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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Aerospace Science and Engineering".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 20844

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Special Issue Editors

Dr. Luís Santos

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Guest Editor

1. ISEC Lisboa, Alameda das Linhas de Torres 179, 1750-142 Lisboa, Portugal
2. AEROG, Universidade da Beira Interior, Calçada Fonte do Lameiro, 6200-358 Covilhã, Portugal
Interests: aerospace; management; safety; planetary defense

Dr. André Silva

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Guest Editor

1. Aerospace Sciences Department, Universidade da Beira Interior, Calçada Fonte do Lameiro, 6200-358 Covilhã, Portugal
2. AEROG, Universidade da Beira Interior, Calçada Fonte do Lameiro, 6200-358 Covilhã, Portugal
Interests: aerodynamics and propulsion; two-phase flows; combustion; planetary defence

Dr. Rui Melicio

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Guest Editor

1. LAETA/IDMEC, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisboa, Portugal
2. LAETA/AEROG, University of Beira Interior, 6201-001 Covilhã, Portugal
Interests: power systems management and operation; power electronics and its applications; renewable energies; smart grids; cryogenic systems; avionics systems; aeronautics and space systems
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Special Issue Information

Dear Colleagues,

The technological processes and regulatory transformations that occurred in recent decades transformed aviation in the safest way of transportation. Today, with organizational and human threats, it is important to develop innovative approaches, methodologies, and frameworks to adapt to these new challenges. The aviation ecosystem is still adapting to the currently available technologies, and a new technological transformation is again on the verge, with actions needing to be taken to minimize the impact of these in aviation safety.

The main causes of today’s aviation safety hazards are human and organizational errors. New and enhanced models, using current and emergent technologies, can help to diminish the possibilities of errors.

In this Special Issue, we invite submissions that can help and contribute to overhaul aviation safety. Both theoretical and experimental frameworks are suitable, as well comprehensive review and survey papers.

Dr. Luís Santos
Dr. André Silva
Prof. Dr. Rui Melicio
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

aviation safety
safety management
risk assessment
human error
organizational error
hazard model

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Published Papers (10 papers)

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Research

22 pages, 6276 KiB

Open AccessArticle

Enhanced Soft Error Rate Estimation Technique for Aerospace Electronics Safety Design via Emulation Fault Injection

by Dongmin Lee, Taehyeong Nam, Daeseon Park, Yeju Kim and Jongwhoa Na

Appl. Sci. 2024, 14(4), 1470; https://doi.org/10.3390/app14041470 - 11 Feb 2024

Cited by 1 | Viewed by 1148

Abstract

In this paper, we propose Automatic Configuration Memory Fault Injection (ACMFI), a tool that calculates the architectural vulnerability factor (AVF) and soft error rate (SER) using the emulation fault injection technique. SER, which is essential for the safety design of aerospace electronics, can be obtained by experiments (beam tests) performed in a beam facility equipped with high-energy radiation facilities. However, SER calculation using beam tests has the disadvantage of a high cost and a long waiting time, making it difficult to use in the conceptual design stage, which is the aerospace system development stage and the initial HW/SW development stage. Using the emulation fault injection method, it is possible to estimate the SER, which can be used in the system safety design phase. This paper describes the ACMFI tool, which automatically performs emulation fault injection in SRAM-based FPGAs, which are widely used in aerospace electronic hardware. Unlike the existing methods, the proposed method has the advantage of minimizing the side effects by injecting faults into a dedicated SRAM area. In other words, the SER obtained by the proposed method can be estimated more accurately than the SER result obtained by the existing method. To prove the accuracy of the proposed test method, the SER calculated by performing an emulation fault injection test on the same FPGA was compared with the SER results tested at the beam facility. The method of obtaining SER using the proposed ACMFI gave results that were closer to the SER obtained by testing at the beam facility than the method of obtaining SER using the existing EMFI. The proposed method is used to calculate the failure rate, which is a key variable in determining the development assurance level when performing safety design tests in aerospace system development, enabling the development of safer systems and lower cost/higher quality aerospace electronic equipment than before. Full article

(This article belongs to the Special Issue Research on Aviation Safety)

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14 pages, 2857 KiB

Open AccessArticle

Psychophysical State Aspect during UAV Operations

by Marta Maciejewska, Marta Galant-Gołębiewska and Tomasz Łodygowski

Appl. Sci. 2024, 14(1), 150; https://doi.org/10.3390/app14010150 - 23 Dec 2023

Viewed by 898

Abstract

The development of unmanned aerial vehicles (UAVs) and the increasing air traffic of these devices make it necessary to pay attention to the issue of the human factor in UAV operations. In this article, tests were conducted in real conditions on the unmanned aerial vehicle operator’s (UAVO) psychophysical state during training. The parameters of the human cardiovascular system, and more specifically the heart rate variability (HRV), were used to conduct research and analysis. The purpose of this research is to elaborate the typical HRV parameters for student operators during UAVO training. These reference values could be used during UAVO training to assess candidates’ psychophysical state objectively and could allow for the monitoring of operators’ state and management of their cognitive load. Monitoring operators’ state may have a positive impact on increasing training effectiveness. Research confirmed the thesis that HRV parameters are significantly different during performed tasks with cognitive load and can be used to assess candidates’ psychophysical state objectively. This can help flight instructors perform student assessment, meaning that they would not have to rely only on their subjective feelings. Full article

(This article belongs to the Special Issue Research on Aviation Safety)

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10 pages, 616 KiB

Open AccessArticle

Amphibious Airplane Accidents: An Exploratory Analysis

by Alex de Voogt, Amy Campos and Yi Lu

Appl. Sci. 2023, 13(22), 12224; https://doi.org/10.3390/app132212224 - 10 Nov 2023

Viewed by 1296

Abstract

Causes and contributing factors of amphibious airplane accidents are examined by comparing the proportion of fatal accidents for different causes of accidents, with a focus on landings on water and low-level flying maneuvers. A set of 183 accidents involving amphibious planes from 2005 to 2020 was extracted from the National Transportation Safety Board’s online database. Amphibious airplane accidents are reported to be fatal in 34% of cases, which is higher than the average of 20% for general aviation. Logistic regression analysis shows that the maneuvering flight phase and decision-making factors are significantly more often associated with fatal accidents than other flight phases and causes. In addition, the number of accidents associated with decision-making factors significantly increased during the studied time period. Amphibious airplanes benefit from accident analysis despite the absence of denominator data and the limitations of most general aviation accident reports. Intentional low-level flying is shown to be a central area of concern that may be addressed at the operational as well as the training level. Landing accidents could be avoided by introducing additional warning systems and training regarding (retractable) landing gear as well as general awareness training of decision-making during landings on water. Full article

(This article belongs to the Special Issue Research on Aviation Safety)

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15 pages, 3460 KiB

Open AccessArticle

Initial Student Attention-Allocation and Flight-Performance Improvements Based on Eye-Movement Data

by Junli Yang, Ziang Qu, Zhili Song, Yu Qian, Xing Chen and Xiuyi Li

Appl. Sci. 2023, 13(17), 9876; https://doi.org/10.3390/app13179876 - 31 Aug 2023

Cited by 2 | Viewed by 1411

Abstract

At the onset of their flight careers, novice pilots often lack clarity regarding the standard attention-allocation pattern. Therefore, to enhance the efficiency of initial flight training, it is crucial for students to develop a comprehensive understanding of flight control and attention-allocation behavior during the learning process. In this study, flight-performance data and eye-movement data from experienced instructors in no-power stall scenarios were collected to create an attention-allocation training course. An experimental group underwent the attention-allocation training course, while a control group followed the traditional teaching curriculum. The disparities between the flight performance and eye-movement indices of the two groups after they completed their respective courses were compared to evaluate the effectiveness of the training. The finding indicate significant differences between the speed losses, altitude losses, and mean course deviations of the instructors and the control group; these indicators had p-values of 0.01, 0.004, and 0.001, respectively. Moreover, significant differences were observed between the altitude losses and mean course deviations of the instructors and the experimental group; these indicators had p-values of 0.006 and 0.001, respectively. The experimental group, which underwent attention-allocation training, exhibited eye-movement indices that closely resembled those of the instructor group, and its instrument scanning was more strategic, thereby resulting in improved flight performance from that of the control group. Additionally, correlations were observed between flight-performance indices and eye-movement indices of the students. Overall, this study demonstrates the effectiveness of an attention-allocation training course designed specifically for a no-power stall scenario. It effectively enhanced the training outcomes of novice pilots, promoted an appropriate allocation of attention to instrument displays, introduced a novel approach to flight training, and ultimately contributed to aviation safety. Full article

(This article belongs to the Special Issue Research on Aviation Safety)

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19 pages, 5506 KiB

Open AccessArticle

Uncovering the Hidden Correlations between Socioeconomic Indicators and Aviation Accidents in the United States

by Joana Samarra, Luís F. F. M. Santos, Ana Barqueira, Rui Melicio and Duarte Valério

Appl. Sci. 2023, 13(14), 7997; https://doi.org/10.3390/app13147997 - 8 Jul 2023

Cited by 1 | Viewed by 1280

Abstract

Rules and regulations for accident mitigation have been implemented between all players. It is necessary to use new technologies and resources for human factors to mitigate future accidents to decrease accidents. It has been verified that accidents by sabotage are currently non-existent and that most of the fatalities are during the flight and in the runway approach phase. Severe accidents with associated fatalities are a small number that tend to decrease over time. Human errors, although with all the mitigations over time, are still the most significant cause of accidents; although accidents have decreased, other factors may be related to this type of error, such as the lack of personnel for the operation of a flight. Accidents can also be related to other factors, such as economic factors. GDP growth is positively correlated with accidents, and inflation is negatively correlated. It is also found that the inflation factor is also related to the number of flights due to a lack of demand. Full article

(This article belongs to the Special Issue Research on Aviation Safety)

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18 pages, 899 KiB

Open AccessArticle

Behavioral Indicator-Based Initial Flight Training Competency Assessment Model

by Hong Sun, Fangquan Yang, Peiwen Zhang and Qingqing Hu

Appl. Sci. 2023, 13(10), 6346; https://doi.org/10.3390/app13106346 - 22 May 2023

Cited by 2 | Viewed by 2539

Abstract

Ensuring training safety is paramount to flight schools. In response to the inadequacy of traditional flight training assessment for comprehensive quantitative evaluation of cadet competency, an initial flight training competency assessment standard based on behavioral indicators was developed and optimized using the VENN model. Firstly, the Assessor Score Measurement Form (ASMF) was constructed according to the requirements of the Training Evaluation Worksheet specification, such as typical subjects, observations, and completion criteria. Secondly, based on the basic principles of the experience of the flight expert and the Competency-Based Training and Assessment (CBTA), a matrix of correlations between the observations and each competency-based behavioral indicator was created to construct a competency assessment matrix. In addition, a two-dimensional model for representing competency items characterized by behavioral indicators was established and an optimization model for competency assessment criteria was constructed. Finally, through combining actual flight training data, the proposed method was validated in the flight screening check phase. The results show that the optimized flight training competency assessment scheme can be well quantified and matched to real instructor ratings with an accuracy of 84%. The assessment worksheet, the assessment matrix, and the VENN competency rating model can be adapted to the different teaching requirements of each flight phase, achieving a perfect match between the behavioral indicators and the competency items, which is highly versatile. The proposed model can more accurately reflect the core competencies of flight trainees, enable quantitative assessment of behavioral indicators and competency items, and provide support for subsequent training of trainees. Full article

(This article belongs to the Special Issue Research on Aviation Safety)

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19 pages, 645 KiB

Open AccessArticle

Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety

by Imran Shafi, Amir Sohail, Jamil Ahmad, Julio César Martínez Espinosa, Luis Alonso Dzul López, Ernesto Bautista Thompson and Imran Ashraf

Appl. Sci. 2023, 13(9), 5475; https://doi.org/10.3390/app13095475 - 27 Apr 2023

Cited by 3 | Viewed by 2402

Abstract

Safety critical spare parts hold special importance for aviation organizations. However, accurate forecasting of such parts becomes challenging when the data are lumpy or intermittent. This research paper proposes an artificial neural network (ANN) model that is able to observe the recent trends of error surface and responds efficiently to the local gradient for precise spare prediction results marked by lumpiness. Introduction of the momentum term allows the proposed ANN model to ignore small variations in the error surface and to behave like a low-pass filter and thus to avoid local minima. Using the whole collection of aviation spare parts having the highest demand activity, an ANN model is built to predict the failure of aircraft installed parts. The proposed model is first optimized for its topology and is later trained and validated with known historical demand datasets. The testing phase includes introducing input vector comprising influential factors that dictate sporadic demand. The proposed approach is found to provide superior results due to its simple architecture and fast converging training algorithm once evaluated against some other state-of-the-art models from the literature using related benchmark performance criteria. The experimental results demonstrate the effectiveness of the proposed approach. The accurate prediction of the cost-heavy and critical spare parts is expected to result in huge cost savings, reduce downtime, and improve the operational readiness of drones, fixed wing aircraft and helicopters. This also resolves the dead inventory issue as a result of wrong demands of fast moving spares due to human error. Full article

(This article belongs to the Special Issue Research on Aviation Safety)

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14 pages, 4093 KiB

Open AccessArticle

Implementation of the Mean Time to Failure Indicator in the Control of the Logistical Support of the Operation Process

by Andrzej Żyluk, Mariusz Zieja, Norbert Grzesik, Justyna Tomaszewska, Grzegorz Kozłowski and Michał Jasztal

Appl. Sci. 2023, 13(7), 4608; https://doi.org/10.3390/app13074608 - 5 Apr 2023

Cited by 4 | Viewed by 2322

Abstract

The focus of this paper is to identify a method for defining the needs of logistical operational support based on the mean time to failure (MTTF) factor. The research was based on a helicopter intended for flight training. The MTTF indicator for selected equipment was determined based on failure data from previous flight operations. As the basic operational data for the developed method, the time from the beginning of the operation or the flight time from the last damage and the method of restoring airworthiness were selected. The MTTF and replacement index for the device were determined. The next step was to determine the index, based on selected probability distributions. The results were analyzed and presented in graphical form, and conclusions were drawn. Based on the MTTF index and replacement index, the logistics needs of selected devices were determined. The obtained results were compared with the actual exchanges of devices made in the year in question. The research proved that the MTTF reliability factor and the analysis of trends in value changes could be used to determine the needs for the logistical security of the operation process, particularly in relation to the equipment subject to accidental failures. This is important for maintaining high availability of an aircraft or other technical objects. Full article

(This article belongs to the Special Issue Research on Aviation Safety)

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17 pages, 3960 KiB

Open AccessArticle

Design and Reliability Analysis of a Series/Parallel Hybrid System with a Rotary Engine for Safer Ultralight Aviation

by Teresa Donateo and Ludovica Spada Chiodo

Appl. Sci. 2023, 13(7), 4155; https://doi.org/10.3390/app13074155 - 24 Mar 2023

Cited by 6 | Viewed by 2212

Abstract

The conventional powertrain for ultralight aviation consists of a fixed pitch propeller connected to an internal combustion engine (ICE). Since ICEs have a limited thermal efficiency (<40%), new and more efficient powerplant configurations have recently been proposed in the scientific literature by adopting hybrid electric solutions. Hybridization has the additional benefit of increased safety thanks to redundancy. This is a very important issue in ultralight aviation, where a high percentage of accidents are caused by engine failure. In a previous investigation, the authors proposed the design of a series/parallel hybrid electric power system to increase safety and optimize fuel economy by controlling the engine working points during flight. A new powertrain, derived from an automotive Honda i-MMD system, is analyzed in this study and a reliability analysis is performed to underline the improved safety obtained with the proposed system. Full article

(This article belongs to the Special Issue Research on Aviation Safety)

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15 pages, 1313 KiB

Open AccessArticle

Learning Methods and Predictive Modeling to Identify Failure by Human Factors in the Aviation Industry

by Rui P. R. Nogueira, Rui Melicio, Duarte Valério and Luís F. F. M. Santos

Appl. Sci. 2023, 13(6), 4069; https://doi.org/10.3390/app13064069 - 22 Mar 2023

Cited by 9 | Viewed by 3490

Abstract

This paper proposes a model capable of predicting fatal occurrences in aviation events such as accidents and incidents, using as inputs the human factors that contributed to each incident, together with information about the flight. This is important because aviation demands have increased over the years; while safety standards are very rigorous, managing risk and preventing failures due to human factors, thereby further increasing safety, requires models capable of predicting potential failures or risky situations. The database for this paper’s model was provided by the Aviation Safety Network (ASN). Correlations between leading causes of incident and the human element are proposed, using the Human Factors Analysis Classification System (HFACS). A classification model system is proposed, with the database preprocessed for the use of machine learning techniques. For modeling, two supervised learning algorithms, Random Forest (RF) and Artificial Neural Networks (ANN), and the semi-supervised Active Learning (AL) are considered. Their respective structures are optimized applying hyperparameter analysis to improve the model. The best predictive model, obtained with RF, was able to achieve an accuracy of 90%, macro F1 of 87%, and a recall of 86%, outperforming ANN models, with a lower ability to predict fatal accidents. These performances are expected to assist decision makers in planning actions to avoid human factors that may cause aviation incidents, and to direct efforts to the more important areas. Full article

(This article belongs to the Special Issue Research on Aviation Safety)

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