Flight Safety Research Articles

Design and modeling of a highly compact negative index floral shape metamaterial for flight navigation applications

Abstract The collision avoidance system (CAS) is a mandatory monitoring apparatus equipped in all aircraft to safeguard flight safety. The CAS scans the predefined regions in a systematic manner for a certain length of time to detect any approaching aircraft that could potentially pose a threat. Thus, CAS requires a focused multi-element radiator which can encompass the complete azimuth region. Recent years have seen a growing emphasis on enhancing the efficiency of CAS antennas because of several constraints, such as low gain (3.6 dB), larger dimensions, substantial side-lobe amplitude (− 7 dB), and challenges with beam adaptation. The current research strives to enhance the gain of a CAS antenna by incorporating the basic idea of metamaterials (MTMs). Therefore, a compact floral-shaped double negative (DNG) MTM design is proposed. The CAS antenna routes the signal throughout the complete azimuth region, so the designed MTM must be proficient to withstand its DNG characteristics for different incident angles. Hence, the proposed design is tested at various incident angles spanning between to along the azimuth region, at a deviation. The results indicate that the proposed structure retains its DNG behavior in the desired frequency range, regardless of the incident angles. The computed effective medium ratio of the structure is 13.47 at the CAS central frequency (1.06 GHz), highlighting its compactness and efficacy. Furthermore, to analyze the function of the structure on the antenna, the unit-element (UE) is expanded to a 5 × 4 array and deployed as an additional layer on the radiator at a predetermined distance. The addition of MTM to the radiator outperformed the conventional radiator by enhancing the antenna gain, by 2.6 dB, respectively. Additionally, to confirm the experimental findings, the UE and array designs are fabricated, and the fabrication results align closely with the simulation results.

A Data-Driven Approach for Automatic Aircraft Engine Borescope Inspection Defect Detection Using Computer Vision and Deep Learning

Regular aircraft engine inspections play a crucial role in aviation safety. However, traditional inspections are often performed manually, relying heavily on the judgment and experience of operators. This paper presents a data-driven deep learning framework capable of automatically detecting defects on reactor blades. Specifically, this study develops Deep Neural Network models to detect defects in borescope images using various datasets, based on Computer Vision and YOLOv8n object detection techniques. Firstly, reactor blade images are collected from public resources and then annotated and preprocessed into different groups based on Computer Vision techniques. In addition, synthetic images are generated using Deep Convolutional Generative Adversarial Networks and a manual data augmentation approach by randomly pasting defects onto reactor blade images. YOLOv8n-based deep learning models are subsequently fine-tuned and trained on these dataset groups. The results indicate that the model trained on wide-shot blade images performs better overall at detecting defects on blades compared to the model trained on zoomed-in images. The comparison of multiple models’ results reveals inherent uncertainties in model performance that while some models trained on data enhanced by Computer Vision techniques may appear more reliable in some types of defect detection, the relationship between these techniques and subsequent results cannot be generalized. The impact of epochs and optimizers on the model’s performance indicates that incorporating rotated images and selecting an appropriate optimizer are key factors for effective model training. Furthermore, models trained solely on artificially generated images from collages perform poorly at detecting defects in real images. A potential solution is to train the model on both synthetic and real images. Future work will focus on improving the framework’s performance and conducting a more comprehensive uncertainty analysis by utilizing larger and more diverse datasets, supported by enhanced computational power.

An Examination of UAS Incidents: Characteristics and Safety Considerations

This paper examines the characteristics and implications of reported Unmanned Aircraft Systems (UAS) incidents in the National Aeronautics and Space Administration (NASA) Aviation Safety Reporting System (ASRS) database for UAS incidents operated by remote pilots licensed under Part 107. Characteristics examined include seasonal patterns of incidents, operational mission, and number of contributing factors, as well as crew configuration, timing of incident detection, and airspace class. Results are compared with previous research and with incident data for recreational users. The narratives for each incident are assessed to provide a greater context for the incidents and to determine how the incidents vary in different classes of airspace. Findings reveal that UAS incidents often involve multiple contributing factors, including environmental, human, equipment, and policy issues; there is an increasing prevalence of human-related issues over equipment problems compared to previous research; this reflects historic safety trends in crewed aviation. Near-miss incidents with crewed aircraft are a very real concern, particularly in Class D airspace, which often includes general aviation (GA) and helicopter operations. This research underscores the need for timely communication during urgent nighttime UAS operations as well as enhanced safety culture at both operator and organizational levels.

Assessment of Airfoil Lightning Strikes Characterization on Aircraft in Dynamic Conditions

Lightning is a long-gap high-current discharge event in nature, and its enormous discharge energy can cause structural damage to struck objects. Relevant studies have shown that aircraft in the air are exposed to a higher risk of lightning strikes than ground objects. However, most studies on aircraft lightning strikes have used static models, and there are still shortages of research objects under high-speed movements. Therefore, it is necessary to conduct an investigation of the lightning strike characteristics of aircraft under dynamic conditions to ensure flight safety fully. In this study, the lightning strike characteristics of airfoil under dynamic conditions have been simulated by studying two variables, airspeed and angle of attack, while employing the Leader Progression Model (LPM). The results show that adjustments in the angle of attack cause changes in the atmospheric pressure field, and a maximum pressure difference of up to 5.44 × 104 Pa is observed at the angle of attack of 15°, which results in a change in the average electric field strength Estr, leading to a 33.63% difference in the upward leader trigger height. At an airspeed equal to 120 m/s, the trigger height in the low-pressure region is only 54.7% of that in the high-pressure region (435 m and 795 m, respectively). A higher lightning strike risk is found in areas of higher air pressure and is positively correlated with angle of attack and airspeed.

An active and passive combined fault-tolerant control approach for UAV with the control surface fault

In this paper, the fault-tolerant control (FTC) problem of fixed-wing unmanned aerial vehicle (UAV) with control surface structural damage is studied. An active-passive combined FTC algorithm based on sliding mode and adaptive control is proposed. Considering that the UAV is required to be instantaneously stable in the initial stage of the fault, a one-way auxiliary surface sliding mode controller based on fast dual power reaching law is adopted. During this period, the nonlinear recursive least squares method is used to identify the aerodynamic parameters of the UAV after the fault. Then, an adaptive control combined with aerodynamic parameter identification is designed to actively compensate for the fault. In addition, a hardware-in-the-loop simulation platform is developed to verify the active and passive combined FTC method. The simulation results show that the algorithm reduces the control error of the UAV after the fault and improves the robustness and reliability. This method provides a reliable technical guarantee for the safe flight of UAV under the condition of structural damage of control surface and has important practical significance for improving the reliability and safety of UAV.

Crossroads Between Safety and Environmental Protection: EASA is not Merely a Specialized Safety Agency

Inference from the Chicago Convention and its Annexes suggests that safety and environmental protection can be viewed as two distinct domains. However, as aviation rules develop in the European Union (EU), the two have been increasingly seen as interconnected. There have been many advancements in creating aviation environmental rules, and it is essential to always prioritize safety while implementing these rules. In reality, safety and environmental protection cannot be fully separated. As a specialized agency, the EU Aviation Safety Agency (EASA) operates within this reality. It plays a vital role in ensuring paramount safety while simultaneously implementing and developing environmental protection.The European Commission adopted Regulation (EU) 2021/1119, the ‘European climate law’, which requires relevant EU institutions, including EASA, to ensure continuous progress in enhancing adaptive capacity, strengthening resilience, and reducing vulnerability to climate change. EASA has previously been additionally tasked with environmental protection and must, therefore, continue its progress in the area alongside its main task for aviation safety. The intersections of these two areas can be analysed through the lens of EASA’s tasks. This article explores EASA’s Basic Regulations within the context of environmental roles, particularly in research, monitoring, and rule development.

Simulating the break-up, debris formation and throw of concrete structures under explosive loading

Malicious acts, but likewise unintended accidental explosions, can lead to severe structural damage and resulting debris throw, which poses a significant threat to humans and facilities. Until now, risk analysis is based mainly on empirical data, since the reliable simulation of structural break-up, dissolution and emergence of secondary fragments for real structures is still challenging. In this paper, we investigate the application of a mesoscale description of concrete with finite elements to predict the dispersal of fragments out of dynamically loaded concrete specimens. We demonstrate that the approach delivers accurate predictions for maximum velocity and total debris mass. Further, it is even able to to resolve the debris mass distribution with very reasonable quality, a result rarely found in literature up to today. The detailed resolution of the debris field allows furthermore a more thorough determination of the aerodynamic factors governing the subsequent flight phase. We compare the results using different assumptions in terms of flight distances and safety maps.

T-38 failure analysis of an upper cockpit longeron for safety of flight and sustainment

T-38 failure analysis of an upper cockpit longeron for safety of flight and sustainment

Factors Contributing to Fatalities in Helicopter Emergency Medical Service Accidents

INTRODUCTION: This study aimed to update and reinforce previous research on helicopter emergency medical service accidents in the United States. By investigating predictors of fatalities after helicopter emergency medical service crashes through the application of machine learning techniques, we updated existing data sets and sought to uncover patterns that traditional analysis might not reveal. METHODS: Using the National Transportation Safety Board database, the authors analyzed a dataset of 267 helicopter emergency medical service accidents between 1991–2022. We first calculated fatalities odds ratios for each condition. We then plotted geospatial locations of all reported accidents. Finally, we used XGBoost regression to understand the most important features contributing to fatality after an accident. RESULTS: The findings reaffirm previous research and identify significant predictors of fatalities in helicopter emergency medical service accidents. Key factors such as adverse flight conditions (weather), the absence of a copilot, and postcrash fires are highlighted as critical to understanding and mitigating risks of fatality. DISCUSSION: These findings emphasize the utility of machine learning in extracting meaningful insights from accident data, suggesting that such techniques offer a more nuanced understanding of the conditions leading to fatalities. It points out the potential of these methods to not only enhance aviation safety but also to be applied across other sectors. We conclude by underlining the significant potential of techniques like XGBoost in advancing safety measures within helicopter emergency medical service and possibly other aviation sectors. Korentsides J, Keebler JR, Berezovski M, Chaparro A. Factors contributing to fatalities in helicopter emergency medical service accidents. Aerosp Med Hum Perform. 2025; 96(2):111–115.

Jet fuel oxidation on conventionally and additively manufactured metallic tubes

In the aviation industry, jet fuel is a propellant as well as a coolant. At elevated temperatures, the jet fuel experiences significant thermal stresses, which leads to the oxidation of hydrocarbons when exposed to the heat exchanger walls. As a result, undesirable insoluble carbon deposits on the internal heat exchanger walls impede the effectiveness of the heat exchanger and the aircraft fuel circulatory system, compromising aircraft safety and performance. Here, we study the jet fuel fouling behavior on additively manufactured tubes created using stainless steel, aluminum alloy, titanium, and Inconel, during autoxidation at elevated temperatures. Additively manufactured materials are tested due to their numerous potential benefits for the aerospace industry, which include design flexibility for the creation of complex and optimized parts that enhance performance and aerodynamics. Experiments were conducted using a fuel-fouling open loop system based on the Jet Fuel Thermal Oxidation Test (JFTOT). Scanning electron microscopy and characterization of the internal surface of the tubes showed that the jet fuel reacts differently with different metals and alloys. Comparisons were made with traditionally manufactured materials and benchmarked with the performance of bare copper tubes. We show that additively manufactured tubes were more prone to fuel fouling due to the larger inherent roughness associated with the additive manufacturing process. We show that applying a thin layer of commercially available Silcolloy 2000 coating onto the internal surface minimizes jet fuel degradation significantly. Our work helps to enable the application of additive manufacturing for aircraft thermal management component manufacture by alleviating concerns related to fuel blockage and performance deterioration stemming from surface deposition.

Simulated microgravity predisposes kidney to injury through promoting intrarenal artery remodeling.

Spaceflight-induced multi-organ dysfunction affects the health of astronauts and the safety of in-orbit flight. However, the effect of microgravity on the kidney and the underlying mechanisms are unknown. In the current study, we used a hindlimb unweighting (HU) animal model to simulate microgravity and employed histological analysis, ischemia-reperfusion experiments, renal ultrasonography, bioinformatics analysis, isometric force measurement, and other molecular experimental settings to evaluate the effects of microgravity on the kidneys and the underlying mechanisms involved in this transition. Relative to controls, 31-day hindlimb unweighting had no obvious effect on serum creatinine and urea nitrogen levels as well as on renal injury scores; however, animals in the HU group showed an impaired renal recovery to ischemia-reperfusion injury. Ultrasonography showed that renal resistive index was increased, which indicated an altered renal hemodynamics induced by simulated microgravity. The enhanced vasoconstriction mediated by the Rho-kinase pathway and impaired vasodilation mediated by NO-eNOS of the intrarenal artery contributed to the altered renal hemodynamics. Simulated microgravity predisposes the kidney to ischemia-reperfusion injury. The altered renal hemodynamics induced by renal arterial remodeling may account for the increased renal injury susceptibility, providing a target for early intervention in kidney dysfunction under microgravity.

Inhibitor treatment and subcellular localization analysis reveal the contribution of a cytosolic terpene synthase to the substantial release of anti-insect monoterpenes by Sphagneticola trilobata (L.) Pruski.

Bird strikes are one of the greatest threats to aviation safety in the worldwide. Sphagneticola trilobata (L.) Pruski has anti-insect effects that may indirectly decrease bird populations, thereby reducing bird strikes. However, it is unclear how S. trilobata exerts its anti-insect effects. Moreover, the mechanism mediating the biosynthesis of its main volatile compounds is unknown. This study was conducted to identify the major anti-insect volatile compounds in S. trilobata and elucidate their biosynthetic mechanisms. A continuous sampling method was used to analyze the released volatiles of S. trilobata. Direct feeding or fumigation treatments with the main monoterpenes were followed by the evaluation of anti-insect functions. Evolutionary and enzyme activity analyses were performed to verify the functions of target enzymes. The subcellular localization and potential functions of the target enzymes were revealed by quantitative analyses of synthase gene expression, subcellular localization experiments, inhibitor experiments, and enzyme activity analysis of proteins from different subcellular organelles. α-Phellandrene, limonene, and p-cymene, which had a circadian release pattern, were the major volatiles in S. trilobata. These three monoterpenes have anti-insect functions. Additionally, StTPS3 has a relatively broad substrate specificity in vitro, which may result in the production of limonene, p-cymene, and β-caryophyllene. The circadian rhythm in StTPS3 expression was consistent with the changes in volatile compound levels. The encoded enzyme was localized in the cytoplasm. Inhibition of the mevalonate pathway reduced monoterpene formation. Proteins extracted from the cytoplasm and chloroplasts may catalyze the synthesis and conversion of monoterpene precursors. The study data provide direct evidence for the anti-insect effects of S. trilobata, while positively elucidating the biosynthesis of key volatiles from cytoplasmic GPP and NPP. Furthermore, the findings may be relevant for the control of bird populations at airports and the reduction of the risk of bird strikes.

Air pollution and safety incidents: a Health policy case study with property and violent incidents in Medellín, Colombia, 2017–2019

ABSTRACT This paper explores the short-term relationship between the levels of air pollution and safety incidents in Medellín, Colombia, between 2017 and 2019. The data for this paper come from public sources from Medellín. While policymakers are grappling with the cost of these policies, in the middle of an air quality crisis, the city of Medellín has been implementing measures to clean up the air; this paper seeks to inform that process by outlying the public safety implications of better air quality. We find a positive relationship between PM2.5 and daily incidents, where a 10% increase in PM2.5 is associated with a 0.21% increase in incidents per day, and this relationship holds for different categories, such as property and violent incidents. Both safety and environmental concerns are key issues in the policy-agenda in Latin America, and so the findings become a key contribution to research and a valuable tool for cost–benefit analysis of environmental policy.

Identifying causes of aviation safety events using wW2V-tCNN with data augmentation

Identifying the causes of these safety events is crucial for safety agencies to create recommendations and for airlines to enhance procedures and mitigate hazards. This paper proposes a model to identify the causes of civil aviation safety events using a weighted Word2Vec-based Text-CNN (wW2V-tCNN) algorithm and data augmentation techniques. A corpus is built by matching narrative texts from investigation reports with cause labels from the Aviation Safety Network database. This corpus is transformed into Text-CNN inputs using a weighted sentence vector method based on word embeddings, considering word frequency and part-of-speech weighting. Additionally, a novel document balancing method is introduced for data augmentation. The proposed identification model achieves Macro-F1 and Macro-accuracy scores of 0.9803 and 0.9699, outperforming traditional methods and showing significant improvement over models like Doc2vec and SBERT. This model provides an accurate tool for safety agencies and airlines to analyze and effectively mitigate civil aviation safety events.

Analyzing Aviation Safety Trends in Nepal over the Past Fourteen Years

Analyzing Aviation Safety Trends in Nepal over the Past Fourteen Years

Pilot-Related Factors Involved in Runway Incursions: An Epidemiological Approach

ABSTRACT Objective This research investigates the role of pilot deviation in runway incursions, a persistent safety risk for aviation safety. Background Despite many research efforts focusing primarily on the evaluation of advanced technical systems as mitigation solutions, there remains a significant lack of studies investigating the central figure in this issue: the pilot, as well as the underlying human factors and mitigating circumstances that contribute to runway incursions. Method An in-depth analysis of incident and accident reports from 1993 to 2021, sourced from both Australia and the United States was employed. A comprehensive codebook was developed to capture key information for investigating significant relationships between variables. An extended taxonomy derived from the U.S. Department of Defence - DoD HFACS was used to systematically analyze pilot-related causal factors. Results The results highlight critical causal factors from a human factors perspective, with wide-ranging issue such as miscommunication being prevalent. Statistically significant relationships were observed between the severity of runway incursions and other key factors. Conclusion The study underscores the importance of considering pilot-related causal factors of runway incursions to gain better understanding of potential mitigation solutions.

Research on Unmanned Aerial Vehicle Intelligent Maneuvering Method Based on Hierarchical Proximal Policy Optimization

Improving decision-making in the autonomous maneuvering of unmanned aerial vehicles (UAVs) is of great significance to improving flight safety, the mission execution rate, and environmental adaptability. The method of deep reinforcement learning makes the autonomous maneuvering decision of UAVs possible. However, the current algorithm is prone to low training efficiency and poor performance when dealing with complex continuous maneuvering problems. In order to further improve the autonomous maneuvering level of UAVs and explore safe and efficient maneuvering methods in complex environments, a maneuvering decision-making method based on hierarchical reinforcement learning and ‌Proximal Policy Optimization (PPO) is proposed in this paper. By introducing the idea of hierarchical reinforcement learning into the PPO algorithm, the complex problem of UAV maneuvering and obstacle avoidance is separated into high-level macro-maneuver guidance and low-level micro-action execution, greatly simplifying the task of addressing complex maneuvering decisions using a single-layer PPO. In addition, by designing static/dynamic threat zones and varying their quantity, size, and location, the complexity of the environment is enhanced, thereby improving the algorithm’s adaptability and robustness to different conditions. The experimental results indicate that when the number of threat targets is five, the success rate of the H-PPO algorithm for maneuvering to the designated target point is 80%, which is significantly higher than the 58% rate achieved by the original PPO algorithm. Additionally, both the average maneuvering distance and time are lower than those of the PPO, and the network computation time is only 1.64 s, which is shorter than the 2.46 s computation time of the PPO. Additionally, as the complexity of the environment increases, the H-PPO algorithm outperforms other compared networks, demonstrating the effectiveness of the algorithm constructed in this paper for guiding intelligent agents to autonomously maneuver and avoid obstacles in complex and time-varying environments. This provides a feasible technical approach and theoretical support for realizing autonomous maneuvering decisions in UAVs.

Distributed planning method for detection array of UAV formation with bearing-only sensor network

Abstract Unmanned aerial vehicle (UAV) formations for bearing-only passive detection are increasingly important in modern military confrontations, and the array of the formation is one of the decisive factors affecting the detection accuracy of the system. How to plan the optimal geometric array in bearing-only detection is a complex nondeterministic polynomial problem, and this paper proposed the distributed stochastic subgradient projection algorithm (DSSPA) with layered constraints to solve this challenge. Firstly, based on the constraints of safe flight altitude and fixed baseline, the UAV formation is layered, and the system model for bearing-only cooperative localisation is constructed and analysed. Then, the calculation formula for geometric dilution of precision (GDOP) in the observation plane is provided, this nonlinear objective function is appropriately simplified to obtain its quadratic form, ensuring that it can be adapted and used efficiently in the system model. Finally, the proposed distributed stochastic subgradient projection algorithm (DSSPA) combines the idea of stochastic gradient descent with the projection method. By performing a projection operation on each feasible solution, it ensures that the updated parameters can satisfy the constraints while efficiently solving the convex optimisation problem of array planning. In addition to theoretical proof, this paper also conducts three simulation experiments of different scales, validating the effectiveness and superiority of the proposed method for bearing-only detection array planning in UAV formations. This research provides essential guidance and technical reference for the deployment of UAV formations and path planning of detection platforms.

Development and Experimental Validation of a Sense-and-Avoid System for a Mini-UAV

This paper provides an overview of the three-year effort to design and implement a prototypical sense-and-avoid (SAA) system based on a multisensory architecture leveraging data fusion between optical and radar sensors. The work was carried out within the context of the Italian research project named TERSA (electrical and radar technologies for remotely piloted aircraft systems) undertaken by the University of Pisa in collaboration with its industrial partners, aimed at the design and development of a series of innovative technologies for remotely piloted aircraft systems of small scale (MTOW < 25 Kgf). The system leverages advanced computer vision algorithms and an extended Kalman filter to enhance obstacle detection and tracking capabilities. The “Sense” module processes environmental data through a radar and an electro-optical sensor, while the “Avoid” module utilizes efficient geometric algorithms for collision prediction and evasive maneuver computation. A novel hardware-in-the-loop (HIL) simulation environment was developed and used for validation, enabling the evaluation of closed-loop real-time interaction between the “Sense” and “Avoid” subsystems. Extensive numerical simulations and a flight test campaign demonstrate the system’s effectiveness in real-time detection and the avoidance of non-cooperative obstacles, ensuring compliance with UAV aero mechanical and safety constraints in terms of minimum separation requirements. The novelty of this research lies in (1) the design of an innovative and efficient visual processing pipeline tailored for SWaP-constrained mini-UAVs, (2) the formulation an EKF-based data fusion strategy integrating optical data with a custom-built Doppler radar, and (3) the development of a unique HIL simulation environment with realistic scenery generation for comprehensive system evaluation. The findings underscore the potential for deploying such advanced SAA systems in tactical UAV operations, significantly contributing to the safety of flight in non-segregated airspaces

Integrative Path Planning for Multi-Rotor Logistics UAVs Considering UAV Dynamics, Energy Efficiency, and Obstacle Avoidance

Due to their high flexibility, low cost, and energy-saving advantages, applying Unmanned Aerial Vehicles (UAVs) in logistics is a promising field to achieve better social and economic benefits. Since UAVs’ energy storage capacity is generally low, it is essential to reduce energy costs to improve their system’s energy efficiency. In this paper, we proposed a novel trajectory planning framework to achieve the optimal trajectory with the minimum amount of energy consumption under the constraints of obstacles in a static environment. Based on UAV dynamics, we first derived the required power functions of multi-rotor UAVs in vertical and horizontal flight. To generate a feasible trajectory, we first adopted the A* algorithm to find a path and developed a safe flight corridor for the UAV to fly across by expanding the waypoints against the environment, and then proposed a time-discretization method to formulate the trajectory generation problem and solve it by the convex optimization algorithm. The optimization results in a static environment with obstacles demonstrated that the proposed method could efficiently and effectively obtain the optimal trajectory with the minimum amount of energy consumption under different allowed mission times and payloads. The framework would promote a variety of logistics UAV applications relevant to trajectory planning.