Abstract Fog-related flight disruption is costing big international airports more than Rs 2.5 crores for each such event, while the traditional countermeasures, chemical seeding and thermal heating, are expensive, slow and environmentally damaging. This paper proposes the first field-validated airport fog dispersal autonomous UAV system that combines deep reinforcement learning with targeted UV-C photolysis technology. Conventional ways of fog dispersal take 30–45 min for runway clearance, cost Rs 15,000 per operation and produce 500 kg of CO2 emissions. These strategies evaporate fog droplets without tackling the condensation nuclei that are causing them and so the fog can quickly reform. We use a 4-UAV swarm with UV-C LED arrays (254 nm wavelength) for the degradation of hygroscopic aerosols which act as cloud condensation nuclei (CCN). Unlike thermal approaches that only evaporate droplets, our photolysis-based approach can reduce the efficiency of CCN by 35–45% so that the fog does not re-form. A deep Q-Network (DQN), based on 625-256-256-8 architecture, autonomously coordinates swarm positioning based on real-time sensor fusion from LiDAR (25 × 25 m resolution), thermal imaging (640 × 480 at 30fps) and meteorological arrays. 96% accuracy of fog detection. Our operational flights took place at Sri Guru Ram Dass Jee International Airport Amritsar, India, with 120 flights starting from November 2024 till March 2025. Results show: 83.1% reduction in time of fog clearance (from 30 to 5.06 min), 80% improvement of runway visibility range (from 450 to 810 m), 95% reduction in cost (from Rs 800 to Rs 15000 per sortie), 96% reduction in CO2 emission (from 20 to 500 kg per operation).Randomized complete block design using Friedman analysis (kh2 = 128.45, p < 0.001, Cohen’s d effect sizes of 4.85–7.92 show very large practical significance for all of the metrics. Zero incidents during 120 flights with ground exposure from UV-C (0.008 mJ/cm 2 ) 375x below ICNIRP occupational limits. Real-time DQN inference latency (183+-27ms) is the aviation safety-critical requirement (< 250ms). This research sets up a scalable paradigm for fog management at fog-prone airports anywhere in the world economically and environmentally and the potential savings is Rs. 2.5 Crores every year at major international airports.

The modern cockpit is a high-performance environment that demands continuous, accurate decision-making under conditions of uncertainty, time pressure, and dynamic risk. Despite decades of progress in procedural training and cockpit resource management (CRM), stress remains a major contributor to human error in aviation accidents and incidents. This paper explores the multidimensional nature of stress in flight operations and its impact on decision-making processes among pilots. Drawing from cognitive psychology, neurophysiology, and human factors research, the study outlines key mechanisms by which stress degrades cognitive performance, impairs attention allocation, and alters judgment. Additionally, the paper evaluates both traditional and emerging methods for stress detection and management, including biometric monitoring and AI-enhanced adaptive training systems. By integrating scientific findings with case-based analysis and simulation-based scenarios, the study aims to inform next-generation training protocols that promote cognitive resilience, situational awareness, and safer decision-making under pressure.

Abstract The aviation industry’s growing carbon footprint necessitates data-driven evaluation tools.This study assesses the CO 2 efficiency of airlines operating at Istanbul Airport by integrating operational flight data with the Atmosfair Airline Index through a machine learning framework. A multiple linear regression model was developed to predict CO 2 Efficiency Points (EP) using two primary predictors: total payload and daily landing frequency. Flight observations were collected from FlightRadar24 for passenger aircraft operating on March 28, 2025, while EP values were obtained from the 2024 Atmosfair Index. The model demonstrated a strong explanatory capacity (Adjusted R 2 ≈ 0.73) and acceptable predictive accuracy (MAE = 3.82; RMSE = 4.45), indicating that flight frequency and payload are statistically significant determinants of CO 2 efficiency.The findings underscore that larger payloads and higher operational intensity are associated with improved efficiency scores, reflecting the critical role of data-informed scheduling and capacity management in sustainable aviation. Despite the limited sample size, the model exhibits robust internal validity and offers a transparent, reproducible approach for airport-level carbon performance assessment. By linking empirical aviation data with environmental performance metrics, this research contributes a lightweight yet scalable analytical framework that aligns with the International Civil Aviation Organization’s (ICAO) net-zero carbon target for 2050. The proposed model provides practical implications for airport operators and policymakers aiming to integrate predictive analytics into emissions monitoring and green airport management systems.

The variable-pitch connecting rod of a helicopter bears axial tensile and compressive loads during operation. The traditional load monitoring method using strain gauge is easily affected by external conditions. Therefore, a giant magnetostrictive (GM) tension and compression force sensor with permanent magnet bias is proposed and optimized. Because the bias magnetic field plays a decisive role in the performance of the sensor, this paper has carried out in-depth research on this. Firstly, the mathematical model of the magnetic circuit is established, and the various magnetic circuits of the sensor are simulated and analyzed. Secondly, the magnetic flux uniformity of the GMM rod is used as the evaluation index, and the relative permeability of the magnetic material and the structure are systematically studied. The influence of parameters on the magnetic flux of the magnetic circuit, and finally the optimal parameter combination of the magnetic circuit is determined by orthogonal test. The results show that when the magnetic circuit without the magnetic side wall is used, the magnetic material can better guide the magnetic flux through the GMM rod; the magnetic flux uniformity of the optimized GMM force sensor is increased by 7.44%, the magnetic flux density is increased by 13.9 mT and the Hall output voltage increases linearly by 1.125% in the same proportion. This provides an important reference for improving the utilization rate of GMM rods and also improves the safety of flight operation and reduces maintenance costs.

Purpose This study investigates the longitudinal control of aircraft by integrating classical feedback strategies with advanced feedforward and intelligent methods. The aim is to enhance system stability, minimize response time, and maintain robust performance under environmental uncertainties, contributing to safer and more efficient flight operations. Design/methodology/approach A hybrid control framework is developed that combines feedback, feedforward, and nearest-neighbor-based adaptive techniques. Detailed simulation and flight test analyses are conducted to evaluate system performance across a wide range of operating conditions, demonstrating improved disturbance rejection and trajectory tracking capabilities. Findings This paper provides a comprehensive analysis of various aircraft longitudinal control methods, using modern techniques combined with feedback and feedforward control strategies. The results demonstrate notable improvements in stability and dealing with uncertainties. This study aims to achieve optimal performance of longitudinal control systems by analyzing data in detail and comparing different methods. The findings indicate that the application of hybrid techniques can significantly enhance flight stability, reduce response times and address environmental uncertainties effectively. The outcomes of this research can serve as a guide for the development and improvement of control systems for the next generation of aircraft and pave the way for further research in this field. Originality/value This research presents a novel combination of traditional and intelligent control strategies for longitudinal aircraft dynamics, providing a practical methodology for real-time adaptive flight control. The findings offer guidance for future aircraft control system design and contribute to the advancement of intelligent flight technologies.

A hierarchical Bayesian methodology for aircraft performance monitoring addresses two main objectives: identifying variations in aircraft fuel consumption with 1% accuracy and distinguishing between airframe and engine effects as causes of these variations. The computational framework decomposes inference into fleet-level and aircraft-specific scales, embedding interpretable physics-based aerodynamic and propulsion models at each level and quantifying uncertainty in deterioration-related parameters through posterior distributions rather than point estimates. Verification using synthetic operational data with embedded ground truth yields mean absolute errors of 0.32% for drag variations and 0.25% for engine fuel flow deviations, achieving better detection sensitivity than current monitoring systems. Validation with operational flight data confirms the method’s ability to distinguish between deterioration sources under real-world conditions, a fundamental diagnostic limitation of current methods. The framework also estimates aircraft gross weight with a mean absolute error of 0.73%, a useful level of accuracy given the airline industry’s reliance on statistical averages of passenger weights rather than direct measurements. In contrast to some machine learning models that lack interpretability, generalizability, and uncertainty quantification, this physics-informed statistical approach enables targeted maintenance interventions for specific aircraft while requiring only modest adaptation to model different aircraft types.

Safety is the most important requirement in flight operations. This also affects the application for an extreme lightweight glider in this paper. Essential properties are the target weight below 120 kg and the electric propulsion. The unsymmetric inertia from the two-blade propeller at the rear in combination with the light and flexible aluminium tube support makes it necessary to investigate the risk of mechanical instability. Starting from the equations of motion, the time-variant system matrices are set up. The simulation of Floquet multiplier and Hill’s hyper-eigenvalue problem provide the necessary information about the system stability. The conclusion is that the potential instability due to structural damping in the observed system can be avoided in the range of operation. The damping, experimentally determined by approximately 2%, is sufficient.

The Indonesian Aeronautical Information Publication (AIP) is an official document containing essential aeronautical information for the safety and efficiency of flight operations. However, in its implementation, data discrepancies are still found, especially in AIP Volume I (General and En-Route), which can impact the safety and accuracy of information for users. This study aims to analyze weaknesses in the current data verification and validation system and to formulate strategies to strengthen the system, thereby improving the accuracy of AIP data. The method used in this study is descriptive qualitative with a case study approach at the Aeronautical Information Management Unit. Data were collected through interviews, direct observation, and related document studies. The results of the study indicate that the current data verification and validation process has not been fully documented, remains manual, and lacks a multi-layered supervision system. This research opens up the possibility of human error and delays in data updates. Recommended strengthening efforts include the implementation of a digitalization system, the formation of more structured SOPs, and improving personnel competency through ongoing training. With the strengthening of the data verification and validation system, it is hoped that the accuracy of information on the Indonesian AIP can be maintained and support the creation of safe, efficient, and reliable flight navigation services

Abstract Unstable approaches are one of the main safety concerns that contribute to approach and landing accidents. The International Air Transport Association reports that, between 2012 and 2016, 61% of accidents occurred during the approach and landing phase, of which 16% involved unstable approaches. This study addresses this issue by applying the Functional Resonance Analysis Method to examine the dynamics of stable approaches. A total of 195 aviation safety reports, which referred to near-miss data from a single airline, were used in the analysis to identify both actual and aggregated variability. The findings revealed that variability mainly occurred in the following functions: control speed, configure aircraft for landing, communicate with air traffic control and manage flight paths. Effective communication, coordination and collaboration, as well as monitoring, briefings and checklists, were key factors in managing the variability of a stable approach. The study reveals how adopting a perspective of ‘how things go right’ provides insightful findings regarding approach stability, complementing traditional approaches focused on ‘what went wrong’. This study also highlights the value of utilising the Functional Resonance Analysis Method to analyse near-miss data and uncover systemic patterns in everyday flight operations.

Autonomous Unmanned Aerial Vehicles (UAVs) are widely used in smart agriculture, logistics, and warehouse management, where precise trajectory prediction is important for safety and efficiency. Traditional approaches require complex physical modeling including mass properties, moment of inertia measurements, and aerodynamic coefficient calculations, which creates significant barriers for custom-built UAVs. Existing trajectory prediction methods are primarily designed for motion forecasting from dense historical observations, which are unsuitable for scenarios lacking historical data (e.g., takeoff phases) or requiring trajectory generation from sparse waypoint specifications (4–6 constraint points). This distinction necessitates architectural designs optimized for spatial interpolation rather than temporal extrapolation. To address these limitations, we present a segmented LSTM framework for complete autonomous flight trajectory prediction. Given target waypoints, our architecture decomposes flight operations, predicts different maneuver types, and outputs the complete trajectory, demonstrating new possibilities for mission-level trajectory planning in autonomous UAV systems. The system consists of a global duration predictor (0.124 MB) and five segment-specific trajectory generators (∼1.17 MB each), with a total size of 5.98 MB and can be deployed in various edge devices. Validated on real Crazyflie 2.1 data, our framework demonstrates high accuracy and provides reliable arrival time predictions, with an Average Displacement Error ranging from 0.0252 m to 0.1136 m. This data-driven approach avoids complex parameter configuration requirements, supports lightweight deployment in edge computing environments, and provides an effective solution for multi-UAV coordination and mission planning applications.

Single-Pilot Operations (SPO) represent a focal point in the current development direction of commercial aircraft, where monitoring the workload of a single pilot is crucial for ensuring flight safety. The detection results must exhibit both accuracy and real-time performance. This study leverages the widely adopted OpenPose model, which is employed for posture detection due to its capability to effectively extract human skeletal keypoints. However, the conventional OpenPose model suffers from issues such as large data volume, high computational demands, and significant redundancy. This paper systematically reviews relevant improvement methods across three key aspects of the OpenPose model: image preprocessing, feature extraction, and pose estimation. Additionally, it summarizes a fatigue assessment method based on this model. Research findings indicate that the enhanced OpenPose model, compared to the traditional version, demonstrates greater adaptability to complex environments, faster response and data processing capabilities, and improved accuracy in results. By incorporating fatigue detection criteria, the model enables real-time alerting.

Reducing the mass of supersonic aerodynamic surfaces is a critical challenge in the development of high-speed rockets to further their potential range. This study presents the redesign of a supersonic fin with the primary objective of reducing its thickness from 25 mm. Two designs are investigated, with thicknesses of 10 and 12 mm, respectively, to ensure structural integrity under extreme flight conditions. A comprehensive computational approach is employed, combining static structural analysis, modal analysis, and aeroelastic analysis. Modal analysis is validated through an experimental method using a hammer impulse test for modal frequencies. The 10 mm rocket fin cannot withstand the static load simulated under the flight condition of 15-degree angle of attack, maximum operational flight speed of Mach 3.27, and air density at sea level. The 12 mm thick fin meets the requirements and demonstrates a flutter speed of Mach 11, significantly exceeding the required flutter speed of Mach 3.99. This research highlights the feasibility of substantial weight reduction in supersonic fins without compromising stability, offering a pathway for future advancements in lightweight, high-speed control surfaces.

Introduction. The development of critical thinking is an important aspect of military professional education. The ability to critically analyze information and apply various approaches to solving problems is practically realized not only in the educational process, but also in professional activities. Theoretical analysis. The study of scientific publications on the topic of the development of critical thinking and the use of the technology of its formation allowed us to come to the following conclusion: the problem of methodological support for the educational process of professional training of foreign military personnel in a technical specialty is practically not considered, since a significant part of scientific and methodological works is devoted to teaching students in the humanities and economics. Empirical analysis. The article presents the results of experimental work on the use of various techniques aimed at developing critical thinking. The presented techniques were adapted to the content of the disciplines of the professional cycle of specialty 25.05.04 “Flight operation and use of aviation complexes” taking into account the fact that students are foreign military personnel and Russian is not their native language. Conclusion. The presented results show that the use of techniques adapted to the process of training foreign cadets contributed to the development of analytical skills that found practical application in writing term papers and thesis.

Throughout the history of civil aviation, radio navigation aids have played a crucial role in ensuring the safety and continuity of air transportation. Although the development of Global Navigation Satellite Systems (GNSS) over the past half-century has significantly improved positioning accuracy, the system’s vulnerability to interference considerably reduces its reliability and poses a risk to civil aviation safety. This limitation highlights the crucial role of ground-based radio navigation networks in ensuring nominal flight operations. This study presents a comprehensive analysis of the global coverage and performance of radio navigation aid networks and assesses the implementation level of Performance-Based Navigation (PBN) by Air Navigation Service Providers (ANSPs) worldwide. A novel methodology is proposed for network performance evaluation, incorporating spatial characteristics of parameter distribution across global airspace using a geospatial indexing framework to determine airspace configurations compliant with various area navigation (RNAV) specifications. The performance of DME/DME, VOR/DME, and VOR/VOR positioning methods is evaluated within the official ICAO regional airspace structure. The results indicate that the European and North American regions currently maintain the most developed DME and VOR networks and propose reliable infrastructure sustainability. Globally, RNAV 1 capability is supported within approximately 20.2% of airspace using DME/DME and 3.45% using VOR/DME, while RNAV 5 coverage extends over 23.61% of global airspace, which approves resource efficiency distribution. RNAV 10 coverage could be supported by the VOR/VOR positioning method only in 13.48% of global airspace. Overall, the obtained results confirm the limited positioning performance of VOR network compared with DME, supporting the continuation of VOR network rationalization strategies and highlighting the need for optimized resource sharing to ensure the resilience and safety of the global air navigation system.

The integration of non-intrusive load monitoring (NILM) into solar-powered aviation systems presents a transformative approach for achieving sustainable, lightweight, and intelligent flight operations. However, the sector’s stringent constraints on weight, latency, and computational resources pose critical challenges to real-time NILM deployment. This study develops a resource-efficient machine learning framework that systematically evaluates six machine learning (ML) and deep learning (DL) models using high-resolution (200 kHz) power data that capture both transient and steady-state load characteristics across all flight phases. Advanced preprocessing techniques—comprising moving average smoothing and non-overlapping downsampling—were applied to suppress noise while preserving essential features. The comparative analysis reveals that K-Nearest neighbors (KNN) delivers the most effective balance between accuracy and computational cost, achieving an R² of 0.9403 with an execution time of 0.20 s, substantially outperforming ensemble models such as random forest (RF) and XGBoost in real-time feasibility. Conversely, the hybrid CNN-LSTM architecture attained the lowest mean squared error (MSE = 0.0048) and superior temporal sensitivity but required 271.53 s, demonstrating its suitability for offline analysis rather than onboard deployment. Through comprehensive hardware-in-the-loop validation using Opal-RT and Launchpad-F28379D DSP controllers, the framework verified appliance-level disaggregation accuracy under dynamic flight scenarios. The findings underscore a critical accuracy–efficiency trade-off in NILM model selection, establishing that traditional ML algorithms can outperform complex DL models when optimized for real-time, resource-constrained environments. This research provides actionable design insights for next-generation solar-powered aircraft energy management systems, demonstrating that model selection must prioritize computational efficiency, predictive reliability, and real-time responsiveness to enable sustainable and intelligent aviation energy control.

The study aimed to develop a comprehensive flight performance evaluation model based on flight parameters, covering the entire flight and applicable to normal and abnormal conditions. Thirty-seven pilots performed one normal traffic pattern flight and one single-engine failure emergency flight using a Cessna-172 simulator. The complete flight was divided into distinct phases – takeoff, climb, cruise, descent, approach/landing, and emergency, with evaluation metrics defined for each phase. The analytic hierarchy process was employed to determine the weights of flight phases and evaluation metrics. Two flight instructors provided ratings of performance after reviewing video recordings of the flights. ChatGPT generated five sets of performance scores based on the flight data. Intraclass correlation coefficient and correlation analyses indicated good consistency across multiple evaluation sources. Significant correlations were found among model-derived scores, instructor ratings, and ChatGPT-generated scores. These findings demonstrate that the model is reliable, and potentially applicable to real-world flight training and operations.

The paper considers the problem of constructing safe routes for a group of unmanned aerial vehicles in a limited airspace over an agricultural area. The relevance of the study is due to the growing use of UAV groups in the agro-industrial complex for monitoring, mapping, and processing fields, which requires ensuring flight safety in conditions of high air traffic density, limited communication, and exposure to external factors. A particular challenge is the need for autonomous missions in the presence of navigation errors and natural impacts. A route planning method is proposed based on representing the trajectory of each device as a capsule air corridor – a three-dimensional volume of a fixed radius formed along the trajectory segments. Spatial redundancy ensures safe spacing of trajectories at the planning stage, eliminating conflicts during subsequent autonomous flight operations without the need for continuous coordination between agents. The capsule radius includes a reserve for possible deviations from the planned trajectory, which ensures resistance to navigation errors. The method is based on the sequential formation of routes for each device according to a four-phase scheme, including a vertical ascent from the starting point to the operating altitude, a horizontal transition to the entrance to the processing zone, a return from the exit from the zone to the starting point of the descent, and a vertical descent to the initial position. Each new route is built considering the already reserved air corridors through an analytical check of geometric intersections between the capsules of different trajectories and convex polyhedrons of the processing zones. To improve computational efficiency, hierarchical spatial filtering is used based on bounding parallelepipeds, which allows for the rapid cutting off of obviously non-intersecting objects at the preliminary stage and performing an accurate geometric check only for potentially conflicting route segments. Numerical experiments were carried out for groups of 2 to 32 devices on a typical agricultural plot of one square kilometer. A nonlinear increase in the planning time and the number of iterations with an increase in the number of agents was found, which is due to the need to build each subsequent route in an already partially occupied space with an increasing number of spatial constraints. The length of routes shows a tendency to increase, especially pronounced at the initial stages of scaling, which is associated with the need to bypass already reserved air corridors.

The opening of the international flight route between Manado and Jeddah presents a strategic opportunity to enhance connectivity between eastern Indonesia and major centers of religious activities, trade, and global mobility in the Middle East. This study aims to analyze the prospects of establishing this route through a comprehensive approach that encompasses market, operational, economic, and policy aspects. Data were obtained through a combination of demand analysis for Umrah and Hajj travel from North Sulawesi and surrounding regions, evaluation of airport capacity and supporting infrastructure readiness, as well as an economic feasibility assessment based on projected operational costs and airline revenue potential. The results indicate that the demand for religious travel shows a significant upward trend, providing a strong market potential. Additionally, Sam Ratulangi International Airport is considered capable of supporting medium-haul flight operations with several technical adjustments. Economically, the route has the potential to generate profits for airlines if fare strategies, flight frequency, and market segmentation are properly optimized. This study concludes that the opening of the Manado–Jeddah route holds promising prospects but requires synergy among airlines, airport authorities, and regional governments in infrastructure development and market promotion.

Public use of unmanned aircraft or drones continues to affect emergency response operations in ways that responders, aviation teams, and agencies cannot ignore. Recreational drone ownership has expanded rapidly, and more individuals are flying these devices near active disaster scenes. Although many operators believe they are assisting, uncoordinated drone flights introduce risks that slow operations and place both responders and survivors in danger. As researchers and practitioners working in disaster operations, we have observed a significant rise in uncoordinated drone activity at active response sites. This letter aims to highlight that trend and call on response agencies, regulators, drone manufacturers, and the public to address this issue directly. The July 2025 floods in the Texas Hill Country demonstrate how quickly these hazards can develop. As helicopter crews conducted hoist operations and reconnaissance in unstable conditions, unauthorized drones entered the airspace. Several near misses were reported, and one rescue helicopter in Kerr County was struck by a drone and forced to land, removing a vital aircraft from service during an active rescue cycle (KSAT News, 2025). Responders noted that unauthorized drones complicated flight paths and reduced available decision time during aerial search operations (DroneLife, 2025; Stokel-Walker, 2025). Similar interference has been documented internationally. In January 2025 a privately operated drone collided with a Super Scooper aircraft working an active wildfire in California, causing damage significant enough to remove the aircraft from service during suppression efforts (Los Angeles Times, 2025). Unmanned aircraft sightings reported by aircrews indicate a steady increase in unsafe operations, with pilots reporting evasive maneuvers in nearly three percent of encounters in 2025 (Wallace, 2025). The risks of drone collisions with helicopters or fixed wing aircraft are well established. Federal Aviation Administration impact testing shows that even lightweight drones can damage rotors, engines, or windshields upon impact (Federal Aviation Administration, 2017). When pilots see or suspect a drone in their airspace, they must slow, alter, or temporarily suspend flight operations until the area is confirmed safe, which introduces complex operational risks. These delays also reduce the speed and effectiveness of rescues and wildfire suppression. Although many recreational operators intend to help, uncoordinated drone flights do not support responders. Incident commanders cannot verify or integrate imagery or data collected by personal drones, and such information may conflict with operational formats or create liability concerns. During Hurricane Harvey, unauthorized drones initially interfered with air operations, prompting the Texas Military Department to warn publicly that “civilian drones pose EXTREME risks to our rescue pilots and crews in high need areas” (IoT World Today, 2017). After this warning, several volunteer drone groups worked with agencies to coordinate flight patterns, ground aircraft upon request, and share imagery only through official channels. This shift improved safety and demonstrated that civilian groups can contribute meaningfully when they operate under unified direction rather than independently. The DroneUp partnerships used during Harvey further illustrate how civilian operators, when organized and aligned with official command, can support search efforts without adding risk (AirSight, 2017). That experience underscores the difference between organized support and self-directed flight. Legal restrictions prohibiting drone operation near disaster scenes exist in the United States, the United Kingdom, Canada, and Australia. Enforcement remains challenging. Responders cannot divert personnel to locate drone operators during an active emergency, and counter-drone technologies are not universally available or authorized for local agencies. Despite these concerns, drones have meaningful value when deployed within coordinated response systems. Agencies in the United Kingdom, Canada, Australia, and the United States use drones to map fire behavior, document search areas, assess structural conditions, and deliver real-time situational awareness. Research from Sweden shows that automated external defibrillator-equipped drones arrived before ambulances in a majority of trials and reduced time to first shock by nearly two minutes, demonstrating the potential of well-integrated drone systems to save lives (Karolinska Institute, 2020). Emergency response agencies should strengthen public education that emphasizes how unauthorized drone flights restrict aviation safety. The West Midlands Fire Service has set a strong example with its direct messaging urging the public to keep personal drones away from emergency scenes (West Midlands Fire Service, 2023). Regulators and manufacturers should expand geofencing and develop automatic restrictions that activate during declared emergencies. Agencies should also create accredited pathways that allow trained civilian pilots to support operations safely rather than through uncoordinated flights. Uncoordinated drone flights place responders and communities at unnecessary risk. As severe flooding, drought conditions, and wildfire activity increase across multiple regions, safe and predictable airspace will remain essential to effective emergency aviation. Keeping uncoordinated, personal drones grounded during active incidents is a necessary and achievable step toward protecting both responders and the people they are working to reach.

This paper describes a comprehensive numerical investigation into the aerodynamic characteristics of a full-scale UH-60 helicopter–inlet integrated flow field under the ground effect. The impact of both the internal and external flow parameters on the coupled flow field is analyzed, and the aerodynamic performance, streamline distributions, surface pressure, velocity fields, and vorticity magnitude are examined in detail. The numerical results demonstrate that the ground effect effectively reduces the flow losses within the air intake. Furthermore, the ground effect exhibits a significant attenuation in the presence of incoming flow, accompanied by substantial modifications in the three-dimensional flow field characteristics at the entrance of the intake. The internal parameters of the intake exert a substantial influence on the coupled flow field dynamics. This research elucidates the aerodynamic characteristics of the coupled interference in the near-ground flow fields across various operational conditions, providing valuable insights for helicopter flight operations under the ground effect.