Search Results (8,443)

One of the engineering applications inspired by nature is bio-inspired wings. The aerodynamic properties and autorotation characteristics of samara wing models have been studied extensively using both experimental and numerical methods. However, the three-dimensional flow behavior and angle of attack interaction around a natural samara wing are not yet fully understood. This study investigates the flow behavior around a samara wing model, with the aim of underlying physics and qualitatively analyzing the flow field, as well as the aerodynamic forces and stresses. Since the samara wing and the flow around it are three-dimensional, the difficulty of experimental investigation was taken into account, and the numerical analysis was performed using Computational Fluid Dynamics techniques. The results obtained from the numerical solution of the governing equations for three-dimensional turbulent flow were verified with experimental data. The calculations were performed by varying the angle of attack of the model wing between 0 and 50 degrees at 10-degree intervals. Depending on the angle of attack, the velocity field around the wing, surface pressure, and stress distributions, vortex structures formed on the wing and streamlines were analyzed, and the results were presented. This study and its results on this model may lead to the development and optimization of the model and its use in turbines or air vehicles. Full article

Laser fiber-based collimation straightness measurement can eliminate the intrinsic drift of the laser source while offering a simple configuration and simultaneous measurement of straightness in two orthogonal directions. As a high-precision optoelectronic sensing method, it has been widely used for the measurement of straightness, parallelism, perpendicularity, and multi-degree-of-freedom geometric errors. However, two common issues remain in practical applications. One is the nonlinear response of the four-quadrant detector, the core position-sensitive sensor, which is caused by detector nonuniformity and the quasi-Gaussian distribution of the spot. The other is the degradation of measurement performance by atmospheric inhomogeneity and air turbulence along the optical path, particularly in long-distance measurements. To address these issues, a two-dimensional planar calibration method is first proposed to replace conventional one-dimensional linear calibration. A polynomial surface-fitting model is introduced to correct the nonlinear response and inter-axis coupling errors of the four-quadrant photoelectric sensor. Simulation and experimental results show that the proposed method significantly reduces the standard deviation of calibration residuals and improves measurement accuracy. In addition, based on our previously developed common-path beam-drift digital compensation method, comparative experiments were carried out on double-pass common-path and single-pass optical configurations employing corner-cube retroreflectors, and theoretical simulations were performed to analyze the influence of air-turbulence disturbances on measurement stability. Both theoretical and experimental results show that the double-pass common-path configuration exhibits more pronounced temporal drift. Therefore, a real-time digital compensation method for beam drift in long-distance single-pass common-path measurements is proposed. Experimental results demonstrate that the proposed method effectively suppresses drift induced by environmental air turbulence and thereby improving the accuracy and stability of long-travel geometric-error and related straightness measurement for machine-tool linear axes. Full article

This study investigates the roles gold and the US dollar play as safe-haven, hedging, or diversifier assets relating to six important financial stock market indices: the S&P 500, FTSE 100, Hang Seng, CAC 40 (Paris), Shanghai Composite Index, and Nikkei 225. This paper applies the bivariate dynamic copula technique and the DCC-GARCH econometric advanced methods from January 2013 to July 2024 by focusing on four serious market crashes: the Chinese stock market meltdown (2015–2016), the trade war between the US and China (2018–2020), the COVID-19 pandemic (2020–2022), and the conflict between Russia and Ukraine (2022–2024). The results show that the US dollar displays reliable hedging and safe-haven characteristics with strong evidence mainly for its role as a safe-haven asset against the FTSE 100, Hang Seng, and S&P 500. Our findings support the idea that the US dollar serves consistently as a safe-haven asset. In contrast, gold showcased a twofold function, serving as a hedge for the FTSE 100 and the S&P 500 during crisis times and acting as a diversifier for the CAC 40 and the Shanghai Composite Index in times of market stability. This dynamic was specifically noticeable in the COVID-19 period, when gold’s hedging properties were outstanding and its role as a diversifier became more pronounced in the Paris and Shanghai markets. Our results suggest that the consistent reliability of the US dollar as a safe-haven asset combined with gold’s dual role presents a compelling argument for including both in well-diversified portfolios. This strategy enables investors to mitigate risk and safeguard their wealth, especially during periods of financial market volatility. Full article

Coastally Trapped disturbances (CTDs) are shifts in wind direction from the pre-dominant direction to equatorward to poleward for a period of time. These CTDs occur during the warm season off the California coast and impact coastal weather conditions and planned offshore wind plants. This study assesses the characteristics of CTD events as observed by lidar and other offshore buoys, then evaluates the ability of modeling systems to capture the correct characteristics, leveraging model output from the High-Resolution Rapid Refresh (HRRR) operational modeling system and the NOW-23 (National Offshore Wind) model dataset. CTDs were analyzed for October 2020 and May through to October of 2021, identifying 18 unique CTD events, confirmed by a nearby National Data Buoy Center (NDBC) buoy. The HRRR model captured most of these events, but the NOW-23 model output contained only 12 events. Composites of the wind, temperature, and pressure perturbations pre-, during, and post-event demonstrated the diminishment in wind speed, particularly for the alongshore component. Although the NOW-23 model captured the alongshore wind component and pressure perturbations well, the cross-shore wind component and temperature perturbations varied substantially. When the turbulent kinetic energy deviation and wind shear was positive across all levels pre-event, the NOW-23 modeling system was less likely to capture the CTD event. In contrast, the events that were captured by the model tended to have negative wind shear aloft pre-event. Full article

A miniaturized (70 × 7.7 cm) multi-parameter synchronous observation system was developed for in situ ocean turbulence measurement, integrating micro-electromechanical system (MEMS)-based two-dimensional (2D) turbulence, pressure, temperature, conductivity, and attitude sensors. Field tests conducted at a depth of 1800 m in the northern South China Sea validated the system’s accuracy through comparisons with standard CTD (Conductivity, Temperature, and Depth) sensors, dual-probe consistency analysis, and Nasmyth spectrum fitting. The system precisely captured thermoclines, internal waves, and turbulent shear fluctuations at a depth of approximately 125 m, revealing enhanced turbulence near the thermocline due to intensified shear effects. With high spatiotemporal synchronization and reliability, the system provides an effective solution for studying multiscale ocean turbulence and associated dynamic processes. Full article

Artificial intelligence and machine learning techniques can be used for performing magnetic testing on spacecraft that has historically been difficult and risky to perform. Some of the difficulty arises from the need to take these measurements from within the turbulent near-field area of the spacecraft. Some methods of testing require the spacecraft to be hoisted in the air and swung while the measurements are being taken so that any magnetic signatures in the test area can be removed. These new artificial intelligence and machine learning techniques can be used to determine the magnetic torque of complex magnetic systems. Here we will describe a test method that collects such data and poses much less risk to the spacecraft. We will also show some combinations of hyper-parameters that can be used to increase the speed and accuracy of the models. Some models were able to achieve over 96.6% accuracy of multipole determination on simulated data and over a 99.99% accuracy of dipole moment determination on simulated data. Applications include attitude control systems (ACS), science instrument locations, and data analysis. Full article

This study numerically investigates the flow characteristics of submerged impinging jets at a standoff distance of H/d = 3. The computational analysis is performed utilizing large eddy simulation (LES) alongside the one-equation Wray-Agarwal and the two-equation SST k-ω and RNG k-ε turbulence models. The current work emphasizes the hydrodynamic structures developing in the unconfined jet region and the variations in flow behavior at the stagnation zone across a range of impact angles (θ ≤ 90°) at Re (Reynolds number) = 23,400. Compared with PIV data, the Wray-Agarwal model accurately predicts the free-jet flow, whereas the RNG k-ε model excels in the wall-jet region. As the impingement angle increases, the pressure distribution calculated by the LES method gradually approaches the experimental results. When the impinging angle θ = 90°, LES has high prediction accuracy in both regions. In general, under the grid scheme used in this study, RNG k-ε can make a more accurate prediction of the average characteristics of the submerged impinging jet flow field. Full article

As a prominent frontier in ultrafast laser–matter interaction, femtosecond laser filamentation holds great potential for atmospheric pollutant detection and remote sensing. However, its practical application in the open atmosphere is severely hampered by atmospheric turbulence, which induces beam wander, wavefront distortion, and intensity scintillations. In this study, we numerically investigated the propagation dynamics of femtosecond laser filaments in a turbulent medium and elucidated the underlying physical mechanisms. The results show that, compared to linear propagation, the nonlinear self-guiding effect inherent to filamentation effectively suppresses turbulence-induced beam wander. Furthermore, by employing vortex beams carrying orbital angular momentum (OAM), we significantly suppressed the stochastic generation of multiple filaments, thereby notably improving the stability of long-range filament propagation in complex atmospheric conditions. These findings provide new insights into the physical mechanisms and novel strategies for improving the robustness of laser filamentation technology in real-world turbulent environments. Full article

Under turbulent wind conditions, rapid wind speed fluctuations can markedly increase the fatigue loads borne by wind turbine blades and towers. In practice, conventional PID pitch control based on speed feedback often struggles to deliver satisfactory load mitigation, mainly because the wind turbine system is highly nonlinear, strongly coupled, and subject to time-delay effects. To overcome these limitations, this paper proposes a load-predictive pitch control strategy built on a Long Short-Term Memory (LSTM) network. Specifically, the LSTM model is first employed to predict the hub-fixed tilt and yaw moments ahead of time. These predicted values are then introduced as feedforward signals and combined with the conventional speed-based pitch control signal as well as a proportional feedback term. After that, the inverse Coleman transformation is used to generate the individual pitch commands for each blade. To verify the effectiveness of the proposed method, co-simulations were carried out in FAST and MATLAB/Simulink on a 5000 KW distributed pitch-controlled wind turbine under IEC Kaimal spectrum wind conditions, with a mean wind speed of 18 m/s and Class B turbulence intensity. The results show that the LSTM prediction model achieves an R² of 0.998 on the test dataset, with an RMSE as low as 0.0051. Compared with the conventional pitch-based power control strategy, the proposed approach maintains the same average power output while significantly reducing fatigue loads, thereby contributing to a longer service life for the wind turbine. Full article

The aerodynamic characteristics of wingsails on unmanned surface vessels (USVs) play a crucial role in enhancing propulsion performance. Two-dimensional wingsail airfoils of owl wings, merganser wings, seagull wings, and teal wings were obtained through biomimetic design. Then a numerical investigation was conducted on the four biomimetic airfoils using the SST k-ω turbulence model to evaluate their aerodynamic performance. The results demonstrate that the bionic merganser airfoil exhibits the most superior lift performance, achieving a maximum lift coefficient of 3.21 across angles of attack ranging from 0° to 60° among the four biomimetic wingsails, and the bionic seagull airfoil is second, while the bionic teal airfoil shows the weakest lift characteristics. As the angle of attack increases, flow separation emerges at the trailing edge of the biomimetic airfoils, leading to the formation of separation vortices. For example, the backflow zone on the suction surface of the biomimetic merganser wingsail, caused by unsteady flow, persists at an angle of attack of 16 degrees. The vortex structure at the trailing edge of the biomimetic merganser wingsail periodically generates, develops, detaches, and dissipates, which affects the backflow of the suction surface of the wingsail and interferes with its lift coefficient. The study provides an excellent reference for selecting high-performance USV wingsails. 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

Inorganic scale formation in oil wells is a major flow assurance challenge, causing production losses, increased intervention costs and reduced operational efficiency. In Brazil, recent discoveries in pre-salt reservoirs have increased the relevance of calcium carbonate (CaCO${}_3$) scaling under high-pressure and high-temperature (HPHT) conditions. Experimental data representative of petroleum environments under such conditions, particularly regarding the influence of CO${}_2$ and flow conditions, remain limited. In this study, a compact pressurized experimental unit was designed and constructed to investigate the dynamic formation, deposition and adhesion of CaCO${}_3$ under conditions close to those encountered in oil production systems. A dedicated experimental methodology was developed to promote controlled mixing of aqueous sodium bicarbonate (NaHCO${}_3$) and calcium chloride (CaCl${}_2$) solutions and CO${}_2$ injection, enabling precise control of pressure, temperature and flow regime. The effects of turbulent flow, expressed by different Reynolds numbers, on the deposited CaCO${}_3$ mass and its adhesion to the substrate were systematically evaluated under controlled conditions of 40 °C and a pressure drop of 15 bar was imposed in the control valve in order to promote the flash of CO${}_2$ and CaCO${}_3$ precipitation. Complementary characterization analyses were performed to assess crystal morphology and adhesion detachment strength. The results provide new experimental insights into CaCO${}_3$ scaling mechanisms under CO${}_2$-rich flowing conditions, contributing to improved understanding of scale adhesion and the development of mitigation strategies for flow assurance in oil and gas operations. Full article

We develop a structural framework for the three-dimensional incompressible Navier–Stokes equations in which the nonlinear dynamics are reorganized in terms of triadic interactions, dyadic shells, and helical modes. Within this formulation, all interactions are classified into Low–Low, Low–High, and High–High channels, and it is shown that the Low–Low and Low–High contributions are perturbatively controlled through scale-localized estimates without introducing external assumptions. Consequently, potentially non-perturbative contributions are confined, within the present framework, to a class of same-scale High–High interactions. This class is further reduced, through geometric and dynamical constraints, to a coherent core characterized by amplitude activity and low phase drift. The resulting reduced dynamics is expressed in terms of family-level phase variables and associated curvature quantities. The main result establishes a quantitative residence-time compression principle for this coherent regime. Specifically, it is shown that intervals on which both amplitude activity and low phase drift persist must have small total measures, due to an absolute-value coercivity property of the curvature combined with bounded-variation control of the phase dynamics. This implies that coherent same-scale interactions cannot occupy a macroscopic portion of any bounded time interval, even though local re-entry into low-drift configurations is not excluded. Consequently, the nonlinear transfer associated with coherent triads becomes temporally localized and admits a shellwise compressed representation. These results provide a structurally reduced description of a candidate mechanism for cumulative same-scale amplification within the present dyadic–triadic framework. They do not claim a framework-level structural exclusion of the global regularity problem. Rather, they identify and analyze, within an explicit structural setting, a minimal mechanism for non-perturbative amplification, and establish a quantitative constraint on its temporal persistence. Full article

Due to acute resource constraints and environmental turbulence, many small and medium-sized construction enterprises (SMEs) prioritize short-term survival over corporate social responsibility (CSR) initiatives. Grounded in social exchange theory (SET), this study investigates how CSR implementation drives financial performance (FP) via the mediating role of non-financial performance (NP), aiming to deconstruct the “psychological black box” of this transformation. Drawing on a sequential mixed-methods design involving PLS-SEM analysis of 380 responses and 10 semi-structured interviews, the results confirm that CSR practices, particularly ethical practices and community engagement, can be effectively translated into improved NP, which acts as a vital strategic conduit for enhancing FP. However, skills development and training showed limited immediate impact due to a systemic “digital mismatch” and significant time-lag effects. Theoretically, this research refines SET by identifying a hierarchical transition where socio-emotional assets serve as compensatory resources in volatile and resource-constrained environments. Practically, the findings offer a strategic roadmap for SMEs to mitigate technological and systemic barriers, providing novel pathways for fostering CSR to achieve sustainable growth. Full article

Hydrogen-enriched fuel injection in staged gas-turbine combustors is commonly achieved through jet-in-crossflow (JICF) configurations, where flame stabilization is governed by a local balance between flow-induced strain/mixing and chemical reaction rates. This work investigates turbulent reacting JICF relevant to staged combustion conditions using high-fidelity simulations with adaptive mesh refinement (AMR) and differential-diffusion effects together with Lagrangian particle statistics. Chemistry model uncertainties are incorporated by using a projection method that maps uncertainty estimates from detailed mechanisms into the model used in this work. Results show that the macroscopic flame topology remains in a stable two-branch regime (lee-stabilized and lifted) and is primarily controlled by the jet momentum–flux ratio J. Visualization of the normalized scalar dissipation rate reveals that the flame front resides on the low-dissipation side of intense mixing layers, occupying an intermediate region between over-strained and under-mixed regions. While hydrogen content does not significantly change the global stabilization mode for the cases studied, uncertainty analysis reveals composition-dependent differences that are not apparent in the mean behavior alone. In particular, visualization in Eulerian (χ, T) state-space analysis and particle statistics conditioned on the stoichiometric surface demonstrate that higher-hydrogen cases observe a lower scalar dissipation rate and exhibit substantially reduced variability in OH production under kinetic-parameter perturbations, whereas lower-hydrogen blends experience higher dissipation and amplified chemical sensitivity. These findings highlight that, even in globally similar JICF regimes, the hydrogen content can modify the local response of the flame to kinetic-parameter uncertainty, motivating uncertainty-aware interpretation and design for hydrogen-fueled staging systems. Full article