This work investigates the feasibility and performance of aerogravity assist (AGA) maneuvers for missions to Ceres, with emphasis on both interplanetary trajectory design and atmospheric flight dynamics. AGAs provide substantially greater turning capability than pure gravity assists by exploiting aerodynamic lift during atmospheric passes, thereby enabling greater heliocentric energy changes than pure gravity assists. To quantify these benefits, this study integrates a broad interplanetary trajectory search with high-fidelity atmospheric performance modeling. Candidate trajectories for launch years 2030–2050 are generated for multiple inner-planet encounter sequences using vehicles with lift-to-drag ratios (L/D) between 1 and 7. Atmospheric flight segments are simulated using a rotating, spherical, 3-DOF model with a multilayer atmospheric profile to determine AGA turning angles, stagnation-point peak heat rates, integrated heat load and peak g-load. Trajectories employing a single AGA at Mars using moderate-to-high L/D vehicles yield the most favorable combination of reduced time of flight and manageable thermal and structural design requirements. Venus AGAs produce larger turning angles but incur significantly higher heating loads, constraining their applicability. Overall, this combined interplanetary-atmospheric analysis demonstrates that AGA substantially broaden the feasible design space for Ceres missions, offering practical, non-propulsive mechanisms for achieving large trajectory deflections and enabling rapid, efficient access to small bodies in the outer main belt.
Advanced Air Mobility (AAM) systems introduce certification challenges associated with distributed propulsion, increasing software reliance, and tightly coupled architectures. These characteristics evolve within regulatory environments that are still adapting to emerging vehicle concepts. While digital engineering practices are increasingly adopted during system development, certification workflows remain largely document-centric, limiting lifecycle traceability and consistent reasoning about change impacts as designs and regulatory interpretations evolve. This paper proposes a Digital Twin–oriented certification architecture that integrates regulatory knowledge, system representations, Means of Compliance (MoCs), and verification artifacts within a unified model-based environment. The architecture is organized around four elements: regulatory structuring, Model-Based Systems Engineering (MBSE)–based embedding, dependency-aware change reasoning, and authority-oriented compliance organization. A simplified scenario, illustrated using a simplified and abstracted eVTOL configuration as a representative AAM example, is used to demonstrate the structure of certification relationships rather than vehicle-specific implementation details. The proposed architecture supports improved traceability, enhances visibility of dependencies, and provides a structured basis for future digital certification practices across AAM ecosystems. Future work will focus on implementation pathways, evaluation in representative scenarios, and integration with model-based verification approaches.
Despite sustained regulatory reforms, infrastructural development and capacity-building initiatives, Nigeria, one of Africa’s largest economies and a key regional aviation market, continues to experience significant safety deficiencies. This study examines how national cultural norms shape safety practices and performance in Nigeria’s aviation sector. Using an exploratory mixed-methods approach, it combines a systematic literature review, structured safety culture surveys, and semi-structured expert interviews to analyze interactions among regulatory frameworks, organizational practices, and human factors. Findings show that deeply embedded norms, particularly authority gradients, rule adherence, reporting behavior, and accountability, significantly influence safety outcomes. Although leadership commitment and safety awareness appear relatively strong, their effectiveness is constrained by operational pressures, resource limitations, and uneven confidence in reporting. The study argues that national culture is a critical determinant of aviation safety performance and that sustainable safety improvements require multi-level interventions that address regulatory robustness, organizational capacity, and culturally conditioned behaviors.
Professional pilots face distinct mental health challenges arising from irregular schedules, fatigue, high responsibility, and the need to maintain medical certification. These factors contribute to elevated stress, anxiety, and depression, while concerns about stigma, confidentiality, and potential career consequences often discourage pilots from seeking professional psychological support. As a result, barriers to early identification and intervention of mental health challenges remain a persistent concern within the aviation industry. This ongoing study utilizes a non-experimental, survey-based design to examine the mental wellness of professional pilots worldwide and their willingness to seek mental health support. The survey includes demographic items, standardized mental health assessments, and open-ended reflection questions addressing perceived barriers to help-seeking. Validated instruments include the Generalized Anxiety Disorder scale (GAD-7) to assess anxiety, the Patient Health Questionnaire (PHQ-9) to evaluate depressive symptoms, and the Self-Stigma of Seeking Help scale (SSOSH) to measure internalized stigma. Reflection items further explore pilots’ perceptions of civil aviation authority medical disclosure requirements, confidentiality concerns, and perceived career impacts associated with reporting mental health conditions. The study aims to assess the prevalence of anxiety and depression symptoms among professional pilots and to identify key factors influencing help-seeking behavior. Findings are expected to inform airlines, regulators, and aviation medical examiners by providing evidence-based insights to strengthen support systems, reduce stigma, and promote early mental health intervention, ultimately enhancing pilot well-being and aviation safety.
Modern aviation maintenance operates within increasingly data-intensive technological environments, yet the operational integration of predictive maintenance into routine decision-making remains inconsistent across sectors. Contemporary aircraft incorporate centralized maintenance computers and condition monitoring architectures capable of fault aggregation, exceedance capture, and trend-based data collection. Although prognostics and health management research continues to advance detection and prediction methods, implementation outcomes often depend on factors beyond analytical performance alone. This paper examines predictive maintenance adoption in distributed-authority aviation environments and argues that constraints emerge less from sensing capability than from organizational structure, decision authority dispersion, and institutional trust dynamics. For purposes of analysis, distributed-authority environments are defined as those in which ownership, maintenance execution, engineering oversight, and financial decision-making are not institutionally unified. In such contexts, preemptive maintenance decisions frequently require mediation among multiple stakeholders, introducing complexity into the translation of predictive insight into action. To address this gap, the paper proposes a conceptual hybrid trust-mediated adoption framework consisting of five stages: procedural institutionalization of data review, centralized interpretive oversight, mediated authority negotiation, operational validation through performance outcomes, and cultural normalization with distributed competency. The framework links predictive maintenance integration to staged processes of trust formation, authority alignment, and institutional embedding, and is intended as an analytical lens to guide future empirical research rather than a prescriptive implementation sequence. Predictive maintenance is further situated within Safety Management System (SMS) processes, where it may function as a leading indicator within safety assurance and performance monitoring activities. Potential accelerants of adoption include demonstrated reductions in unscheduled maintenance events, regulatory recognition, and financial incentives that align safety outcomes with economic decision-making. The paper concludes by outlining a research agenda for examining measurable indicators of stage progression, trust formation, and operational impact within maintenance decision systems.
The United States (US) airline industry is struggling to find enough commercial pilots and to diversify a profession that has the reputation of historically being male and white. To attract a younger and more diverse generation of pilots who have been historically underrepresented, women and people of color, a major US airline announced a diversity plan in March 2021 to hire thousands of pilots, at least 50% of them women and people of color. In 2022, several other US airlines launched similar initiatives. The purpose of this study was to identify perceptions of collegiate flight students regarding the airline industry’s new training and hiring initiatives focused on this underrepresented population. There were 183 flight students from seven collegiate flight programs who participated in this study. The findings indicated (1) a small majority of the collegiate flight students (56%) agreed that women and people of color are underrepresented in the US airline pilot career field, (2) only 46% of students agreed US airlines should diversify their pilot ranks and create new opportunities for women and people of color, (3) only 27% agreed white pilots are perceived as a racial barrier and males are considered a gender barrier for potential pilot candidates, (4) 79% agreed passengers want airlines to hire pilots based upon aptitude and competency and not gender and ethnicity, and (5) 48% agreed the airline industry’s new diversity, equity, and inclusion initiatives would negatively impact a collegiate flight student’s employment opportunity. The dissemination of findings will foster needed dialog among researchers, airline industry professionals, and collegiate flight programs regarding the diversification of the flight deck and the effects of these new training and hiring initiatives on the sociocultural dynamics within collegiate aviation programs.
This study examines the role of national cultural determinants in shaping aviation safety culture across the Middle East. Employing an exploratory research design that integrates a literature review and structured safety culture surveys, it analyzes how national, organizational, and traditional values influence safety attitudes, communication practices, and reporting behaviors. While the Middle Eastern countries, such as UAE and Qatar possesses advanced aviation infrastructure and regulatory systems, persistent Arab cultural norms, including deference to authority, collective responsibility, and avoidance of public blame, continue to shape safety management and organizational learning. The preliminary findings indicate that these values promote discipline, cooperation, and moral accountability, but may also limit openness and the development of a just culture, both of which are essential for proactive safety management. The study argues that strong aviation safety systems in the region depend on adaptive leadership, culturally responsive training, and institutional mechanisms that reconcile local value systems with global safety governance frameworks.
In recent years primary flight training in the United States has experienced a significant bottleneck in the form of long waits to schedule check rides with Designated Pilot Examiners. This challenge has been significant enough to warrant Congressional attention, with reforms mandated in the past two FAA Re-authorization Acts. In 2022 a first national survey of flight schools and DPE’s was conducted to determine their perceptions of the DPE system, including feedback on the recommendations from the then recently concluded DPE Reforms Working Group. Given the anecdotal evidence of continuing long waits for check rides, a second survey was conducted in the summer of 2025. As with the original study, both DPE and flight school perceptions of the DPE system including wait times for check rides, costs of check rides, and the extent to which both applicants and DPE’s travel for the conduct of check rides were examined. Additionally, perceptions of the effectiveness of the measures taken by the FAA in the last three years including the move to a national oversight model for DPE’s and the allowance for BasicMed for DPE’s were evaluated. Other improvements which were recommended by the DPE Reforms Working Group but which have not been implemented were also revisited. Finally, participant feedback on their perception of the Airman Certification Organization Designation Authorization order and the Part 141 modernization effort was sought. Overall, the study results showed the DPE problem has become more severe over the last 3 years, with a 5 week average check ride wait reported by flight schools and 91% of schools responding that check ride availability is an issue for their operation. There continues to be support for an applicant confidential survey system regarding DPE performance, for the ability to start the ground portion of a check ride even if weather will likely not allow the conduct of the flight portion on the same day, agreement that the ability of DPE’s to use BasicMed as appropriate is beneficial, and that the current DPE Locator website should be modernized.
This study aims to investigate the potential benefits and drawbacks of Single-Pilot Operations (SPO) and to provide actionable insights for commercial airlines. A qualitative research design was employed, with data collected via online interviews and analyzed thematically and descriptively using MAXQDA 24. The findings indicate that pilots perceive several advantages of SPO, including the power of automation, learning artificial intelligence, minimizing human error, cockpit systems designed for artificial intelligence, more effective decision-making/speed-up in response time, reduced labor costs, reduced pilot fatigue, and reduced need for human factor/performance. Conversely, the perceived disadvantages include diminished lack of situational awareness, lack of communication and coordination, lack of cognitive flexibility, negative impact on passenger psychology, increased workload for ground operations, and lack of need to exercise authority/take initiative. Among these, the most frequently cited advantages were power of automation and learning artificial intelligence, whereas reduced pilot fatigue and reduced need for human factor/performance were least mentioned. Similarly, lack of situational awareness and the lack of crew communication and coordination were the most frequently reported disadvantages, while lack of need to exercise authority/take initiative was least mentioned. Notably, a key contribution of this study is the identification of interrelationships between advantages and disadvantages, which may have important cause and effect implications for operational safety. These findings underscore the need for a holistic evaluation of SPO, considering the interactions between technological, humanistic, and organizational factors.
The global aviation industry plays a critical role in economic development and international connectivity. However, it also contributes significantly to environmental challenges, particularly through fuel consumption and carbon emissions. The aviation sector is responsible for approximately 1–2% of global CO₂ emissions, and its environmental impact is expected to increase substantially by 2050. This study proposes a machine learning (ML)-based decision support framework to forecast carbon emissions in the airline sector. Using Turkish Airlines’ quarterly operational data from 2008 to 2023, Decision Trees, Random Forest, XGBoost, and Extra Trees models were applied to predict carbon emissions. Key variables include revenue passenger kilometers (RPK), available seat kilometers (ASK), load factor (LF), fuel cost, number of aircraft, aircraft utilization, and fleet age. Among the tested models, the Extra Trees model achieved the highest accuracy, with an RMSE of 906.98, a MAPE of 6%, and an R² of 0.95. Beyond prediction accuracy, model explainability was enhanced through SHAP (Shapley Additive exPlanations) analysis, which confirmed the dominant influence of RPK and LF and provided interpretable insights for decision-makers. These results demonstrate the effectiveness of machine learning techniques in improving fuel efficiency, minimizing carbon emissions, and supporting sustainable airline operations. Furthermore, this study highlights the importance of integrating predictive analytics into strategic decision-making for emission control and operational optimization. The research contributes to both academic literature and industry practice, particularly in the context of emerging aviation markets. Future studies may incorporate external shocks, hybrid model designs, and adaptive time series approaches.
This study critically examines the Kansai Airports Group's 44-year Public-Private Partnership (PPP) monopoly-type concession encompassing Kansai International (KIX), Osaka Itami (ITM), and Kobe (UKB) airports. The research uses financial analysis, Monte Carlo simulation, and NPVat-risk modeling to evaluate how Japan's fixed-term private monopoly model allocates financial, operational, and environmental risks. In 2015, there were significant developments in the promotion of the Private Finance Initiative (PFI) Act. It was revised in 2015; as a result, it finds that the underestimated long-term maintenance liabilities, especially Kansai airport (KIX) foundation subsidence, and lacked flexibility to manage exogenous shocks like COVID-19 and Typhoon Jebi. Comparative insights from various global models reveal structural weaknesses in Japan's legal and institutional frameworks. The study also identifies investment disincentives linked to unclear terminal asset reversion and overly rigid performance monitoring. Drawing on international best practices, the research recommends embedding dynamic risk forecasting tools and renegotiation triggers to create more resilient, adaptive infrastructure concessions in Japan's aviation sector.
This study explores the reasons why first officers choose to remain silent in the cockpit or refrain from reporting errors after a flight. Effective communication and error reporting is crucial for flight safety, yet limited research has examined the factors contributing to silence among first officers. To address this gap, semi-structured interviews were conducted with 19 first officers (13 males, 6 females) working in the Turkish aviation sector. Data were collected through online Zoom interviews and analyzed using creative coding techniques in MAXQDA 22. Content analysis revealed 14 themes related to cockpit silence, including the captain’s manner, power distance, lack of knowledge of rules and procedures, lack of trust in the system, and reliance on the captain’s experience. Additional factors, such as fear of damaging relationships, negative labeling, conflict avoidance, inefficient communication skills, and a sense of futility, highlight social and organizational pressures affecting first officers' willingness to speak up. Findings align with Turkiye’s high-power distance and collectivist cultural traits, suggesting that hierarchical structures and social norms contribute to this reluctance. To overcome these barriers, training programs should integrate real-life case studies that foster open communication and encourage first officers to express concerns confidently.
Notwithstanding significant efforts to reduce the risk of bird strikes at and around airports, as well as advancements in technology, aviation safety continues to be threatened by this hazard. Our study of recent data shows a clear historical increase in accidents, therefore underscoring the flaws in present technologies. Although among the technologies in common use are radar, drones, cameras, and acoustic approaches, they are not always able to completely meet the challenging needs of bird strike risk mitigation. This approach creatively blends these four fundamental technologies by means of enhanced real-time data processing and targeted drone deployment. While providing more efficient and comprehensive preventive activities than present methods, this synergy helps quick and precise decision-making in crucial events. It incorporates a diagnostic system using contemporary information technology that forecasts medium- and short-term dangers, therefore enhancing the accuracy and efficacy of preventive measures. This combination of technology also makes it feasible to follow migrating birds and assemble all the gathered data in a single dashboard, therefore facilitating dynamic analysis of bird movements and improved visualization. This approach helps airports to concentrate more on administrative and preventive aspects. Thanks to more coverage and more flexibility to fit particular climatic conditions, this method covers all significant stages of flight, including takeoff, approach, and landing. It lowers false alerts, maximizes operating costs, and offers a sustainable means to prevent bird attacks, therefore enhancing aviation safety completely and efficiently.
To address the challenges of aviation safety information overload in Pre-flight Information Bulletin (PIB) systems, this study proposes an intelligent classification framework (ERNIE-DPCNN) that integrates knowledge-enhanced semantic representation with a Deep Pyramid Convolutional Network. Traditional systems relying on rule-based filtering mechanisms suffer from inefficiencies in critical information identification and high risks of human misjudgment. The proposed framework achieves breakthroughs through three technical innovations: (1) An aviation domain-adapted ERNIE model is constructed, leveraging phrase-level masking strategies to enhance semantic representation of compound identifiers; (2) A Deep Pyramid Convolutional Network (DPCNN) is designed to extract multi-granularity features via hierarchical convolution-pooling architecture, optimized with residual connections for long-text gradient propagation; (3) The AdamW optimizer is introduced to dynamically adjust learning rates, improving model convergence efficiency. Evaluated on real-world NOTAM records from airlines, the framework achieves a weighted F1-score of 98.8% in binary classification (Flight Advisory/Restriction) and 91.5% in multi fine-grained classification, outperforming baseline models such as ERNIE-CNN and ERNIELSTM. Ablation studies demonstrated the effectiveness of the domain adaptive masking strategy and dynamic learning rate mechanism. The framework provides an interpretable and scalable technical approach for aviation safety information processing, and its hierarchical feature extraction mechanism facilitates the subsequent simplified deployment scenarios of PIB.
Abstract Investigations into aviation accidents aim to identify root causes and enhance safety. Despite advancements in safety measures, technology, and education, general aviation accident rates remain stable, with loss of control identified as the leading cause. Spatial disorientation is a significant contributing factor in these incidents, accounting for approximately 10% of general aviation accidents—90% of which are fatal. Addressing spatial disorientation is increasingly critical with the emergence of Advanced Air Mobility (AAM) vehicles, a sector projected to reach a $30 billion market by 2030. While AAM is expected to transition toward full autonomy, human pilots will operate these vehicles during the early adoption phases, increasing their exposure to spatial disorientation. This research aims to enhance spatial disorientation training through the MOTUS 600 Flight Simulator, incorporating necessary upgrades such as X-Plane virtual simulation software. Various training scenarios have been developed to expose pilots to spatial disorientation effects, including the pitch-up illusion, also known as the somatogravic illusion, graveyard spiral illusion, runway width illusion, and a general motion decoupling illusion. These scenarios integrate task-loading elements such as navigation and air traffic control interactions to simulate real-world challenges. This study details the simulator upgrades and the intended effects of these training methods in mitigating spatial disorientation and improving pilot safety. Keywords: spatial disorientation, Advanced Air Mobility, flight simulator, X-Plane simulation, illusion scenarios.
Ground handling delays at major airports pose significant challenges, impacting operational efficiency, airline schedules, and passenger satisfaction. This study explores the root causes of these delays, including resource constraints, operational inefficiencies, adverse weather, technical failures, and security issues. By examining case studies from major airports, the report identifies key solutions such as technology integration, infrastructure upgrades, staff training, standardized procedures, and enhanced communication systems. The findings underscore the importance of adopting proactive and collaborative approaches to optimize ground handling operations, ensuring smoother airport workflows and improved service delivery in the face of growing air traffic demands.
With the rapid evolution of commercial space markets in the new space era, satellite design philosophy is transitioning from "performance supremacy" toward "cost-effectiveness priority." This research concentrates on solar arrays, a critical component representing 15%-20% of satellite costs, addressing limitations in traditional design methodologies that inadequately coordinate user requirements and lack systematic frameworks for cost-performance collaborative optimization. We propose a solar array optimization methodology predicated on the integration of Quality Function Deployment (QFD) and the Theory of Inventive Problem Solving (TRIZ). This methodology incorporates cost dimensions through an extended QFD matrix, employs TRIZ to resolve technical contradictions, and develops a multi-constraint optimization model utilizing the NSGA-II multi-objective optimization algorithm. Through empirical validation with a commercial remote sensing satellite case study, results demonstrate that the optimized solar array design achieves a 12.5% cost reduction while simultaneously realizing a 20.8% improvement in specific power ratio, 0.04% enhancement in reliability, and 39.4% extension in operational lifespan. This cost-performance balanced optimization methodology provides practical engineering guidance for the commercial aerospace sector and offers valuable reference for promoting innovative satellite design in cost-sensitive environments.
The rise of urban air mobility (UAM) and electric vertical takeoff and landing (eVTOL) aircraft necessitates the development of well-designed vertiports that align with consumer expectations. This study examines consumer preferences and willingness to pay (WTP) for vertiport design, amenities, and services to inform infrastructure planning and investment strategies. Using a survey-based quantitative research design, data were collected from 500 participants to assess key factors influencing WTP, including convenience, security measures, service efficiency, and premium offerings. Findings indicate that location accessibility, affordability, and streamlined security procedures are primary determinants of vertiport adoption. While aesthetic and retail enhancements contribute to passenger experience, practical features such as climate control, intuitive wayfinding, and noise reduction were rated as more critical. Additionally, three consumer segments were identified—cost-sensitive users, convenience-oriented users, and premium experience seekers—highlighting the need for tiered service models. The study underscores the importance of integrating vertiport infrastructure with existing transportation networks while ensuring a balance between operational efficiency and passenger comfort. These insights provide guidance for policymakers, UAM operators, and urban planners in designing consumer-centric vertiport systems that enhance market acceptance and financial viability.
One of the most promising uses of aerial applications by a sUAS is on small farms where traditional crewed aerial applicators were not practical due to the limited size of the operations. This controlled field test aimed to assess the feasibility and cost-effectiveness of using a sUAS spreading system compared to the traditional manual application of weed killer and fertilizer on cranberry bogs in Western Washington that were less than 10 acres. The aerial application took place over two separate days as scheduled by the farmer for maximum product efficiency. A total of sixty-three flights were necessary to apply the grower-defined dosages of 60 pounds per acre of weed killer and 100 pounds per acre of fertilizer across the 7.35-acre cranberry farm. The average flight time required to dispense a full hopper load was 3.7 minutes. Several new terms for efficiency were developed, including Spreading Efficiency (ESP), Flight Efficiency (EFL), Operational Efficiency (EOP), and Payload Efficiency (EPL). The ESP for both trials was higher than expected and yielded returns above 70%, indicating that over 70% of the flight time was spent dispensing material. EFL can be optimized by minimizing reloading time through a quick-flow filling system rather than the manual funnel method. The EOP varied between 34% and 44%. The EFL played a significant role in improving EOP. There were several other factors to consider when comparing manual and UAS aerial applications, including crop damage, precision, time investment, and cost. In the specific case of cranberry farms, any intrusion into the bogs, whether by foot or mechanical means, should be minimized to limit damage to the crop. Each spreading event took a single worker 3-5 days to apply a product to a 10-acre cranberry farm by traditional methods compared to less than 4 hours with an sUAS, which did not require bog intrusion. Keywords: sUAS, drone, precision agriculture, aerial applications, aerial spraying, crop dusting, Part 137 certification.
Within the context of learning, there poses difficulty when objectively measuring human performance. In this work, we investigate the evaluation of human performance via its relation to the individual's mental capacity by classification of cognitive load within the domain of aviation. By utilizing a mixed virtual and physical flight simulation environment in conjunction with biometric sensing, we create and evaluate the predictive capabilities of a Joint-Embedding Predictive Architecture (JEPA) and compare the architecture and results to traditional methods for transfer learning and domain adaptation. We find that our JEPA inspired architecture can achieve more than 70% accuracy of cognitive workload, compared to the 63% and 56% accuracies of traditional transfer learning methods. Through this foundation, we have made advancements in multi-modal and multi-task learning to classify various features across numerous pilots, operators, and novices within aviation. Our predictive model can automate the evaluation of cognitive load, enabling creation of generalizing features even when labeled examples are scarce.
