Aviation Flight Research Articles

Assessment of the sustainable development of the flight route

The article provides an assessment of the sustainable development flight route servicing in the absence of a regular schedule. It is not possible to establish a schedule of such flights due to poor forecasting of the requirements for applications for transport, so a calculation mechanism is required to adapt the situation to the usual conditions for formalization in a short time. The most convenient mathematical device in this case is the application of a multiplier form of integral penalty function, considering the operating costs that depend on the downtime of free aircraft. In the case of short-term transportation planning for service in local emergency zones, it is expected to reduce the waiting time for operational applications by 15-20% compared to the known method of routing planning using a “greedy algorithm”. The paper provides a specific example of a practical performance assessment, influenced by these criteria of operational nature and urgency of execution, which are expressed by weights. This methodology can be applied to the planning of small aviation flights, including those in agriculture.

Safety analysis of Civil aviation Flight and UAV Operation based on STAMP/STPA

In order to prevent the occurrence of dangerous approaches or accidents in the low-altitude UAV conflict resolution process, the security problem in the process was transformed into the control problem, and a conflict resolution security analysis method for low altitude UAV based on STAMP/STPA was proposed. Firstly, based on STAMP (System Theoretic Accident Modeling and Processes), the security control structure in the conflict resolution system was constructed. Then STPA (Systems Theoretic Process Analysis) was used to determine system-level accidents and hazards based on the context information of system operation, identify the unsafe control behaviors in the process of conflict resolution, and analyze the key causes of unsafe control behaviors.

Analysis of Mental Health Status and Related Factors of Civil Aviation Flight Cadets

Analysis of Mental Health Status and Related Factors of Civil Aviation Flight Cadets

From Aviation to Web Standarts: Transformation of SSIM Files into JSON Format

Considering dynamic changes in the aviation sector, successful data exchange and integration planning plays a pivotal role in maintaining operational efficiency of airlines. Although the SSIM (Standard Schedules Information Manual) file is considered a reliable format, it has certain limitations regarding interaction and compatibility with other web technologies and programs due to its text format strictly defined by the specification. In this work, we propose an approach to convert SSIM files into the JSON (JavaScript Object Notation) format. One of the central aspects of this work is a detailed transformation model that considers all the important structures and levels of information of the SSIM file, conducts their syntactic analysis, and converts them into a flexible and easily recoverable JSON format. To transform a set of aviation flights into a combined flying line, a modified branch and bound method is used. With a series of examples and experiments, the article aims to demonstrate the effectiveness of this methodology in terms of preserving data integrity and ensuring their accuracy. In particular, the problems faced during the transformation, for example, how to consider the consistency of data rows with fixed width in the SSIM file, are considered. The transition from aviation-oriented formats, such as the SSIM file, to global web formats, such as JSON, is a process that goes beyond technologies and aims to increase the flexibility and convenience of collaboration, as well as data processing in the aviation industry. This research helps to implement this change, offering a simple, effective, and scalable approach for converting airline schedules into a format that by its nature meets the needs of modern information applications and systems.

МЕТОДОЛОГИЧЕСКОЕ ОБЕСПЕЧЕНИЕ ИМПЛЕМЕНТАЦИИ МЕЖДУНАРОДНЫХ СТАНДАРТОВ ПО БЕЗОПАСНОСТИ ПОЛЕТОВ В РОССИЙСКИХ НОРМАТИВНЫХ ДОКУМЕНТАХ

The present paper delves into the challenge of implementing international flight safety standards into the Russian regulatory framework governing the establishment, application, and refinement of aviation safety management systems (SMS) in the civil aviation sector in Russia. It underscores the reliance of SMS regulatory adherence on the Standards and Recommended Practices (SARPs) set forth by the International Civil Aviation Organization (ICAO). The paper conducts an examination of prevailing discrepancies, indeterminacies, and ambiguities in delineating core terms and concepts within the English and Russian renditions of ICAO SARPs documentation. Furthermore, it offers an evaluation of the implementation hurdles associated with SARPs in regulatory documentation pertaining to civil aviation flight safety in Russia. The deficiencies in the regulatory infrastructure for flight safety in Russia are substantiated. Drawing from practical insights gained in formulating and improving regulatory and methodological support for SMS in prominent Russian airlines, the paper proposes measures to mitigate inaccuracies and uncertainties in the integration of ICAO SARPs editions within the Russian regulatory framework for aviation industry service providers.

Substantiation of source data on the parametric algorithms for the classification of weather hazards

The meteorological situation is one of the decisive factors determining the safety and frequency of civil aviation flights. Weather hazards (WH), associated with cumulonimbus clouds, such as a heavy shower, thunderstorm, hail, combined with high atmosphere turbulence, quite often lead to aviation events and even accidents. Currently, a domestic weather radar system of the near airfield zone (WR) “Monocle” has been developed and successfully operated. The criteria for the classification of meteorological phenomena (MP), used in the WR, have been developed individually for each phenomenon and have some heuristic character. These criteria are cumbersome and complicate the process of automating the WH classification. In this case, there is a natural desire to generalize the criteria and optimize them in accordance with the theory of distinguishing statistical hypotheses. This article discusses the application of the Bayesian approach to the WH classification. The statistical Bayesian decision theory assumes decision-making in terms of the probability theory when all significant probabilistic values, so-called sufficient statistics, are known. In order to obtain statistical descriptions of the probability distributions of reflectivity and the eddy dissipation rate (EDR), an analysis of radar signals, reflected from such MP as a rain shower, thunderstorm, hail was carried out. The article provides brief descriptions of the methods of conducting experiments to form statistical database and its analysis. Based on the above methods, the statistical parameter H(EDRmax) analysis for a rain shower, the amplitude distribution of reflectivity parameters and the EDR (Zmax, EDRmax) for thunderstorms and hail was carried out, which showed the low distinguishing ability of each individual parameter when solving the problem to classify MP within the assigned alphabet. The obvious solution is dictated by the theory of recognition. To increase the classification confidence, it is essential to share information parameters, for example, in the form of multidimensional distribution densities of the probabilities of random parameters. The article presents a parametric description of the MP “rain shower-thunderstorm-hail” classification features. An analysis to evaluate the probabilistic characteristics of the WH classification for the adopted empirical classification criteria in the WR shows that the adopted criteria are far from optimal in terms of the probabilities of the correct classification, especially in the rain shower case. It is obvious that a problem solution of the assigned classification confidence is associated with the optimization of the feature space and classification criteria. Based on the data obtained, it is necessary to build an algorithm to classify the WH “rain shower-thunderstorm-hail”.

MODE AND CONDITIONS OF FORMATION OF THUNDERSTORMS IN ODESA REGION

Active thunderstorm activity is dangerous for various sectors of the economy, and especially for aviation flights. The study determined the modern spatio-temporal distribution of thunderstorm activity at meteorological stations of the Odesa region for 2005–2022, as well as the circulation and thermodynamic conditions of thunderstorm formation. Revealed that thunderstorm activity in 2005-2022 at the stations of Odesa region was less active compared to 1961-1990, but the geographic distribution of the intensity of thunderstorm formation did not change. Almost all thunderstorms formed from April to October. The maximum annual variation over the entire region occurred in June, the minimum in January. May and June 2013 and June 2021 were marked by the most active thunderstorm formation. Exposed that in 2018-2022 intra-mass thunderstorms and thunderstorms on the main cold fronts formed more often over Odesa. Most often, before the storm in the Whiting and Total Totals indices reached critical criteria.

Research on a post-competency model of civil aviation flight cadets

ABSTRACT The initial evaluation index system of civil aviation flight cadets’ competency was established using NVivo version 11.0 through qualitative analysis. The competency evaluation index system of civil aviation flight cadets was determined using questionnaire surveys and SPSS version 26.0, and includes four dimensions – core quality, ethical discipline, psychological quality and leadership – and another 23 indexes. The competency model was constructed using AMOS version 23.0 through factor analysis and a structural equation model, and the weight coefficient of each index at all levels was finally determined. The results showed that self-confidence and optimism, attention distribution and situational awareness are the three most important indicators affecting the competency of civil aviation flight cadets. Therefore, the model can provide references for the scientific evaluation of civil aviation flight cadets’ competence and provide support for airlines and colleges to train flight cadets.

Comparative Analysis of Aviation Occurrence Patterns among Three Northeast Asian Countries: South Korea, China and Japan

This study investigates the commonalities and differences in aviation occurrences among three Northeast Asian countries–South Korea, China and Japan, using the ICAO's Integrated Safety Trend Analysis and Reporting System (iSTARS) dataset. Logistic regression and latent class analysis are employed to identify factors influencing the likelihood of fatalities and to classify occurrences into distinct groups. The findings indicate that operational issues in airspace pose greater risks than runway-related issues in this region. The ground and landing flight phases have lower fatality likelihoods compared to the cruise phase, and winter seasons show reduced odds of fatalities. Latent class analysis identifies five distinct occurrence classes, highlighting specific risks and areas for targeted interventions. Technical and environmental issues are prominent in Japan, while operational issues during the cruise and maneuvering phases are significant in South Korea, associated with a high fatality rate. Light recreational general aviation flights pose a considerable risk of fatalities in China and Japan. The findings of this study highlight the need for comprehensive research to explore the causes and contributing factors of aviation occurrences in different countries to enhance safety practices and mitigate fatality risks.

Hemostasis System in Airline Passengers and Pilots

This review contains the results of the studies of the hemostasis system in airline passengers and crew members, as well as ground-based model experiments simulating the effect of specific flight factors. According to most researchers, flight factors shift the coagulation balance towards increasing the procoagulant potential. However, the occurrence of thrombotic conditions in passengers is associated with the individual presence of certain endogenous risk factors, such as age, constitution, the diseases of the cardiovascular system and hereditary pathology of the hemostasis system, some types of hormone therapy. Aircraft pilots whose flight time significantly exceeds that of passengers have described cases of thrombosis. No mention of cases of hemorrhagic conditions in civil aviation flight personnel has been found in the available literature.

AMU-LED Cranfield Flight Trials for Demonstrating the Advanced Air Mobility Concept

Advanced Air Mobility (AAM) is a concept that is expected to transform the current air transportation system and provide more flexibility, agility, and accessibility by extending the operations to urban environments. This study focuses on flight test, integration, and analysis considerations for the feasibility of the future AAM concept and showcases the outputs of the Air Mobility Urban-Large Experimental Demonstration (AMU-LED) project demonstrations at Cranfield University. The purpose of the Cranfield demonstrations is to explore the integrated decentralized architecture of the AAM concept with layered airspace structure through various use cases within a co-simulation environment consisting of real and simulated standard-performing vehicle (SPV) and high-performing vehicle (HPV) flights, manned, and general aviation flights. Throughout the real and simulated flights, advanced U-space services are demonstrated and contingency management activities, including emergency operations and landing, are tested within the developed co-simulation environment. Moreover, flight tests are verified and validated through key performance indicator analysis, along with a social acceptance study. Future recommendations on relevant industrial and regulative activities are provided.

Study on Machine Learning Methods for General Aviation Flight Phase Identification

Accurate identification of phases of flight is an essential step in analyses such as airport operation counts, fuel burn estimation, and safety studies. Past research has focused primarily on using positional data with rule-based or probabilistic-based decision-making to identify the phases of flight. Many of these efforts note that the task of correctly identifying phases of flight is challenging, often requiring extreme fine-tuning of methods. In this paper, we initially study whether combinations of dimensionality reduction of flight data records from general aviation aircraft impact clustering into the correct flight phases (climb, cruise, or descent) without any preprocessing or fine-tuning. For dimensionality reduction, we considered the low variance filter, the high correlation filter, principal component analysis, and autoencoders. We found that these dimensionality reduction algorithms do not offer any benefit for the phase identification task, as compared to feature selection that simply omits engine-specific features. For the clustering task, we considered -means and Gaussian mixture models. After performing clustering on eight test flights, we conclude that both methods are adequate at identifying the phases of flight in various general aviation flights and yield similar results.

NOAA Space Weather Prediction Center Radiation Advisories for the International Civil Aviation Organization

AbstractThe National Oceanic and Atmospheric Administration's Space Weather Prediction Center (NOAA/SWPC) issues several solar radiation storm products: the long standing proton Warnings and Alerts that are based on particle intensity levels observed by the Geostationary Operational Environmental Satellites; and the more recent International Civil Aviation Organization (ICAO) radiation advisories which specify effective dose rates at aviation flight levels. SWPC ICAO advisories are supported by the U.S. Federal Aviation Administration (FAA) CARI‐7A model. In this paper we use CARI‐7A modeling results for the Ground Level Enhancement 69 (GLE69) solar radiation storm which occurred on the 20th of January 2005 to demonstrate the ICAO advisory format. For the onset and peak of GLE69, we find that a severe (SEV) radiation advisory would have been issued for altitudes above 32,000 ft, for polar and mid latitude regions of the northern and southern hemisphere. At lower altitudes, down to 25,000 ft, the moderate (MOD) radiation threshold would have been exceeded. In total, 10 ICAO radiation advisories would have been issued over 6.5 hr. From the retrospective modeling of GLE69, and feedback from users, we identify ways in which the ICAO advisories should be improved.

A high-precision and efficient algorithm for space-based ADS-B signal separation

AbstractSpace-based automatic dependent surveillance-broadcast (ADS-B) receivers can cover thousands of aircraft, each transmitting 6 ⋅ 2 signals per second. As a result, ADS-B signals are very prone to overlap. When the number of aircraft covered by a receiver reaches 3,000, about 90 % of the signals will be overlapping. Overlapped signals can reduce the decoding accuracy of receivers, so that aircraft information cannot be accurately transmitted to the air traffic control (ATC) surveillance system, hence threatening aviation flight safety. It is necessary to propose signal separation algorithms for space-based ADS-B systems. An orthogonal projection linear constrained minimum variance (OPLCMV) algorithm is proposed, which can separate two signals simultaneously based on the linearly constrained minimum variance algorithm by exploiting the characteristics of overlapped signals. Compared with the state-of-the-art extended projection algorithm and the fast independent component analysis algorithm, the OPLCMV method has a higher decoding accuracy for multiple overlapping signals with a small direction difference of arrival or frequency shift. Moreover, the OPLCMV algorithm has a low computational complexity when the number of overlapped signal sources is less than seven.

The nature and costs of civil aviation flight training safety occurrences

The continuing growth of the aviation industry will see robust demand for more qualified pilots in the coming years. To meet the standards required to qualify as pilot, individuals must undergo extensive training. Flight training, like all other parts of aviation, is not without risks. Previous research that examined the nature and costs of accidents and incidents within the general aviation industry has put little emphasis on flight training. The present work explores the nature and costs of incident, serious incidents, and accidents within the Australian flight training industry between the 1st of January 2000 and the 31st of December 2019. This study of 7863 flight training safety occurrences is the single largest study of the phenomena to date. This has facilitated the confirmation of some previous noted results, as well as refuting some others. All flight training data categories are statistically significantly different relative to all general aviation occurrences, as reported to the ATSB. It was noted that safety occurrences during landing and approach phases are significantly higher for flight training than previous studies. Occurrences during climb and standing resulted in more injuries and fatalities than expected. While solo instructional flights were found to be less fatal, the probability of an accident or serious incident or aircraft damage being from solo operations has increased over the period of the study. Of particular concern is that the accident and occurrence rates have increased over the period examined. The fact that the safety of flight training has not significantly improved is further shown by the extremely high costs that have resulted from these incidents and accidents.

Nowcast for cloud top height from Himawari‐8 data based on deep learning algorithms

AbstractCloud top height (CTH) indicates the vertical development of clouds. Intensely vertically developing clouds are usually accompanied by extreme weather systems and pose a threat to aviation safety. Therefore, nowcast for CTH is necessary and meaningful to guide aviation flights. In this study, we researched the nowcast for CTH (mainly within 0–2 h) based on deep learning algorithms. With Sichuan Province as the study area, we collected CTH data of Himawari‐8 satellite from 2018 to 2020. Convolutional‐long‐short‐term‐memory (ConvLSTM) and trajectory‐gated‐recurrent‐unit (TrajGRU) were used to build nowcast models in the encoder‐forecaster framework. The optical flow model and persistence were used as benchmarks. The results showed that the deep learning models did not have significant advantages over the benchmarks in the first 20 min. However, with increasing nowcast time, the nowcast skills of the deep learning models were gradually exhibited. For all four seasons, the TrajGRU‐based model showed superior performance over the ConvLSTM‐based model and the benchmarks. In spring, autumn and winter, the results yielded by the ConvLSTM‐based model were second only to those of the TrajGRU‐based model. However, in summer, the ConvLSTM‐based model did not outperform the persistence. The results of the optical flow model worsened significantly with increasing nowcast time. In contrast to the persistence, the optical flow model had almost no nowcast skills after 40 min.

UPLC-MS/MS simultaneous quantification of urinary circadian rhythm hormones and related metabolites: Application to air traffic controllers

Civil aviation flight crew and civil aviation air traffic controllers are prone to circadian rhythm abnormalities, which can lead to a slew of other maladies. It could endanger people's health and provide a serious threat to the safety of civil aviation flights if it is not appropriately evaluated and addressed. Early detection of rhythm irregularities and prompt treatment for particular populations that are vulnerable to rhythm disorders are crucial for enhancing civil aviation safety.In general, monitoring of the classical circadian rhythm biomarkers (melatonin or cortisol) in plasma or saliva is an effective way to evaluate the rhythm status. Due to the challenging sample procedure and the trauma of plasma, urine sample testing has received an increasing amount of attention. While, urine circadian rhythm biomarkers have seldom been examined, and the relationship between urinary steroid hormones and melatonin is still poorly understood. In most cases, hormones are determined by immunoassays respectively, mainly enzyme-linked immunosorbent assay (ELISA) or radioimmunoassay (RIA). There are also reports describing the liquid chromatography with tandem mass spectrometry (LC-MS/MS) technique as a method of melatonin or few steroid hormones quantification, however, the simultaneous detection of multiple rhythmic hormones in human urine is rarely reported.For the quantification of the rhythmic hormones in human urine, an accurate approach using ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was devised in this work. Nine endogenous hormones (melatonin, 6-hydroxymelatonin, 6-sulfatoxymelatonin, cortisol, corticosterone, cortisone, testosterone, epitestosterone and androsterone), in human overnight urine, were quantified after solid phase extraction (SPE). A reverse phase HSS C18 column was used for chromatographic separation with a 9-minute gradient elution and deuterated analogues of each analyte were applied as internal standards. This method was successfully applied to the analysis of 596 overnight urine samples (23:00–9:00) collected from 84 air traffic controllers in the Beijing area during shift work. This study's findings showed a clear correlation not only between melatonin and its metabolites; cortisol-related metabolites, but also between melatonin metabolites and endogenous metabolites upstream and downstream of cortisol, implying that these two categories of hormones can be used as potential biological rhythm indicators to provide circadian rhythm data support for future studies on circadian rhythm disorders.

Improved space weather observations and modeling for aviation radiation

In recent years there has been a growing interest from the aviation community for space weather radiation forecasts tailored to the needs of the aviation industry. In 2019 several space weather centers began issuing advisories for the International Civil Aviation Organization alerting users to enhancements in the radiation environment at aviation flight levels. Due to a lack of routine observations, radiation modeling is required to specify the dose rates experienced by flight crew and passengers. While mature models exist, support for key observational inputs and further modeling advancements are needed. Observational inputs required from the ground-based neutron monitor network must be financially supported for research studies and operations to ensure real-time data is available for forecast operations and actionable end user decision making. An improved understanding of the geomagnetic field is required to reduce dose rate uncertainties in regions close to the open/closed geomagnetic field boundary, important for flights such as those between the continental US and Europe which operate in this region. Airborne radiation measurements, which are crucial for model validation and improvement, are lacking, particularly during solar energetic particle events. New measurement campaigns must be carried out to ensure progress and in situ atmospheric radiation measurements made available for real-time situational awareness. Furthermore, solar energetic particle forecasting must be improved to move aviation radiation nowcasts to forecasts in order to meet customer requirements for longer lead times for planning and mitigation.

일반항공 비행교관 역량별 행동지표 및 비행교관 교육요구 분석

This study aims to provide information necessary for developing a “Competency-Based Training and Evaluation Plan” for general aviation flight instructors in Korea. General aviation training experts designed a survey by selecting “Behavior Indicators” from the ICAO Flight Instructor Competency Model. This study analyses the importance of Behavioral Indicators, the level of instructor performance, and the training needs for flight instructors. According to the factor analysis, ICAO “Flight Instructor Competency Model” is found as a valid tool to measure general aviation flight instructor competencies. According to the training needs analysis, flight instructors training program needs to be improved on “Training Environment Management”, “Teaching Method”, and “Interaction” competencies. When compared with trainee group, flight instructors & examiners have a higher training demand for “Teaching & Learning Environment Management”. This study suggests the flight instructor training program requires to be reorganized based on the competencies related to the flight instructor duties in the training environments.

Forecasting solar energetic proton integral fluxes with bi-directional long short-term memory neural networks

Solar energetic particles are mainly protons and originate from the Sun during solar flares or coronal shock waves. Forecasting the Solar Energetic Protons (SEP) flux is critical for several operational sectors, such as communication and navigation systems, space exploration missions, and aviation flights, as the hazardous radiation may endanger astronauts’, aviation crew, and passengers’ health, the delicate electronic components of satellites, space stations, and ground power stations. Therefore, the prediction of the SEP flux is of high importance to our lives and may help mitigate the negative impacts of one of the serious space weather transient phenomena on the near-Earth space environment. Numerous SEP prediction models are being developed with a variety of approaches, such as empirical models, probabilistic models, physics-based models, and AI-based models. In this work, we use the bidirectional long short-term memory (BiLSTM) neural network model architecture to train SEP forecasting models for three standard integral GOES channels (>10 MeV, >30 MeV, >60 MeV) with three forecast windows (1-day, 2-day, and 3-day ahead) based on daily data obtained from the OMNIWeb database from 1976 to 2019. As the SEP variability is modulated by the solar cycle, we select input parameters that capture the short-term, typically within a span of a few hours, and long-term, typically spanning several days, fluctuations in solar activity. We take the F10.7 index, the sunspot number, the time series of the logarithm of the X-ray flux, the solar wind speed, and the average strength of the interplanetary magnetic field as input parameters to our model. The results are validated with an out-of-sample testing set and benchmarked with other types of models.