Search Results (971)

A three-dimensional numerical simulation of hot air anti-icing was conducted on the full-annular realistic model of engine intake components, comprising the intake ducts, intake casing, struts, axial flow casing, and zero-stage guide vanes, based on the intermittent maximum icing conditions and the actual engine operating parameters. The simulation integrated multi-physics modules, including air-supercooled water droplet two-phase flow around components, water film flow and heat transfer on anti-icing surfaces, solid heat conduction within structural components, hot air flow dynamics in anti-icing cavities, and their coupled heat transfer interactions. Simulation results indicate that water droplet impingement primarily localizes at the leading edge roots and pressure surfaces of struts, as well as the leading edges and pressure surfaces of guide vanes. The peak water droplet collection coefficient reaches 4.2 at the guide vane leading edge. Except for the outlet end wall of the axial flow casing, all anti-icing surfaces of intake components maintain temperatures above the freezing point, demonstrating effective anti-icing performance. The anti-icing characteristics of the intake components are governed by two critical factors: cumulative heat loss along the hot air flow path and heat load consumption for heating and evaporating impinging water droplets. The former induces a 53.9 °C temperature disparity between the first and last struts in the heating sequence. For zero-stage guide vanes, the latter factor exerts a more pronounced influence. Notable temperature reductions occur on the trailing edges of three struts downstream of the hot air flow and at the roots of zero-stage guide vanes. Full article

As cities transition toward intelligent traffic systems, hybrid networks combining AI and traditional intersections raise challenges for efficiency and sustainability. Existing studies primarily focus on global intelligence assumptions, overlooking the practical complexities of hybrid control environments. Moreover, the decision-making processes of AI-based controllers remain opaque, limiting their reliability in dynamic traffic conditions. To address these challenges, this study investigates the following realistic scenario: a Deep Reinforcement Learning (DRL) intersection surrounded by max–pressure-controlled neighbors. A spatiotemporal pressure perception agent is proposed, which (a) uses a novel Holistic Traffic Dynamo State (HTDS) representation that integrates real-time queue, predicted vehicle merging patterns, and approaching traffic flows and (b) innovatively proposes Neighbor–Pressure–Adaptive Reward Weighting (NP-ARW) mechanism to dynamically adjust queue penalties at incoming lanes based on relative pressure differences. Additionally, spatial–temporal pressure features are modeled using 1D convolutional layers (Conv1D) and attention mechanisms. Finally, our Strategy Imitation–Mechanism Attribution framework leverages XGBoost and Decision Trees to systematically analyze traffic condition impacts on phase selection, fundamentally enabling explainable control logic. Experimental results demonstrate the following significant improvements: compared to fixed-time control, our method reduces average travel time by 65.45% and loss time by 85.04%, while simultaneously decreasing average queue lengths and pressure at neighboring intersections by 91.20% and 95.21%, respectively. Full article

Spacecraft and satellites are equipped with cryogenic storage systems to maintain instruments and engines at optimal operating temperatures. However, in cryogenic storage tanks, the steep temperature gradient along the pipeline (arising from sections inside and outside the tank) may induce instability in stored gases such as helium or hydrogen, leading to large-amplitude, self-excited thermoacoustic oscillations, known as Taconis oscillations. Taconis oscillations not only cause structural damage to pipelines, jeopardizing the safety of the cryogenic storage system, but also produce significant heat leakage and boil-off losses of cryogens. This study employs computational fluid dynamics (CFD) to simulate Taconis oscillations within a U-shaped cryogenic helium pipeline. The flow dynamics and acoustic field characteristics of the cryogenic helium pipeline are first analyzed. Fast Fourier transform and wavelet transform are employed to characterize the Taconis oscillations. A subsequent parametric study investigates the influence of the location and magnitude of temperature gradients on the dynamic behavior of Taconis oscillations. Simulation results reveal that the onset temperature gradient is at a minimum when the temperature gradient is applied at one-quarter of the cryogenic pipeline. To prevent the occurrence of Taconis oscillations, the transition between the warm and cold sections should be away from one-quarter of the cryogenic helium pipe. Moreover, increasing the temperature gradient leads to the emergence of multiple oscillation modes and an upward shift in their natural frequencies. This research gives deeper insights into the dynamics of thermally induced thermoacoustic oscillations in cryogenic pipelines, providing guidelines for improving the efficiency and safety of cryogenic storage systems in aerospace engineering. Full article

To alleviate the pressure on grid regulation and ensure grid safety, pumped storage power stations need to frequently start and stop and change operating conditions, leading to the pump-turbine easily entering the hump characteristic zone, causing flow oscillation within the unit and significant changes in its input power, resulting in increased vibration and grid connection failure. The spatial distribution of energy losses and the hydrodynamic flow features within the hump zone of a pump-turbine under pumped storage operation are the focus of the study. The SST k-ω turbulence model is applied in CFD simulations of the pump-turbine within this work, focusing on the unstable operating range of the positive slope, with model testing providing experimental support. The model test method combines numerical simulation with experimental verification. The LEPR method is used to quantitatively investigate the unstable phenomenon in the hump zone, and the distribution law of energy loss is discussed. The results show that, at operating points in the hump zone, up to 72–86% of the energy dissipation is attributed to the runner, the guide vane passage, and the double vane row assembly within the guide vane system. The flow separation in the runner’s bladeless area evolves into a vortex group, leading to an increase in runner energy loss. With decreasing flow rate, the impact and separation of the water flow intensify the energy dissipation. The high-speed gradient change and dynamic–static interference in the bladeless area cause high energy loss in the double vane row area, and energy loss mainly occurs near the bottom ring. In the hump operation zone, the interaction between adverse flows such as vortices and recirculation and the passage walls directly drive the sharp rise in energy dissipation. Full article

Video stabilization is a critical technology for enhancing visual content quality in dynamic shooting scenarios, especially with the widespread adoption of mobile photography devices and Unmanned Aerial Vehicle (UAV) platforms. While traditional digital stabilization algorithms can improve frame stability by modeling global motion trajectories, they often suffer from excessive cropping or boundary distortion, leading to a significant loss of valid image regions. To address this persistent challenge, we propose the View Out-boundary Synthesis Algorithm (VOSA), a symmetry-aware spatio-temporal consistency framework. By leveraging rotational and translational symmetry principles in motion dynamics, VOSA realizes optical flow field extrapolation through an encoder–decoder architecture and an iterative boundary extension strategy. Experimental results demonstrate that VOSA enhances conventional stabilization by increasing content retention by 6.3% while maintaining a 0.943 distortion score, outperforming mainstream methods in dynamic environments. The symmetry-informed design resolves stability–content conflicts and outperforms mainstream methods in dynamic environments, establishing a new paradigm for full-frame stabilization. Full article

This study proposes a novel vehicle speed prediction model for plateau transportation—CLS-DW Stacking (Constrained Least Squares Dynamic Weighting Model Stacking)—which holds significant implications for the sustainable development of transportation systems in high-altitude regions. Research on sharp-curved roads on mountainous plateaus remains scarce. Compared with plain areas, data acquisition in such regions is constrained by government confidentiality policies, while complex environmental and topographical conditions lead to substantial variations in road alignment and elevation. To address these challenges, this study presents a sustainable data acquisition and construction method: unmanned aerial vehicle (UAV) video data are processed through road image segmentation, trajectory tracking, and three-dimensional modeling to generate multi-source heterogeneous datasets for both single-curve and continuous-curve scenarios. Building upon these datasets, the proposed framework integrates constrained least squares with multiple deep learning methods to achieve accurate traffic flow prediction. Bi-LSTM (Bidirectional Long Short-Term Memory), Informer, and GRU (Gated Recurrent Unit) are employed as base learners, and the loss function is redefined with non-negativity and normalization constraints on the weights. This ensures optimal weight coefficients for each base learner, with the final prediction obtained via weighted summation. The experimental results show that, compared with single deep learning models such as Informer, the proposed model reduces the mean squared error (MSE) by 1.9% on the single curve dataset and by 7.7% on the continuous curve dataset. Furthermore, by combining vehicle speed predictions across different altitude gradients with decision tree-based interpretable analysis, this research provides scientific support for developing altitude-specific and precision-oriented speed limit policies. The outcomes contribute to accident risk reduction, traffic congestion mitigation, and carbon emission reduction, thereby improving road resource utilization efficiency. This work not only fills the research gap in traffic prediction for sharp-curved plateau roads but also supports the construction of green transportation systems and the broader objectives of sustainable development in high-altitude regions. Full article

The Vietnamese Mekong Delta (VMD) is experiencing accelerated coastal erosion, driven by upstream sediment trapping, sea-level rise, and local anthropogenic pressures. This study evaluates the effectiveness of pilot breakwater structures in mitigating erosion and supporting mangrove regeneration along the western coast of Ca Mau Province—one of the delta’s most vulnerable shorelines. An integrated methodology combining field-based wave monitoring, remote sensing analysis of shoreline and mangrove changes (2000–2024), and high-resolution Flow-3D hydrodynamic modeling was employed to assess the performance of four breakwater typologies: semi-circular, pile-rock, Busadco, and floating structures. The results show that semi-circular breakwaters achieved the highest wave attenuation, reducing maximum wave height (H_max) by up to 76%, followed by pile-rock (69%), Busadco (66%), and floating structures (50%). Sediment accretion and mangrove stabilization were most consistent around the semi-circular and pile-rock types. Notably, mangrove loss slowed significantly after breakwater installation, with the annual deforestation rate dropping from 7.67 ha/year (2000–2021) to 1.1 ha/year (2021–2024). Simulations further revealed that mangrove width strongly influences wave dissipation, with belts under 5 m offering minimal protection. The findings highlight the potential of hybrid coastal protection strategies that combine engineered structures with ecological buffers. Modular solutions such as floating breakwaters offer flexibility to adapt with evolving shoreline dynamics. These findings inform scalable coastal protection strategies under sediment-deficit conditions. This study contributes to Vietnam’s Coastal Development Master Plan and broader resilience efforts under Sustainable Development Goals (SDGs) 13 and 14, providing evidence to inform the design and scaling of adaptive, nature-based infrastructure in sediment-challenged deltaic environments. Full article

The present study reports the outcome of an experimental study of organic vapor trailing edge flows. As a working fluid, the organic vapor Novec 649 was used under representative pressure and temperature conditions for organic Rankine cycle (ORC) turbine applications characterized by values of the fundamental derivative of gas dynamics below unity. An idealized vane configuration was placed in the test section of a closed-loop organic vapor wind tunnel. The effect of the Reynolds number was assessed independently from the Mach number by charging the closed wind tunnel. The airfoil surface roughness and the trailing edge shape were evaluated by experimenting with different test blades. The flow and the loss behavior were obtained using Pitot probes, static wall pressure taps, and background-oriented schlieren (BOS) optics. Isentropic exit Mach numbers up to 1.5 were investigated. Features predicted via a simple flow model proposed by Denton and Xu in 1989 were observed for organic vapor flows. Still, roughness affected the downstream loss behavior significantly due to shockwave boundary-layer interactions and flow separation. The new experimental results obtained for this organic vapor are compared with correlations from the literature and available loss data. Full article

Gas coolers are critical elements of turbogenerator cooling systems, which ensure the reliability and stability of the thermal mode of high-power electric machines. The aim of this research is to improve the accuracy of thermal calculations of gas coolers by combining analytical methods with numerical CFD-modeling (Computation Fluid Dynamics). The cooler’s total cooling capacity is approximately 3.8 MW, distributed across three identical sections.An analytical calculation of heat transfer for a hydrogen-water gas cooler with finned tubes was performed, using classical dependencies to determine the heat transfer coefficients and pressure losses. The results were verified using three-dimensional CFD-modeling of the hydrogen flow through the cooler using the standard k-ε (k-epsilon) turbulence model. The discrepancy between the results of analytical and numerical calculations is less than 10%. The temperature of the cooled hydrogen at the outlet meets the design requirements (+40 °C); however, areas of uneven temperature distribution were identified that require further design optimization. The study introduces, for the first time, a combined approach using analytical calculations and CFD by thoroughly evaluating the heat exchange between the cooling tube fins and hydrogen. This scientific solution enabled the simulation of hydrogen flow within the multi-stage cooler system. The proposed method has proven to be reliable and can be applied both at the design stage and for the analysis of upgraded cooling systems of turbogenerators. Full article

To address compacted soils with high power consumption and waterlogging risks in rice–rapeseed rotation areas of the Yangtze River, this study designed a ditching machine combining a stepped cutter head and trapezoidal cleaning blade, where the mechanical synergy between components minimizes energy loss during soil-cutting and -throwing processes. We mathematically modeled soil cutting–throwing dynamics and blade traction forces, integrating soil rheological properties to refine parameter interactions. Discrete Element Method (DEM) simulations and single-factor experiments analyzed impacts of the inner/outer blade widths, blade group distance, and blade opening on power consumption. Results indicated that increasing the inner/outer blade widths (200–300 mm) by expanding the direct cutting area significantly reduced the cutter torque by 32% and traction resistance by 48.6% from reduced soil-blockage drag; larger blade group distance (0–300 mm) initially decreased but later increased power consumption due to soil backflow interference, with peak efficiency at 200 mm spacing; the optimal blade opening (586 mm) minimized the soil accumulation-induced power loss, validated by DEM trajectory analysis showing continuous soil flow. Box–Behnken experiments and genetic algorithm optimization determined the optimal parameters: inner blade width: 200 mm; outer blade width: 300 mm; blade group distance: 200 mm; and blade opening: 586 mm, yielding a simulated power consumption of 27.07 kW. Field tests under typical 18.7% soil moisture conditions confirmed a <10% error between simulated and actual power consumption (28.73 kW), with a 17.3 ± 0.5% reduction versus controls. Stability coefficients for the ditch depth, top/bottom widths exceeded 90%, and the backfill rate was 4.5 ± 0.3%, ensuring effective drainage for rapeseed cultivation. This provides practical theoretical and technical support for efficient ditching equipment in rice–rapeseed rotations, enabling resource-saving design for clay loam soils. Full article

In wind tunnel tests for aerospace and bridge engineering, the accurate prediction of Mach number remains a core challenge to ensure the reliability of airflow dynamics characterization. Pure data-driven models often fail to meet high-precision prediction requirements due to the lack of physical mechanism constraints and insufficient generalization capability. This paper proposes a physical information-based long short-term memory network (P-LSTM), which constructs a physical loss function by embedding isentropic flow equations from gas dynamics, thereby constraining the Mach number prediction solution space within the physically feasible domain. This approach effectively balances the neural network’s ability to capture temporal features with the interpretability of physical mechanisms. Aiming at the scarcity of data in new wind tunnel scenarios, an adaptive weight transfer learning method (AWTL) is further proposed, realizing efficient knowledge transfer across different-scale wind tunnels via cross-domain data calibration, adaptive source-domain weight reweighting, and target-domain fine-tuning. Experimental results show that the P-LSTM method achieves a 50.65–62.54% reduction in RMSE, 48.00–54.05% in MAE, and 47.88–73.68% in MD compared with traditional LSTM for Mach number prediction in the 0.6 m continuous wind tunnel flow field. The AWTL model also outperforms the direct training model significantly in the 2.4 m continuous wind tunnel, with RMSE, MAE, and MD reduced by 85.26%, 95.12%, and 71.14%, respectively. These results validate that the proposed models achieve high-precision Mach number prediction with strong generalization capability. Full article

This study aims to clarify the nonlinear pressure loss patterns of the pneumatic system in a pneumatic seeder under varying pipeline structures and airflow parameters, and to develop a rapid prediction equation for the main pipe’s pressure loss. The studied multi-branch pipeline system consists of a main pipe, a header, and ten branch pipes. The main pipe is vertically installed at the center of the header in a straight-line configuration. The ten branch pipes are symmetrically and evenly spaced along the axial direction of the header, distributed on both sides of the main pipe. The outlet directions of the branch pipes are arranged in a 180° orientation opposite to the inlet direction of the main pipe, forming a symmetric multi-branch configuration. Firstly, this study investigated the flow characteristics within the multi-branch pipeline of the pneumatic system and elaborated on the mechanism of flow division in the pipeline. The key geometric factors affecting airflow were identified. Secondly, from a microscopic perspective, CFD simulations were employed to analyze the fundamental causes of pressure loss in the multi-branch pipeline system. Finally, from a macroscopic perspective, a dimensional analysis method was used to establish an empirical equation describing the relationship between the pressure loss (P) and several influencing factors, including the air density (ρ), air’s dynamic viscosity (μ), closed-end length of the header (Δl), branch pipe 1’s flow rate (Q), main pipe’s inner diameter (D), header’s inner diameter (γ), branch pipe’s inner diameter (d), and the spacing of the branch pipe (δ). The results of the bench tests indicate that when 0.0018 m³·s⁻¹ ≤ Q ≤ 0.0045 m³·s⁻¹, 0.0272 m < d ≤ 0.036 m, 0.225 m < δ ≤ 0.26 m, 0.057 m ≤ γ ≤ 0.0814 m, and 0.0426 m ≤ D ≤ 0.0536 m, the prediction accuracy of the empirical equation can be controlled within 10%. Therefore, the equation provides a reference for the structural design and optimization of pneumatic seeders’ multi-branch pipelines. Full article

The formation mechanism of Virtual Reality (VR) Immersive Experience (VRIE) is notably complex; this study aimed to dynamically decode its underlying drivers by innovatively integrating Flow Theory and Privacy Calculus Theory, focusing on Perceptual-Interactive Fidelity (PIF), Consumer Willingness to Immerse in Technology (CWTI), and the applicability of Loss Aversion Theory. To achieve this, we analyzed approximately 30,000 user reviews from Amazon using Latent Semantic Analysis (LSA) and regression analysis. The findings reveal that user attention’s impact on VRIE is non-linear, suggesting an optimal threshold, and confirm PIF as a central influencing mechanism; furthermore, CWTI significantly moderates users’ privacy calculus, thereby affecting VRIE, while Loss Aversion Theory showed limited explanatory power in the VR context. These results provide a deeper understanding of VR user behavior, offering significant theoretical guidance and practical implications for future VR system design, particularly in strategically balancing user cognition, PIF, privacy concerns, and individual willingness. Full article

Background/Objectives: Magnetic iron oxide nanoparticles (IONPs), human serum albumin (HSA) and folic acid (FA) are prospective components for hybrid nanosystems for various biomedical applications. The magnetic nanosystems FA-HSA@IONPs (FAMs) containing IONPs, HSA, and FA residue are engineered in the study. Methods: Composition, stability and integrity of the coating, and peroxidase-like activity of FAMs are characterized using UV/Vis spectrophotometry (colorimetric test using o-phenylenediamine (OPD), Bradford protein assay, etc.), spectrofluorimetry, dynamic light scattering (DLS) and electron magnetic resonance (EMR). The selectivity of the FAMs accumulation in cancer cells is analyzed using flow cytometry and confocal laser scanning microscopy. Results: FAMs (d_N~55 nm by DLS) as a drug delivery platform have been administered to cancer cells (human breast adenocarcinoma MCF-7 and MDA-MB-231 cell lines) in vitro. Methylene blue, as a model photosensitizer, has been non-covalently bound to FAMs. An increase in photoinduced cytotoxicity has been found upon excitation of the photosensitizer bound to the coating of FAMs compared to the single photosensitizer at equivalent concentrations. The suitability of the nanosystems for photodynamic therapy has been confirmed. Conclusions: FAMs are able to effectively enter cells with increased folate receptor expression and thus allow antitumor photosensitizers to be delivered to cells without any loss of their in vitro photodynamic efficiency. Therapeutic and diagnostic applications of FAMs in oncology are discussed. Full article

The efficiency decline of Francis turbines, a key component of hydroelectric power generation, presents a multifaceted challenge influenced by interconnected factors such as water quality, incidence angle, erosion, and runner wear. This paper is structured into two main sections to address these issues. The first section applies the Buckingham π theorem to establish a dimensional analysis (DA) framework, providing insights into the relationships among the operational variables and their impact on turbine wear and efficiency loss. Dimensional analysis offers a theoretical basis for understanding the relationships among operational variables and efficiency within the scope of this study. This understanding, in turn, informs the selection and interpretation of features for machine learning (ML) models aimed at the predictive maintenance of the target variable and important features for the next stage. The second section analyzes an extensive dataset collected from a Francis turbine in Colombia, a country that is heavily reliant on hydroelectric power. The dataset consisted of 60,501 samples recorded over 15 days, offering a robust basis for assessing turbine behavior under real-world operating conditions. An exploratory data analysis (EDA) was conducted by integrating linear regression and a time-series analysis to investigate efficiency dynamics. Key variables, including power output, water flow rate, and operational time, were extracted and analyzed to identify patterns and correlations affecting turbine performance. This study seeks to develop a comprehensive understanding of the factors driving Francis turbine efficiency loss and to propose strategies for mitigating wear-induced performance degradation. The synergy lies in DA’s ability to reduce dimensionality and identify meaningful features, which enhances the ML models’ interpretability, while ML leverages these features to model non-linear and time-dependent patterns that DA alone cannot address. This integrated approach results in a linear regression model with a performance (R²-Test = 0.994) and a time series using ARIMA with a performance (R²-Test = 0.999) that allows for the identification of better generalization, demonstrating the power of combining physical principles with advanced data analysis. The preliminary findings provide valuable insights into the dynamic interplay of operational parameters, contributing to the optimization of turbine operation, efficiency enhancement, and lifespan extension. Ultimately, this study supports the sustainability and economic viability of hydroelectric power generation by advancing tools for predictive maintenance and performance optimization. Full article