Search Results (2,060)

Atmospheric turbulence, quantified by the refractive index structure parameter (${C_n}^2$), degrades the performance of optical systems. Reliable ${C_n}^2$ estimation is critical for free-space optical communication, remote sensing, and astronomy. This study compares frame-based and event-based approaches to turbulence strength estimation. A high-speed CMOS camera (180/90/30 frames per second (FPS)) and an event camera were deployed along a 300 m outdoor path, with a scintillometer providing ground truth. Event streams were segmented into 5 s windows, features were extracted, and predictions were made using an Extreme Gradient Boosting regressor (XGBoost). A hybrid model was also tested, combining CMOS-based predictions with event features. Results show that CMOS accuracy is strongly dependent on frame rate, with diminishing returns beyond 90 FPS under weak turbulence. Event-based models achieved higher correlation with ground truth in strong turbulence but produced larger errors in weak regimes. The hybrid approach yielded the best overall performance in moderate-to-strong turbulence, reducing mean estimation error by ~35% compared to CMOS-only at 180 FPS. These findings demonstrate the complementary strengths of frame and event modalities. Frame cameras provide stability in weak turbulence, while event sensors capture fast fluctuations under stronger conditions. Together, they enable more robust ${C_n}^2$ estimation and motivate further research into advanced hybrid sensing strategies for operational turbulence monitoring. Full article

Fretting wear due to flow-induced vibration (FIV) remains a primary cause of fuel failure in light water nuclear reactors. In the study of axial FIV, i.e., FIV caused by axial flows, three vibration characteristics, namely natural frequency, damping ratio, and root-mean-square (RMS) amplitude, are critical for mitigating fretting wear by avoiding resonance, maximising overdamping, and preventing large-amplitude instability motion, respectively. This paper presents a set of best practices for simulating axial FIV with a focus on predicting these parameters based on a URANS-FSI numerical framework, utilising high-Reynolds-number Unsteady Reynolds-Averaged Navier–Stokes (URANS) turbulence modelling and two-way fluid–structure interaction (FSI) coupling. This strategy enables accurate and efficient prediction of vibration parameters and offers promising scalability for full-scale nuclear fuel assembly applications. Validation is performed against a semi-empirical model to predict RMS amplitude and experimental benchmarking. The validation experiments involve two setups: vibration of a square beam with fixed and roller-supported ends in annular flow tested at Vattenfall AB, and self-excited vibration of a cantilever beam in annular flow tested at the University of Manchester. The study recommends best practices for numerical schemes, mesh strategies, and convergence criteria, tailored to improve the accuracy and efficiency for each validated parameter. Full article

Vertical-axis wind turbines (VAWTs) have received increasing research interest due to their structurally simple design and superior adaptability to gusty, multidirectional, and highly turbulent wind conditions. However, their relatively low efficiency of wind utilization remains a significant limitation, necessitating extensive research into design optimization and performance enhancement strategies. As we show, efficiency can be achieved by arranging the blades not evenly around the circumference, as in a traditional VAWT, but in groups called “blocks”, which extracts more energy from the air flow using aerodynamic and thermodynamic phenomena. The experimental results of a 20 kW VAWT in an independent certified laboratory strengthen the theoretical study and prove that the efficiency of the proposed system is 1.7 times higher than that of known VAWTs, as well as horizontal-axis wind turbines (HAWTs). Full article

This study systematically investigates the propagation characteristics of ring-shaped Airy-Gaussian vortex (RAiGV) beams in a 50 m marine turbulent channel. Utilizing a combined angular spectrum-phase screen model, numerical simulations were conducted to analyze the evolution of light intensity, scintillation index (SI), and detection probability (DP) under varying distribution factors b, topological charge l, and turbulence intensity σ². Results reveal that the SI of RAiGV exhibits a three-stage pattern: initial rise, decline, and subsequent rise. The valley positions of SI correspond one-to-one with self-focusing foci. Smaller b values result in closer foci, with short-range SI reaching its minimum but eventually surpassing long-range SI. At b = 0.15, the beam maintains a flatter SI curve and higher DP over long distances. The l = 1 vortex structure, characterized by its simplicity, demonstrates superior robustness against turbulence compared to higher-order modes. Appropriate selection of b and l enables a trade-off between near-field peak intensity and far-field stability, providing valuable design guidance for underwater OAM multiplexing communications. Full article

Financial markets evolve through recurring phases of stability, turbulence, and structural transformation. Standard Hidden Markov Models (HMMs) assume fixed transition probabilities, which limits their ability to capture such higher-order changes in market behavior. This study introduces an Adaptive Hierarchical Hidden Markov Model (AH-HMM), where regime transitions depend on an unobserved meta-regime that reflects the broader macro-financial environment. Each meta-regime defines its own transition matrix across market states such as bull, bear, and turbulent phases. In this way, the model adapts dynamically to structural changes arising from crises, policy shifts, or variations in investor sentiment. Using weekly data for major equity indices, aggregated from daily prices, together with macro-uncertainty indicators, we show that the AH-HMM identifies key turning points including the Global Financial Crisis, the COVID-19 shock, and the post-2022 tightening cycle. In our empirical application, where we approximate the latent structural layer by low- and high-uncertainty environments defined from the VIX, the adaptive model attains a higher in-sample likelihood and delivers competitive out-of-sample forecasts and Value-at-Risk coverage relative to conventional HMMs and time-varying transition alternatives. Overall, the results highlight a mechanism of structural learning within market regimes and offer tools for risk management and policy analysis under uncertainty. Full article

ANSYS-Fluent was applied to simulate diffusion combustion flame in a two-dimensional (2D) industrial burner to determine the contours of the mass fraction of gas emissions, velocity, and combustion temperature. The effects of the boundary conditions, including momentum, thermal, and species (inlet air, inlet fuel, and outlet pressure) on combustion temperature and mass fraction (gas emissions) were analyzed in the designed burner. The present study focused on using and analyzing the volumetric reaction and the turbulence-chemistry interaction of the eddy dissipation model for the diffusion flame model. The simulation used the discrete ordinate model and p1 for radiation and the k-ε model for turbulence with enhanced wall treatment. Based on the results, the magnitude velocities of air and fuel, inlet temperature, and mass fractions of oxygen and inert gas can influence the parameters of flame temperature and gas emissions in the industrial burner. The flame shape for all the cases of inlet velocity was predominantly symmetric about the x = 0 mm for all the axial distances towards the outlet. The radial velocity contour at 0.01 m/s (300 K) gave better results with an area of 1.31 m/s to 4.08 m/s, which was wider than that of the case at 0.01 m/s (700 K). By varying the inlet temperature and oxygen mass fraction, the flame configurations on temperature, CO₂, and H₂O formed a symmetric flame structure. The temperature distribution resulted in the centerline being hotter than other radial positions for all of the inlet temperatures. The emissions of CO₂ and H₂O generally increased with the addition of the oxygen mass fraction. Full article

Background: Impaired mucus drainage from the paranasal sinuses is often associated with nasal obstruction and reduced airway function in growing patients. Orthopedic maxillary protraction and expansion techniques can enhance airway dynamics, but their underlying fluid–structure mechanisms remain insufficiently understood. Objective: To validate that the Right Angle Maxillary Protraction Appliance (RAMPA), combined with a semi-rapid maxillary expansion (sRME) intraoral device gHu-1, improves mucus drainage by enhancing nasal airflow through nasal cavity expansion. Methods: The effects of RAMPA therapy were analyzed using computational fluid dynamics (CFD) for single-phase (air) and two-phase (air–mucus) flows within the nasal cavity, employing the unsteady RANS turbulence model. Finite element method (FEM) results from prior studies were synthesized to assess changes in the center and radius of maxillary rotation induced by RAMPA-assisted sRME. A male patient (aged 8 years 7 months to 11 years 7 months) treated with extraoral RAMPA and the intraoral appliance (gHu-1) underwent pre- and post-treatment cone-beam computed tomography (CBCT) and ear, nose, and throat (ENT) evaluation. Results: FEM analysis revealed an increased radius and elevated center of maxillary rotation, producing expansion that was more parallel to the palatal plane. CFD simulations showed that nasal cavity expansion increased airflow velocity and pressure drop, enhancing the suction effect that promotes mucus clearance from the frontal sinus. Clinically, nasal passages widened, paranasal opacities resolved, and occlusal and intermolar widths improved. Conclusions: RAMPA combined with sRME improves nasal airflow and maxillary skeletal expansion, facilitating paranasal mucus clearance and offering a promising adjunctive approach for enhancing upper airway function in growing patients. Full article

To address the thermal management requirements of unmanned underwater vehicles (UUVs), this study designs a small high-speed water tunnel test section. Combining numerical simulations and experimental methods, we systematically investigate how outlet gauge pressure regulates flow structure and cooling performance from perspectives of vortex dynamics and turbulent energy scaling. Results demonstrate that increasing outlet pressure from 1.0 to 2.0 atm reduces system pressure loss by 26.60%, drag coefficient by 26.56%, and power consumption by 27.30%. The test section maintains flow uniformity below 1.0% with over 75% high-speed zone coverage, satisfying the ≥25 m/s design requirement. Mechanism analysis reveals that elevated pressure suppresses cavitation and boundary layer separation, attenuates large-scale vortex generation, and promotes turbulence transition to smaller scales, thereby optimizing energy transport and thermal uniformity. Experimental validation confirms the numerical model’s reliability in predicting flow characteristics, providing theoretical and technical support for advanced water tunnel design and battery thermal management optimization. Full article

We study one of the world’s largest cattle markets by revisiting and extending previous work on the forecasting of Brazil’s Boi Gordo Index (BGI). Using an updated daily dataset (July 2006–September 2025, inflation-adjusted), we evaluate classical and machine learning (ML) approaches for price prediction. Methods include Exponential Smoothing (Simple, Holt, and Holt–Winters), ARMA/ARIMA/SARIMA, GARMA variants, GARCH, Theta, Prophet, and XGBoost; models are compared under a strictly chronological 90/10 holdout (~476 test days) using RMSE, MAE, and MSE, with the AIC guiding within-family selection. Results show that, for the full out-of-sample window, GARMA delivers the best overall accuracy, with ARMA and Holt–Winters close behind, while Prophet and XGBoost perform comparatively worse in this volatile setting. Performance is horizon-dependent: in the first 180 test days, prior to the late-2024 level shift, Holt attains the lowest RMSE/MSE, and XGBoost achieves the lowest MAE. No method anticipates the October–November 2024 exogenous jump and subsequent correction, highlighting the difficulty of structural breaks and the need for timely re-specification. We conclude that GARMA is a robust default for long, turbulent windows, whereas smoothing and ML methods can be competitive on shorter horizons. These findings inform risk measurement and risk mitigation strategies in Brazil’s cattle futures market. Full article

As an active flow-control technology, the counterflowing jet can reduce drag by reconstructing the flow field structure during the re-entry of a vehicle, thereby mitigating the adverse effects of high overload on personnel. However, variations in the angle of attack (AoA) and nozzle mass flow rate tend to induce transitions in its flow field modes and fluctuations in drag reduction performance. To further investigate the aerodynamic interference characteristics of the counterflowing jet during the re-entry process, this study focused on a 2.6% subscale model of the Apollo return capsule. The Reynolds-averaged Navier–Stokes (RANS) equations turbulence model was employed to numerically analyze the effects of different mass flow rates and freestream AoAs on the flow field modes and the drag behavior. The results indicate that with an increase in AoA, the flow field structure of the long penetration mode (LPM) is likely to be destroyed, and the shock wave shape exhibits significant asymmetric distortion. In contrast, the flow field structure of the short penetration mode (SPM) remains relatively stable; however, the bow shock and Mach disk exhibit two typical offset patterns, whose offset characteristics are jointly regulated by the mass flow rate and AoA. In terms of drag characteristics, the AoA significantly weakens the drag reduction effect of the LPM. In contrast, the SPM can maintain a stable drag reduction efficiency of approximately 50% within a certain AoA range. Nevertheless, as the AoA further increases, the drag reduction effect of the SPM gradually diminishes. Full article

River-training structures such as spur dikes are frequently used in the field of river engineering, which play a critical role in flow regulation and stabilization of the riverbank. However, previous studies lack a precise prediction of factors inducing scour and turbulence phenomena, such as tip velocity, for optimal design of the spur dikes. This study addresses a key gap in previous research by predicting tip velocity around spur dikes using advanced and interpretable machine learning models while simultaneously evaluating the influence of key geometric and hydraulic parameters. For this purpose, the current study utilized advanced artificial intelligence (AI) techniques like Gaussian Process Regression (GPR), Categorical Boosting (CatBoost), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), optimized with Particle Swarm Optimization (PSO), to predict tip velocity in the vicinity of the spur dike. In this paper, a small dataset of 69 laboratory-scale experimental trials was collected; therefore, the chosen AI models were selected for their ability to handle such limited data points. In this study, the input parameters included Froude number (Fr), separation length to spur dike length ratio (L/l), and incidence angle (β), while the output parameter was tip velocity. The selected four AI models were trained on 70%, 15%, and 15% of the data for the training, testing, and validation phases, respectively. SHapley Additive exPlanations (SHAP) analysis was used to observe the influence of the critical parameters on the tip velocity. The results demonstrated the superior performance of GPR, followed by the CatBoost model, compared to other models. GPR and CatBoost show greater values of coefficient of determination (R²) (GPR R² = 0.972 and CatBoost R² = 0.970) and lower values of root mean square error (RMSE) (GPR RMSE = 0.0107 and CatBoost RMSE = 0.0236). The result of the heatmap and SHAP analysis indicated a greater influence of Fr and L/l and a lower impact of β on the tip velocity. The results of this study recommend the utilization of GPR and CatBoost for precise and robust performance of the hydrodynamic phenomenon around the spur dikes, supporting scour mitigation strategies in river engineering. Full article

To investigate the aerodynamic characteristics of the subsonic transport standard model (DLR-F4 wing–body configuration), this study uses the Spalart–Allmaras Detached Eddy Simulation (SA-DES) turbulence model as the core, coupling it with fifth-order WENO/WCNSs and HLLC approximate Riemann solver for numerical simulations under different angles of attack (AOA). Through comparative simulations, effects of grid density, turbulence models (URANS/DES), and spatial discretization schemes (second-order CDS, fifth-order WENO-JS/WCNS-JS) on accuracy are analyzed, focusing on grid convergence and numerical scheme dissipation in separated flows. The results show medium-density grid results are stable, balancing accuracy and efficiency. Under high AOA, DES outperforms URANS in capturing separated vortex structures, effectively reproducing small-scale vortices in the wing–body junction. High-order WCNS performs best in predicting wing-tip vortices and wake turbulence due to lower dissipation. WCNS-JS/WCNS-T (different weight functions) affect lift/drag coefficient errors: WCNS-JS has smaller lift prediction errors, while WCNS-T better reduces dissipation and maintains wing-tip vortex integrity. This study provides key references for high-accuracy simulations of complex separated flows, supporting efficiency improvement and accuracy optimization in aerospace vehicle aerodynamic design. Full article

The increasing global demand for cleaner transportation has intensified the importance of efficient AdBlue^® (AUS32) production, a key chemical in selective catalytic reduction (SCR) systems that reduces nitrogen oxides (NOx) emissions from diesel engines. This work presents a computational fluid dynamics (CFD) simulation study of the urea–water mixing process within a high shear mixer (HSM), aiming to enhance the sustainability of AdBlue^® manufacturing. The model evaluates the hydrodynamic characteristics critical to optimising the dissolution of urea pellets in deionised water, which conventionally requires significant preheating. Experimental validation was conducted by comparing pressure drop simulation results with operational data from an active industrial facility in the United Kingdom. Therefore, this study validates the CFD model against an industrial two-stage Rotor–stator under real operating conditions. The computational framework combines a refined mesh with the k-ω SST turbulent model to resolve flow structures and capture near-wall effects and shear stress transport in complex flow domains. The results reveal opportunities for process optimisation, particularly in reducing thermal energy input without compromising solubility, thus offering a more sustainable pathway for AdBlue^® production. The main contribution of this work is to close existing gaps in industrial practice and propose and computationally validate strategies to improve the numerical design of HSM for solid dissolution. Full article

Hydrogen internal combustion engines are a promising propulsion technology due to their zero-carbon emission potential and high efficiency. However, achieving stable mixture formation during direct hydrogen injection remains a key challenge affecting ignition stability and NO_x emissions. Although numerous studies address the combustion characteristics of hydrogen, only a limited number have examined the transient behavior of hydrogen/air mixing during the intake stroke, particularly its interaction with in-cylinder flow structures prior to ignition. This lack of detailed insight into early mixture stratification and jet-driven turbulence represents a significant research gap that currently limits further optimization of DI-H₂ICE systems. This study therefore deals with the numerical analysis of the process of mixing hydrogen with air in the combustion chamber of a direct hydrogen injection engine (DI-H₂ICE). A 3D CFD model of a hydrogen direct-injection engine was used to evaluate in-cylinder mixing during the intake and early compression strokes. Unlike most existing publications that focus primarily on combustion or emission formation, this work examines the mixing process from the beginning of the intake stroke and provides a new evaluation of the evolution of the hydrogen jet and its interaction with the piston-induced swirl as the crankshaft angle changes. The simulation covers the section from the exhaust top dead center (TDC) to the early compression phase, during which hydrogen is injected at a high pressure. The results show that the shape of the combustion chamber and the interaction of the hydrogen jet with the piston significantly affect the distribution of the equivalent ratio and the intensity of the swirl. Quantitative evaluation showed that the mixture remained lean overall throughout the cycle: typical hydrogen mass fractions in the cylinder ranged from 0.01 to 0.05, corresponding to equivalence ratios of φ = 0.35–1.81 (λ = 2.85–0.55). Only the core of the jet reached an instantaneous local mass fraction of 0.96, representing undiluted hydrogen and not a combustible mixture. No persistent zones with φ > 1 were detected, confirming that the chosen injection strategy prevents the formation of locally rich pockets. This study confirmed that a suitably selected injection configuration and combustion chamber geometry can significantly contribute to a uniform mixture distribution, a more stable combustion process, and lower NO_x production. The presented findings provide a methodological basis for improving mixture formation strategies in hydrogen engines and may support the development of efficient, zero-carbon powertrains in future mobility systems. Full article

In recent years, honeycomb sandwich structures have seen continuous development due to their excellent structural performance and design flexibility in heat dissipation. However, their complex heat transfer mechanisms and diverse modes of thermal exchange necessitate research on the air flow behavior and temperature distribution characteristics of micro-channels and lattice pores. This study investigates the internal flow field within a ventilated honeycomb sandwich structure through numerical simulation. The spatial flow characteristics and temperature distribution are analyzed, with a focus on the effects of turbulent kinetic energy, heat flux distribution on the heated surface, and varying pressure drop conditions on the thermal performance. The results indicate that the micro-channels inside the honeycomb core lead to a strong correlation between temperature distribution, flow velocity, and turbulence intensity. Regions with higher flow velocity and turbulent kinetic energy exhibit lower temperatures, confirming the critical role of flow motion in heat transfer. Heat flux analysis further verifies that heat is primarily removed by airflow, with superior heat exchange occurring inside the honeycomb cells compared to the solid regions. The intensive mixing induced by highly turbulent flow within the small cells enhances contact with the solid surface, thereby improving heat conduction from the solid to the flow. Moreover, as the inlet pressure increases, the overall temperature gradually decreases but exhibits a saturation trend. This indicates that beyond a certain pressure level, further increasing the inlet pressure yields diminishing returns in heat dissipation enhancement. Full article