Aviation Industry Research Articles

The Role of Organizational Trust in Shaping Commitment and Reducing Turnover Intention in Aviation

In the contemporary business landscape, advancements in artificial intelligence and robotics are rapidly transforming industries, including air transportation. Although the demand for human resources may diminish with technological progress, the necessity for skilled personnel remains critical. The air transportation sector, in particular, relies heavily on qualified professionals, whose contribution to sustainable business practices is substantial. However, despite their importance, these trained professionals may occasionally exhibit a turnover intention their positions. Such turnover in an industry where technology plays a pivotal role represents a significant loss for organizations. Mitigating turnover intentions may depend on the application of effective organizational behavior models, one of which is fostering an environment of trust within the organization. Increased trust in the organization can enhance employees’ commitment and job satisfaction, thereby reducing their turnover intention. The primary aim of this study is to identify the factors influencing turnover intention, with a specific focus on whether organizational trust affects this intention. Furthermore, the study seeks to determine whether organizational commitment mediates the relationship between organizational trust and turnover intention. To explore these dynamics, a survey was conducted with 404 ground and flight personnel employed in both public and private aviation sectors in Türkiye. The research findings show that trust has important implications in an organizational setting. As a result of the analysis conducted within the scope of the research, it was determined that organizational trust has no effect on turnover intention. The reason for the different result from the literature may be the sensitivity of the aviation industry and the high staff turnover rate. Due to these characteristics of the sector, employees of the aviation industry may show turnover intention even if they trust their organizations.

Electrochemical removal of ethylene glycol monobutyl ether in aviation industry wastewater using a porous Ti4O7/Ti electrode

Titanium alloy chemical milling wastewater contains a significant amount of ethylene glycol monobutyl ether (EGBE), which is toxic and difficult to be degraded by microorganisms, and the emission will lead to severe environmental pollution. To solve this problem, the electrocatalytic oxidation method was employed to remove EGBE in the wastewater, in which a porous Ti4O7/Ti electrode were prepared and employed as the anode, while stainless steel acted as the cathode. The effects of current density, interelectrode distance, and stirring mode on the COD removal rate were also investigated. Results show that the COD of the wastewater significantly decreases from 12410 to 15 mg L−1 after 16 h of electrolysis at 10 mA cm−2 and electrode spacing of 1.0 cm as well as aeration stirring at 100 mL min−1. Moreover, pilot-plant scale testing reveals that the COD decreasing from 11980 to 183 mg L−1 after 24 h of electrolysis. Consequently, the utilization of porous Ti4O7/Ti electrode as anode in the electrocatalytic oxidation method presents significant potential in removal of EGBE in aviation industry wastewater.

From Text to Safety: A Novel Framework for Mining Unsafe Aviation Events Using Advanced Neural Network and Feature Network

The rapid growth of the aviation industry highlights the need for strong safety management. Analyzing data on unsafe aviation events is crucial for preventing risks. This paper presents a new method that integrates the Transformer network model, clustering analysis, and feature network modeling to analyze Chinese text data on unsafe aviation events. Initially, the Transformer model is used to generate summaries of event texts, and the performance of three pre-trained Chinese models is evaluated and compared. Next, the Jieba tool is applied to segment both summarized and original texts to extract key features of unsafe events and prove the effectiveness of the pre-trained Transformer model in simplifying lengthy and redundant original texts. Then, cluster analysis based on text similarity categorizes the extracted features. By solving the correlation matrix of these features, this paper constructs a feature network for unsafe aviation events. The network’s global and individual metrics are calculated and then used to identify key feature nodes, which alert aviation professionals to focus more on the decision-making process for safety management. Based on the established network and these metrics, a data-driven hidden danger warning strategy is proposed and illustrated. Overall, the proposed method can effectively analyze Chinese texts of unsafe aviation events and provide a basis for improving aviation safety management.

Numerical Investigation of Film Formation Characteristics and Mechanisms through Airless Spraying on Spherical Surfaces

This paper focuses on key engineering issues, particularly the overall turbulent transport of paint spray and coating film distribution characteristics, in the process of airless spraying film formation. By deeply considering the geometric features of spherical surfaces and their impact on the near-wall region of the flow field, an airless spraying film formation model consisting of the Eulerian multiphase model, the realizable k–ε turbulence model, and the Eulerian Wall Film model was established. Through numerical simulations of static spraying on the inner and outer walls of spherical surfaces with different radii, the influence of geometric features on the spray flow field and film formation characteristics on spherical surfaces was investigated. Subsequently, based on numerical simulations of dynamic spraying on different nozzle trajectories, the film formation characteristics were analyzed, and the optimal spray trajectory planning method was determined. Additionally, this study examined the coating distribution characteristics during dynamic spraying on spherical surfaces with varying geometric dimensions. Finally, a kind of chlorinated rubber anti-corrosion primer was chosen to carry out spraying experiments, which validated that the airless spray coating model and the corresponding numerical simulation methods established in this paper were reasonable and feasible for investigating the film formation characteristics on spherical surfaces. This work is expected to further promote the application of airless spray techniques in machinery, automotive, shipbuilding, and aviation industries.

The Safety Management System (SMS) As A Tool for Building Safety Culture in Aviation: A Qualitative Research

Safety culture is conceptualized as the combination of beliefs, values, norms, attitudes, roles, and practices—both social and technical—within an organization, aimed at mitigating the exposure to adverse or hazardous conditions for both internal and external individuals. In entities where a safety culture is deeply ingrained, efforts are made to formulate a framework that encourages employee adherence to safety regulations, promoting safe behaviours, particularly in high-risk environments. The efficacy of safety culture in substantially reducing accidents, incidents, errors, hazards, and risks, especially those related to human factors, is pivotal in attaining elevated safety standards. At the heart of fostering such a culture lies the implementation of a robust Safety Management System (SMS), which, through its effective and dynamic execution, paves the way for cultivating a safety-centric ethos. This study conducts a detailed empirical analysis of aviation enterprises in Türkiye to evaluate the hypothesis that the Safety Management System (SMS) is a crucial instrument in cultivating a safety culture within the aviation industry. The study employs qualitative research methodologies to discern the correlation between the effectiveness of SMS and its components and the level of safety culture within these organizations. Findings from the study reveal a direct relationship where entities with inadequately implemented SMS and its elements exhibit lower levels of safety culture. Conversely, enterprises that demonstrate effective implementation of SMS are characterized by a high level of safety culture, affirming the critical role of SMS in fostering safety within aviation organizations. This inquiry not only substantiates the integral role of SMS in developing a safety culture in aviation but also highlights the necessity for meticulous implementation of SMS components and processes to achieve a superior safety standard in the industry.

Body Image, Stress, and Performance: Analysing the Aviation industry’s Physical Appearance Standards

Body Image, Stress, and Performance: Analysing the Aviation industry’s Physical Appearance Standards

A Review of Aircraft Engine Fault Prediction and Performance Optimization Based on Deep Learning

This paper is aimed at presenting a systematic review of the recent studies on the use of deep learning techniques in aircraft engine health management for fault detection and performance enhancement. This comes in the light of increased growth in the air transport industry, the efficiency of aircraft engines and the efficiency of their maintenance have emerged as key issues in the safety and efficiency of air transport. This paper also identifies some of the key issues emerging from this area such as the data issues and the need to develop very accurate models for faulting and performance enhancement. Some of the most advanced techniques, namely the Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) are discussed as potential solutions to these challenges. The theoretical descriptions of the working principles, as well as the strengths and weaknesses of the methods in question with regard to the problems of aircraft engine health management, are elaborated. Furthermore, this paper introduces the application of Reinforcement Learning as a new technique in the area of optimal control of engine parameters with the hope of improving the dependability and maintainability of aircraft engines and in the process of achieving sustainable growth in the aviation industry. The issues on model interpretability, model generalization and model deployment are also discussed and research guidelines for the future work in this field are also outlined. In this paper, the state of the art and the importance of deep learning as well as the applications of deep learning in aircraft engine health management are discussed based on the literature and case studies.

Delamination and thrust force mitigation during drilling of unidirectional carbon fibre-reinforced plastic laminate using magnetorheological elastomer

Abstract In new improvements to the aviation industry, carbon fibre-reinforced plastic (CFRP) is a buoyant material due to its noteworthy and application-friendly properties. The behaviour of transversely isotropic CFRP, which prompts drilling-induced delamination, causes critical damage that leads to the rejection of the final product. The cause of the delamination damage is the thrust force generated by the drilling tool during the machining operation. The present work proposes an indigenous approach to suppress delamination significantly using magnetorheological elastomer (MRE). The thrust force generated by the drilling tool is recorded for varying magnetic field strengths. Delamination damage was computed using the MATLAB script. Meanwhile, specific focus was given to studying the interlaminar mechanics of a drilled hole through scanning electron microscopy. The results show that nearly 45% of the thrust force is reduced using this MRE at a maximum field strength of 0.4T compared to a conventional one. The results are further supported by a 22% and 30% smoothening of the delamination at the hole’s entry and exit, respectively. Thus, this approach helps to reduce delamination during drilling.

Shock buffet onset prediction with flow feature-informed neural network

Transonic shock buffet is a significant self-excited shock oscillations and aerodynamic instability phenomenon induced by shock-boundary layer interaction, which limits the flight envelope and even causes flight accidents. The aviation industry has a significant interest in accurately predicting the shock buffet onset boundary, defined by a specific combination of Mach number and angle of attack. While the current methods of steady and unsteady numerical simulation suffer from a contradiction of efficiency and accuracy. In the current paper, a flow feature-informed neural network (FINN) model is constructed to predict the buffet onset boundary over airfoils. Typical features associated with buffet onset are extracted from the steady base flow and subsequently integrated into the hidden layer of the neural network to impose physical constraints. With the test cases of the NACA0012 airfoil at various Mach numbers, the FINN model can accurately predict the damping representing the unsteady instability margin. Compared to the direct mapping input-output neural network (NN) model, the proposed method with shock wave feature-informed has enhanced accuracy, with an average relative error decreased by 70% at extrapolated Mach numbers. This research demonstrates the effectiveness of the FINN model in predicting the buffet onset, which leverages physics features derived from the more economical steady solution far from the onset boundary at a given predicted Mach number.

Speed of sound measurements of binary n-octane+ ethylcyclohexane mixture at liquid-gas phase transition curve

Bio-jet fuel is a key element in the aviation industry to reduce operating costs and environmental impacts. Bio-jet fuel is a complex mixture of four hydrocarbons (n-alkanes, isoalkanes, cycloalkanes and aromatics). In the present work, the speed of sound in pure n-octane, ethylcyclohexane, and their mixtures with six selected compositions of (0.3004, 0.4191, 0.4999, 0.5538, 0.6991, and 0.7852 mole fraction of ethylcyclohexane) has been measured along the l-G saturation curve in the temperature ranges from (286 to 443) K using the pulse method with a constant (acoustic) sounding base. The combined expanded absolute and relative uncertainties (0.95 level of confidence, k = 2) of the temperature, concentration, and speed of sound measurements are estimated to be 20 mK, 0.0006 mole fraction, and 0.2 %, respectively. The measured speed of sound data together with our previous reported density data for the pure component (ethylcyclohexane) and the mixture were used to calculate derived thermodynamic properties, such as isentropic compressibility kS and heat capacity ratios CPCV as a function of temperature along the l-G saturation curve for the pure components and the mixture for selected concentration of x = 0.8 mole fraction of ethylcyclohexane. The deviation of the measured speed of sound data for the mixture from the linear additive rule has been determined using the pure component data.

Optimizing IT Project Management in Ukraine's Aviation Sector: Challenges, Opportunities, and Strategic Insights

The Ukrainian aviation industry is undergoing digital transformation; however, the pace remains behind global and European standards due to several challenges. These include limited funding, a lack of skilled IT professionals, and a slow rate of modern technology adoption. The research, titled "Optimizing IT Project Management in Ukraine’s Aviation Sector: Challenges, Opportunities, and Strategic Insights," addresses the urgent need for improvements in IT project management to enhance efficiency, competitiveness, and technological advancement within the aviation sector in Ukraine. This article analyzes the sector's current challenges, including insufficient government and private sector investment, internet infrastructure issues, and a shortage of IT specialists. It also identifies opportunities in areas like automation, digitization, and the potential for public-private collaborations. The study's primary objective is to develop and evaluate an optimal IT project management framework tailored to Ukraine’s aviation industry. This framework aims to optimize resource usage, integrate modern technologies like AI, automation, and cybersecurity, and improve global competitiveness. Furthermore, the research offers a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis of Ukraine’s aviation IT landscape, benchmarking it against European standards. A substantial gap exists between Ukraine and Europe regarding IT investment and automation, with European countries leading in adopting AI-driven systems, enhanced cybersecurity, and automated business and customer service processes. To bridge this divide, the study proposes an optimized IT project management model focused on strategic priorities such as investment in skill development, strengthening public-private partnerships, enhancing cybersecurity, implementing agile work methods, and supporting innovation through government initiatives. In conclusion, the study underscores that enhancing IT project management in Ukraine’s aviation industry is crucial for maintaining global competitiveness and improving operational efficiency. The proposed model offers actionable insights to address current challenges while capitalizing on new opportunities, thereby ensuring resilience in the face of ongoing global digitization and technological advancements.

Features and prospects of the Russian civil aircraft industry development under sanctions pressure from Western countries

The specifics and prospects of development of the Russian civil aircraft industry under sanctions pressure from Western countries have been considered. The study analyzes secondary data from open sources. The main provisions and conclusions of the work are as follows. The United States of America and its European satellites have imposed comprehensive sanctions against Russian air carriers, including a ban on aircraft deliveries. Lessors who owned foreign airplanes made demands for their return, and foreign registrars suspended airworthiness certificates for domestic airplanes. Western sanctions against Russian airlines have caused significant damage to Russia, but have also become a serious stimulus for the domestic aviation industry development. Significant time and investment will be required to resolve the problems encountered. Russia has the necessary potential to restore the aviation industry and ensure safe passenger and cargo transportation in the required volume. The country is currently working systematically to revitalize the industry, implementing advanced developments and establishing serial production of a wide range of civil aircraft. The success of the domestic aviation industry development depends mainly on the state’s efforts, as it has the access to almost all production and design capacities. In addition, effective measures are needed to influence the air transportation industry in order to balance the supply and demand for aircraft.

Optimal Design of High Specific Power Electric Machines for Fully Electric Regional Aircraft: A Case Study of 1MW S-PMSM

The aviation industry is undergoing electrification due to the increased global focus on reducing emissions in air traffic. Regarding the volatility of raw material prices, one main objective is the increase in the specific power of the motor. This matches the ambitious targets of the CoE project (Center of Excellence) in Finland on high-speed electric motors. The targeted specific power is 20 kW/kg. In this work, motors are designed and optimized for a fully electric regional aircraft. motors with different slot/pole configurations and rotational speed values are studied to determine the advantage of increasing speed in terms of weight reduction. As increasing speed requires the use of a gearbox, the overall weight of the motor and the gearbox is evaluated in post-processing, which allows for determining the impact of high speed on the overall weight. An optimization tool coupled with an electromagnetic and mechanical analysis is used to optimize 1 MW surface mounted permanent magnet synchronous motors (S-PMSMs) for given specifications of regional electric aircraft. Optimization results indicate that there is considerable gain in terms of overall weight only when increasing the speed to the range of 10,000–15,000 rpm.

Capabilities of Software Package Gearacl for Design Gearings and Transmission Elements

Creation of a mathematical model of gearing operation belongs to one of the most knowledge-intensive fields of knowledge. Interdisciplinary research is being conducted to develop the model. For practical use, the results of this work are embodied in strength analysis standards. Due to the complexity of the processes occurring in gearings, performing analysis of one pair of gears today requires not only significant time costs from the engineer, but also a deep understanding of hundreds of pages of calculation techniques described by groups of interrelated standards. The calculation formulae include clarifying coefficients, the contribution and values of which are determined by a group of specialists developing the standard, based on research of analysis result coordination, experimental data and calculations performed using the finite element method. To perform the analysis, the designer needs knowledge about the principles of choosing coefficient values and their meaning. Performing the analysis of gearings according to the standard is a time-consuming process that can be correctly performed only by an engineer with experience in designing gearings. To facilitate the work of the engineer, software products both domestic and foreign were created. However, the trends of recent years require replacement of foreign software products. In 2022, the specialists of the Central Institute of Aviation Motors developed the Gearacl software package, which allows replacing foreign products in the volume required by the aviation industry enterprises in the field of cylindrical gearings, shafts and bearings analysis. Modern analysis methods have been introduced into the software package. This article is devoted to the capabilities of the Gearacl software package.

Air travellers’ attitudes towards carbon emissions: evidence from the Google Flights interface

Reducing carbon dioxide emissions in the aviation industry is a formidable challenge. Understanding airline travellers’ willingness to pay for emission reductions could lead to impactful changes in flight pricing and government policies. In this study, we employ a discrete choice experiment to more accurately gauge this willingness to pay. The experiment features a comprehensive analysis of both leisure and business traveller categories, utilising a realistic interface mirroring Google Flights for journeys between Los Angeles and New York. The findings demonstrate that, on average, business flyers exhibit a significantly higher willingness to pay for emission reductions compared to leisure flyers. Notably, a subset of emissions conscious leisure flyers also exhibits a markedly higher willingness to pay. Latent class modelling indicates that this emissions conscious group accounts for about 42% of the leisure flyer sample. The larger group of price focused leisure flyers does not display any significant willingness to pay for emission reduction. Another key finding is that ethical consumption behaviour, not just stronger perceptions related to climate change, is linked to emissions-reducing preferences among both types of flyers. These results provide valuable insights for policy and pricing strategies, emphasising the heterogeneity across and within the business and leisure flyer segments.

Bangalore International Airport Limited: A Hub for the Future

Bangalore International Airport Limited (BIAL) has been operating the Kempegowda International Airport near Bengaluru, India, since 2008. Bengaluru, the capital of Karnataka, is a major hub for startups and the IT industry. The COVID-19 pandemic significantly impacted the aviation industry, including BIAL, leading to a substantial decline in passenger traffic and cargo handling, weakening profitability. Mr Agarwal, the Corporate Strategist for BIAL, has been tasked with developing new strategies to address the various challenges the company faces. These challenges include the impending expiration of a concession agreement, decreasing non-aeronautical revenues and making critical strategic decisions related to infrastructure development. This case allows students to explore strategic decision-making using the Ansoff matrix. It encourages them to identify the key pillars of airport business growth that are internationally recognized, which can help BIAL expand its business portfolio, meet increasing demand, ensure infrastructure development in the right direction and achieve sustainability over the next 30 years.

Unlocking the Power: How Digital Transformation Fuels Intellectual Capital

Purpose–This paper aims to explore the transformative impact of digitalization within the aviation sector, with a specific focus on airport terminal management. The primary objective is to understand how digitalization influences and reshapes the various facetsof the industry, particularly in terms of intellectual capital.Design/Methodology/Approach –This study employs a conceptual overview to examine the impact of digitalization on intellectual capital in aviation management. By analyzing the integration of digital technologies, the study explores their effects on human, structural, and relational capital. The conceptual framework is enhanced by detailed network analysis using the NetworkX library, offering an in-depth examination of the relationships between elements of intellectual capital and digital transformation criteria.Findings–The study uncovers the intricate relationships defining TAV Holding and its subsidiary entities, providing insights into the dynamic interplay of power, influence, and intellectual capital within the aviation sector. It reveals how digital transformation criteria are crucial in enhancing intellectual capital, which includes human, structural, and relational capital. The findings emphasize the role of digital technologies in streamlining processes, improving decision-making, and fostering innovation.Discussion–This paper contributes to the body of knowledge by highlighting the original value of studying the impact of digitalization on intellectual capital in the aviation sector. It underscores the importance of digital transformation criteria in enriching intellectual capital and fostering resilience in a rapidly evolving industry landscape. The discussion also addresses the broader implications of these findings for the aviation industry and its stakeholders, suggesting that embracing digitalizationcan lead to more effective and efficient airport terminal management.

The ‘hangar of the future’ for sustainable aviation

AbstractSustainability is becoming a major strategic driver within the aviation industry, which has moved from providing primarily economic benefits to delivering the ‘triple bottom line’, including social and environmental impact as well as financial performance. Sustainable aviation is also being tracked by the International Civil Aviation Organisation (ICAO) Global Collation for Sustainable Aviation. Operations and Infrastructure is an important near-term opportunity to deliver sustainability benefits. Digital Technologies, Integrated Vehicle Health Management (IVHM) and Maintenance Repair and Overhaul (MRO) play a prominent role in implementing these benefits, with a particular focus on operational efficiencies. As part of this, the sustainable smart hangar of the future is a concept that is becoming more and more important in forming the future of the aviation industry. The Hangar of the Future is an excellent opportunity for innovation, combining the progress in manufacturing, materials, robotics and artificial intelligence technologies. Succeeding in developing a hangar with these characteristics will provide us with potential benefits ranging from reduced downtime and costs to improved safety and environmental impact. This work explores some of the key features related to the sustainable smart hangar of the future by discussing research that takes place in DARTeC’s (Digital Aviation Research and Technology Centre) hangar led by the IVHM Centre in Cranfield. Additionally, the paper touches on some longer-term aspirations.

Multi-objective optimization of airfoils with integral tubular high-pressure tanks for hydrogen storage

Reducing the environmental impact of air transport is one of today's most important challenges in aviation industry and research. A promising key enabler is the use of hydrogen as an alternative to fossil fuels. The development of hydrogen-powered aircraft poses new engineering challenges due to its low volumetric energy density requiring high-pressure or cryogenic storage. If the volume in the wing shall be further used for storing hydrogen under high pressure, new demands arise to airfoil design. The present work focuses on this issue by presenting a multi-objective optimization approach aiming for airfoils with both low drag and high volume for internal tubular high-pressure tanks. This allows to directly address the new design objective and to find novel airfoil shapes providing the best compromise between aerodynamic efficiency and high storage volume for pressurized hydrogen. The resulting optimization problem is solved using Evolutionary Algorithms. For an efficient aerodynamic evaluation, the open source viscous-inviscid panel method XFOIL is used. An application example, based on the flight conditions of a general aviation aircraft, demonstrates the applicability of the method. Comparisons of the resulting aerodynamic characteristics obtained by XFOIL with RANS simulations confirm the feasibility of the results.

A Dual-Dimension Convolutional-Attention Module for Remaining Useful Life Prediction of Aeroengines

Remaining useful life (RUL) prediction of aeroengines not only enhances aviation safety and operational efficiency but also significantly lowers operational costs, offering substantial economic and social benefits to the aviation industry. Aiming at RUL prediction, this paper proposes a novel dual-dimension convolutional-attention (DDCA) mechanism. DDCA consists of two branches: one includes channel attention and spatial attention mechanisms, while the other applies these mechanisms to the inverted dimensions. Pooling and feature-wise pooling operations are employed to extract features from different dimensions of the input data. These branches operate in parallel to capture more complex temporal and spatial feature correlations in multivariate time series data. Subsequently, an end-to-end DDCA-TCN network is constructed by integrating DDCA with a temporal convolutional network (TCN) for RUL prediction. The proposed prediction model is evaluated using the C-MAPSS dataset and compared to several state-of-the-art RUL prediction models. The results show that the RMSE and SCORE metrics of DDCA-TCN decreased by at least 12.8% and 4.6%, respectively, compared to other models on the FD002 subset, and by at least 10.6% and 18.4%, respectively, on the FD004 subset, which demonstrates that the DDCA-TCN model exhibits excellent performance in RUL prediction, particularly under multiple operating conditions.