Search Results (703)

To enhance the reliability of electric propulsion in electric aircraft and address power interruptions caused by current sensor failures, this study proposes a current sensorless fault-tolerant control strategy for permanent magnet synchronous motors (PMSMs) based on a long short-term memory (LSTM) network. First, a hierarchical architecture is constructed to fuse multi-phase electrical signals in the fault diagnosis layer (sliding mode observer). A symbolic function for the reaching law observer is designed based on Lyapunov theory, in order to generate current predictions for fault diagnosis. Second, when a fault occurs, the system switches to the LSTM reconstruction layer. Finally, gating units are used to model nonlinear dynamics to achieve direct mapping of speed/position to phase current. Verification using a physical prototype shows that the proposed method can complete mode switching within 10 ms after a sensor failure, which is 80% faster than EKF, and its speed error is less than 2.5%, fully meeting the high speed error requirements of electric aircraft propulsion systems (i.e., ≤3%). The current reconstruction RMSE is reduced by more than 50% compared with that of the EKF, which ensures continuous and reliable control while maintaining the stable operation of the motor and realizing rapid switching. The intelligent algorithm and sliding mode control fusion strategy meet the requirements of high real-time performance and provide a highly reliable fault-tolerant scheme for electric aircraft propulsion. Full article

Ultrahigh-temperature ceramic composites based on hafnium diboride have a wide range of applications, including as components for high-speed aircraft and energy generation and storage devices. Consequently, developing methodologies for their fabrication and studying their properties are of paramount importance, in particular in using them as an electrode material for energy storage devices with increased oxidation resistance. This study investigates the behavior of ceramic composites based on the HfB₂-HfO₂-SiC system, obtained using 15 vol% Ti₂AlC MAX-phase as a sintering component, under the influence of subsonic flow of dissociated air. It was determined that incorporating the modifying component (Ti₂AlC) altered the composition of the silicate melt formed on the surface during ceramic oxidation. This modification led to the observation of a protective antioxidant function. Consequently, liquation was observed in the silicate melt layer, resulting in the formation of spherical phase inhomogeneities in its volume with increased content of titanium, aluminum, and hafnium. It is hypothesized that the increase in the high-temperature viscosity of this melt prevents it from being carried away in the form of drops, even at a surface temperature of ~1900–2000 °C. Despite the established temperature, there is no sharp increase in its values above 2400–2500 °C. This is due to the evaporation of silicate melt from the surface. In addition, the electrochemical behavior of the obtained material in a liquid electrolyte medium (KOH, 3 mol/L) was examined, and it was shown that according to the value of electrical conductivity and specific capacitance, it is a promising electrode material for supercapacitors. Full article

Synthetic aperture radar (SAR), as an active microwave imaging system, has the capability of all-weather and all-time observation. In response to the challenges of aircraft detection in SAR images due to the complex background interference caused by the continuous scattering of airport buildings and the demand for real-time processing, this paper proposes a YOLOv7-MTI recognition model that combines the attention mechanism and involution. By integrating the MTCN module and involution, performance is enhanced. The Multi-TASP-Conv network (MTCN) module aims to effectively extract low-level semantic and spatial information using a shared lightweight attention gate structure to achieve cross-dimensional interaction between “channels and space” with very few parameters, capturing the dependencies among multiple dimensions and improving feature representation ability. Involution helps the model adaptively adjust the weights of spatial positions through dynamic parameterized convolution kernels, strengthening the discrete strong scattering points specific to aircraft and suppressing the continuous scattering of the background, thereby alleviating the interference of complex backgrounds. Experiments on the SAR-AIRcraft-1.0 dataset, which includes seven categories such as A220, A320/321, A330, ARJ21, Boeing737, Boeing787, and others, show that the mAP and mRecall of YOLOv7-MTI reach 93.51% and 96.45%, respectively, outperforming Faster R-CNN, SSD, YOLOv5, YOLOv7, and YOLOv8. Compared with the basic YOLOv7, mAP is improved by 1.47%, mRecall by 1.64%, and FPS by 8.27%, achieving an effective balance between accuracy and speed, providing research ideas for SAR aircraft recognition. Full article

In this study, we evaluated the spatial variability in droplet deposition in herbicide applications using a remotely piloted aircraft (RPA) in pasture areas. The investigation was conducted in a square grid (50.0 m × 50.0 m), with 121 sampling points, at two operational flight heights (3.0 and 4.0 m). Droplet deposition was quantified using the fluorescent dye rhodamine B, and the droplet spectrum was characterised using water-sensitive paper tags. Geostatistical analysis was implemented to characterise spatial dependence, complemented by multivariate statistical analysis. Droplet deposition ranged from 1.01 to 9.02 and 1.10–6.10 μL cm⁻² at 3.0 and 4.0 m flight heights, respectively, with the coefficients of variation between 19.72 and 23.06% for droplet spectrum parameters. All droplet spectrum parameters exhibited a moderate to strong spatial dependence (relative nugget effect ≤75%) and a predominance of adjustment to the exponential model, with spatial dependence indices ranging from 12.55 to 47.49% between the two flight heights. Significant positive correlations were observed between droplet deposition and droplet spectrum parameters (r = 0.60–0.79 at 3.0 m; r = 0.37–0.66 at 4.0 m), with the correlation magnitude decreasing as the operational flight height increased. Cross-validation indices demonstrated acceptable accuracy in spatial prediction, with a mean estimation error ranging from −0.030 to 0.044 and a root mean square error ranging from 0.81 to 2.25 across parameters and flight heights. Principal component analysis explained 99.14 and 85.72% of the total variation at 3.0 and 4.0 m flight heights, respectively. The methodological integration of geostatistics and multivariate statistics provides a comprehensive understanding of the spatial variability in droplet deposition, with relevant implications for the optimisation of phytosanitary applications performed using RPAs. Full article

We investigate how the factors influencing aircraft utilization have changed during and post-Pandemic depending on the business model before. We classify the Pandemic into three periods (pre-, during and post- Pandemic) and the business models into three types (Total, FSC and LCC). For each group, we analyze the importance of factors using the SHAP and Random Forest models. Through group-difference tests on factor importance, we examine whether there are significant differences across the three periods and business models. According to the findings of the ANOVA (Analysis of Variance) and the Kruskal–Wallis assay, the importance of factors influencing aircraft utilization has changed across all business models over the three periods. Pre-Pandemic, a coincident index and a consumer price index were the principal factors. However, the exchange rate (KRW/EUR) gained significant importance during the Pandemic. This suggests that the Pandemic’s impact on the aviation industry was not limited to reduced demand but was also associated with changes in the importance of exchange rates and key business indicators for airline operations. Pre-Pandemic, there were significant differences among the business model groups. However, no meaningful differences were observed during and post-Pandemic. In other words, it seems that the leading indexes were closely interconnected pre-Pandemic, whereas lagging indexes and exchange rate became closely interconnected afterward. A group-difference test confirmed that no differences were observed among the business models, but differences were evident when considering the groups in their entirety. We presented the implications for changes in airline decision-making to understand changes in the aviation industry caused by the Pandemic, by identifying how the factors influencing aircraft utilization were altered. Full article

Span-wise homogeneous supersonic cavity flows display complicated structures due to shear layer breakdown, flow acoustic resonance, and even non-linear hydrodynamic-acoustic interactions. In practical applications, such as aircraft bays, the cavity is of finite width and has doors, both of which introduce distinctive phenomena that couple with the shear layer at the cavity lip, further modulating shear layer bifurcations and tonal mechanisms. In particular, asymmetric states manifest as ‘tornado’ vortices with significant practical consequences on the design and operation. Both inward- and outward-facing leading-wedge doors, resulting in leading edge shocks directed into and away from the cavity, are examined at select opening angles ranging from $22.5°$ to $90°$ (fully open) at Mach 1.6. The computational approach utilizes the Reynolds-Averaged Navier–Stokes equations with a one-equation model and is augmented by experimental observations of cavity floor pressure and surface oil-flow patterns. For the no-doors configuration, the asymmetric results are consistent with a long-time series DDES simulation, previously validated with two experimental databases. When fully open, outer wedge doors (OWD) yield an asymmetric flow, while inner wedge doors (IWD) display only mildly asymmetric behavior. At lower door angles (partially closed cavity), both types of doors display a successive bifurcation of the shear layer, ultimately resulting in a symmetric flow. IWD tend to promote symmetry for all angles observed, with the shear layer experiencing a pitchfork bifurcation at the ‘critical angle’ ($67.5°$). This is also true for the OWD at the ‘critical angle’ ($45°$), though an entirely different symmetric flow field is established. The first observation of pitchfork bifurcations (‘critical angle’) for the IWD is at $67.5°$ and for the OWD, $45°$, complementing experimental observations. The back wall signature of the bifurcated shear layer (impingement preference) was found to be indicative of the 3D cavity dynamics and may be used to establish a correspondence between 3D cavity dynamics and the shear layer. Below the critical angle, the symmetric flow field is comprised of counter-rotating vortex pairs at the front and back wall corners. The existence of a critical angle and the process of door opening versus closing indicate the possibility of hysteresis, a preliminary discussion of which is presented. Full article

This study addresses the limitations of conventional filtering methods in handling irregular outliers and missing observations, which can compromise filter robustness and accuracy. We propose the Transformer-based Outlier-Robust Kalman Filter (TORKF), a hybrid data and knowledge hybrid-driven framework for stochastic discrete-time systems. Initially, this study derives the filtering formulas applicable when outliers exist in observation vectors and, based on these formulations, proposes a novel method capable of accurately identifying observation vectors containing outliers. In addition, a transformer-based prediction compensation approach is employed to compute the prediction vector compensation value in scenarios involving outliers. This method utilizes a specially designed data structure to ensure the transformer encoder fully extracts the input features. Furthermore, to address outlier-induced inaccuracy in prediction error covariance, a compensation method aggregating all prediction outcomes is proposed, leading to enhanced filtering accuracy. Aircraft tracking presents challenges from complex motion models and outlier-prone observations, making it an ideal testbed for robust filtering algorithms. TORKF demonstrates superior performance, with a 12.7% lower RMSE than state-of-the-art methods across both propeller and jet datasets, while maintaining sub-90 ms single-frame processing to meet real-time requirements. Ablation studies confirm that all three proposed methods enhance accuracy and demonstrate synergistic improvements. Full article

The tandem tilt-wing UAV features an advanced aerodynamic layout design and is regarded as a solution for small-scale urban air mobility. However, the tandem wing configuration exhibits complex aerodynamic interactions between the front and rear wings during cruise flight and the wing tilt transition process. The objective of this paper is to investigate the aerodynamic coupling characteristics between the front and rear wings of the tandem tilt-wing UAV under level flight and tilt transition conditions while also assessing the influence of the propellers on the aircraft’s aerodynamic performance. Through CFD numerical analysis, the aerodynamic characteristics of various aircraft components are examined at different angles of attack and wing tilt angles, and the underlying reasons for the observed differences and variations are explored. The results indicate that, during level flight, the aerodynamic interference between the wings is primarily dominated by the detrimental influence of the front wing on the rear wing. During the tilt transition process, mutual interactions between the front and rear wings occur as wing tilt angle changes, leading to more drastic variations in lift coefficients and increased control difficulty. However, the propeller’s effect contributes to smoother changes in lift and drag, thereby enhancing aircraft stability. Full article

In this paper, the problem of pitching attitude finite-horizon optimization for aircraft is posed with system uncertainties, external disturbances, and input constraints. First, a neural network (NN) and a nonlinear disturbance observer (NDO) are employed to estimate the value of system uncertainties and external disturbances. Taking input constraints into account, an auxiliary system is designed to compensate for the constrained input. Subsequently, the backstepping control containing NN and NDO is used to ensure the stability of systems and suppress the adverse effects caused by the system uncertainties and external disturbances. In order to avoid the derivation operation in the process of backstepping, a dynamic surface control (DSC) technique is utilized. Simultaneously, the estimations of the NN and NDO are applied to derive the backstepping control law. For the purpose of achieving finite-horizon optimization for pitching attitude control, an adaptive method termed adaptive dynamic programming (ADP) with a single NN-termed critic is applied to obtain the optimal control. Time-varying feature functions are applied to construct the critic NN in order to approximate the value function in the Hamilton–Jacobi–Bellman (HJB) equation. Furthermore, a supplementary term is added to the weight update law to minimize the terminal constraint. Lyapunov stability theory is used to prove that the signals in the control system are uniformly ultimately bounded (UUB). Finally, simulation results illustrate the effectiveness of the proposed finite-horizon optimal attitude control method. Full article

Thin plates are commonly used in mechanical structures such as ship hulls, offshore platforms, aircraft, automobiles, and bridges. When subjected to in-plane compressive loads, these structures may experience buckling. In some applications, perforations are introduced, altering membrane stress distribution and buckling behavior. This study investigates the elasto-plastic buckling behavior of perforated plates using the Finite Element Method (FEM), Constructal Design (CD), and Exhaustive Search (ES) techniques. Simply supported thin rectangular plates with central elliptical perforations were analyzed under biaxial elasto-plastic buckling. Three shapes of holes were considered—circular, horizontal elliptical, and vertical elliptical—along with sixteen aspect ratios and two different materials. Results showed that higher yield stress leads to higher ultimate stress for perforated plates. Regardless of material, plates exhibited a similar trend: ultimate stress decreased as the aspect ratio dropped from 1.00 to around 0.40 and then increased from 0.35 to 0.25. A similar pattern was observed in the stress components along both horizontal (x) and vertical (y) directions, once the y-component became considerably higher than the x-component for the same range of 0.40 to 0.25. For longer plates, in general, the vertical elliptical hole brings more benefits in structural terms, due to the facility in the distribution of y-components of stress. Full article

Space-based radar systems offer significant advantages for air surveillance, including wide-area coverage and extended early-warning capabilities. The integrated design of detection and imaging in space-based wideband radar further enhances its accuracy. However, in the wideband tracking mode, large aircraft targets exhibit extended characteristics. Measurements from the same target cross multiple range resolution cells. Additionally, the nonlinear observation model and uncertain measurement noise characteristics under space-based long-distance observation substantially increase the tracking complexity. To address these challenges, we propose a robust aerial target tracking method for space-based wideband radar applications. First, we extend the observation model of the gamma Gaussian inverse Wishart probability hypothesis density filter to three-dimensional space by incorporating a spherical–radial cubature rule for improved nonlinear filtering. Second, variational Bayesian processing is integrated to enable the joint estimation of the target state and measurement noise parameters, and a recursive process is derived for both Gaussian and Student’s t-distributed measurement noise, enhancing the method’s robustness against noise uncertainty. Comprehensive simulations evaluating varying target extension parameters and noise conditions demonstrate that the proposed method achieves superior tracking accuracy and robustness. Full article

Wind shear at the Hong Kong International Airport (HKIA) poses a significant safety risk due to terrain-induced airflow disruptions near the runways. Accurate assessment is essential for safeguarding aircraft during take-off and landing, as abrupt changes in wind speed or direction can compromise flight stability. This study introduces a hybrid framework for short-term wind shear prediction based on data collected from Doppler LiDAR systems positioned near the central and south runways of the HKIA. These systems provide high-resolution measurements of wind shear magnitude along critical flight paths. To predict wind shear more effectively, the proposed framework integrates a signal processing technique with a deep learning strategy. It begins with optimized variational mode decomposition (OVMD), which decomposes the wind shear time series into intrinsic mode functions (IMFs), each capturing distinct temporal characteristics. These IMFs are then modeled using bidirectional gated recurrent units (BiGRU), with hyperparameters optimized via the Tree-structured Parzen Estimator (TPE). To further enhance prediction accuracy, residual errors are corrected using Extreme Gradient Boosting (XGBoost), which captures discrepancies between the reconstructed signal and actual observations. The resulting OVMD–BiGRU–XGBoost framework exhibits strong predictive performance on testing data, achieving R² values of 0.729 and 0.926, RMSE values of 0.931 and 0.709, and MAE values of 0.624 and 0.521 for the central and south runways, respectively. Compared with GRUs, LSTM, BiLSTM, and ResNet-based baselines, the proposed framework achieves higher accuracy and a more effective representation of multi-scale temporal dynamics. It contributes to improving short-term wind shear prediction and supports operational planning and safety management in airport environments. Full article

This paper proposes a global, predefined time control method based on a predefined time disturbance observer to address the issues of wide flight airspace, large aerodynamic deviations, and high precision requirements for the entire process of aerospace aircraft re-entry. Firstly, this method proposes an adjustable predefined time nonsingular sliding mode disturbance observer, which can not only accurately estimate the modeling uncertainty and external aerodynamic disturbances of the aerospace aircraft, but also quickly converge while suppressing chattering. Then, based on the disturbance observation results, combined with a new performance function and nonsingular predefined-time sliding mode, a global predefined-time controller suitable for any order system was designed. Unlike existing methods that can only ensure that the initial deviation converges to the deviation boundary within a predefined time and then remains within the deviation boundary, it can ensure that any deviation generated within the error boundary also converges within the predefined time. Finally, the effectiveness and superiority of the proposed control scheme were verified through comparative simulation. Full article

Mapping methods for loosened rock mass in mountainous areas are useful for risk management of landslide disasters. Depending on the type of aircraft and sensor, there are several different aerial electromagnetic measurement methods for estimating subsurface structures. Helicopter-borne electromagnetic methods are commonly used. Recently, unmanned aerial vehicles (drones) have been used. By understanding the characteristics of each method, it is possible to choose a suitable method for the target of observation. In this study, resistivity from the frequency-domain helicopter-borne electromagnetic (HEM) method and resistivity from the time-domain drone-grounded electrical-source airborne transient electromagnetic (D-GREATEM) method were compared to estimate loosened zones in mountainous areas. The resistivity cross-sectional profiles were largely similar, but differences were observed near the surface in some zones. The comparative analysis of both methods with outcrop observations revealed that D-GREATEM resistivity data can detect both loosened rock mass from the surface to an approximately 30 m depth located above the groundwater and saturated rock mass. It is because D-GREATEM resistivity was obtained by assuming five layers from the surface to a depth of 40 m. This indicates that D-GREATEM is suitable for estimating near-surface loosened rock mass distribution in the valleys. However, D-GREATEM has a limited observation range. Therefore, it was concluded that the D-GREATEM method is suitable for a detailed and localized estimation of landslide susceptibility near the surface, whereas the HEM method is suitable for wide-area analysis. Full article

Remotely Piloted Aircraft (RPA) pilots frequently experience difficulties with depth perception, particularly when estimating distances between the drone and environmental obstacles. This study evaluates whether the use of onboard camera imagery can improve exocentric distance estimation accuracy among ab initio drone pilots operating under visual line-of-sight (VLOS) conditions. Two groups of undergraduate students performed distance estimation tasks at 20 and 50 m. One group used direct observation only to estimate the exocentric distance between the drone and an obstacle. The second group, as well as direct observation, had access to a live video feed from the drone’s onboard camera via a ground control station. At 20 m, there was no statistically significant difference in estimation accuracy between the groups. However, at 50 m, the camera-assisted group demonstrated significantly improved accuracy in distance estimation and reduced variance in estimation error. These findings suggest that a ubiquitous and low-cost technology, originally intended for imaging, can offer measurable benefits for depth perception at greater operational distances. The inclusion of camera-assisted perception training during early-stage licensing may enhance safety and spatial judgement in RPAS operations. Full article