Search Results (397)

The study of the dynamics during droplet breakup is fascinating to engineers. Some industrial applications include fire extinguishing by sprinkler systems, painting of various components, washing processes, and fuel spraying in internal combustion engines, which involve the interaction between liquid droplets, gaseous flow field, and walls. In this work, washing operations effectiveness of civil aviation aircraft engines is analyzed. Periodic washing operations are necessary to slow down the effects of particle deposition, e.g., gas turbine fouling, to reduce the specific fuel consumption and the environmental impact of the gas turbine operation. This analysis describes the dynamics in the primary breakup, related to the breakup of droplets due to aerodynamic forces, which occur when the droplets are set in motion in a fluid domain. The secondary breakup is also considered, which more generally refers to the impact of droplets on surfaces. The latter is studied with particular attention to dry surfaces, investigating the limits for different breakup regimes and how these limits change when the impact occurs with surfaces characterized by different wettability. Surfaces with different roughness are also compared. All the tested cases are referred to surfaces at ambient temperature. Dimensionless numbers generalize the analysis to describe the droplet behavior. The analysis is based on several data reported in the open literature, demonstrating how different washing operations involve different droplet breakup regimes, generating a non-trivial data interpretation. Impact dynamics, droplet characteristics, and erosion issues are analyzed, showing differences and similarities between the literature data proposed in the last twenty years. Washing operation and the effects of gas turbine fouling on the aero-engine performance are still under investigation, demonstrating how experiments and numerical simulations are needed to tackle this detrimental issue. Full article

This study applied clustering algorithms to reveal latent structures in 9791 Brazilian civil aviation occurrences recorded from 2007 to 2023. We tested K-means, hierarchical clustering, and K-medoids, using aircraft type, flight phase, and severity as variables in different configurations. The K-medoids method with Manhattan distance produced the best separation. It formed clusters that isolated accidents involving helicopters, ultralights, and critical phases such as takeoff and landing. It also highlighted a specific group of specialized operations. Results confirm that occurrences with similar operational profiles tend to group together, which may help prioritize investigation and prevention actions. The analysis also shows that combining different types of aviation in the same dataset reduces specificity, as heterogeneous operations are mixed. Even so, the findings provide a first overview of safety dynamics in Brazilian civil aviation. The study concludes that clustering can expose latent structures not detected by traditional descriptive analyses and may support the development of more targeted safety policies. Full article

With the rapid development of the civil aviation industry, the reliability and real-time performance of airborne data transmission are becoming increasingly important. The traditional airborne network cannot meet the future flight requirements of the aircraft. To ensure the reliable and real-time transmission of data, the time-sensitive network introduces the Frame Replication and Elimination for Reliability (FRER) mechanism. The standard FRER mechanism defines the methods of frame replication and elimination of redundant frames. However, the description of how the replicated frames are transmitted is not in-depth. The frame replication and elimination function at the source and destination nodes will also reduce the reliability and real-time performance of the network. In order to realize the application of the time-sensitive network in the airborne network, this article independently builds an airborne time-sensitive network test simulation platform. It carries out in-depth research on improving the reliability of the network. It puts forward a path-finding algorithm based on a time-sensitive network with the FRER mechanism in response to the problem of low reliability of the selected data transmission paths in the airborne network. The algorithm integrates the constraints of transmission link delay and packet loss rate. It performs link reliability calculation before selecting redundant paths to obtain non-overlapping data transmission paths. The experimental results show that, compared with the dynamic link redundancy selection algorithm, the path delay is reduced by 21.51%. Compared with the multilevel P-cycle cascading algorithm, the path delay is reduced by 19.70%. At a 120 Mbps data transmission rate, the packet loss rate is reduced by 18.67% compared with the dynamic link redundancy selection algorithm. It is also reduced by 24.00% compared with the multilevel P-cycle cascading algorithm. These results show that the proposed method improves the reliability of data transmission in the airborne network. Full article

The aviation industry needs to develop sustainable, fire-safe cabin interior materials. Although wood is eco-friendly, its high flammability makes it challenging to meet flame retardant standards. Enhancing wood fire safety requires the creation of an environmentally friendly and flame retardant coating. In this study, a new type of intumescent flame retardant (IFR) coating was applied to the wood surface using the layer-by-layer (LBL) technique, with fully bio-based chitosan (CS), agar, and phytic acid (PA) as key components. The coated wood demonstrated improved durability, flame resistance, and thermal stability. Particularly, the Wood-2 sample achieved a vertical burning test (UL-94) V-0 rate and a limiting oxygen index (LOI) of 53.1%, which exceeded most previous reported flame retardant coatings. Cone calorimeter test and infrared thermography analysis confirmed that a thick layer of intumescent char formed when the coating was exposed to heat, effectively hindering heat transfer and oxygen supply. This flame retardant effect is attributed to a synergistic mechanism involving nitrogen/phosphorus (N/P) elements. This study offers an environmentally friendly solution for wood flame retardancy and lays an experimental and theoretical foundation for the development of green aviation interior materials. Full article

The wake vortex of an aircraft can be hazardous to aviation operations. Therefore, the International Civil Aviation Organization has established requirements regarding the separation of aircraft. In light of the current implementation of regulations, this systematic study was the first of its kind investigating wake vortices of aircraft at the new north runway of Hong Kong International Airport (HKIA). A short-range light detection and ranging (SR-LIDAR) system, previously installed by the Hong Kong Observatory at HKIA, performed range–height indicator scans at the recently commissioned north runway end to capture wake vortices of arriving aircraft. The lifetimes of the wake vortices were calculated, and the exit times of the vortices away from the runway were determined. Based on an analysis of data from a period of approximately eight weeks—mostly during summer with its prevailing southwestern monsoon—it was found that, as in a previous study, the displacement of vortices increased with the radial background velocity. Moreover, approximately 0.6% of aircraft may be susceptible to encountering the vortex left behind by the preceding aircraft. Analysis of data from a second period of approximately four weeks revealed that vortex lifetimes were negatively correlated with the magnitude of the turbulence intensity expressed in terms of the eddy dissipation rate. Correlations with various other meteorological and non-meteorological factors were not apparent. The results of the present study supplement previous work in Hong Kong with a site-specific dataset for the new commissioned north runway, provide validation of established principles with an initial assessment of operational risk of turbulence encounter, and pave the way for longer-term statistical analysis of the behaviour of aircraft wake vortices in the climate of Hong Kong. Full article

Doped perovskites are widely studied in the domain of perovskite material design. However, due to the limited data available for the target materials, machine learning methods based on small datasets become particularly important. In this study, we propose a transfer learning strategy aimed at predicting doped perovskites on limited data samples. This strategy first utilizes the ABO₃-type perovskite dataset to develop a deep learning source model based on its formation energies. Then, fine-tuning is performed on the doped perovskite structure dataset to obtain a model with good transferability, applicable to the doped perovskite oxide target domain. Based on the transfer learning model, we further predict the formation energies of 12,897 A₂BB′O₆ compounds, 10,401 AA′B₂O₆ compounds, and 49,723 AA′BB′O₆ compounds. With the tolerance factor $\mathrm{t} \in [0.7–1.1]$, octahedral factor $\mathsf{\mu } \in [0.45–0.7]$, and the modified tolerance factor $\mathsf{\tau } \in [0, 4.18]$ for screening, we successfully predict 3389 A₂B′BO₆, 3002 AA′B₂O₆, and 13,563 AA′BB′O₆ structures as potential stable doped perovskite candidates. Among these filtered results, 821 A₂B′BO₆, 69 AA′B₂O₆, and 6 AA′BB′O₆ compounds have been reported in the OQMD database. For each doped perovskite, we select the candidate with the lowest formation energy and perform DFT validation. This resulted in three newly reported stable doped perovskite materials: CaSrHfScO₆, BaSrHf₂O₆, and Ba₂HfNdO₆. The transfer learning-based perovskite material design method proposed in this study not only effectively addresses the challenges of model training on small datasets but also significantly improves the accuracy and stability of doped perovskite material predictions. Through transfer learning, the model can fully leverage the data and knowledge from the ABO₃-type perovskite, effectively overcoming the problem of limited data. This strategy provides a new approach for efficient perovskite material design, enabling broader structural and performance predictions under limited experimental data conditions, and offering a powerful tool for the development of novel functional materials. Full article

Minimum Ignition Energy (MIE) is a critical parameter for assessing the combustion and explosion risks of liquid fuels under specific conditions. However, systematic testing methods for long-chain alkanes remain underdeveloped. In this study, an experimental apparatus was developed based on American Society for Testing and Materials Standard ASTM E582-21 to measure the MIE of liquid fuel vapors. Through systematic measurements of the minimum ignition energy (MIE) of alkane vapors, this study examines the influence of vapor concentration on MIE and elucidates the dependence of ignition energy on carbon chain length. System sensitivity parameters were calibrated using propane/air mixtures, establishing optimal testing conditions as a 2.0 mm electrode gap and a 14.0 pF capacitance. The measured minimum ignition energy (MIE) values for C₅–C₈ alkane vapors at their respective sensitive volume fractions were 0.197 mJ (at 3.4 vol%), 0.253 mJ (at 3.3 vol%), 0.303 mJ (at 3.0 vol%), and 0.323 mJ (at 2.8 vol%). The experimentally determined MIE values for C₅–C₈ alkane vapors demonstrate close agreement with literature data, confirming the reliability of the experimental system and methodology for MIE determination of liquid fuel vapors. Furthermore, the study reveals a characteristic V-shaped correlation between MIE and vapor concentration, along with a consistent shift in the sensitive concentration toward fuel-rich conditions relative to stoichiometric proportions. Extended measurements of C₉–C₁₁ alkanes revealed MIE values of 0.523 mJ (at 2.8 vol%) for n-nonane, 0.857 mJ (at 2.5 vol%) for n-decane, and 1.127 mJ (at 2.0 vol%) for n-undecane. Notably, the results demonstrate a substantial increase in MIE with carbon chain length, showing a 471% rise from C₅ to C₁₁. A nonlinear regression analysis confirmed a strong correlation between MIE and carbon chain length (R² = 0.98). Full article

The optimization of material design for subway interior structure is crucial for noise reduction and sustainability. Plant fiber-reinforced composites (PFRCs) used as interior structures offer both adequate load-bearing capacity and vibration reduction. In this study, a hybrid fiber technique was employed, integrating the Hashin failure criterion and complex eigenvalue method to investigate bending and damping performances of five distinct carbon/flax fiber-reinforced epoxy composite (CFFRC) stacking sequences (C80, C20F20C20, F15C20F15, F10C10F10C10F10, and F40) of an interior structure. The CFFRCs were fabricated via a hot press platen process with a consistent 60% overall fiber volume fraction. The experimental modal behaviors (damping ratios, frequencies, and mode shapes) were clarified by vibration tests using a non-contacting 3D Scanning Laser Doppler Vibrometer. The results revealed that hybrid composites can effectively balance the mechanical and damping properties. Hybrid composites with the flax fiber positioned in the outermost layer demonstrated superior damping performances. The optimal hybrid composite (F10C10F10C10F10) achieved a first-order modal damping ratio of 0.75% (numerically), which is significantly higher than the 0.30% observed for pure carbon fiber composites (C80). The numerical model’s validity was confirmed by a strong correlation with experimental results. It provides valuable parameters for designing safe and reliable subway interior structures, integrating load-bearing and damping capabilities. Full article

The use of artificial oxygenation to counteract the effects of hypoxia and improve living standards in high-altitude, low-oxygen settings is widespread. A recognized consequence of this intervention is that it elevates the risk of fire occurrence. In this study, we simulated a real fire environment with low-pressure oxygen enrichment in a plateau area. A new multi-measuring apparatus was constructed by integrating an electronic control cone heater and a low-pressure oxygen enrichment combustion platform to enable the simultaneous measurement of multiple parameters. The combined effects of varying oxygen concentrations and thermal irradiance on the combustion behavior of wood plastic composites (WPCs) under specific low-pressure conditions were investigated, and alterations in crucial combustion parameters were examined and evaluated. Increasing the oxygen concentration and heat flux significantly reduced the ignition and combustion times. For instance, at 50 kW/m², the ignition time decreased from 75 s to 16 s as the oxygen concentration increased from 21% to 35%. This effect was suppressed by higher heat fluxes. Compared with low oxygen concentrations and low thermal radiation environments, the ignition time of the material under high oxygen concentrations and high thermal radiation conditions was shortened by more than 78%, indicating that its flammability is enhanced under extreme conditions. Higher oxygen concentrations enhanced the heat feedback to the fuel surface, which accelerated pyrolysis and yielded a more compact flame with reduced dimensions and a color transition from blue-yellow to bright yellow. This intensified combustion was further manifested by an increased mass loss rate (MLR), elevated flame temperature, and a decline in residual mass percentage. The combustion of WPCs displayed distinct stage characteristics, exhibiting “double peak” features in both the MLR and flame temperature, which were attributed to the staged pyrolysis of its wood fiber and plastic components. Full article

With the rapid expansion of the civil aviation industry, the surge in flight numbers has led to increasingly pronounced issues of air route congestion and flight conflicts. 4D trajectory prediction, by dynamically adjusting aircraft paths in real time, can prevent air route collisions, alleviate air traffic pressure, and ensure flight safety. Therefore, this paper proposes a combined model—GAT-BiGRU-TPA—based on the Spatio-Temporal Graph Neural Network (STGNN) framework to achieve refined 4D trajectory prediction. This model integrates Graph Attention Networks (GAT) to extract multidimensional spatial features, Bidirectional Gated Recurrent Units (BiGRU) to capture temporal dependencies, and incorporates a Temporal Pattern Attention (TPA) mechanism to emphasize learning critical temporal patterns. This enables the extraction of key information and the deep fusion of spatio-temporal features. Experiments were conducted using real trajectory data, employing a grid search to optimize the observation window size and label length. Results demonstrate that under optimal model parameters (observation window: 30, labels: 4), the proposed model achieves a 45.72% reduction in mean Root Mean Square Error (RMSE) and a 43.40% decrease in Mean Absolute Error (MAE) across longitude, latitude, and altitude compared to the optimal baseline BiLSTM model. Prediction accuracy significantly outperforms multiple mainstream benchmark models. In summary, the proposed GAT-BiGRU-TPA model demonstrates superior accuracy in 4D trajectory prediction, providing an effective approach for refined trajectory management in complex airspace environments. Full article

The widespread adoption of fifth-generation Reduced Instruction Set Computing (RISC-V) processors in embedded systems has driven advancements in domestic processor design. However, research on processor performance optimization methods predominantly focuses on two- to three-stage pipeline architectures, with relatively few studies addressing complex five-stage pipeline processors. This study addresses this gap by analyzing optimization strategies for a five-stage pipeline processor architecture. Key areas examined include RISC-V jump instruction branch prediction (speed optimization), memory structure (memory access and resource optimization), and data-correlation-based division operations (fetch optimization). The processor core underwent CoreMark benchmark testing via a Field Programmable Gate Array (FPGA), analyzing the impact of optimizations such as branch prediction and cache on processor performance. The final processor achieved a CoreMark score of 2.92 CoreMark/MHz, outperforming most open-source processors and validating the effectiveness of the optimization strategies. Full article

Carbon fiber-reinforced polymer (CFRP) composite–metal joint structures are susceptible to localized discharge and thermal damage under lightning current, posing serious safety concerns for critical aircraft components such as fuel tanks. In this study, we investigated the conductive behavior of composite–metal lap joint structures subjected to multiple continuous lightning current components (A, B, and C*) through a combination of experimental testing and numerical simulations. The effects of fastener assembly methods on ignition events were systematically examined, and the ignition source generation mechanisms under interference-fit and clearance-fit conditions were revealed. The protective performance of different assembly approaches against ignition sources was also evaluated. The results indicate that the assembly type and installation method have a pronounced influence on the ignition threshold and damage modes. Specifically, interference-fit joints with wet installation exhibited no ignition even at a current of 91 kA, whereas clearance-fit joints without wet installation generated potential ignition sources at 14 kA. Wet installation effectively increased the ignition threshold by approximately twofold. Copper mesh on the composite surface played a crucial role in current conduction. The simulation results further demonstrated that the current became concentrated at the composite–metal interface upon removal of the copper mesh, causing local temperatures to exceed the resin pyrolysis temperature (893 K), thereby creating potential ignition sites. This study enhances the understanding of lightning ignition mechanisms in composite–metal lap joint structures and provides both theoretical and experimental foundations for improving lightning protection design in aircraft fuel tank structures. Full article

The vertical tail plays a crucial role in aircraft directional stability and lateral control, especially during low-speed operations such as takeoff and landing. This study examines the effect of aircraft mass on vertical tail geometry through a statistical analysis of 65 design parameters from civil jet aircraft. Aerodynamic performance of a sub-scale Boeing 777-200 vertical tail model was further investigated in a low-speed wind tunnel under rudder deflections and sideslip angles. Experiments were conducted at freestream speeds of 20 and 30 m/s, corresponding to Reynolds numbers of 5 × 10⁵ and 7.5 × 10⁵, with model blockage ratios below 2% in all configurations. Side force and drag coefficients were measured for rudder deflections from −30° to +30° and sideslip angles from −7.5° to +7.5°. Results show a nearly linear variation of side force with rudder deflection, while drag exhibits noticeable nonlinearity at higher deflections. At zero sideslip, increasing rudder deflection from 0° to 30° raised the side force coefficient from 0 to 0.65, with a maximum uncertainty of ±0.011, while drag coefficient uncertainty remained below ±0.0055. Furthermore, the application of positive or negative sideslip resulted in substantial variations in the side force coefficient, reaching values of up to ±1.1 depending on the direction. By integrating experimental data with statistical analysis of real aircraft geometries, this study provides reliable quantitative benchmarks and highlights the vertical tail’s aerodynamic importance. Full article

Clear-air turbulence (CAT), as a key meteorological hazard threatening aviation safety, urgently requires the revelation of its spatiotemporal distribution patterns and formation mechanisms within the China region. Based on the first release of 12,539 aircraft turbulence voice reports from China’s civil aviation from 2022 to 2024 and ERA5 high-resolution reanalysis data, this study constructs for the first time a climatological portrait of aircraft turbulence over China, revealing the spatiotemporal distribution characteristics and formation mechanisms of CAT in the region: turbulence occurs predominantly at 3000–8000 m (accounting for 61.0%), peaking at 7000–8000 m, driven by strong low-level jet wind shear and Kelvin–Helmholtz instability (KHI); wintertime exhibits a high frequency (33.4%) stemming from strong upper-level jets (>30 m s⁻¹), while summer is dominated by low-level thermal convection (21.0%); the high-incidence zones of Central-South and Southwest China (>2800 events) are jointly governed by a mid-level strong horizontal gradient of vertical vorticity, divergence perturbations, and jet shear, with the winter jet shifting southward (22–30° N), further intensifying the turbulence risk. The findings establish a dynamic–thermodynamic coupling mechanism for CAT over China, providing a scientific basis for aviation safety early warning. Full article

Foreign object debris (FOD) in civil aviation environments poses severe risks to flight safety. Conventional detection primarily relies on manual visual inspection, which is inefficient, susceptible to fatigue-related errors, and carries a high risk of missed detections. Therefore, there is an urgent need to develop an efficient and convenient intelligent method for detecting aircraft FOD. This study proposes a detection model based on a Siamese network architecture integrated with a spatial transformation module. The proposed model identifies FOD by comparing the registered features of evidence-retention images with their corresponding normally distributed features. A dedicated aircraft FOD dataset was constructed for evaluation, and extensive experiments were conducted. The results indicate that the proposed model achieves an average improvement of 0.1365 in image-level AUC (Area Under the Curve) and 0.0834 in pixel-level AUC compared to the Patch Distribution Modeling (PaDiM) method. Additionally, the effects of the spatial transformation module and training dataset on detection performance were systematically investigated, confirming the robustness of the model and providing guidance for parameter selection in practical deployment. Overall, this research introduces a novel and effective approach for intelligent aircraft FOD detection, offering both methodological innovation and practical applicability. Full article