Search Results (142)

The National Transport Safety Board found that Wheels-Up Landing was the second highest defining event for aircraft accidents from 2008 to 2022. The increasing push for sustainable propulsion and accompanying novel airframe architectures present new integration and safety challenges for aircraft design and development, among which is the structural integrity and crashworthiness of the aircraft under such extreme events. This paper examines the regulations and design requirements governing aircraft emergency Wheels-Up Landing scenarios, emphasising their implications for aircraft safety and structural integrity. It consolidates standards from aviation authorities, such as the FAA and EASA, which identify and define key requirements relating to occupant safety and fire prevention and protection during such events. The paper then considers the Wheels-Up Landing scenario and its design requirements within the context of future novel aircraft employing sustainable propulsion systems, from higher bypass turbofan to electric- and hydrogen-based technologies. The unique characteristics and challenges of these emerging propulsion technologies are described, highlighting how alternative structural configurations, weight distributions and powerplant architectures may influence the aircraft response under a Wheels-Up Landing event. Finally, an exploration of predictive modelling strategies and methods currently used in Wheels-Up Landing analysis was conducted. While reviewing the breadth of accurate, high-fidelity modelling methods targeting fuselage impact, it also highlighted the gap in both considering the increasingly relevant and frequent powerplant impact scenarios, and the provision of lightweight modelling approaches necessary to rapidly and adequately address the emergency Wheels-Up Landing response early in the aircraft design process. Full article

Human factors remain the predominant contributors to aviation accidents, yet the cognitive foundations of pilot performance have not been systematically defined. Existing cognitive frameworks inadequately represent the complex, high-demand environment of flight operations. This systematic review and meta-analysis aimed to identify a core cognition set for piloting, defined as the minimal group of cognitive modules most consistently related to flight performance, and to examine how these modules are affected by aviation-specific risk factors. A total of 93 studies were included in this review. Of these, 31 reported quantitative associations between cognitive performance and flight outcomes. A three-level mixed-effects meta-regression model was applied to estimate pooled effect sizes for each cognitive module. Meanwhile, 74 studies examined the influence of aviation factors such as fatigue, hypoxia, gravitational load, and aging. Meta-analytic findings indicated four cognitive modules (Perception, Working Memory, Multitasking Flexibility, and Psychomotor) as showing the strongest and most reliable associations with flight performance (Fisher’s z = 0.356–0.440, p < 0.001). The narrative synthesis corroborated that these four modules were most sensitive to operational and physiological risks such as fatigue, sleep deprivation, hypoxia, and aging. Working memory and cognitive flexibility consistently emerged as the earliest indicators of cognitive deterioration. Full article

Aviation accidents during the final approach phase of transport aircraft account for nearly half of all accidents in the flight stage, with unstable approaches being a notable contributing factor. In related accident analysis research, traditional single-parameter threshold monitoring methods have shown difficulty in capturing the complex coupling relationship between kinetic energy and potential energy. This weakness results in insufficient adaptability under variable meteorological disturbances and poor risk identification. To address this limitation, this study establishes an evaluation framework based on the concepts of “energy altitude” and “balance energy, shifting the analytical focus of aircraft state variations to energy evolution. A hybrid dynamic safety boundary function is further constructed by integrating flight mechanics principles with civil aviation regulatory constraints. This boundary integrates a height attenuation mechanism, enhancing adaptability to environmental disturbances. The study adopts QAR flight data of Boeing aircraft collected at an international airport from 2015 to 2020 as the database for machine learning modeling, and selects two additional independent flight datasets under calm-air and wind-shear conditions respectively for model verification. The research results indicate that this framework provides a robust theoretical foundation for the early identification of unstable approaches and provides actionable insights for optimizing energy control strategies, thus improving flight safety under complex operational conditions. Nevertheless, the verification only relies on two groups of typical flight cases under limited meteorological conditions, which restricts the generalizability of the research conclusions. Follow-up work will expand multi-type and multi-meteorological flight samples to carry out quantitative performance evaluation and further optimize the model’s practicality under diverse operational environments. Full article

Increased flight volumes necessitate urgent reforms in Airspace Management (ASM) to mitigate risks of fatalities and near-misses. In order to enhance aviation system safety, the International Civil Aviation Organization (ICAO) mandates that state parties must conduct the Universal Safety Oversight Audit Program (USOAP) to continuously monitor civil aviation. This research aims to identify critical factors influencing Thailand’s ASM by employing experimental design and Structural Equation Modeling (SEM) to analyze influences and relationships among communication, surveillance, navigation, Air Traffic Management (ATM), and ASM. The methodology includes stimulation and a questionnaire-based survey conducted with aviation professionals and mapping out their answers to find the influences, relationships, and importance of the different factors. The results were validated using various statistical tools. The findings indicate signi1ficant direct and indirect effects on ASM, emphasizing that effective communication and robust surveillance are essential for safety and operational efficiency. This study highlights the need to increase the ASM framework, providing actionable insights for optimizing air traffic control in response to the growing air traffic demand. Furthermore, SEM for Airspace optimization can be applied internationally to significantly reduce accidents and incidents in the future. Full article

Autonomous navigation technologies have been widely adopted in the automotive and aviation sectors, significantly reducing human-error-induced accidents and operational costs. However, their application to maritime systems remains limited due to the complexity of conventional propulsion systems. Electric propulsion ships, with well-defined system boundaries and accessible operational data, offer a promising platform for autonomous navigation. In this study, we propose an FMEA-guided selective multi-fidelity digital twin framework for fault detection, where model fidelity is adaptively selected between low- and high-fidelity models based on risk priority numbers derived from failure mode and effects analysis. This approach enables selective execution of computationally expensive models only under high-risk conditions, thereby improving computational efficiency. In addition, a sliding window-based algebraic aggregation method is employed to achieve lightweight and real-time fault diagnosis. The proposed framework is validated using operational sensor data from a 100 kW electric propulsion ship under multiple fault scenarios, including power supply faults and signal anomalies. Experimental results show that the proposed method reduces computational cost while maintaining stable real-time performance, compared to conventional data-driven AI-based approaches. These results demonstrate that the proposed framework provides an effective and efficient solution for enhancing the reliability and safety of autonomous ship systems. Full article

Low-level flight or maneuvering defines a flight phase that is particularly common and, in some cases, central to helicopter operations, but brings several safety concerns. At low altitude, helicopters are more susceptible to collisions with objects, while there is also limited time and space in which to perform an emergency landing. A total of 403 helicopter accidents in the low-level flight phase that occurred between 1 January 2009 and 31 December 2022 in the US were analyzed for their most common causes and differentiated based on the type of flight operation to gain insight into low-level flight accidents. It is shown that, for low-level flights, the proportion of fatal accidents in flights conducted under Federal Aviation Regulations Part 91, General Aviation, is 30%, but in flights conducted under Part 137, aerial application or agricultural flights, only 12%. Logistic regression analysis shows that while controlling for other factors, the proportion of fatal accidents was significantly higher in Part 91 operations. Flight experience measured as total flight hours was not a significant factor for estimating fatality. It is recommended that low-level helicopter training includes low-altitude autorotations in simulators to optimize the mitigating effect of this emergency procedure in this flight phase with a specific focus on Part 91 operations. Full article

The seemingly endless expanse of the sky might suggest that it could support a large volume of aerial traffic with minimal risk of collisions. However, mid-air collisions do occur and are a significant concern for aviation safety. Pilots are trained in scanning the sky for other aircraft and maneuvering to avoid such accidents, which is known as the basic see-and-avoid principle. While this method has proven effective, it is not infallible because human vision has limitations, and pilot performance can be affected by fatigue or distraction. Despite progress in electronic conspicuity (EC) systems, which effectively increases the visibility of aircraft to other airspace users, their utility as collision avoidance systems remains limited. This is because they are recommended but not mandatory in uncontrolled airspace, where most mid-air accidents occur, so other aircraft may not mount a compatible device or have it inactive. In addition, their use carries some risks, such as causing pilots to over-focus on them. In response to these concerns, this paper presents evidence on the utility of using an optical flow-based obstacle detection system that can complement the pilot and electronic visibility in collision avoidance, but that, unlike pilots, neither gets tired like the pilot does nor depends on whether other aircraft have mounted devices, such as EC devices. The current investigation demonstrates that the proposed optical flow-based obstacle detection system meets or exceeds the critical minimum time required for pilots to detect and react to flying obstacles (12.5 s) using a mid-air collision simulator in various test environments. Full article

The causal analysis of historical aviation accidents documented in investigation reports is important for the design, manufacture, operation, and maintenance of aircraft. However, given that most accident data are unstructured or semi-structured, identifying and extracting causal information remain labor intensive and inefficient. This gap is further deepened by tasks, such as system identification from component information, that require extensive domain-specific knowledge. In addition, there is a consequential demand for causation pattern analysis across multiple accidents and the extraction of critical causation chains. To bridge those gaps, this study proposes an aviation accident causation and relation analysis framework that integrates prompt engineering with a retrieval-augmented generation approach. A total of 343 real-world accident reports from the NTSB were analyzed to extract causation factors and their interrelations. An innovative causation classification schema was also developed to cluster the extracted causations. The clustering accuracy for the four main causation categories—Human, Aircraft, Environment, and Organization—reached 0.958, 0.865, 0.979, and 0.903, respectively. Based on the clustering results, a causation knowledge graph for aviation accidents was constructed, and by designing a set of safety evaluation indicators, “pilot—decision error” and “landing gear system malfunction” are identified as high-risk causations. For each high-risk causation, critical combinations of causation chains are identified and “Aircraft operator—policy or procedural deficiency/pilot—procedural violation/Runway contamination → pilot—decision error → pilot procedural violation/32 landing gear/57 wings” was identified as the critical causation combinations for “pilot—decision error”. Finally, safety recommendations for organizations and personnel were proposed based on the analysis results, which offer practical guidance for aviation risk prevention and mitigation. The proposed approach demonstrates the potential of combining AI techniques with domain knowledge to achieve scalable, data-driven causation analysis and strengthen proactive safety decision-making in aviation. Full article

Crew fatigue is a major cause of aviation accidents. Current civil aviation crew scheduling relies on civil aviation regulations, managing fatigue via restricting flight, duty and rest time; however, fatigue is also affected by time difference, circadian rhythm and mental fatigue severity in different work modes, which makes time-only constraints insufficient for scientific fatigue measurement and management and disregards safety hazards. Thus, integrating fatigue management into crew scheduling has practical value. This paper studies fatigue-oriented crew scheduling by first establishing a GA-LSSVM-based multiple regression model to predict crew fatigue in different flight loop tasks—achieved by exploring the quantitative relationship between pilot fatigue and key influencing factors—then investigating fatigue-considered crew ring formation, building a multi-objective optimization model and verifying its effectiveness via the NSGA-II algorithm, and finally studying fatigue balance-oriented task assignment, constructing a multi-objective model, solving the optimal scheme with the NSGA-II algorithm. Results show the proposed method reduces fatigue by 2.86% (based on the paper’s subjective evaluation data) in airline operations, with only a 0.28% increase in operating costs, while also improving crew satisfaction and ensuring fatigue balance. 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

General aviation (GA), comprised mainly of piston engine airplanes, has an inferior safety history compared with air carriers in the United States. Most studies addressing this safety disparity has focused on pilot deficiencies. Herein, we determined the rates/causes of equipment failure-related GA fatal accidents for type-certificated and experimental-amateur-built airplanes. Aviation accidents/injury severity were per the NTSB Access^R database. Statistical tests employed proportion/binomial tests/a Poisson distribution. The rate of fatal accidents (1990–2019) due to equipment failure was unchanged (p > 0.026), whereas the fatal mishap rate related to other causes declined (p < 0.001). A disproportionate (2× higher) count (p < 0.001) of equipment-related fatal accidents was evident for experimental-amateur-built aircraft with type-certificated references. Propulsion system (67%) and airframe (36%) failures were the most frequent causes of fatal accidents for type-certificated and experimental-amateur-built aircraft, respectively. The components “fatigue/corrosion” and “manufacturer–builder error” resulted in 60% and 55% of powerplant and airframe failures, respectively. Most (>90%) type-certificated aircraft propulsion system failures were within the manufacturer-prescribed engine time-between-overhaul (TBO) and involved components inaccessible for examination during an annual inspection. There is little evidence for a decline in equipment failure-related fatal accident rate over three decades. Considering the fact that powerplant failures mostly occur within the TBO and involve fatigue/corrosion of one or more components inaccessible for examination, GA pilots should avoid operations where a safe off-field landing within glide-range is not assured. Full article

Shipborne fighters play a critical role in far-sea operations. However, their landing process on aircraft carrier decks involves significant risks, where accidents can lead to substantial losses. Timely and accurate risk prediction is, therefore, essential for improving flight training efficiency and enhancing the combat capability of naval aviation forces. Machine-learning algorithms have been explored for predicting landing risks in land-based aircraft. However, owing to the challenges in acquiring relevant data, the application of such methods to shipborne aircraft remains limited. To address this gap, the present study proposes a deep learning-based method for predicting landing risks of shipborne aircraft. A dataset was constructed using simulated ship movements recorded during the sliding phase along with relevant flight parameters. Model training and prediction were conducted using up to ten different input combinations with artificial neural networks, long short-term memory, and transformer neural networks. Experimental results demonstrate that all three models can effectively predict landing parameters, with the lowest average test error reaching 3.5620. The study offers a comprehensive comparison of traditional machine learning and deep learning methods, providing practical insights into input variable selection and model performance evaluation. Although deep learning models, particularly the Transformer, achieved the highest accuracy, in practical applications, the support of hardware performance still needs to be fully considered. Full article

Aviation safety remains a critical area of research, requiring accurate and efficient classification of incident reports to enhance risk assessment and accident prevention strategies. This study evaluates the performance of three deep learning models, BERT, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) for classifying incidents based on injury severity levels: Nil, Minor, Serious, and Fatal. The dataset, drawn from ATSB records covering the years 2013 to 2023, consists of 53,273 records and was used. The models were trained using a standardized preprocessing pipeline, with hyperparameter tuning to optimize performance. Model performance was evaluated using metrics such as F1-score accuracy, recall, and precision. Results revealed that BERT outperformed both LSTM and CNN across all metrics, achieving near-perfect scores (1.00) for precision, recall, F1-score, and accuracy in all classes. In comparison, LSTM achieved an accuracy of 99.01%, with strong performance in the “Nil” class, but less favorable results for the “Minor” class. CNN, with an accuracy of 98.99%, excelled in the “Fatal” and “Serious” classes, though it showed moderate performance in the “Minor” class. BERT’s flawless performance highlights the strengths of transformer architecture in processing sophisticated text classification problems. These findings underscore the strengths and limitations of traditional deep learning models versus transformer-based approaches, providing valuable insights for future research in aviation safety analysis. Future work will explore integrating ensemble methods, domain-specific embeddings, and model interpretability to further improve classification performance and transparency in aviation safety prediction. Full article

Background: General aviation pilots are, anecdotally, referred to as “weekend warriors” due to their flying infrequency. Considering that flight skills erode with irregular practice/reinforcement, we determined whether private pilots (PPLs) fly/train sufficiently to operate safely in the context of slow flight, a skill critical for safe operations and which rapidly atrophies with <~51 h flight time/8 months per prior research. Method: Slow-flight-related aviation accidents (2008–2019) were per the NTSB Access^R database, and fatal mishap rates were calculated using general aviation fleet times. Eight-month flight histories of airplanes in single PPL ownership were captured retrospectively using FlightAware^R. PPL survey responses were collected between January and March 2025. Statistical tests employed proportion/Independent-Samples Median Tests and a Poisson Distribution. Results: The slow-flight-related fatal accident rate (2017–2019) trended downwards (p = 0.077). In-flight tracking of 90 airplanes revealed an 8-month median flight time of 6 h, which is well below the aforementioned 51 h requisite for safe operations. Of the aircraft flown < 51 h, only 9% engaged in slow-flight practice. In the online survey, only the upper quartile of 126 PPLs achieved the aforementioned time requisite for preserving slow-flight skills, but nevertheless, 89% of respondents attested to being flight-proficient. Conclusions: Persistence in slow-flight-related fatal accidents likely partly reflects PPLs’ deficiency in in-flight time/slow-flight practice. Full article

This article examines the impact of an undetected Instrument Landing System (ILS) failure on crew situational awareness. A literature review of similar aviation accidents is presented, highlighting the recurring challenge of misleading instrument indications and their influence on approach safety. The research environment consisted of flight simulator replicating both ideal and accident-weather conditions at two airports, with the final scenario involving a simulated ILS receiver malfunction providing erroneous yet seemingly valid indications. Six pilots with varying flight hours participated, conducting four simulated approaches under different conditions. Flight path stability, deviation from glide slope and course, approach speed, and decision-making were recorded and analyzed. The results indicate that experienced pilots detected inconsistencies more quickly, maintained more stable control inputs, and initiated go-arounds earlier, while less experienced pilots required more time but were still able to correctly assess the risks. The primary goal of this research was to identify cognitive mechanisms and operational decision-making processes under simulated conditions, not to establish universally generalizable outcomes. The findings underline the importance of simulator-based training incorporating unexpected navigation system failures to reinforce cross-checking habits, enhance situational awareness, and improve decision-making during critical phases of flight. Full article