Search Results (4,539)

Sediment erosion under turbulent wave action is a highly dynamic process shaped by the interaction between wave properties and sediment characteristics. Despite extensive empirical research, the underlying mechanisms of wave-induced erosion remain insufficiently understood, particularly regarding the threshold energy required for particle mobilization and the factors governing displacement patterns. This study employed a custom-built wave flume and a 3D-printed sampler to examine sediment behavior under controlled wave conditions. Rounded glass beads, chosen to eliminate the influence of particle shape, were used as sediment analogs with a similar specific gravity to natural sand. Ten experiments were conducted to systematically assess the effects of particle size, particle number, input voltage (wave power), and water depth on sediment response. The results revealed that (1) only a fraction of particles were mobilized, with the remainder forming stable interlocking structures; (2) the number of displaced particles increased with particle size, particle count, and water depth; (3) a threshold wave power is required to initiate erosion, though buoyancy under shallow conditions reduces this threshold; and (4) wave steepness, rather than voltage or wave height alone, provided the strongest predictor of sediment displacement. These findings highlight the central role of wave steepness in erosion modeling and call for its integration into predictive frameworks. The study concludes with methodological limitations and proposes future research directions, including expanded soil types, large-scale flume testing, and advanced flow field measurements. Full article

Cardiovascular disease diagnosis heavily relies on accurate blood flow assessments, traditionally performed using invasive and often uncomfortable methods like catheterization. This research introduces PPG-Net 4, an innovative deep learning approach for non-invasive blood flow pattern classification using dual photoplethysmography (PPG) signals. By leveraging advanced machine learning techniques, the proposed method addresses critical limitations in current diagnostic technologies. The study employed a novel dual-sensor arrangement capturing PPG signals from two body locations, generating a comprehensive dataset from 75 participants. Advanced signal processing techniques, including mel spectrogram generation and mel-frequency cepstral coefficient extraction, enabled sophisticated feature representation. The deep learning model, PPG-Net 4, demonstrated good capability at classifying the following five distinct blood flow patterns: laminar, turbulent, stagnant, pulsatile, and oscillatory. The experimental results revealed strong classification performance, with F1-scores ranging from 0.86 to 0.92 across different flow patterns. The highest accuracy was observed for pulsatile flow (F1-score: 0.92), underscoring the model’s precision and reliability. This approach not only provides a non-invasive alternative to traditional diagnostic methods but also offers a potentially useful technique for early cardiovascular disease detection and continuous monitoring. Full article

This study examines the evolution characteristics of ship waves generated by large vessels in shallow waters. A CFD-based numerical wave tank, incorporating Torsvik’s ship wave theory, was developed using the VOF multiphase approach and the RNG k-ε turbulence model to capture free-surface evolution and turbulence effects. Results indicate that wave heights vary significantly near the critical depth-based Froude number ($F_h$). Comparative analyses between CFD results for a Wigley hull and proposed empirical correction formulas show strong agreement in predicting maximum wave heights in transcritical and supercritical regimes, accurately capturing the nonlinear surge of wave amplitude in the transcritical range. Simulations of 2000-ton and 6000-ton class vessels further reveal that wave heights increase with $F_h$, peak in the transcritical regime, and subsequently decay. Lateral wave attenuation was also observed with increasing transverse distance, highlighting the role of vessel dimensions and bulbous bow structures in modulating wave propagation. These findings provide theoretical and practical references for risk assessment and navigational safety in shallow waterways. Full article

This study investigates the hydrodynamic loads of “Ningde No. 1” offshore aquaculture under current-only conditions using a fluid–structure interaction (FSI) approach with the computational fluid dynamics (CFD) solver OpenFOAM. A porous-media-based model is applied to simulate net-induced drag, while the rigid framework is resolved using a large eddy simulation (LES) turbulence model. A comprehensive set of 350 CFD simulations is performed, with varying flow velocities, flow directions, draft depths, and existence of nets. The results reveal that the load on this fishing facility in the streamwise direction (Fx) increases monotonically with flow velocity, direction, and draft. The lateral (Fy) and vertical (Fz) loads exhibit non-linear trends, peaking at a specific flow direction (approximately 60°) and draft levels (around 11.5 m). The fishing nets substantially increase the streamwise load by up to 80%, while their influence on the lateral forces is dependent on submergence depth. To efficiently predict hydrodynamic loads without performing additional and lengthy CFD simulations, a physics-informed neural network (PINN) is trained using the simulated data. The PINN model is found able to accurately reproduce the hydrodynamic force across a wide range of current conditions, offering a practical and interpretable surrogate approach for structural design optimization and mooring system development in offshore aquaculture industry. Full article

Flow stabilization and velocity uniformity in a Göttingen-type closed-circuit subsonic aerodynamic wind tunnel with an expanded test section are investigated in this study. Both experimental and numerical approaches were employed. The experiments were performed by using Laser Doppler Anemometry, Pitot tubes, and thermal anemometry. For numerical simulations, Reynolds-averaged Navier–Stokes simulations with a standard k-ε turbulence model were employed to evaluate flow characteristics in the velocity range of 0.05–20 m/s. The study shows that a properly contoured contraction nozzle suppresses inlet turbulence and ensures stable Reynolds-independent core flow. The contraction nozzle significantly accelerates and redistributes the flow, allowing rapid hydrodynamic stabilization and ensuring velocity measurements with high repeatability. These characteristics are inherent in a benchmark facility. Additionally, the study shows that the outlet-to-inlet diameter has the most prominent role in longitudinal velocity distribution in the test section. An optimal ratio of 1.10 was identified, stabilizing the pressure distribution and providing the most uniform longitudinal velocity profile. These findings offer geometry-dependent design guidelines for achieving high-quality measurements in Göttingen-type wind tunnels with expanded test sections and support accurate velocity measurement instrument calibration and aerodynamic testing. Full article

As the operational speeds of subway trains in China incrementally increase to 160 km/h, the enclosed nature of tunnel environments poses significant challenges by restricting free airflow. This limitation leads to intense airflow disturbances and turbulence phenomena within tunnels, consequently exacerbating aerodynamic noise issues. This study utilizes compressible Large Eddy Simulation (LES) and acoustic finite element methods to construct a computational model of aerodynamic noise for subway trains within tunnels. It employs this model to compare and analyze the near-field noise characteristics of subway trains traveling at 120 km/h on open tracks versus in infinitely long tunnels. The findings indicate that the distribution of sound pressure levels on the surfaces of trains within tunnels is comparatively uniform, overall being 15 dB higher than those on open tracks. The presence of a high blockage ratio in tunnels intensifies the cavity flow between two air conditioning units, making it the region with the highest sound pressure level. The surface sound pressure spectrum within the tunnel shows greater similarity across different segments, with low-frequency sound pressure levels notably enhanced and high-frequency levels attenuating more rapidly compared to open tracks. It is recommended that in tunnels with high blockage ratios, the positioning of subway train air conditioning should not be too high, overly concentrated, submerged, or without the use of sound-absorbing materials. Such adjustments can effectively reduce the sound pressure levels in these areas, thereby enhancing the acoustic performance of the train within the tunnel. Full article

Understanding the variability of turbulence intensity (TI) under different wind regimes is essential for the design and safety of offshore wind turbines. The IEC Normal Turbulence Model (NTM), though widely adopted in industry, does not incorporate directional dependence or account for extreme wind events such as typhoons, which can lead to substantial underestimation of turbulence in complex offshore environments. In this study, field measurements from two coastal sites in China, Huilai and Pingtan, were analyzed. At Pingtan, two months of observations captured both normal and typhoon-affected winds, providing a unique dataset for assessing turbulence under typhoon-affected conditions. The results show that wind speeds during the typhoon-affected period were approximately 14% higher than those during normal periods. At Huilai, TI was evaluated under northeasterly and southeasterly sea breezes, revealing that the IEC NTM underestimated TI by 15–42%, with more pronounced discrepancies under northeasterly winds. Based on these findings, revised NTM parameters and correction factors are proposed for different wind conditions, enhancing the applicability of the model to offshore wind turbine design. This work underscores the importance of incorporating directional and event-specific modifications into IEC turbulence standards to ensure reliable structural assessment across diverse wind regimes. Full article

Background: Deep learning (DL) models using Holter-ECG may enhance risk stratification after heart failure (HF) or myocardial infarction (MI). Objective: To evaluate the prognostic performance of a Holter-based DL model for predicting major adverse cardiac events (MACE), compared with conventional noninvasive markers. Methods: In the K-REDEFINE study, 1108 patients with acute MI or HF underwent 24 h Holter monitoring. A DL model was trained using raw Holter-ECG data and tested for predicting a composite of cardiac death and ventricular arrhythmias. Its performance was compared with heart rate turbulence (HRT), T-wave alternans (TWA), and ejection fraction (EF). Results: During follow-up, 56 adjudicated cardiac deaths (1.18%/yr) and 21 ventricular arrhythmias (0.44%/yr) occurred. The DL model showed an area under the receiver operating characteristic curve (AUROC) of 0.74 (95% CI, 0.70–0.77) for the composite outcome, improving to 0.77 (0.74–0.81) when combined with EF. In comparison, HRT and TWA showed lower AUROCs of 0.62 and 0.55, respectively. For cardiac death alone, the AUROC reached 0.79, further improving to 0.82 with EF. Model-derived risk stratification revealed a seven-fold increase in cardiac death risk in the high-risk group compared to the low-risk group (HR 7.47, 95% CI 2.24–24.96, p < 0.001). This stratification remained particularly effective in patients with EF > 40%. Conclusions: A DL algorithm trained on single-lead Holter-ECG data effectively predicted cardiac death and ventricular arrhythmia. Its performance surpassed conventional markers and was further enhanced when integrated with EF, supporting its potential for noninvasive, scalable risk stratification. Full article

To meet the stringent reliability requirements of rotor blades in turbomachines, greater effort should be devoted to improving both aerodynamic and structural performance in blade design. This paper introduces an aero-structural multi-disciplinary design optimization (MDO) method for compressor rotor blades using a discrete adjoint method and an equivalent stress model (ESM). The principles of the ESM are firstly introduced, and its accuracy in calculating equivalent stress is validated through comparison with a commercial program. Both the aerodynamic performance and the maximum equivalent stress (MES) are selected as optimization objectives. To modify the blade profile, the steepest descent optimization method is utilized, in which the necessary sensitivities of the cost function to the design parameters are calculated by solving the adjoint equations. Finally, the aero-structural MDO of a transonic compressor rotor, NASA Rotor 67, is conducted, and the Pareto solutions are obtained. The optimization results demonstrate that the adiabatic efficiency and the MES are competitive in improving multi-disciplinary performance. For most of the Pareto solutions, the MES can be considerably reduced with increased adiabatic efficiency. Full article

Nowadays, the potential of hydrokinetic turbines as a sustainable alternative to complement traditional hydropower is widely recognized. This study presents a comprehensive numerical analysis of twin straight-bladed Darrieus hydrokinetic turbines, characterizing their hydrodynamic interactions and performance characteristics. The influence of turbine configuration spacing and flow parameters on efficiency and wake dynamics are investigated. The employed 3D computational approach combines the overset mesh technique, used to capture the unsteady flow around the turbines, with the URANS k-ω Shear Stress Transport (SST) turbulence model. Results show that turbine spacing improves power coefficients and overall efficiency, albeit at the cost of slower wake recovery. A noticeable performance increase is observed when the turbines are spaced between 1.5 and 2 diameters apart, which is predicted to reach up to 40% regarding the single turbine. Furthermore, the effect of flow interaction between the turbines is examined by analyzing the influence of turbine spacing on flow structures as well as pressure and skin friction coefficients on the blades. The performed analysis reveals that vortex detachment is delayed in the twin-turbine configuration compared to the isolated case, which partially explains the observed performance enhancement. The insights gained from this work are expected to contribute to the advancement of renewable hydrokinetic energy technologies. Full article

The interaction between groundwater and surface water can be significant in lakes or irrigation channels, as well as in large dam reservoirs or along portions of them. To evaluate this interaction at a sampling location directly controlled by a large dam equipped with reversible pump-turbines, data from Rn-222 and physicochemical parameters at specific depths and times were obtained and studied using Principal Component Analysis and Hierarchical Clustering. Dimension 1 explains 45.3% of the total variability in the original data, which can be interpreted as the result of external factors related to seasonal variability (e.g., temperature, turbulent flow, and precipitation), while Dimension 2 explains up to 31.2% and can be interpreted as the variability related to groundwater inputs. Five hierarchical clusters based on these dimensions were considered and were related to the temporal variability observed in the water column throughout the year, as well as the depth relationships observed between successive surveys. A hypothesis-driven conceptual piston-like effect model is proposed for groundwater–surface water interactions, considering the identified relationships between variables, including higher Rn-222 concentrations in surface water after heavy rain. According to this simplified conceptual model, water infiltrates in a weathered granitic recharging area; during heavy rain, it is forced through the fracture systems of a lesser-weathered granite. Thus, an overall increase in pressure over the hydrological system forces the older radon-enriched water to discharge into the Mondego River. This work highlights the importance of exploratory techniques such as PCA and Hierarchical Clustering, in addition to underlying knowledge of the geological setting, for the proposal of simplified conceptual models that help in the management of important reservoirs. This work also demonstrates the utility of Rn-222 as a simple tracer of groundwater discharge into surface water. Full article

As aviation operations extend into complex natural environments, dust particles present significant challenges to flight stability and safety, particularly in dust-prone regions like southern Xinjiang. This study employs high-fidelity computational fluid dynamics (CFD) simulations, combined with the SST turbulence model and the Lagrangian discrete phase model, to analyze the aerodynamic response of the NACA 0012 airfoil at varying wind speeds (5, 15, and 30 m/s) and angles of attack (3°, 8°, and 12°). The results indicate that, at low speeds and moderate to high angles of attack, dust particles reduce lift by over 70%, primarily due to boundary layer instability, weakened suction-side pressure, and premature flow separation. Higher wind speeds slightly delay flow separation, but cannot counteract the disturbances caused by the particles. At higher angles of attack, drag increases by more than 60%, driven by wake expansion, shear dissipation, and delayed pressure recovery. Pitching moment frequently reverses from negative to positive, reflecting a forward shift in the aerodynamic center and a loss of pitching stability. An increase in dust concentration amplifies these effects, leading to earlier moment reversal and more abrupt stall behavior. These findings underscore the urgent need to improve aircraft design, control, and safety strategies for operations in dusty environments. Full article

Nearly homogeneous and isotropic turbulence, generated in flows through grids of various forms in wind tunnels or by towing or oscillating grids in stationary samples of classical viscous fluids and the superfluid phases of helium, have played an essential role in studies of the still partly unresolved problem of turbulence in fluids. This review describes a selected class of complementary grid experiments performed with classical viscous fluids such as air or water and with the superfluid liquid phases of ⁴He (He II) and ³He-B, which led to a deeper understanding of the underlying physics of turbulent quantum flows. In particular, we discuss the pioneering experiments on generating and probing quantum turbulence by oscillating grids in He II in the zero temperature limit, performed by Peter McClintock’s group in Lancaster. Full article

In industrial equipment containing hydraulic lines for power transmission, the lines have boundary conditions defined by components such as pumps, valves, and actuators located at the ends of the lines. Sudden changes in any of the boundary conditions may result in significant pressure/flow dynamics (fluid transients) in the lines that may be detrimental or favorable to the performance of the equipment. Accurate models for line transients are defined by the exact solution to a set of simultaneous partial differential equations. In this paper, analytical solutions to the partial differential equations provide Laplace transform transfer functions applicable to any set of boundary conditions yet to be specified that satisfy the requirements of causality. Analytical solutions of these partial differential equations from previous publications are reviewed for cases of laminar and turbulent flow for Newtonian and a class of non-Newtonian fluids. This paper focuses on a method for obtaining total system analytical models and time domain solutions for cases in which the end-of-line components can be modeled with linear equations for perturbations relative to pre-transient flow conditions. Examples with pumps, valves, and actuators demonstrate the process of coupling equations for components at the ends of a line to obtain total system transfer functions and then obtain time domain solutions for outputs of interest associated with system inputs and load variations. Full article

Modern gas turbines for aeroengines operate at ever-increasing inlet temperatures to maximize thermal efficiency, power, output and thrust, subjecting turbine blades to severe thermal and mechanical stresses. To ensure component durability, effective cooling strategies are indispensable, yet they strongly influence the underlying aerothermal behavior, particularly in transonic regimes where shock–boundary layer interactions are critical. In this work, a comprehensive Reynolds-Averaged Navier–Stokes (RANS) investigation is carried out on the LS89 transonic turbine cascade, considering both adiabatic and cooled wall conditions. Three operating cases, spanning progressively higher outlet Mach numbers (0.84, 0.875, and 1.020), are analyzed using multiple turbulence closures. To mitigate the well-known model dependence of RANS predictions, a model-averaging strategy is introduced, providing a more robust prediction framework and reducing the uncertainty associated with single-model results. A systematic mesh convergence study is also performed to ensure grid-independent solutions. The results show that while wall pressure and isentropic Mach number remain largely unaffected by wall cooling, viscous near-wall quantities and wake characteristics exhibit a pronounced sensitivity to the wall-to-recovery temperature ratio. To support further research and model benchmarking, the complete RANS database generated in this work is released as an open-source resource and made publicly. Full article