-per-revolution (1P) fundamental frequency of the rotor’s flap-lag coupling response under VRS aerodynamic turbulence. This research provides direct visual evidence bridging black-box AI decisions with classical fluid mechanics, proposing a “Mechanism-Guided Verification” framework that offers a trustworthy pathway for the future certification of AI in safety-critical aerospace systems. Full article

The quality of meteorological data from a certain type of commercial aircraft flying between Hong Kong and Shanghai is investigated in this study with a special focus on wind-related parameters, including the horizontal wind speed, horizontal wind direction, and eddy dissipation rate (EDR). The novelty of the study is the analysis of flight data on a new route between Hong Kong and Shanghai. The method for calculating the EDR from Quick Access Recorder (QAR) data of the studied aircraft type is first described. Then, we analyze seven flights operating between Hong Kong and Shanghai in 2025, when Hong Kong was affected by two typhoons, Wipha and Ragasa. Both low-level and enroute wind data are considered. The quality of QAR-based wind data is established through comparison with (a) QAR data from other airline flights separated by 10 min and by one runway from the studied aircraft; (b) headwind and EDR observations from Doppler Light Detection and Ranging (LIDAR) systems at Hong Kong International Airport (HKIA); and (c) reanalysis data of a global numerical weather prediction (NWP) model for the enroute phase of the studied aircraft type. The QAR-based wind data is found to have sufficient quality for the study of low-level windshear and turbulence as well as meteorological applications such as upper-air wind monitoring and data assimilation into NWP models. The wind data collected in the enroute phase is studied further by considering an extended period of July and September 2025 with 151 sets of valid QAR data. The horizontal wind speed and wind direction from the QAR are in general agreement with the model reanalysis data, noting the different nature of the matched data (e.g., averaging period, model grid resolution). Full article

Accurate three-dimensional characterization of turbulent flows in natural waterways is essential for the effective design of tidal farms and other critical infrastructure situated along or across rivers. High-fidelity predictions based on the large-eddy simulation (LES) method capture the necessary physics but incur computational costs that hinder rapid scenario testing. Statistically, a relatively long history of instantaneous flow fields is required to generate reliable turbulence statistics, e.g., mean velocity and Reynolds stresses, of river flow. Such a requirement often incurs high simulation runtime and data storage costs. This study seeks to develop a neural network surrogate model that learns from a limited number of instantaneous flow realizations and approximates the outputs of the corresponding time-averaged fields with LES-level accuracy. Such a surrogate would eliminate the need to accumulate extensive ensembles, enabling faster hydrodynamic assessment and making LES-informed analyses more accessible for practical engineering decisions. Full article

Loading fluctuations cause structural responses such as deformations and vibrations on the propeller. Structural response of propellers results in vibrations on the shaft system or even the hull. Considering the demand for structural safety, the correlation between structural response of propellers and inflow conditions is numerically studied in the present paper. The interaction between the propeller and turbulence structures and vortex shedding from upstream structures is considered. Loading fluctuations on the propeller blade are obtained by a turbulence model of improved delayed detached eddy simulations (IDDESs). The deformations and vibrations of propeller blades fixed at their roots are captured considering fluid–structure interaction. Results show that the loading fluctuations and vibrations on the propeller contain tonal components occurring at harmonics of shaft frequency and broadband components. Inhomogeneous inflow amplifies pressure fluctuations as a product of space frequency and shaft frequency (SF). Inhomogeneous inflow also results in more intense fluctuations of velocity in the tip vortex at SF and blade wake at blade passing frequency and encounter frequency. As a result of loading fluctuations, the vibration of the blade is a superposition of excited vibrations and natural vibrations. Inhomogeneous inflow amplifies the vibrations at the encounter frequency. Resonance of the blade can be observed when the excited frequency approaches the first natural frequency. Full article

As a key enabler for carbon neutrality, hydrogen-blended natural gas utilization requires a thorough safety assessment. This study investigates leakage dispersion in a typical kitchen (4 m × 3 m × 2.8 m) using validated CFD methods. Simulations examined coupled effects of hydrogen blending ratios (0–20%), leakage types (pipe cracks/hose pinholes), and ventilation conditions. Results show hydrogen enrichment accelerates overall dispersion but intensifies methane accumulation near the source. In all scenarios, buoyancy-driven stratification formed stable combustible layers (>0.3 m thick) exceeding the mixture-specific lower flammability limit (recalculated using Le Chatelier’s law) after 25 min in confined spaces. While ventilation promoted global diffusion, it created persistent high-concentration zones (≥5% volume fraction) near leakage sources. The spatial overlap of these hazardous clouds with ignition sources significantly increases explosion risks. This research provides critical insights for developing safety standards and emergency protocols for hydrogen–natural gas mixtures. Full article