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The airport apron is a high-risk area for aircraft collisions due to its heavy operational load and high aircraft density. Currently, existing quantitative models for apron collision risk provide limited consideration and classification of risk areas. In response, this paper proposes a Petri net-based method for assessing aircraft collision risk. The method predicts the probability of aircraft reaching different areas at different times based on operational data, enabling the calculation of collision risks within the Petri net framework. This approach highlights areas with potential collision risks and provides a classification evaluation. Subsequently, aircraft path re-planning is carried out to reduce collision risks. The model simplifies the complex operations of the apron system, making the calculation process clearer. The results show that, during the mid-phase of aircraft taxiing, there is a significant deviation between the actual and ideal positions of aircraft. Areas with high taxiway occupancy are more prone to collision risks. On peak days, due to relatively high flight volumes, the frequency of collision risks is 14% higher than on regular days, with an average risk increase of 23.3%, and the risks are more concentrated. Therefore, reducing collision risks through path planning becomes more challenging. It is recommended to focus attention on areas with high taxiway occupancy during peak periods and carefully plan routes to ensure apron safety. [ABSTRACT FROM AUTHOR]

The article argues on the loss of the Malaysian flight MH370 in airplane crash which was not found.

The industry has been significantly enhanced by recent developments in UAV collision avoidance systems. They made collision avoidance controllers for self-driving drones both affordable and hazardous. These low-maintenance, portable devices provide continuous monitoring in near-real time. It is inaccurate due to the fact that collision avoidance controllers necessitate trade-offs regarding data reliability. Collision avoidance control research is expanding significantly and is disseminated through publications, initiatives, and grey literature. This paper provides a concise overview of the most recent research on the development of autonomous vehicle collision avoidance systems from 2017 to 2024. In this paper, the state-of-the-art collision avoidance system used in drone systems, the capabilities of the sensors used, and the distinctions between each type of drone are discussed. The pros and cons of current approaches are analyzed using seven metrics: complexity, communication dependency, pre-mission planning, resilience, 3D compatibility, real-time performance, and escape trajectories. [ABSTRACT FROM AUTHOR]

To reveal complex causes of aircraft events, this paper aims to mine association rules between the trigger probability and relative strength via a modified Apriori algorithm. Clustering is adopted for data preprocessing and TF–IDF value calculation. Causative item sets of aircraft events are obtained based on the accident causation 2–4 model and are coded to establish code indicators. By avoiding the use of statistical methodologies to resolve not-a-number (NaN) values for altering the interrelations among causes, an enhancement in the Apriori algorithm is proposed by considering frequent items. By extracting frequent patterns, in this paper, all the association rules that satisfy three perspectives (support, confidence and lift) are determined by constantly generating and pruning candidate item sets. A network graph is used to visualize the association rules between different unsafe events and all types of causes. Finally, 9835 representative pieces of data, including general unsafe events, general incidents and serious incidents from the Southwest Air Traffic Management Bureau, are selected for analysis. The results show that improper energy allocation, poor conflict resolution ability, inadequate onsite management duties, adoption of a luck mentality, and occurrence of controller oversight are highly correlated with general unsafe events, and failure to rectify incorrect recitation is notably correlated with general incidents, while inadequate manual promotion, lack of conflict judgement and insufficient safety management are strongly correlated with serious incidents. This study quantitatively reveals the potential patterns and characteristics of mutual interactions among various types of historical aircraft events and highlights directions for controllable prevention and prediction of aircraft events. [ABSTRACT FROM AUTHOR]

In order to accurately record the entry and departure times of helicopters and reduce the incidence of general aviation accidents, this paper proposes a helicopter entry and departure recognition method based on a self-learning mechanism, which is supplemented by a lightweight object detection module and an image classification module. The original image data obtained from the lightweight object detection module are used to construct an Automatic Selector of Data (Auto-SD) and an Adjustment Evaluator of Data Bias (Ad-EDB), whereby Auto-SD automatically generates a pseudo-clustering of the original image data. Ad-EDB then performs the adjustment evaluation and selects the best matching module for image classification. The self-learning mechanism constructed in this paper is applied to the helicopter entry and departure recognition scenario, and the ResNet18 residual network is selected for state classification. As regards the self-built helicopter entry and departure data set, the accuracy reaches 97.83%, which is 6.51% better than the bounding box detection method. To a certain extent, the strong reliance on manual annotation for helicopter entry and departure status classification scenarios is lifted, and the data auto-selector is continuously optimized using the preorder classification results to establish a circular learning loop in the algorithm. [ABSTRACT FROM AUTHOR]

In risk and safety assessments of aviation systems, engineers generally pay more attention to the risks of hardware or software failure and focus less on the risks caused by human errors. In this paper, a (FRAHE) method is proposed for identifying this critical error type and determining the risk severity of human errors. This method accounts for the human error probability as well as the impacts of human errors on the system. The fuzzy inference approach is employed in this paper to address the uncertainty and issues of imprecision that arise from insufficient information and scarce error data and a risk assessment model of human error is developed. The model can be used to precisely describe the relationship between the output risk severity and the input risk indicators, including the human error probability, the error impact probability, and the human error consequence. A case study of the approach task is presented to demonstrate the availability and reasonability of the model. The risk-based modeling method can not only provide valuable information for reducing the occurrence of critical errors but also be used to conduct prospective analyses to prevent unsafe incidents or aviation accidents. [ABSTRACT FROM AUTHOR]

Purpose: About 70% of all aircraft accidents are caused by human–machine interaction, thus identifying and quantifying performance shaping factors is a significant challenge in the study of human reliability. An information flow field model of human–machine interaction is put forward to help better pinpoint the factors influencing performance and to make up for the lack of a model of information flow and feedback processes in the aircraft cockpit. To enhance the efficacy of the human–machine interaction, this paper aims to examine the important coupling factors in the system using the findings of the simulation. Design/methodology/approach: The performance-shaping factors were retrieved from the model, which was created to thoroughly describe the information flow. The coupling degree between the performance shaping factors was calculated, and simulation and sensitivity analysis are based on system dynamics. Findings: The results show that the efficacy of human–computer interaction is significantly influenced by individual important factors and coupling factors. To decrease the frequency of accidents after seven hours, attention should be paid to these factors. Originality/value: The novelty of this work lies in proposing a theoretical model of cockpit information flow and using system dynamics to analyse the effect of the factors in the human–machine loop on human–machine efficacy. [ABSTRACT FROM AUTHOR]

Aircraft health management has been researched at both component and system levels. In instances of certain aircraft faults, like the Boeing 777 fuel icing problem, there is evidence suggesting that a platform approach using an Integrated Vehicle Health Management (IVHM) system could have helped detect faults and their interaction effects earlier, before they became catastrophic. This paper reviews aircraft health management from the aircraft maintenance point of view. It emphasizes the potential of a platform solution to diagnose faults, and their interaction effects, at an early stage. The paper conducts a thorough analysis of existing literature concerning maintenance and its evolution, delves into the application of Artificial Intelligence (AI) techniques in maintenance, explains the rationale behind their employment, and illustrates how AI implementation can enhance fault detection using platform sensor data. Further, it discusses how computational severity and criticality indexes (health indexes) can potentially be complementary to the use of AI for the provision of maintenance information on aircraft components, for assisting operational decisions. [ABSTRACT FROM AUTHOR]

BACKGROUND: Fighter pilots' team situation awareness (TSA) has been studied from the perspective of tsa accuracy, which represents how closely the pilots' collective knowledge is aligned with the real world. When tsa accuracy is low, the pilots can have similarly or dissimilarly inaccurate sa. the concept of TSA similarity represents the similarity of team members' collective knowledge. this paper investigates how TSA accuracy and similarity of F/a-18 pilots are associated with performance. METHOD: Data were extracted from simulated air combat missions. Performance and tsa were investigated in 58 engagements. the accuracy and similarity of pilots' sa were elicited and performance was evaluated. TSA accuracy and similarity were analyzed with respect to the flights' performance, and the independent variables were events in which the flights initiated engagements with enemy aircraft versus events in which the flights were engaged by enemy aircraft. RESULTS: With the mentioned events as the main effect, there were statistically significant differences at all levels of tsa accuracy and similarity. With performance as the main effect, there were also significant differences at all levels of TSA accuracy and similarity. TSA accuracy and similarity were superior in offensive engagements and when engagements were successful. DISCUSSION: the main contribution of this paper is the extension of the concept of tsa similarity to air combat: both RSA similarity and accuracy were higher when the flight was engaging the enemy aircraft, compared to situations when the flight itself was being engaged. the results also suggest that low tsa accuracy and similarity have a statistically significantly negative impact on the flights' performance. [ABSTRACT FROM AUTHOR]

The methods and procedures within United States naval aviation to certify an aircraft safe for flight are well established. However, these methods and procedures are based on clearing a system that is operated or monitored by a human. A fully autonomous system will not have a human in or on the loop and will therefore require a new method for certifying it safe for flight. This paper details how to use run time assurance as the framework for a safety of flight certification of autonomous behavior within United States naval aviation. We present an aerial refueling task with run time assurance as use case for the framework for certification. Within the use case we then give more details on the mechanics of using RTA to enable autonomous functionality within naval aviation. [ABSTRACT FROM AUTHOR]

Purpose: The research aimed to verify the usefulness of selected subjective methods of task load assessment for evaluating task load in general aviation organizations and forecasting preflight load. The study also highlighted the importance of the pilot's task load in the context of flight safety. Design/methodology/approach: The study included a comprehensive literature review to present the specifics of general aviation organization and the role of the human factor in aviation accidents. The research discussed the division of task load methods and characterized four selected subjective task load methods (NASA-TLX, SWAT, ISA, Bedford Scale). Expert assessments were conducted to evaluate these methods' effectiveness from the perspective of post-flight load assessment and pre-flight load prediction, considering the feasibility of implementation in general aviation organizations. Findings: The findings emphasized the need to develop a dedicated method for predicting task load before a flight. The study concluded that the methods used should be simple and require minimal financial outlay, considering the specificity of general aviation organizations. Research limitations/implications: Future research should focus on developing a dedicated pre-flight task load prediction method tailored to the unique requirements of general aviation. Identified limitations include the need for further validation of the subjective methods in different organizational contexts. Practical implications: The research suggests that implementing simple and cost-effective task load assessment methods can enhance flight safety in general aviation. The development of a specialized pre-flight load prediction tool could significantly improve operational planning and pilot performance. Social implications: By improving task load assessment and prediction in general aviation, the research can contribute to enhanced flight safety, thereby potentially reducing accident rates and improving public trust in aviation safety measures. Originality/value: This paper contributes to the field by addressing the gap in pre-flight task load prediction in general aviation. It provides valuable insights for aviation safety researchers, practitioners, and policymakers interested in optimizing pilot performance and ensuring flight safety. [ABSTRACT FROM AUTHOR]

Ice formation on aircraft surfaces poses significant safety risks, and current detection systems often struggle to provide accurate, real-time predictions. This paper presents the development and comprehensive evaluation of a smart ice control system using a suite of machine learning models. The system utilizes various sensors to detect temperature anomalies and signal potential ice formation. We trained and tested supervised learning models (Logistic Regression, Support Vector Machine, and Random Forest), unsupervised learning models (K-Means Clustering), and neural networks (Multilayer Perceptron) to predict and identify ice formation patterns. The experimental results demonstrate that our smart system, driven by machine learning, accurately predicts ice formation in real time, optimizes deicing processes, and enhances safety while reducing power consumption. This solution holds the potential for improving ice detection accuracy in aviation and other critical industries requiring robust predictive maintenance. [ABSTRACT FROM AUTHOR]

Most of the existing literature on crisis communication focuses on the crisis response immediately following a crisis. There is a dearth of research examining postcrisis communication efforts. Postcrisis communication is especially important in the context of tragic crises, which leave stakeholders deeply affected and traumatized. This paper examines the multifaceted nature of memorials and their significance in postcrisis communication. Using the 2015 Germanwings crash as an illustrative case study, the paper highlights how memorials serve as artifacts of grief, remembrance and celebration of life. Postcrisis communication that centres on victim needs is a keystone to better crisis management. Moreover, I argue that postcrisis communication should facilitate healing by serving the needs of victims, which supersede those of organizations. The paper examines the notions of victim‐centred crisis communication and ethics of care by looking at memorials as artefacts of postcrisis communication. [ABSTRACT FROM AUTHOR]

Aviation safety reports can provide detailed records of past aviation safety accidents, analyze their problems and hidden dangers, and help airlines and other aviation enterprises avoid similar accidents from happening again. In a novel way, we plan to use named entity recognition technology to quickly mine important information in reports, helping safety personnel improve efficiency. The development of intelligent civil aviation creates demands for the incorporation of big data and artificial intelligence. Because of the aviation-specific terms and the complexity of identifying named entity boundaries, the mining of aviation safety report texts is a challenging domain. This paper proposes a novel method for aviation safety report entity extraction. First, ten kinds of entities and sequences, such as event, company, city, operation, date, aircraft type, personnel, flight number, aircraft registration and aircraft part, were annotated using the BIO format. Second, we present a semantic representation enhancement approach through the fusion of enhanced representation through knowledge integration embedding (ERNIE), pinyin embedding and glyph embedding. Then, in order to improve the accuracy of specific entity extraction, we constructed and utilized the aviation domain dictionary which includes high-frequency technical aviation terms. After that, we adopted bilinear attention networks (BANs), the feature fusion approach originally used in multi-modal analysis, in our study to incorporate features extracted from both iterated dilated convolutional neural network (IDCNN) and bi-directional long short-term memory (BiLSTM) architectures. A case study of specific entity extraction for an aviation safety events dataset was conducted. The experimental results demonstrate that our proposed algorithm, with an F1 score reaching 97.93%, is superior to several baseline and advanced algorithms. Therefore, the proposed approach offers a robust methodological foundation for the relationship extraction and knowledge graph construction of aviation safety reports. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors. Design/methodology/approach: This paper proposes an improved gray correlation analysis (IGCA) theory to make the relational analysis of aviation accidents and influencing factors and find out the critical causes of aviation accidents. The optimal varying weight combination model (OVW-CM) is constructed based on gradient boosted regression tree (GBRT), extreme gradient boosting (XGBoost) and support vector regression (SVR) to predict aviation accidents due to critical factors. Findings: The global aviation accident data from 1919 to 2020 is selected as the experimental data. The airplane, takeoff/landing and unexpected results are the leading causes of the aviation accidents based on IGCA. Then GBRT, XGBoost, SVR, equal-weight combination model (EQ-CM), variance-covariance combination model (VCW-CM) and OVW-CM are used to predict aviation accidents caused by airplane, takeoff/landing and unexpected results, respectively. The experimental results show that OVW-CM has a better prediction effect, and the prediction accuracy and stability are higher than other models. Originality/value: Unlike the traditional gray correlation analysis (GCA), IGCA weights the sample by distance analysis to more objectively reflect the degree of influence of different factors on aviation accidents. OVW-CM is built by minimizing the combined prediction error at sample points and assigns different weights to different individual models at different moments, which can make full use of the advantages of each model and has higher prediction accuracy. And the model parameters of GBRT, XGBoost and SVR are optimized by the particle swarm algorithm. The study can guide the analysis and prediction of aviation accidents and provide a scientific basis for aviation safety management. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to conduct the aircraft electrical wiring interconnection system (EWIS) safety risk assessment process abundantly and hierarchically and establish the assessment index system considering the weights and interrelationships of different levels of indices. Design/methodology/approach: Due to the failure of EWIS being multifactorial, hidden and diverse, this paper divides the factors influencing the failure of EWIS into 3 primary indices, 13 secondary indices and 38 tertiary indices. Taking open circuit failure (OCF) and short circuit failure (SCF) as examples, calculate the weights of assessment indices based on the triangular fuzzy number analytic hierarchy process (TFNAHP) and triangular fuzzy number decision-making trial and evaluation laboratory (TFNDEMATEL). The cloud model (CM) divides the risk levels and obtains the safety risk assessment results. The comparative analyses of different weight calculation methods, different failure modes and different aircraft EWIS zones verify the effectiveness and practicability of the proposed method. Findings: The results show that the proposed method aligns more with the actual situation than other methods. Also, the results identify key focus objects in EWIS safety risk assessment, such as the surrounding environmental factors among the primary indices having the most significant influence on OCF and SCF, the risk level of SCF being higher than that of OCF, etc. Originality/value: This paper proposes a safety risk assessment index system for aircraft EWIS based on the cable parameters, surrounding environmental factors, installation and protection methods. The weight assignment is added to the assessment index system, and the safety risk assessment model is constructed by combining TFNAHP, TFNDEMATEL and CM. [ABSTRACT FROM AUTHOR]

Prognostic and health management (PHM) plays a vital role in ensuring the safety and reliability of aircraft systems. The process entails the proactive surveillance and evaluation of the state and functional effectiveness of crucial subsystems. The principal aim of PHM is to predict the remaining useful life (RUL) of subsystems and proactively mitigate future breakdowns in order to minimize consequences. The achievement of this objective is helped by employing predictive modeling techniques and doing real-time data analysis. The incorporation of prognostic methodologies is of utmost importance in the execution of condition-based maintenance (CBM), a strategic approach that emphasizes the prioritization of repairing components that have experienced quantifiable damage. Multiple methodologies are employed to support the advancement of prognostics for aviation systems, encompassing physics-based modeling, data-driven techniques, and hybrid prognosis. These methodologies enable the prediction and mitigation of failures by identifying relevant health indicators. Despite the promising outcomes in the aviation sector pertaining to the implementation of PHM, there exists a deficiency in the research concerning the efficient integration of hybrid PHM applications. The primary aim of this paper is to provide a thorough analysis of the current state of research advancements in prognostics for aircraft systems, with a specific focus on prominent algorithms and their practical applications and challenges. The paper concludes by providing a detailed analysis of prospective directions for future research within the field. [ABSTRACT FROM AUTHOR]

This review paper addresses the critical need for structural prognostics and health management (SPHM) in aircraft maintenance, highlighting its role in identifying potential structural issues and proactively managing aircraft health. With a comprehensive assessment of various SPHM techniques, the paper contributes by comparing traditional and modern approaches, evaluating their limitations, and showcasing advancements in data-driven and model-based methodologies. It explores the implementation of machine learning and deep learning algorithms, emphasizing their effectiveness in improving prognostic capabilities. Furthermore, it explores model-based approaches, including finite element analysis and damage mechanics, illuminating their potential in the diagnosis and prediction of structural health issues. The impact of digital twin technology in SPHM is also examined, presenting real-life case studies that demonstrate its practical implications and benefits. Overall, this review paper will inform and guide researchers, engineers, and maintenance professionals in developing effective strategies to ensure aircraft safety and structural integrity. [ABSTRACT FROM AUTHOR]

The functional resonance analysis method (FRAM) is a system-based method to understand highly complex sociotechnical systems. Besides learning from safety occurrences or undesirable states, FRAM can be used to understand how things go well in a system, by identifying gaps between "work as imagined" (WAI) and "work as done" (WAD). FRAM is increasingly used in many domains and can enhance our understanding of a complex system and proposes strategies to refine the work design. This systematic review identified 108 FRAM research papers from 2006–2019. Most of these papers were conducted by European researchers and employed qualitative methods such as document analysis, interviews, and focus groups with subject matter experts (SMEs) and observations to develop WAI and WAD. Despite being used in healthcare, construction, and maritime sectors among others, aviation was the most commonly explored domain in FRAM studies. The 26 FRAM studies in aviation explored many aspects of the aviation industry, including Air Traffic Control (ATC) systems, cockpit operation, ground handling, maintenance, and a range of past safety incidents, like runway incursions. This paper also characterises the FRAM studies focused on aviation in terms of the common methods and steps used to build FRAM and the available software tools to build FRAM nets. Current FRAM illustrates its advantages in capturing the dynamic and nonlinear nature of complex systems and facilitates our understanding and continual improvement of complex systems. However, there are some critical issues in FRAM use and interpretation, such as the consistency of methods and complexity and reliability of data collection methods, which should be considered by researchers and FRAM users in industry. [ABSTRACT FROM AUTHOR]

Machinery prognostics play a crucial role in upgrading machinery service and optimizing machinery operation and maintenance schedule by forecasting the remaining useful life (RUL) of the monitored equipment, which has become more and more popular in recent years. The safety of aviation is one of the issues that people are most concerned about in the field of transportation, since it might cause disastrous loss of life and property once accident happened. The turbofan engine is an important part of the aircraft that provides thrust for plane. With aging, the turbofan engine becomes prone to failures. As a result, it would be worth studying prognostics in turbofan engine to improve the reliability of machinery and reduce unnecessary maintenance cost. Recently, a data-driven prognostics modeling strategy called the classification of predictions strategy (CPS) was proposed, in which the continuous signal and the discrete modes of an actual system come together to achieve RUL estimation. However, machine health states measured from classification rarely have just one potential situation, and this strategy cannot determine whether the fault occurs or not by a certain probability which comes closer to reality. Moreover, since there is no information and prior knowledge of prognostics application, it is hard to obtain the probability of various situations from raw measured data. Hence, based on previous work, this paper proposes an improved prognostics modeling method named the classification of predictions strategy with decision probability (CPS-DP), whose key innovations mainly include three parts: (1) decision probability process (DPP) where each step of multi-step prediction obeys geometric distribution and can judge whether the failure state occurs using the decision probability; (2) decision probability calculation (DPC) algorithm, which is first proposed by this paper and can calculate the values of decision probability without prior knowledge of prognostics application; and (3) withdrawal mechanism optimizer (WMO), which is specially designed to compensate for the shortcomings of DPP and further enhance the performance of the prognostics model. In brief, first, CPS is used to build a basic prognostics model to acquire RUL estimation results, in which the information applied to find the probability has been contained. Later, the mean of RUL estimation errors is figured from the results, which is further employed to calculate the probability using DPC algorithm. Then, CPS-DP can be achieved by means of integrating two parts: DPP and CPS. Furthermore, to further improve the performance, WMO is utilized to optimize CPS-DP with rolling back predictions. Ultimately, an enhanced prognostic model based on CPS-DP is set up through uniting CPS, DPP, and WMO. To validate the proposed method, experimental results on the turbofan engine in 2008 prognostics and health management competition are investigated. [ABSTRACT FROM AUTHOR]

The aerospace industry prioritises safety protocols to prevent accidents that can result in injuries, fatalities, or aircraft damage. One of the potential hazards that can occur while manoeuvring aircraft in and out of a hangar is collisions with other aircraft or buildings, which can lead to operational disruption and costly repairs. To tackle this issue, we have developed the Smart Hangar project, which aims to alert personnel of increased risks and prevent incidents from happening. The Smart Hangar project uses computer vision, LiDAR, and ultra-wideband sensors to track all objects and individuals within the hangar space. These data inputs are combined to form a real-time 3D Digital Twin (DT) of the hangar environment. The Active Safety system then uses the DT to perform real-time path planning, collision prediction, and safety alerts for tow truck drivers and hangar personnel. This paper provides a detailed overview of the system architecture, including the technologies used, and highlights the system's performance. By implementing this system, we aim to reduce the risk of accidents in the aerospace industry and increase safety for all personnel involved. Additionally, we identify future research directions for the Smart Hangar project. [ABSTRACT FROM AUTHOR]

As the information technology (IT) industry advances, embedded systems are being applied in various industrial sectors. With the expansion of application areas, there is a growing demand for high-precision, high-specification embedded systems, leading to the increased complexity of embedded software. Consequently, software errors can cause system malfunctions, resulting in accidents such as airplane crashes and the sudden acceleration of cars, leading to significant loss of life and property damage. Therefore, measures to ensure the safety and stability of increasing embedded systems malfunctions are necessary. This paper proposes a system that monitors the operation of target embedded systems in real-time and compares the extracted normal operation current/voltage patterns with the current/voltage data of a target embedded system (TES). It compares the operation data of the TES with automatically generated normal operation patterns by forcibly exposing them. It suggests algorithms for immediately detecting and efficiently recovering from the TES malfunctions. The proposed system applies two algorithms. (a) Monitoring TES current: When a malfunction is detected, a monitoring embedded systme (MES) resets the TES to restore normal operation. If malfunctions persist, it controls TES by using an algorithm to shut it down. Additionally, a proportional integral derivation (PID) control is applied to stabilize the current state. (b) Monitoring TES voltage: If a voltage drop occurs, the MES immediately stops the TES operation to minimize damage. The proposed algorithms were validated through experiments. For a normal TES consuming up to 95 mA, an error detection rate of 20% was applied. The TES was reset if it consumed over 114 mA. It was confirmed that the TES was stopped upon detecting the third malfunction. Regarding voltage, when the normal operating voltage of the system was around 5 V, if the TES operating voltage dropped below 4.3 V, it was detected as a malfunction, and the algorithm to stop the TES operation was validated. [ABSTRACT FROM AUTHOR]

Aircraft icing has historically been a critical cause of airplane crashes. The electro-impulse de-icing system has a wide range of applications in aircraft de-icing due to its lightweight design, low energy consumption, high efficiency, and other advantages. However, there has been little study into accurate wing electric-impulse de-icing simulation methods and the parameters impacting de-icing efficacy. Based on the damage mechanics principle and considering the influence mechanisms of interface debonding and ice fracture on ice shedding, this paper establishes a more accurate numerical model of wing electric-impulse de-icing using the Cohesive Zone Model (CZM). It simulates the process of electric-impulse de-icing at the leading edge of the NACA 0012 wing. The numerical results are compared to the experimental results, revealing that the constructed wing electro-impulse de-icing numerical model is superior. Lastly, the effects of varying ice–skin interface shear adhesion strengths, doubler loading positions, and impulse sequences on de-icing effectiveness were studied. The de-icing rate is a quantitative description of the electro-impulse's de-icing action, defined in the numerical model as the ratio of cohesive element deletions to the total elements at the ice–skin interface. The findings reveal that varying shear adhesion strengths at the ice–skin interface significantly impact the de-icing effect. The de-icing rate steadily falls with increasing shear adhesion strength, from 66% to 56%. When two, four, and seven impulses were applied to doubler two, the de-icing rates were 59%, 71%, and 71%, respectively, significantly increasing the de-icing efficiency compared to when impulses were applied to doubler one. Doubler one and two impulse responses are overlaid differently depending on the impulse sequences, resulting in varying de-icing rates. When the impulse sequence is 20 ms, the superposition results are optimal, and the de-icing rate reaches 100%. These studies can guide the development and implementation of a wing electric-impulse de-icing system. [ABSTRACT FROM AUTHOR]

The paper explores the potential to enhance aviation safety, particularly in militarized regions, by outfitting aircraft with Side Looking Airborne Radar (SLAR) and employing space-time adaptive processing (STAP) algorithms. The research objective revolves around implementing a model of side-looking airborne radar and the corresponding STAP algorithms. This technology enables the detection of slow-moving targets amidst strong interference, encompassing both passive (clutter) and active (jammer) elements. Slow-moving targets relative to the aircraft's speed include tanks, combat vehicles, command vehicles, artillery, and logistical assets of enemy forces. The theoretical framework of space-time adaptive processing is presented, elucidating the sequential steps of the classical Sample Matrix Inversion Space-Time Adaptive Processing (SMI STAP) algorithm. The paper underscores the significance of characteristic parameters delineating a linear STAP processor. The proposed solution facilitates the detection of enemy combat measures and enhances aviation safety. It outlines a radar model installed beneath the aircraft's fuselage and elucidates algorithms for space-time adaptive processing of radar signals. The simulations conducted within the article were executed using the MATLAB environment. The simulation results indeed suggest that the proposed solution holds promise for deployment in equipping aircraft of one's own military and those engaged in operations within conflict zones. This paper stands as one of the few contributions in the literature addressing the augmentation of aircraft safety through radar and space-time adaptive processing. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics of complex structure, strong coupling and different multi-modal safety levels of more electric aircraft starter generator system, a safety analysis method based on the operating process and a multi-modal failure rate calculation method are proposed. This paper analyses the architecture, operating process and modals of more electric aircraft starter generator system and decomposes the system into eight operating modals. Based on the construction of SafetyLab, a domestic safety analysis platform, the structural models and failure rate calculation models of eight modals of starter generator system are established, and the top event failure rate of each modal, the highest transient failure rate and the steady state failure rate of the system are calculated for a complete safety analysis, taking a typical starter generator system as an example. The method proposed in this paper helps to solve the problem of multi-modal failure rate analysis of complex systems with different equipment involved in the operating process. The multi-modal failure rate calculation method proposed in this paper is also applicable to the safety analysis of other multimodal complex systems. [ABSTRACT FROM AUTHOR]

This paper presents a methodology that involves the development of high-fidelity modeling and simulation procedures aimed at supporting virtual certification for crashworthiness requirements specific to tiltrotor aircraft, addressing the critical need for accurate safety requirement fulfillment predictions and weight containment of wing. The unique crashworthiness requirement for tiltrotor wings necessitates a design that can ensure a controlled failure during survivable crash events. This is to alleviate the inertial load acting on the fuselage, thereby protecting occupants from injuries and fire while ensuring the integrity of escape paths. The objective of this methodology is to simulate the crash effects on the entire wing using explicit, non-linear, and time-dependent FE analysis. This approach verifies the spanwise placement of the frangible sections, the mode of failure, the loads acting on the fuselage links, and the acceleration transmitted to the structure. This study focuses on a standalone analysis. [ABSTRACT FROM AUTHOR]

In online public opinion events, key figures are crucial to the formation and diffusion of public opinion, to the evolution and dissemination of topics, and to the guidance and transformation of the direction of public opinion. Based on the four-dimensional public opinion communication supernetwork (social-psychology-opinion-convergent), this study proposes a classification and recognition algorithm of key figures in online public opinion that integrates multidimensional similarity and K-shell to identify the key figures with differentiation in online public opinion events. The research finds that the evolutionary process of public opinion events is the joint action of key figures with different roles. The opinion leader is the key figure in the global communication of public opinion. The focus figure is the core figure that promotes the dissemination of public opinion on local subnetworks. The communication figure is the "bridge" node in the cross-regional communication of public opinion. Through the algorithm verification of the case "China Eastern Airlines Passenger Plane Crash Event", we find that the algorithm proposed in this paper has advantages in feasibility, sensitivity, and effectiveness, compared with traditional algorithms such as CI, forwarding volume, degree centrality, K-shell, and multidimensional similarity. The classification and recognition algorithm proposed in this study can not only identify multirole key figures simultaneously but also improve the recognition granularity and eliminate the interference of core-like nodes. [ABSTRACT FROM AUTHOR]

The lightning effects in aircraft have been known since the early days of aviation, and still they are a subject of active research. Lightning or atmospheric electric discharge is a natural phenomenon that cannot be affected by man. The goal of this paper is to analyze the impact of atmospheric electrical discharges on flight safety by providing risk assessment techniques. The effects that occur as a result of lightning strikes are presented, and it is explained how lightning strikes can be reported. All relevant events recorded in the database Aviation Safety Network - Database are shown in the tables and there are a total of 29 events caused by lightning strikes in the aircraft during the summer. The main part of this paper is the analysis of aircraft accidents caused by lightning. This analysis involves defining the hazards, assessing the safety and assessing each risk individually. By providing this analysis, safety and mitigating measures could be defined for future lightning strikes occurrences. [ABSTRACT FROM AUTHOR]

This paper presents a Bayesian search methodology in the context of missing aircraft, as well as a few other related search operations. The search seeks an item hidden in one of n cells. The parameters controlling the search are the prior probabilities (updated during each phase of the search) and the search quality. Assume the search begins in the area with the maximal prior. The expected length of the search depends on how far the item is from that locale (in essence a measure of the quality of the prior), and the search effectiveness parameter. A perfect (error free) search could find the item in a number of steps as a function of the distance of the object from the starting location. Lower quality search can take a lot longer, though it can ultimately be effective. The Bayesian process works by guiding us to the higher likelihood areas based on the results of failed search. It adds value by eliminating unlikely possibilities. The search can have an element of luck in starting its exploration close to the actual item. Real searches, where this was true, were in fact ultimately successful; real searches which were not so fortunate ended in failure. [ABSTRACT FROM AUTHOR]

To address growing system complexity in the aerospace industry, a Model‐Based Systems Engineering (MBSE) approach has been increasingly adopted for the development of aircraft systems. This calls for a corresponding approach for performing safety assessment to maintain consistency between the system and safety domains. One of the key safety assessment processes for aircraft development is the Functional Hazard Assessment (FHA). The purpose of this paper is to build upon previously published works and introduce the MBFHA framework which describes the language, method, and tool needed for implementing a model‐based approach to performing FHA and integrating it into MBSE activities. A customised FHA profile is introduced for the modelling language, an overall workflow along with processes for FHA report and safety requirements generation is presented for the method, and a list of tool constraints is provided. A proof‐of‐concept is subsequently presented using safety data for the landing gear extension and retraction system of a generic business aircraft. [ABSTRACT FROM AUTHOR]