Journal of Aviation

Rocket technology has a very important place for many engineering fields such as space research, communication and military applications. It is very important for the success of the mission that rockets achieve their goals against all factors while performing their tasks in these fields. Therefore, all internal and external disturbing factors should be taken into consideration in rocket design. This study focuses on the performance of the nose geometry, which is one of the most important sub-parts affecting rocket performance, under different conditions. The geometry of the nose cone of the rocket is of vital importance for optimizing performance, safety and mission success. Its geometry is the most important parameter for determining aerodynamic efficiency. The design of the nose cone also affects stability and control, allowing the rocket to maintain its orientation. This study was conducted to examine the effect of nose cone geometry on aerodynamic performance at subsonic speeds. Using Computational Fluid Dynamics (CFD), analyses were carried out to evaluate the drag force, pressure, and velocity distributions of rockets with different nose designs. According to the results, the drag force was found to be 40.141N for the conical nose, 38.136N for the elliptical nose, and 37.092N for the tangent ogive nose. These values indicate that nose cone geometry has a significant effect on improving aerodynamic efficiency in certain applications.

This study is conducted to address the inability to predict design objectives, such as weight and cost, that cannot be analytically determined during the preliminary design phase of jet engines. For this purpose, a model establishing the relationship between the design parameters used in the preliminary design stage and engine weight is developed. The engine design parameters affecting weight, along with the corresponding weight data, are obtained from engine data available in the literature, and a database is formed. Using this database, a model is developed in the MATLAB® environment through a regression method. During model development, two criteria reported in the literature—the minimum error criterion and the weight trend reflection criterion—are employed. The distinguishing feature of this study and its contribution to the literature lies in the application of weight model development criteria to the development of a turboshaft engine weight model, thereby introducing a turboshaft engine weight model to support the turboshaft engine design process. To increase model accuracy, turboshaft engines are classified into two categories: light-weight and heavy-weight engines. For the light-weight turboshaft engine category, a model is developed based on 48 engine cases, yielding a maximum error (E) of approximately ±40%, a Root Mean Squared Error (RMSE) of 35, an R-Squared (R^2) value of 0.255, and an Adjusted R-Squared (〖R^2〗_adj) value of 0.238. For the heavy-weight turboshaft engine category, a model is developed based on 16 engine cases, resulting in a maximum E of approximately ±30%, an RMSE of 36.1, an R^2 value of 0.306, and an 〖R^2〗_adj value of 0.256. Examination of the statistical parameters indicates that the model’s ability to represent reality is limited; this outcome is a natural consequence of developing a model based on a relatively small sample size, and differences in the technological levels of the engines included in the database constitute an additional contributing factor.

In this article, it is aimed to minimize cost of automatic flight control system (i.e., AFCS) for a mini unmanned helicopter (MUH) by simultaneously and stochastically redesigning main rotor blades’ taper and PID gains of the AFCS. For minimization of autonomous flight cost index (AFCI) stochastical and simultaneous design approach is used over certain parameters (i.e., blade taper and gains of longitudinal and lateral PID controllers) while there are lower and upper constraints on these design parameters. A MUH is produced in Erciyes University Drone Laboratory (i.e., ERUDL) and called as Erciyes-Qtar-MUH. Its main rotor blades’ taper ratio can change before flight. AFCS parameters and main rotor redesign parameter previously mentioned are stochastically and simultaneously designed for minimization of AFCI that captures rise time, settling time and overshoot of relevant trajectory trackings by using a certain stochastical optimization tool (i.e., simultaneous perturbation stochastical approximation: SPSA). Eventual results are used for making simulations of MUH. Via using simultaneous and stochastical redesign of passively morphing main rotor blade taper having MUH (i.e., Erciyes-Qtar-MUH) over previously mentioned redesign variables, a best MUH autonomous flight performance and a minimum AFCI are found. Simultaneous and stochastical redesign of passively morphing main rotor taper having MUH and its AFCS notion is honestly valuable for minimizing AFCS and maximizing autonomous flight performance any MUH. Composing an original notion for recovering AFCI of a MUH and contributing a new procedure performing simultaneous and stochastical redesign of a MUH having passively morphing main rotor taper and its AFCS strategy meanwhile existence of upper and lower constraints on design variables are main novelties of this research paper. Substantial progress for MUH AFCI save almost %38 with regard to the original MUH is found in this research paper.

In this study, the effects of artificial intelligence (AI) technologies on the flight control, motion dynamics, environmental perception, energy management, and autonomous mission execution capabilities of unmanned aerial vehicles (UAVs) are comprehensively analyzed. The integration of AI subfields machine learning, deep learning, fuzzy logic, and reinforcement learning into UAV systems is examined, and the advantages these methods offer compared to classical control systems are evaluated. The role of data processing, sensor fusion, and decision support mechanisms within modern UAV architectures is discussed in detail. Three different AI based approaches computational intelligence, fuzzy logic based control, and reinforcement learning based control are compared within the scope of this study. The findings indicate that genetic algorithms outperform other methods in route planning, fuzzy logic provides superior performance in environments with uncertainty and dynamic conditions, and reinforcement learning excels in fully autonomous control and adaptive learning. Owing to this methodological diversity, UAV behaviors were analyzed from multiple perspectives, including optimization, uncertainty management, and learning based control. The results reveal that AI driven methods significantly enhance adaptive decision making abilities in UAV operations, allow more effective management of environmental uncertainties, and substantially improve mission success rates. In light of these findings, hybrid AI control systems are evaluated as the most promising architectural solution for next generation UAV design. This study presents the technical, theoretical, and practical contributions of AI to UAV technologies and provides an integrated framework for the development of future fully autonomous UAV systems.

This study explores how managers in the aviation sector perceive the characteristics of Generation Z employees within the context of the new generation workforce. A qualitative research design was adopted, and semi-structured interviews were conducted with twelve managers working in different areas of the aviation industry. The data were analysed using MAXQDA 2020 program. The findings suggest that managers commonly associate Generation Z employees with characteristics such as technological competence, rapid learning ability, adaptability, self-confidence, and innovativeness. However, managers also highlighted certain challenges, including impatience, screen dependency, limited face-to-face communication, and a tendency to expect immediate feedback. Overall, managers emphasized positive attributes more frequently than negative ones. The findings also indicate that Generation Z employees tend to prefer flexible working conditions and technology-supported work environments. These results contribute to a better understanding of generational dynamics in the aviation sector and provide implications for human resource management practices, particularly in designing communication strategies, training approaches, and work arrangements tailored to younger employees.

Agile manufacturing is increasingly important in aerospace. Firms must respond to technological change, supply-chain disruptions, customization pressure, and strict regulations. However, many manufacturers still lack a clear framework for deciding which agile capabilities to prioritize under uncertainty. This study addresses that gap by analyzing causal relationships among agile manufacturing capabilities in Türkiye’s aerospace industry. It uses an Intuitionistic Fuzzy DEMATEL (IF-DEMATEL) approach based on expert evaluations from the OSTİM Defense and Aviation Cluster (OSSA). The method captures uncertainty in expert judgments. It also identifies capabilities as driver (cause) or dependent (effect) factors within the agility system. The results show that multi-skilled workforce, leadership support, and cross-functional collaboration are the main driver capabilities. These factors shape downstream capabilities such as modular design, supplier responsiveness, real-time data integration, and rapid decision-making. The findings suggest that agile transformation should begin with organizational and human-centered enablers. Technical and process-oriented initiatives should follow. For managers, the study provides a practical prioritization roadmap. It shows where to focus investments first, especially in leadership-enabled change, workforce versatility, and cross-functional coordination. This can improve the success of later digital and operational agility initiatives. The study is limited by its Türkiye-specific expert sample and its reliance on expert-judgment-based causal modeling rather than longitudinal operational performance data. Future research can extend the model through cross-country comparisons, mixed-method validation with firm-level KPIs, and longitudinal analysis of capability interactions across production stages.

Accurate and control-oriented simulation environments are essential for analyzing aircraft dynamics and developing advanced flight control strategies without the cost and risk of real flight testing. This study presents a nonlinear flight simulator for transport aircraft implemented in MATLAB/Simulink, in which aerodynamic, propulsion, actuator, and atmospheric subsystems are integrated within a unified six-degree-of-freedom framework. Unlike conventional approaches based on simplified or partially decoupled models, the proposed framework preserves nonlinear coupling effects while incorporating physically constrained actuator dynamics, asymmetric propulsion behavior, and stochastic disturbance modeling within a consistent architecture. This enables systematic scenario-based evaluation of both nominal and degraded operating conditions. The Boeing 747 transport aircraft is used as a benchmark case to demonstrate the capability of the framework under cruise maneuvers, actuator degradation, asymmetric engine failure, and turbulence disturbances. The results demonstrate physically consistent dynamic responses and capture key cross-coupling characteristics of transport aircraft. Owing to its modular and extensible structure, the proposed platform provides a scalable and control-oriented environment for the integration of advanced control strategies, including fault-tolerant and delay-aware methods, thereby bridging the gap between flight dynamics modeling and control-oriented research.

Reliably forecasting airfoil-generated aerodynamic noise is a prerequisite for designing quieter aircraft and wind turbines. Although machine learning (ML) models deliver strong predictive performance in aeroacoustics tasks, their opaque, "black-box" nature frequently impedes adoption in engineering design processes that demand transparent reasoning. In this work, three ML paradigms—Linear Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGB)—are systematically evaluated for airfoil self-noise prediction on the NASA benchmark dataset. Generalization is quantified through 10-fold cross-validation using the coefficient of determination (𝑅²), root mean squared error (RMSE), and mean absolute error (MAE). To overcome the interpretability barrier, SHapley Additive exPlanations (SHAP) is applied, providing both global feature-importance rankings and instance-level explanations of model outputs. XGB attains the highest accuracy, with a cross-validated R² of 0.9498 ± 0.0138, a test R² of 0.9577, and an RMSE of 1.4553 dB. SHAP reveals that frequency, suction-side displacement thickness, and chord length exert the strongest influence on predicted sound pressure levels, whereas angle of attack ranks lowest—an initially surprising result that is nonetheless consistent with the limited angular range covered in the original NASA experiments. These findings illustrate that pairing gradient boosting with explainable AI yields a credible and interpretable prediction framework for aeroacoustic engineering.

In this study, airline passenger satisfaction was predicted using the Random Forest technique. For this purpose, an open-access dataset consisting of 129,880 passenger observations was used. The dataset includes demographic characteristics, travel information, operational indicators, and evaluations of perceived service quality. Passenger satisfaction was treated as a binary outcome and was estimated using a tree-based classification framework. Model performance was evaluated using accuracy, precision, recall, F1 score, and threshold-independent metrics including ROC–AUC and PR–AUC. The results were analyzed comparatively with a logistic regression baseline model, and a 5-fold cross-validation procedure was applied to assess predictive robustness. The Random Forest model demonstrated high discriminative performance (Accuracy = 0.9585; F1 = 0.9618; ROC–AUC = 0.9936) and consistently outperformed the linear reference model. Feature importance analysis, supported by permutation-based robustness checks, shows that passenger satisfaction is primarily shaped by experiential service attributes and digitally mediated service elements. In particular, seat comfort and online boarding emerged as dominant predictors, while demographic and operational variables exhibited relatively lower predictive influence. By combining traditional hypothesis testing with predictive modelling, the study shows that airline passenger satisfaction does not follow simple linear patterns but is shaped by complex interactions among experiential service factors. The findings provide methodological refinement for academic research in aviation and practical implications for data-driven decision-making in airline management.

This study examines how trade openness, institutional quality, digital innovation capability, and economic growth are associated with aviation activities across a balanced panel of 72 countries from 2005 to 2023. Air passenger transport carried (ATPC) and air freight transport carried (ATFR) are analyzed separately to capture possible structural differences. The empirical procedure includes descriptive statistics, diagnostic tests, Fisher-type ADF unit root tests, Westerlund cointegration tests, and two-way fixed-effects regressions with Driscoll-Kraay standard errors. Interaction and threshold models are further estimated to test whether the effect of trade openness varies across levels of digital innovation capability, and Dumitrescu-Hurlin causality tests are used to identify directional linkages. The results show that government effectiveness is a stable and positive determinant of both ATPC and ATFR. Aviation activities also respond differently to regulatory quality and the components of digital innovation capability. Passenger aviation is more closely associated with mobile connectivity, whereas freight aviation is more sensitive to institutional quality and broadband-based digital infrastructure. Threshold findings indicate a regime-dependent trade-aviation relationship shaped by digital capacity.

This research aims to classify the nature and characteristics of market-entry barriers encountered by low-cost carriers (LCCs) and to elucidate the strategies employed to counteract them. The emergence of the LCC business model fundamentally transformed the service paradigm within the aviation sector. These airlines have captured a rapidly increasing market share, reaching a global market penetration of 35% in 2020. Supported by the passage of the Turkish Civil Aviation Law in 1983, a shift in transportation policies in 2003, and its strategic geographical position, Turkey has recorded a growth rate surpassing the global average. Passenger numbers surged from 34 million in 2003 to an estimated 230 million in 2024. Turkish-registered airlines, leveraging this geographical advantage, gain access to 99 countries, 3.8 billion people, and significant economic markets within the narrow-body aircraft range. This expansion has intensified competition and recently facilitated the entry of new airline companies into the sector. The paucity of comprehensive studies on the LCC market in Turkey underscores the originality and significance of this research. Employing a qualitative methodology, this study draws on interviews conducted in April and May 2023 with sector experts and representatives of Turkish-registered LCCs. The data obtained from expert consultations underwent a rigorous thematic analysis, leading to the consolidation of market entry barriers into seven main themes. Foremost among these are governmental regulations, particularly those stemming from Turkey's relatively restrictive Asian and African markets due to its location. Strategies developed to overcome these barriers were grouped into four key themes, with managerial flexibility being the most critical. Due to the inherent nature of a qualitative research approach, the findings of this study are not generalizable. Future research could be substantiated with quantitative data, deepened by focusing on specific entry barriers, and expanded to incorporate the consumer perspective.

This study examines the macroeconomic determinants of air passenger demand in Türkiye using an annual time-series framework that distinguishes short-run adjustment from long-run relationships. The dependent variable is passengers carried by Türkiye-registered air carriers, sourced from the World Development Indicators (World Bank, 2026). Macroeconomic activity is proxied by a chained real GDP index constructed from annual real GDP growth, inflation is measured by consumer price inflation, and the exchange rate is the official exchange rate, local currency units per US dollar, period average (World Bank, 2026). The empirical strategy estimates a baseline autoregressive distributed lag (ARDL) model with policy-regime-sensitive controls and a nonlinear ARDL (NARDL) extension that decomposes exchange-rate movements into positive and negative partial sums to assess potential asymmetry. Augmented Dickey-Fuller diagnostics indicate that the transformed series are suitable for ARDL-type estimation, and bounds-style evidence supports a level relationship among the variables. The results show strong persistence in passenger demand and a positive long-run association with macroeconomic activity, consistent with aviation demand scaling with aggregate economic capacity. Inflation is associated with lower demand, reflecting affordability and macro-stability channels. The exchange-rate channel is economically relevant but theoretically ambiguous in aggregate; while the asymmetric specification allows depreciations and appreciations to have different long-run effects, a formal equality test does not provide statistically decisive evidence of long-run asymmetry in the baseline model. The findings imply that aviation demand projections and planning in Türkiye should account for dynamic adjustment and macro-stability policy regimes rather than rely on static trend extrapolation.

This study investigates the aerodynamic characteristics of the NACA 4412 airfoil using the finite element method under different flow models. A three-dimensional wing section with NACA 4412 geometry was modelled, and the flow was simulated using four commonly employed models: k-omega shear stress transport, k-epsilon, laminar, and inviscid. For each flow model, the angle of attack was varied over a fine range covering prestall, stall, and poststall conditions, and the corresponding lift and drag coefficients were obtained and plotted. The sensitivity and accuracy of the flow models in simulating the behavior of asymmetric airfoils were compared to the expected behavior derived from Navier–Stokes, Euler, and related viscous and nonviscous flow formulations. Particular emphasis was placed on predicting flow separation, resolving the boundary layer close to the airfoil surface, and estimating viscous drag. The results show that the k-omega shear stress transport model most accurately captures the onset of stall, the maximum lift level, and the development of flow separation around the NACA 4412 profile. The k-epsilon model provides acceptable results in fully turbulent regions but predicts stall at higher angles of attack and is less precise near the boundary layer. The laminar and inviscid models fail to reproduce a clear stall region and underestimate drag 23 because the turbulence and viscous losses are not represented adequately. Although the turbulence models require higher computational effort than laminar and inviscid approaches, their superior performance in predicting lift, drag, and stall behavior supports the use of advanced turbulence modelling for reliable aerodynamic analysis and design of airfoils similar to NACA 4412.

This study examines the change in the longitudinal and lateral stability parameters of fixed-wing unmanned aerial vehicles (UAVs) according to changes in wing sweep and dihedral angles. Unlike studies in the literature that focus on changes in wing sweep and dihedral at a single point, no study has been found that examines UAVs with wing sweep and dihedral angles at two different points on the wing. In this study, both dihedral and sweep angles were applied at two different positions on the wing, and models were created accordingly. Numerical analyses examining 16 different cases simultaneously, with initial conditions set at 5°,15° sweep and 5°,15° dihedral angles, resulted in the determination of the damping ratios and natural frequencies of the lateral and longitudinal stability modes. The variation of these parameters with respect to changes in double dihedral (DD) and double sweep (DS) angles was observed.

Istanbul Airport (IGA) is located at the intersection of Europe, Asia, and the Middle East, making it one of the largest international airports by passenger traffic volume in the world and a major hub for worldwide transportation routes, from North America to the Asia-Pacific Region. As a result of its geographical location, IGA is an important hub for intercontinental and intra-regional passenger travel, which helps create a significant economic impact through logistics and service exports, as well as Turkey's integration into global commerce. This research investigates the long-run relationship between air passenger traffic at Istanbul Airport and Turkish export activity over the period 2020-2025 using the Autoregressive Distributed Lag (ARDL) bounds testing approach to analyze monthly time-series data. The analysis shows that there is a statistically significant long-run cointegrating relationship among the variables, and that air passenger traffic, oil prices, exchange rates, and industrial production all have a positive and statistically significant impact on Turkish exports. Accordingly, Istanbul Airport's connectivity provides a measurable, structural contribution to enhancing Turkey's export performance, with important implications for trade policy and aviation infrastructure investment.

Airline firms operate in a context characterized by high operational interdependence, low tolerance for error, and strong stakeholder visibility, making the organizational consequences of crises highly salient. This study examines the relationship between perceived crisis management quality and corporate reputation perception in airline organizations and tests the moderating role of leadership behavior in this relationship. The research was conducted with data collected from cabin crew members employed by civil airline companies operating in Türkiye. A total of 425 valid questionnaires were obtained, and the main analyses were carried out with 408 respondents based on complete responses for the focal variables. Measurement models were assessed through confirmatory factor analysis, while the relationships among variables were tested using Pearson correlation and hierarchical regression analyses. The findings indicate that perceived crisis management quality positively predicts corporate reputation perception. In addition, democratic leadership behavior strengthens this relationship, whereas autocratic and laissez-faire leadership behaviors weaken it. These results suggest that the reputational value of crisis management in airline organizations depends not only on technical and operational preparedness but also on the quality of the leadership climate in which crisis processes are carried out.

Within the scope of this study, it is aimed to address the structural and financial determinants of passenger and freight transportation in the aviation sector. In order to analyze the sensitivity of the traffic in air transportation to various macroeconomic factors, two separate baseline equations were constructed using a semi-logarithmic framework, which covers the period between 1993-2023 and utilizes annual data. In the empirical analysis process, the ARDL Bounds Test approach was adopted to determine the long-term cointegration relationships between the variables; the Fourier Toda-Yamamoto test, which takes structural breaks into account and prevents information loss, was used to analyze causality relationships. In light of the empirical findings obtained, it is demonstrated that domestic credit expansions (DCREDIT) and market valuations (MCAP) play a dominant role in long-term growth in both passenger (PAX) and freight/cargo (FRE) models. Moreover, the findings indicate that the value-added of Machinery and Transport Equipment (MTE) manufacturing is significant and positive at the 1% level in the passenger transportation model and the 10% level in the freight transportation model; thus, technological production, such as machinery and transport equipment, is of vital importance in the development of the sector. In addition to this, the Foreign Direct Investments (FDI), in other words, foreign capital inflows have not been able to trigger structural expansion in the development of air transportation as expected. Finally, the results of the Error Correction Mechanism in the scope of the model showed that short-term shocks converge back to long-run equilibrium at a statistically significant speed.

This study examines the effects of consumer-based brand equity dimensions—brand awareness, brand associations, brand uniqueness, and brand loyalty—on overall brand equity in the aviation industry. It also investigates whether these variables differ according to passengers’ demographic characteristics and travel behaviors. A quantitative research design was employed, and data were collected through an online survey conducted. All constructs were measured using a five-point Likert scale, and 333 valid questionnaires were included in the analysis. The data were analyzed using descriptive statistics, reliability analysis, correlation analysis, independent-samples t-tests, one-way ANOVA, and multiple linear regression analysis. The regression results indicated that brand awareness, brand associations, brand loyalty, and brand uniqueness did not significantly explain brand equity. Group difference analyses revealed significant gender-based differences in brand associations and brand loyalty. In contrast, no significant differences were found according to age, flight type, flight frequency, or travel purpose. Overall, the results suggest that in highly standardized and regulated service contexts such as aviation, explaining brand equity solely through perceptual brand dimensions may be limited. From a managerial perspective, the study highlights the importance of experience design and touchpoint management strategies for strengthening brand equity in the airline industry.

This study investigates the nonlinear dynamics and chaotic microstructure of ten major European airline stocks—comprising eight European carriers and two Turkish airlines—over the period from January 2015 to January 2026, a timeframe that encompasses five structurally distinct crisis regimes: pre-pandemic stability, COVID-19 shock, recovery phase, Ukraine war impact, and post-war normalization. Employing a multi-method chaos framework that integrates Lyapunov exponents, Hurst exponents, BDS tests, and Recurrence Quantification Analysis (RQA)—including rolling-window and cross-recurrence extensions—the study addresses three core research questions: whether chaotic dynamics differ by business model, whether global crises induce permanent structural breaks, and whether Turkish airlines exhibit systematically distinct chaotic profiles from their European counterparts. The empirical results reveal heterogeneous chaos patterns: four carriers exhibit deterministic chaos while six display borderline or stable dynamics, challenging uniform business model categorizations. Crisis periods produce statistically significant and persistent structural transformations, with laminarity increasing 33% and entropy rising 28% between pre-COVID and post-war sub-periods, indicating regime shifts that do not revert to pre-pandemic baselines. Cross-recurrence analysis documents intensifying synchronization during crises, with post-war network density reaching complete integration and thereby eliminating diversification benefits. Turkish airlines demonstrate unexpected stability relative to their European peers, a pattern potentially attributable to domestic monetary policy buffering effects. Rolling-window analysis further reveals that determinism declines 30–40% prior to major market shocks, establishing RQA metrics as early warning indicators complementary to conventional volatility measures.

We report a systematic literature review examining distributed artificial intelligence algorithms and edge computing architectures for autonomous UAV platforms operating under resource and communication constraints, conducted within the PRISMA protocol framework. Existing reviews on UAV systems focus predominantly on physical-layer security and jamming; none provides a unified architectural analysis integrating the CAP Theorem, computational complexity, and hardware-software co-design perspectives. This review addresses that gap by offering a structured design reference for distributed systems researchers working on autonomous UAV platforms. Of 50 candidate studies retrieved from the Web of Science database, 43 were included in full-text analysis following scope and quality assessment. The studies were classified along three axes: (1) distributed AI/ML paradigm (FL, DRL, MARL, CNN, Transformer), (2) system architecture decision (MEC, hierarchical FL, Gossip-based learning), and (3) optimized metric (latency, energy consumption, model size, detection accuracy). FedAvg-based FL and DRL emerge as the leading approaches in distributed swarm learning and online resource allocation, respectively. CAP Theorem analysis shows that the majority of the examined architectures prioritize availability and partition tolerance over consistency, a design decision consistent with adversarial operating conditions. Byzantine-fault tolerant FL and model compression remain under addressed in the reviewed literature. The study concludes with an algorithmic taxonomy and a set of open design problems targeting autonomous UAV platforms in adversarial settings.