Traditional Aircraft Research Articles

Aircraft skin defect detection algorithm based on improved YOLOv8

To solve the problem that the traditional aircraft skin defect detection relies on human eye observation, which leads to reduced efficiency due to human eye fatigue and limited individual cognition, an aircraft skin defect detection algorithm based on improved YOLOv8 is proposed. The data enhancement method is improved, and a slice reasoning + mosaic data enhancement method is proposed; the residual block is integrated into the feature extraction network to enhance the network expression ability and improve the accuracy of the model in the aircraft skin defect detection task; the three-branch (Triplet) attention module is applied to improve the feature fusion network to reduce the false detection rate and missed detection rate of small target samples; the structure of the detection head is optimized so that the network can better combine shallow information with deep information. Experimental results show that compared with the latest YOLOv8 algorithm, the improved algorithm improves the mean average precision (mAP) and recall rate (Recall) on the aircraft skin defect dataset by 3.6%and respectively 3.7%; and improves the mean average precision and recall rate on the public dataset VOC2007 by 2.9%and 2.2%.

Research on Large Hybrid Electric Aircraft Based on Battery and Turbine-Electric

Hybrid electric aircraft use traditional engine and electric propulsion combinations to optimize aircraft architecture, improve propulsion efficiency, and reduce fuel consumption. As a new technology, the fuel and energy consumption calculation of hybrid electric aircraft is more complicated than traditional aircraft due to the usage of different energy forms. The purpose of this paper is to develop the analytical method for fuel and energy consumption for hybrid electric aircraft. This paper summarizes the working principle of hybrid electric aircraft, including the system architecture and power conversion mechanism. The calculation of fuel and energy consumption for hybrid electric aircraft is carried out in detail. In order to evaluate large hybrid electric aircraft, the architecture, based on energy flow, is established, and turbofan engine, electrical system, electric duct fan, and aerodynamic model characteristics are established. With a single-aisle aircraft as an example, the fuel and energy consumption under the 800 nautical mile range is performed. It shows that fuel consumption can be reduced by 10% and energy consumption by 4.7% compared with a traditional aircraft. The effects of different range and battery ratios are analyzed. The payload range for the hybrid electric aircraft is analyzed. The results show that even though the hybrid electric aircraft reduces the payload and range, it can significantly reduce fuel and energy consumption.

Evaluating Reduced-Order Urban Wind Models for Simulating Flight Dynamics of Advanced Aerial Mobility Aircraft

Advanced Aerial Mobility (AAM) platforms are poised to begin high-density operations in urban areas nationwide. This new category of aviation platforms spans a broad range of sizes, from small package delivery drones to passenger-carrying vehicles. Unlike traditional aircraft, AAM vehicles operate within the urban boundary layer, where large structures, such as buildings, interrupt the flow. This study examines the response of a package delivery drone, a general aviation aircraft, and a passenger-carrying urban air mobility aircraft through an urban wind field generated using Large Eddy Simulations (LES). Since it is burdensome to simulate flight dynamics in real-time using the full-order solution, reduced-order wind models are created. Comparing trajectories for each aircraft platform using full-order or reduced-order solutions reveals little difference; reduced-order wind representations appear sufficient to replicate trajectories as long as the spatiotemporal wind field is represented. However, examining control usage statistics and time histories creates a stark difference between the wind fields, especially for the lower wing-loading package delivery drone where control saturation was encountered. The control saturation occurrences were inconsistent across the full-order and reduced-order winds, advising caution when using reduced-order models for lightly wing-loaded aircraft. The results presented demonstrate the effectiveness of using a simulation environment to evaluate reduced-order models by directly comparing their trajectories and control activity metrics with the full-order model. This evaluation provides designers valuable insights for making informed decisions for disturbance rejection systems. Additionally, the results indicate that using Reynolds-averaged Navier–Stokes (RANS) solutions to represent urban wind fields is inappropriate. It was observed that the mean wind field trajectories fall outside the 95% confidence intervals, a finding consistent with the authors’ previous research.

A deep learning approach to predicting UAV sound affect

The most prevalent method by which the impact of an acoustic environment upon people is assessed is the `environmental noise' approach, in which the sounds are quantified based entirely on their recorded weighted sound pressure levels. This does not account, however, for the character of sounds outside of their loudness. Objective metrics such as psychoacoustic annoyance have been developed to attempt to rectify this, however this is still heavily weighted towards loudness as the most important factor and may not provide a good estimate for subjective perceived annoyance for all sound sources, including unconventional aircraft such as unmanned aerial vehicles (UAVs). Here we present a novel method using a convolutional neural network (CNN) to predict perceived annoyance directly from mel-spectrograms derived from recorded audio featuring both traditional and unconventional aircraft sources. The network is trained and tested using data collated from five separate listening tests, and is shown to outperform conventional regression models based on psychoacoustic annoyance as a predictor of perceived annoyance. The time-frequency resolution of the calculated mel-spectrograms is shown to be a key factor in performance.

A comparison of tonal-broadband decomposition algorithms for propeller noise

The usage of propellers is increasing as an alternative to traditional aircraft and has become the primary option for new urban mobility vehicles. However, noise emissions from rotating blades can be a challenge for the certification of these new vehicles. The acoustic sources from a propeller can be decomposed into deterministic and random components. Therefore, academic research has focused on developing methods to separate these components. This study compares the performance of several methods available in the literature to decompose propeller noise signals into tonal and broadband components. Initially, the methods were applied to synthetic data that represents the main characteristics of rotating blade noise. Later, the methods were tested on realistic acoustic data collected from an isolated propeller test rig. The results showed that methods based on phase-averaging recorded signals satisfactorily detected harmonic components. The method based on cross-correlation between data blocks effectively extracted the broadband spectra, but it performed poorly on harmonic detection. Additionally, the wavelet transform and Proper Orthogonal Decomposition methods were capable of reconstructing time series related to both deterministic and random parts. However, further investigation is required regarding the separation of POD modes, as unexpected trends were observed with the wavelet-based method.

Aircraft Electrification: Insights from a Cross-Sectional Thematic and Bibliometric Analysis

Electrifying aircraft, a crucial advancement in the aviation industry, aims to cut pollutive emissions and boost energy efficiency. Traditional aircraft depend on fossil fuels, which contribute significantly to greenhouse gas emissions and environmental pollution. Despite progress in electric propulsion and energy storage technologies, challenges such as low energy density and integration issues persist. This paper provides a comprehensive thematic and bibliometric analysis to map the research landscape in aircraft electrification, identifying key research themes, influential contributors, and emerging trends. This study applies natural language processing to unstructured bibliographic data and cross-sectional statistical methods to analyze publications, citations, and keyword distributions across various categories related to aircraft electrification. The findings reveal significant growth in research output, particularly in energy management and multidisciplinary design analysis. Collaborative networks highlight key international partnerships, with the United States and China being key research hubs, while citation metrics highlight the impact of leading researchers and institutions in these countries. This study provides valuable insights for researchers, policymakers, and industry stakeholders, guiding future research directions and collaborations.

Hybrid electric aircraft design with optimal power management

The aviation industry has continuously improved its fuel efficiency over the last decades with innovations in technology, design and operation. Further improvements are becoming more difficult as current technology reaches complete maturity. Electrification of road vehicles is predicted to completely replace combustion engines in vehicles. As the efficiency and reliability of electric batteries and motors improve, so does the case for electric propulsion systems in aircraft. Jet fuel carries significantly more energy per weight than current state of the technology batteries, making complete replacement of fuel challenging. Hybrid electric propulsion, where both fuel and batteries are used to power propulsion systems could be feasible and improve fuel efficiency. Hybrid electric powertrains use electric power to reduce the power demands from the combustion engine. Electric batteries are discharged and charged during the operation based on power requirements.This paper presents an aircraft design method that includes optimized power management in the design loop. Power management determines battery discharging and charging throughout the design mission to reduce overall fuel consumption. Power management optimization provides the necessary feedback on battery and fuel performance to design battery pack size. This method is applied to regional aircraft, which typically fly the shortest routes of a commercial airline fleet. These short routes have proportionally longer climb and descent segments. These characteristics of regional aircraft routes lead to wider variations in power during operation, where hybrid electric powertrains are the most beneficial.The proposed hybrid electric aircraft design method finds that a serial hybrid electric regional aircraft could achieve significant fuel efficiency improvements to current regional aircraft. This result is in contrast of the current literature, which requires significant improvements to battery energy density, power distribution efficiency and electric motor efficiency to achieve similar fuel performance results. The hybrid electric regional aircraft does not outperform a traditional turboprop aircraft designed with identical objectives. The turboprop aircraft design improves even greater fuel efficiency improvements over current regional turboprop aircraft. This suggests two pathways are possible; the propulsion system of the next generation of regional aircraft could still be non-electric and achieve improved fuel burn improvements considering current technology or serial hybrid-electric powertrains configurations could be adopted in an expanded time horizon if optimistic technology development is done on battery power density to match the possible improvements of the proposed turboprop aircraft.

UAV Wing leading edge crashworthiness behaviour under bird strike events: The added value of CF/PA additive solutions versus traditional metallic wing structures

In recent years, an increasing interest in innovative solutions design of aircraft structural components has been raised through both research and industrial fields, aimed at optimising weight and enhancing the ability to withstand both static and dynamic loads. This study compares the structural response to a bird strike phenomenon of a vertical tail of a UAV in standard metallic configuration with the one obtained from an innovative solution, equal in volume but with an internally designed architecture for an additive approach and manufactured by employing a carbon fibre reinforced filament engineered for metal replacement applications (carbon fibre, CF/polyamide, PA). The additive solution proposes the use of a 10 % infill and a lattice structure that completely replaces the traditional aircraft structure concept. This approach leads to a significant weight reduction, approximately 45 % compared to the traditional metallic configuration. The investigation was conducted through explicit numerical simulations considering different impact angles. The numerical model of the bird strike has been assessed by numerical-experimental comparison, simulating the impact of a bird with a flat plate. For this study, the Coupled Eulerian-Lagrangian (CEL) approach has been adopted to perform the simulation. The results were compared in terms of stress distribution, failure analysis, displacements, and energy-time and force-time diagrams. The work demonstrated that using innovative manufacturing processes, such as additive manufacturing, can significantly improve the bird strike resistance of aerospace structures. This improvement is achieved though the production of lighter, structurally collaborative geometries, by reducing the load transferred to the rest of the UAV by about 47 % and decreasing the displacement on the impact area by 53 %.

Conceptual design and analysis of a box fan-in-split-wing tiltrotor eVTOL aircraft

PurposeWith the advancements in electric vertical take-off and landing (eVTOL) aircraft technology such as batteries, mechanisms, motors, configurations and so on, designers and engineers are encouraged to create unique and unconventional configurations of eVTOL aircraft to provide better capabilities and higher efficiencies to compete in the market. The box fan-in-split-wing tiltrotor eVTOL aircraft is an innovative design that aims to address the aerodynamic inefficiencies such as propeller effects in cruise and engine mounts drag that existed in traditional eVTOL aircraft designs such as vectored thrust, rotorcraft, lift + cruise and multi-copter configurations. This paper aims to propose a multi-disciplinary design process to conceptually design the box fan-in-split-wing Tiltrotor eVTOL aircraft.Design/methodology/approachAn unconventional methodology was used to design the UAM aircraft, and the following parameters are considered: capable of vertical take-off and landing, highly aerodynamic with a high lift-to-drag ratio, low Cd0 modern and appealing, rechargeable or battery swappable and feature to minimise or negate propeller drag. A heavy emphasis on improving performance and weight based on aerodynamics was enforced during the conceptual design phase. MAPLA and XFOIL were used to identify the aerodynamic properties of the aircraft.FindingsUpon determining the key parameters and the mission requirements and objectives, a list of possible VTOL configurations was derived from theoretical and existing designs. The fan in the wing/split wing was selected, as it could stow the propellers. A tiltrotor configuration was selected because of its ability to reduce the total number of lift props/motors, reducing powerplant weight and improving aerodynamic efficiency. For the propulsion configuration, a battery–motor configuration with a hexa-rotor layout was chosen because of its ability to complement the planform of the aircraft, providing redundant motors in case of failure and because of its reliability, efficiency and lack of emissions. Coupled with the fan-in-wing / split wing concept, the box wing seamlessly combines all chosen configurations.Originality/valueThe box fan-in-split-wing Tiltrotor eVTOL aircraft aims to address the aerodynamic inefficiencies of earlier designs such as propeller effects in cruise and engine mounts drag. The potential benefits of this aircraft, such as increased range, endurance and payload capacity, make it an exciting prospect in the field of Urban Air Mobility.

Numerical and experimental investigation of the supercooled large droplets icing of rotating spinner

The icing of aero-engine rotating components is more complex than traditional aircraft icing. A three-dimensional numerical method was developed based on the Eulerian method to study the supercooled large droplets icing of a rotating spinner. Typical dynamic behaviours of supercooled large droplets, such as deformation, breakup, rebound, and splashing, were considered. An icing thermodynamic model applicable to a rotating spinner was established by considering the effects of pressure gradient, air shear force, and centrifugal force on the flow of the water film. Meanwhile, an ice wind tunnel experiment on supercooled large droplets icing of a rotating spinner was carried out. The results showed that the maximum relative deviation of the simulated icing thickness at the cone tip is no more than 15% compared with the experimental result, and the icing thickness on the surface of the rotating spinner is basically consistent with the experimental result. Based on the three-dimensional numerical method developed in this study, the effects of the rotational speed, inflow velocity, and inflow temperature on the supercooled large droplets icing of a rotating spinner were investigated. As the rotational speed increases, the rebound and splashing of supercooled large droplets becomes more obvious, leading to an increase in the mass loss coefficient and a decrease in the collected mass of water droplets on the surface of the rotating spinner. Consequently, the icing thickness at the tail of the rotating spinner decreases with an increase in the rotational speed. Since the liquid water content in the environment is relatively low, water droplets freeze immediately after impacting the surface of the rotating spinner and rime ice is usually formed. When the inflow temperature is relatively high, overflow water exists and glaze ice is formed near the tip of the rotating spinner. The findings of this study can provide valuable support for the design and verification of anti-icing systems for aero-engine rotating spinner.

An asymmetrically variable wingtip anhedral angles morphing aircraft based on incremental sliding mode control: Improving lateral maneuver capability

This paper presents the design of an asymmetrically variable wingtip anhedral angles morphing aircraft, inspired by biomimetic mechanisms, to enhance lateral maneuver capability. Firstly, we establish a lateral dynamic model considering additional forces and moments resulting during the morphing process, and convert it into a Multiple Input Multiple Output (MIMO) virtual control system by importing virtual inputs. Secondly, a classical dynamics inversion controller is designed for the outer-loop system. A new Global Fast Terminal Incremental Sliding Mode Controller (NDO-GFTISMC) is proposed for the inner-loop system, in which an adaptive law is implemented to weaken control surface chattering, and a Nonlinear Disturbance Observer (NDO) is integrated to compensate for unknown disturbances. The whole control system is proven semi-globally uniformly ultimately bounded based on the multi-Lyapunov function method. Furthermore, we consider tracking errors and self-characteristics of actuators, a quadratic programming-based dynamic control allocation law is designed, which allocates virtual control inputs to the asymmetrically deformed wingtip and rudder. Actuator dynamic models are incorporated to ensure physical realizability of designed allocation law. Finally, comparative experimental results validate the effectiveness of the designed control system and control allocation law. The NDO-GFTISMC features faster convergence, stronger robustness, and 81.25% and 75.0% reduction in maximum state tracking error under uncertainty compared to the Incremental Nonlinear Dynamic Inversion Controller based on NDO (NDO-INDI) and Incremental Sliding Mode Controller based on NDO (NDO-ISMC), respectively. The design of the morphing aircraft significantly enhances lateral maneuver capability, maintaining a substantial control margin during lateral maneuvering, reducing the burden of the rudder surface, and effectively solving the actuator saturation problem of traditional aircraft during lateral maneuvering.

Revisiting Legacy Weight Relationships Using Machine Learning Techniques

This paper investigates the application of K‐means clustering algorithms to traditional aircraft conceptual‐level weight estimation techniques. K‐Means clustering was utilized as a means for statistical discovery within the dataset to explore potential paths for improved weight estimation using conceptual‐level information. As a proof‐of‐concept demonstration, scope was limited to fuselage basic weight estimation. A variety of weight sources were parsed and curated to produce a large, diverse dataset consisting of 82 separate aircraft. A corresponding new universal fuselage basic weight regression was generated as a baseline for comparison. K‐Means clustering was then employed to sort aircraft into groupings based on configuration and topology with an associated weight equation created for each grouping. Configuration-based groupings utilized information such as a high-level abstraction of the structural layout as well as whether the aircraft is a fixe‐wing or rotary‐wing vehicle. Topology‐based groupings utilized information such as landing gear location and possession of a cargo ramp or wing. The configuration‐based groupings showed modest improvement compared to the baseline regression which were in turn outperformed by the topology-based regressions across a range of data groupings. Under all conditions, a subset of the data associated with fixed‐wing aircraft was shown to be an outlier in regard to error as a result of a large range of weight and speed scales, in addition to possible secondary pressurization impacts. Special treatment of the winged dataset led to further reduction in error based on unique design features. The weight estimation processes explored within this paper present a methodology that leverages machine learning algorithms that can improve and inform existing best practices.

Motion Equations and Attitude Control in the Vertical Flight of a VTOL Bi-Rotor UAV: Part 2

This paper gathers the dynamical modeling of an unmanned aircraft and the design and simulation of the control system, allowing it to perform a Vertical Take-Off (VTOL) maneuver, a fixed-wing (FW) flight and a transition between the two configurations using two tilting rotors (Bi-Tilt). These Unmanned Aerial Vehicles (UAVs) operating in this configuration are categorized as Hybrid UAVs, for their capability of having a dual flight envelope: flying like a multi-rotor and navigating like a traditional fixed-wing aircraft, allowing the drone to perform complex missions where these two flight configurations are essential. This work exhibits the Bi-Rotor non-linear dynamics, valid for both flight configurations, the design of the control algorithm for stability and navigation, and a simulation of a complete flight mission.

Research on design technology of fiber optic fuel level sensor with novel structure

Traditional aircraft fuel level sensors, represented by capacitive fuel level sensors, have inherent shortcomings, making it difficult to meet the increasing aviation needs in terms of intrinsic safety and electromagnetic interference resistance. Therefore, this article studies the design technology of fiber optic fuel level sensors. By analyzing various optical fiber liquid level measurement methods, the fiber Bragg grating method that best meets practical needs is selected, and a novel and reasonable sensor structure design is achieved. Specifically, in order to improve the measurement accuracy, a lever-type sensitivity enhancing structure based on flexure hinge is studied in this paper. Combined with the metal diaphragm, it can generate higher axial strain on the fiber grating under a given liquid pressure. In order to ensure the practicality of the sensor, this paper analyzes the interference factors that may cause the measurement error in the actual fuel tank. Correspondingly, this paper proposes a design of fixed distance double measuring points to compensate the influence of posture, uses the reference fiber grating method to compensate the influence of temperature, and designs a reasonable assembly and sealing scheme. To verify the rationality of the design, the sensor test platform is built and the sensor function is tested comprehensively. The results show that the sensor has good precision and can achieve expected compensation effect.

Aircraft routing using dynamic programming and reinforcement learning: A customer-centric approach

Over the past few decades, the airline industry has evolved into a sophisticated business, with conventional objectives such as operational efficiency, effective fleet assignment, flight punctuality, and equitable crew scheduling being the primary focus. Nevertheless, given the growing accessibility to customer feedback (either through public forums or private data), it is crucial to make business decisions that cater to the customers’ requirements in addition to conventional goals. Within aircraft operations, there is a series of decisions to be made, such as flight scheduling, fleet assignment, aircraft routing, and crew scheduling. After designating a fleet type to each flight leg, airlines ascertain the sequence of flight legs, or strings, to be operated by individual aircrafts, while complying with obligatory maintenance checks. This constitutes the aircraft routing problem.We introduce a novel approach to aircraft routing problem, by infusing traditional methodologies with insightful customer feedback to create a more responsive and customer-centric model. Unlike conventional techniques of constructing routes, in our work, we construct maintenance feasible strings. Traditional aircraft routing methodologies typically concentrate on minimizing propagated delay. However, by integrating customer feedback, more effective prioritization to minimize delays for flight legs with higher customer dissatisfaction can be achieved. We propose an optimization model that strives to minimize propagated delay while prioritizing flights based on customer feedback, thus resulting in customer-aware routes. To solve the proposed optimization model, we design an iterative alternating optimization scheme where feasible strings are first constructed from the pool of available flight legs using a string generation procedure, followed by solving an integer linear optimization problem over only the feasible set of strings obtained in the first stage. To construct feasible strings of flights at each iteration, we use two different methods. The first method is based on a dynamic programming approach which is useful for mean delay information. To handle practical cases where delays are uncertain, we propose a reinforcement learning (RL) approach to construct the strings. We provide experiments of our proposed methods on synthetic and real data sets and demonstrate the effectiveness of our RL model in comparison to dynamic programming based model. Our experiments affirm that routing decisions informed by customer feedback prioritize minimizing delays for specific flight legs, resulting in routes that are inherently customer-friendly.

Research on inter-shaft bearing fault diagnosis method based on STFT-CNN modeling

Inter-shaft bearings are an essential component of aircraft engines, and their operational status determines the safety of aircraft engine operation. Therefore, to improve the accuracy of fault type prediction and enrich the feature information in vibration signals of aircraft engine inter-shaft bearings, this paper proposes an STFT-CNN model based on the AlexNet architecture, extending its application to the research of aircraft engine inter-shaft bearing fault diagnosis. This approach addresses the common reliance on personnel experience for fault type diagnosis in traditional aircraft engine inter-shaft bearing fault diagnosis. Firstly, real vibration fault signals from inter-shaft bearings are collected through experiments to enrich feature information in non-stationary signals using STFT time-frequency methods. Secondly, utilizing the high interpretability of the STFT-CNN model, fault feature data from inter-shaft bearings under various operating conditions are extracted to refine our understanding of fault feature information. Finally, leveraging the robustness of the STFT-CNN model, fault types are classified and predicted. The training process involves comparative analysis using different pooling algorithms, time-frequency analysis methods, and various deep learning network models. The results demonstrate that the STFT-CNN model, employing the maximum pooling algorithm, outperforms other models in predicting inter-shaft bearing faults, achieving an average fault prediction accuracy of 98.8% .

Urban Air Mobility Profitability and Operational Sensitivity to Battery and Charging Technology

Designs for electric vertical takeoff and landing (VTOL) aircraft deviate from traditional aircraft designs and include a wide variety of different configurations. Significant uncertainty also exists regarding the status of future battery technology, including energy density and charge rates. This paper presents a simple analytic model to estimate the profitability of an urban air mobility electric VTOL aircraft for a variety of vehicle configurations, battery technology parameters, and economic factors. Five main elements are considered by the framework: aircraft performance, battery technology, mission profile, mission economics, and electrical grid parameters. A sensitivity analysis is provided, comparing the operational performance and profitability of three electric VTOL concepts (lift plus cruise, quadcopter, and side by side), with respect to electrical grid and battery technology factors. The takeoff weight to maximize the number of completed routes or the overall profitability is also examined. Interestingly, results show that these two values do not coincide across the entire design space due to the nonlinearity of the battery life cycle with respect to the depth of discharge, which strongly affects battery replacement cost.

Variational Method-Based Trajectory Optimization for Hybrid Airships

Hybrid airships, combining aerodynamic lift and buoyant lift, are efficient near-space aircraft for scientific exploration, observation, and surveillance. Compared to conventional airplanes and airships, hybrid airships offer unique advantages, including stationary hovering and rapid deployment. Due to the different task requirements and strong coupling between flight and environment, trajectory-optimization methods for traditional aircraft are difficult to apply to hybrid airships directly. We propose a trajectory-optimization model based on the variational method to calculate the optimal time and energy paths under weak, uniform, and latitudinal linear wind fields. Our model shows that the influencing factors for the optimization path can be categorized into three types: airship design parameters, wind field parameters, and departure parameters. The result indicates that the optimal time paths are generally straight lines, and the optimal energy paths are piecewise curves with a 24-h cycle under typical hybrid airship design parameters. This work has provided new insight into the trajectory optimization and parameter design of future hybrid airships.

Balancing predictability and flexibility through operation volume-constrained visual flight rule operations in low altitude airspaces

The advancement in uncrewed aircraft systems such as small drones and advanced air mobility vehicles such as Electric Vertical Take Off and Landing aircraft (eVTOL) has called for airspace integration at low altitudes of both traditional aircraft, such as helicopters and new entrants, such as drones and eVTOL. Currently, the trajectories and necessary buffers around them of flights operating under visual flight rules are not possible to predict. This research proposes the use of flight mission characteristics to model the trajectory and evaluates temporal, lateral and vertical deviations to define the safety buffers which can be used to generate operation volumes, geo-fencing such low-altitude flights and separating them from other traffic in a safe and efficient manner. Real flight test data obtained for the purposes of this study and pilots’ interviews assure high fidelity and practicability of the proposal.

Mechanism Analysis of the Impact of COVID-19 on the Whole Process of Aircraft Turnaround Operations

Efficient aircraft turnaround operations at airports are vital to ensure overall air traffic network performance. After the outbreak of COVID-19, the traditional aircraft ground handling process has changed significantly due to new requirements put forward by the pandemic prevention and control policy. To better understand how COVID-19 has affected ground handling operations, a discrete-event simulation model of turnaround is established to analyze the change in the whole turnaround process before and after the pandemic. The critical path of turnaround operations was used to identify the significantly affected subprocesses to which airports should pay attention. For a case study on the two busiest airports in China, the aircraft turnaround time increased by about 18% after COVID-19. Cabin cleaning, catering, and passenger embarking were the main processes in causing this increase. By evaluating the impact mechanism of COVID-19 on turnaround operations, the study sheds light on strategic, tactical, and operational approaches for relevant authorities.