This study presents a systematic review of peer-reviewed academic literature to explore the current landscape of artificial intelligence (AI) applications in sustainable aviation operations. Using a qualitative content analysis approach, four main thematic domains were identified, encompassing emission and fuel efficiency, maintenance reliability, infrastructure sustainability and education- or policy-related applications. In addition to thematic synthesis, the study mapped the annual publication frequency, the AI methods employed and the aviation domains targeted. The results reveal an increasing interest in hybrid and deep learning models, such as long short-term memory (LSTM), convolutional neural networks (CNN) and attention-based architectures, particularly in-flight optimisation and delay prediction tasks. AI-based flight optimisation techniques, such as trajectory prediction and adaptive fuel management, contribute to reducing CO2 emissions through more efficient flight planning and operations. Moreover, predictive maintenance supported by AI-driven digital twin systems has gained prominence due to its potential to reduce downtime and increase safety. The discussion further addresses regulatory challenges, the importance of explainable AI and integration barriers within complex aviation ecosystems. Findings are derived from a focused corpus of 27 peer-reviewed studies, which, although limited in number, offer representative insights into current sectoral trends. This review makes a significant contribution to both academia and industry by offering a comprehensive framework that categorises AI applications and highlights future research directions. Key implications include the need for regulatory harmonisation, real-time decision-support tools, and interdisciplinary approaches that integrate AI with behavioural sciences and sustainability goals.

This study investigates the nonlinear dynamics and control strategies for a Cessna-182 unmanned aerial vehicle (UAV). The longitudinal and lateral dynamic models were derived using aerodynamic analysis conducted in ANSYS Fluent. Various control methodologies, including PID (proportional, integral, derivative) tuning with genetic algorithm (GA), root locus (RL), MATLAB tuned (MT), Ziegler Nichols (ZN), the model reference adaptive controller (MRAC), linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG), were implemented and compared through simulations. Due to the inadequacy of PID control under variable environmental and noise conditions, LQR and LQG controllers, including the full state-space model required for real-time applications, are simulated for both longitudinal and lateral motions in addition to the PID controller. Although the LQR controller has acceptable simulation results for noiseless conditions, the superiority and stable structure of the LQG controller under noise and disturbance effects are highlighted. The results, including noise Dryden turbulence effects, highlight the advantages of proposed MRAC and LQG for robust stability and precise flight performance. Experimental flight tests validate the theoretical findings, demonstrating the practical viability of the proposed control approaches. The results obtained for the Cessna-182 mini-UAV will be effective for future researchers in terms of obtaining UAV dynamics and also evaluating different control strategies of the UAV.

Effectively improving flight fuel efficiency and optimising operational management while ensuring environmentally sustainable and safe operations is crucial for the global aviation to achieve a successful sustainability transition. Addressing the imperative shift from conventional air traffic flow management (ATFM) to trajectory-based operation (TBO), this paper proposes a large-scale flight trajectory optimisation framework under TBO. The study integrates influencing factors including meteorological conditions, airspace capacity and aircraft performance. With the assistance of an air traffic operation simulation system, flight characteristic information is extracted to construct the optimisation model. The model addresses a multi-objective optimisation problem, balancing sector excess traffic flow against cruise fuel consumption within a priority-based framework, to generate global flight plans with optimal altitude profile and waypoint overflight times. Experiment in Chinese airspace shows the TBO scenario achieves substantial reductions of 66.0% in excess sector traffic and 7.8% in cruise fuel consumption versus baseline scenario. This provides a methodological framework for airspace resource allocation supporting energy conservation and emission reduction.

This research is focused on the formulation, implementation and verification of novel transition control strategy for an originally designed tail-sitter VTOL hybrid UAV. A differential thrust transition vehicle was designed to achieve wide-envelope flight control by using four actuators. Attitude-tracking and altitude-hold controllers were developed to provide adequate control actuation for smooth transition between different flight modes. The control system was formulated for flight dynamic model of the aircraft, and implemented in MATLAB/Simulink environment for flight simulations. Tilt-angle-based feedback control was employed to calculate attitude errors. Altitude errors were obtained by direct comparison of commanded signal with the vehicle’s response. To maintain the desired altitude and attitude, required total thrust and differential thrust were calculated from the output of the altitude and attitude controllers, respectively. High-fidelity aerodynamic analysis was performed over the entire flight envelope to obtain the aerodynamic coefficients of the aircraft. Motor dynamics, propeller effects and airframe aerodynamics were incorporated in the flight dynamics model of the vehicle. Flight simulations verified effectiveness of the flight control system in tracking attitude and altitude-hold commands. The flight modes transition manoeuvre was successfully accomplished with the designed transition control system. The control technique for takeoff, hover and landing was successfully validated on manufactured prototype.

This study presents a bespoke hardware platform for indoor navigation, featuring a quadrotor equipped with an FZ3 card incorporating the AMD (formerly Xilinx) Zynq UltraScale+ ZU3EG MPSoC as the onboard computer. A core component of this platform is a field programmable gate array (FPGA) module specifically designed to efficiently compute Delaunay triangulations, enabling enhanced spatial awareness and real-time surface reconstruction. The onboard computer communicates with the flight controller, inertial measurement unit (IMU), ultra-wideband (UWB) localisation system, stereo camera, light detection and ranging (LiDAR) and ultrasonic sensors via robotic operating system (ROS) 2. The primary objective is to develop a cost-effective, modular unmanned aerial vehicle (UAV) system that can be adapted for a range of indoor navigation applications. The modular design supports different onboard computer platforms and sensor configurations, allowing researchers to easily customise the system for various experiments. By providing a practical framework for precise indoor navigation, this platform addresses the limitations of simulated, simplified laboratory setups, accelerating prototyping and supporting the deployment of UAVs in complex real-world environments. This work explores the UAV’s hardware architecture, the implementation of the Delaunay triangulation core on the FPGA system-on-chip (SoC), the ROS 2-based communication system and includes a detailed mass analysis and power estimation.

Aerodynamic modeling of tiltrotor aircraft is inherently limited by incomplete aerofoil data for blades constructed from multiple aerofoil sections. This data deficiency leads fundamental errors into the blade element theory (BET) inputs, resulting in inaccurate induced velocity and unsteady load predictions that compromise flight control design. To overcome this limitation, a corrective induced inflow factor k is introduced into an augmented Pitt–Peters dynamic inflow model. For fixed tilt angles, k is efficiently identified via an optimisation framework. The fmincon algorithm attains the optimal k with a computational speed 5–6 times faster than the genetic algorithm, while maintaining thrust coefficient errors within 1.26%. For continuous tilting, a direction-aware, time-varying quadratic model for k(t) is proposed, which uses the Fréchet distance metric to accurately reconstruct the aerodynamic hysteresis loop and eliminate spurious load predictions during rotation reversal. Validated through wind tunnel tests, the framework reduces the maximum thrust prediction error in dynamic tilting from 34.10 to 10.70%. The corrected aerodynamic model supports real-time simulation and provides a reliable foundation for advanced tiltrotor control system design.

Recent trends in the context of aircraft propulsion and systems electrification are met with challenges, as new sources of on-board waste heat generation are introduced. The development and optimisation of innovative thermal management systems (TMS) for aviation is a key asset in addressing those challenges. Leveraging the intrinsic thermal capacity of Jet-A fuel appears as a promising solution to provide heat source cooling for both conventional and hybrid-electric aircraft configurations. In this paper, the feasibility of a fuel-based thermal management system (F-TMS) is investigated to support thermal control of fuel cells in the context of hybrid-propulsion electrification for a regional transport aircraft. A modelling approach is proposed for the dynamic simulation of the stored fuel mass and temperature, along with a preliminary investigation of the passive heat rejection through the tank walls. The performance of the F-TMS is simulated for two primary cooling architectures, and their thermal endurance is compared across some realistic flight mission profiles. Finally, the influence of altitude, range and alternate flight is investigated to identify critical scenarios of F-TMS utilisation and derive the maximum degree of supported hybridisation.

The demand for renewable energy is increasing rapidly due to the need for environmental sustainability and the reduction of energy costs. Solar energy, with its high potential and minimal environmental impact, has become a key energy source, particularly in innovative applications such as unmanned aerial vehicles (UAVs). The integration of solar cells into fixed-wing UAVs enables extended flight durations and enhances operational efficiency, making them a viable solution for long-endurance missions. In this study, the effects on flight performance, energy consumption and battery management of a fixed wing UAV equipped with solar cells are investigated. Two UAV models with the same aerodynamic characteristics, one with solar cells and the other powered only by the battery, are comparatively analysed. Using a simulation model, the effects of solar irradiation, temperature and environmental factors on the energy management of the UAV were evaluated. The findings indicate that the solar-powered UAV has the capability to sustain uninterrupted 24-hour flight by recharging its battery during daylight hours, whereas the battery-only model is restricted to approximately 14 hours of flight time. In the solar-powered model, dynamic altitude management strategies were employed to extend flight duration, utilising excess energy to determine optimal operational scenarios. Furthermore, the integration of solar cells was found to optimise battery consumption, thereby enabling more sustainable flight operations. These results demonstrate that solar-powered fixed-wing UAVs offer significant advantages for long duration missions and present an efficient solution for energy management.

This study presents a systematic approach to optimising drone airframe geometry with a specific focus on enhancing flight dynamics for racing applications. The proposed method integrates an elitist multi-objective evolutionary algorithm with mathematical procedures of varying natures, encapsulated within an algorithmic system of a simulation platform. The framework combines implicit numerical methods with a custom implementation to seamlessly interact across multiple simulation environments. This approach ensures the mathematical model closely reflects real-world conditions, providing reliable optimisation outcomes. The preliminary findings suggest that optimised airframe geometries lead to notable advancements in thrust efficiency and overall performance. Symmetrical and non-symmetrical designs exhibit distinct benefits, highlighting the potential for significant improvements in trajectory precision and flight efficiency. These results emphasise the practical impact of shape design and dynamic optimisation in drone airframes, offering valuable insights for developing high-performance racing drones.

The privatisation of unmanned aerial vehicles (UAVS) used for situational awareness purposes to ensure their own situational awareness based on parameters gives direction to progress and provides a basic framework for future studies. In this context, a unique communication system architecture was proposed for obtaining state-of-the-art mini-UAV data and evaluations were carried out on the basis of data flow and parameters. Within the scope of the evaluation this study postulates a trailblazing approach as a means of optimising flight data pattern recognition by integrating Cross Industry Standard Process for Data Mining (CRISP-DM) and long short-term memory (LSTM)-based predictive depiction. By leveraging the structured framework of CRISP-DM and the sequential learning capabilities of LSTM, this research aims to enhance the accuracy of mini unmanned aerial vehicle systems (UAVS) flight scenario predictive reliability. This framework is applied to navigation phase, where the overall flight trajectory can be seen, and accurate forecasting and pattern recognition are critical for optimising operational efficiency. The findings have displayed the ability to perform high-accuracy predictions of flight parameters within a structured process. The experimental results demonstrate that key flight parameters can be predicted with near–comma-level numerical accuracy, indicating a high level of estimation precision. Through facilitating immediate data transfer and organised navigation phase evaluation, this research provides a methodical strategy for managing flight data, simultaneously contributing notably to UAV decision support systems and self-qualification engineering.

Predictive maintenance in safety-critical systems like turbofan engines increasingly relies on machine learning (ML) models to estimate remaining useful life (RUL), but the ‘black box’ nature of these models hinders their adoption and trustworthiness. While traditional ex-ante prognostic metrics (e.g. monotonicity, trendability) are used to pre-screen sensor data, a systematic comparison against the post-hoc explanations of what a model actually learns is lacking. We explore the application of SHapley Additive exPlanations (SHAP) from explainable artificial intelligence (XAI) to investigate feature importance in engine failure prediction using the second dataset of the Commercial Modular Aero-Propulsion System Simulation (CMAPSS). The preprocessing pipeline includes z-score normalisation of sensor data and the calculation of a health index (HI) to quantify system degradation. A power-law fit is applied to the HI to capture the underlying trends of engine wear and failure progression. We use the normalisation data to calculate prognostic feature selection metrics: monotonicity, trendability and prognosability. Then, we train two machine learning models – random forest (RF) regressor and gradient boosting (GB) method – directly from the raw data to predict the RUL based on the actual sensor readings. The SHAP values generated for both models are analysed to identify the features with the most significant impact on RUL predictions. By comparing the SHAP value distributions across models and prognostic predictors, we highlight feature robustness and their relative influence on engine degradation and failure prediction. This work provides insights into the interpretability of machine learning models in prognostics and enhances the understanding of sensor contributions to engine health monitoring. The results demonstrate the effectiveness of SHAP in elucidating feature importance, supporting the development of more transparent and reliable prognostic systems.

This paper develops and compares four distinct constraint-aware and adaptive matheuristic approaches based on tabu search, simulated annealing, bacterial foraging optimisation and multi-ant colony optimisation. By integrating metaheuristic search with a mixed-integer linear programming (MILP) model, a hybrid framework is designed to optimise aircraft departure sequencing with rigorous precision. The core novelty lies in how this integration reconciles the exactness of mathematical programming with the interpretability of metaheuristics. Standard MILP models offer precise solutions but function as opaque ‘black boxes’, while standard metaheuristics operate via understandable moves but often rely on simplified feasibility checks. The proposed solution merges these strengths, employing adaptive metaheuristics to generate candidate sequences using interpretable moves while a restricted MILP acts as a high-fidelity evaluator. Fixing the sequence enables the MILP to focus solely on optimising continuous timings and holds. This ensures that every candidate step is strictly feasible and optimally timed, turning the solution process into a sequence of human-readable queue adjustments backed by the numerical precision of an exact solver. In a calibrated Antalya Airport case study with 40 aircraft, all algorithms attain the MILP optimum or remain within 1% of it, achieving a 29% reduction in hold fuel compared to first-come-first-served (FCFS). In denser 50-aircraft scenarios, the approach maintains high solution quality within feasible time limits, proving that explainability does not require sacrificing computational efficiency.

This paper presents the design of a nonlinear adaptive flight control system for the Cessna Citation X longitudinal dynamics. The aircraft pitch rate is controlled using a combination of recursive least squares-based nonlinear dynamic inversion and an adaptive neural network controller. The recursive least squares algorithm provides online parameter estimates to support the inversion, while the neural network compensates for residual modeling errors through online weight adaptation. To enhance robustness and ensure stability, a fixed-gain proportional integral derivative controller is integrated into the control structure. Unlike conventional gain-scheduled controllers, where PID gains vary with flight condition, the proposed adaptive controller uses a single baseline set of fixed gains. The adaptive component updates the control action online, enabling the same controller configuration to operate effectively across all 64 cruise conditions without any gain scheduling. A systematic tuning methodology is introduced for initialising the recursive least squares, selecting forgetting factors and applying covariance resets to ensure accurate adaptation. The controller is able to track a pitch-rate reference model that satisfies longitudinal flight quality requirements. Robustness is assessed under realistic disturbances, including wind gusts, Dryden turbulence, actuator loss-of-effectiveness and actuator noise. Simulation results demonstrate that the controller achieves precise reference tracking while maintaining Level 1 flight qualities. Stability is formally guaranteed using Lyapunov-based analysis. The findings highlight the ability of the designed hybrid adaptive controller to overcome limitations of linearisation, gain scheduling and estimator sensitivity, forecasting a practical and certifiable method for the integration of intelligent adaptive flight control systems into commercial aircraft.

This paper demonstrates a method for quantifying the unmitigated mid-air collision (MAC) rate (${\lambda _{{\textrm{MAC}}}}$) between crewed aircraft and uncrewed aircraft (UA) above different congested area operating environments, to support broadening the definition of an atypical air environment (AAE) within the United Kingdom (UK). The underlying principle of this work is that all crewed aircraft should be operating in accordance with UK Standardised European Rules of the Air (SERA), which dictate minimum operating heights over built-up areas, specifically 1,000 ft in the majority of cases, except during take-off and landing. It is unrealistic, however, to assume that this rule is never breached; therefore, we present a method for objectively evaluating the likely encounter rate. We systematically consider the exclusion of runway protection zones (RPZs), aerodrome traffic zones (ATZs) and helicopter landing sites (HLSs) from the operating areas and evaluate the effect on ${\lambda _{{\textrm{MAC}}}}$. Results are presented that apply the methodology to over 33,000 hours worth of air traffic data recorded at three different sites across the UK. It is concluded that the operation of UA overhead congested areas, outside of RPZs and HLSs, likely satisfies an appropriate target level of safety (TLS) to be considered an AAE up to a height of 100 m above ground level (AGL) both inside and outside of controlled airspace.

Slotted blade technology is a passive flow control strategy that can effectively suppress the boundary layer separation within compressors. To reduce the iteration time of the traditional Design-Experiment-Design method, this study innovatively proposes a fast and universal three-dimensional design method for the slotted blade technology, enabling slot modeling completion within 1 s. Furthermore, combined with machine learning (ML), the mapping relationships between eight design parameters and two key aerodynamic performances – compressor design point efficiency ($\eta$DE) and stator total pressure recovery coefficient at the near-stall point ($\sigma ^{*}_{NS}$) – were pioneeringly established. In this study, the prediction performances of six models were compared: one-dimensional convolutional neural network (1D-CNN), random forest (RF), support vector regression (SVR), Gaussian process regression (GPR), multi-layer perceptron (MLP) and long short-term memory network (LSTM). The results indicate that 1D-CNN achieves the highest prediction accuracy: for the $\eta$DE, the mean absolute error (MAE) and coefficient of determination (R2) are 0.041 and 0.987, respectively; for the $\sigma ^{*}_{NS}$, the MAE and R² are 0.479 × 10−3 and 0.955, respectively. Notably, the computational time of the 1D-CNN model is 99.11% less than that of the computational fluid dynamics (CFD). The Shapley Additive exPlanations (SHAP) method was employed to reveal the effects of design parameters on the compressor aerodynamic performance. Notably, the slot outlet axial position (Zout) exerts the most significant influence on the $\eta$DE, while the slot outlet radial position close to the casing (R1_out) has the strongest impact on the $\sigma ^{*}_{NS}$. This study provides theoretical support and valuable references for the intelligent design of slotted blade technology.

For efficient altitude transition and cruise maintenance, the wing-in-ground craft (WIG) quickly adjusts to and then stabilises at the target flight altitude, with minimal deviation during the fastest transition. Conventional methods typically rely on multi-objective functions to minimise settling time (ST) and integrated absolute error (IAE). These methods achieve the target trajectory through iterative trial-and-error adjustment on their weights that balance the two objectives, leading to time-consuming tuning. A reward function is proposed to prioritise minimising ST first, followed by minimising IAE. When trajectory points deviate from the target altitude, rewards are calculated based on absolute error (AE) values. Once the target altitude is reached, the reward takes the value of an exponential based on the current step number. The proposed reward function ensures that positive rewards for reducing ST outweigh the reward losses from adjusting previous trajectory points, and it is mathematically validated. Example of a WIG’s altitude change compares the proposed reward function and conventional multi-objective methods via deep reinforcement learning. Results show that the proposed reward function directly plans the trajectory that prioritises fast-settling requirements first and then minimising deviation, without needing to introduce or tune any parameter.

Unmanned aerial vehicles (UAVs) tracking one moving target in an oilfield environment with dynamic obstacles has been studied. Firstly, the UAV tracking one target is modeled mathematically, including the oilfield environment, the UAV kinematic model, the moving target model and so on. Considering the oilfield environment with dynamic obstacles, the chance-constraint method is incorporated into the distributed model predictive control (DMPC) to realise the target tracking and avoid the obstacles in the environment. Secondly, to obtain high-quality control-input sequences, the improved salp swarm algorithm (ISSA) is designed as the solver for the chance-constraint DMPC, integrating the sine-tent-cosine chaotic mapping in the initialisation, the differential mutation operation in the leader-position updating and the nonlinear adaptive inertia weights in the follower-position updating. Finally, the simulation results show that the proposed method has great performance in tracking the moving target in an oilfield environment with dynamic obstacles.

To reveal the influence laws of casing abradable coating wear on compressor aerodynamic performance, a numerical simulation study was conducted on the performance of Rotor 37 under high-speed scraping conditions with different hardness coating wear morphologies. The results show that compressor isentropic efficiency and outlet mass flow are sensitive to scraping-induced morphology changes. The medium-hardness coating causes the most significant performance degradation, with maximum reductions reaching 1.96% and 1.39%, respectively, while the pressure ratio shows little variation. Scraping grooves aggravate mixing losses between leading-edge (LE) leakage flow and mid-chord (MID) leakage flow. The tip leakage flow pushes suction-side separation vortices toward the main flow path. Under medium-hardness coating scraping conditions, the maximum entropy generation region affects up to 9.1% blade height and induces tip leakage vortex-shockwave interactions, resulting in substantial tip losses. The compressor aerodynamic performance is less affected by wear zone roughness, with the maximum isentropic efficiency reduction being only 0.31%. When wear zone roughness increases, near-wall turbulence fluctuations intensify and separation zones expand, causing flow structure changes in tip leakage paths and blade wake regions, which shifts the compressor aerodynamic characteristics toward lower flow rates. The study demonstrates that coating hardness alters leakage flow structures through wear morphology depth: medium-hardness coatings with the deepest wear grooves exhibit maximum performance deterioration, while high-hardness coatings show better wear resistance and performance maintenance.

Supersonic wind tunnels are an essential tool for high-speed aerodynamics research, supporting studies ranging from fundamental flow analysis to advancements in supersonic transport. Accurately predicting tunnel performance, however, requires precise mathematical modeling. Previous models have primarily focused on plenum pressure predictions, often assuming an adiabatic process and overlooking temperature dynamics. Temperature changes during a test affect velocity and Reynolds number, influencing experimental measurements and underscoring the need to improve temperature prediction capabilities. In this paper, we develop a new model introducing two key corrections: heat addition from the thermal mass of the wind tunnel and real gas effects, particularly the Joule–Thomson effect, allowing us to capture the critical influence of temperature. Additionally, we account for pressure losses within the piping system. Comparative analysis with experimental data shows that our model reduces temperature prediction errors to within 2%, a marked improvement over the base model’s 9–13% error range. Furthermore, pressure predictions are refined, yielding more accurate assessments of plenum, reservoir and valve inlet pressures. These findings underscore the model’s utility in enhancing control system development and its broader value in advancing experimental design and operational precision in supersonic wind tunnel research.

Design optimisation of hybrid airships consisting of multi-lobed configurations is being projected as the next paradigm shift in implementing sustainable flight operations within the aeronautical industry. To that end, this paper discusses the effect of varying the relative placement of side-lobes pertaining to a tri-lobed airship hull geometry with respect to the middle-lobe. The paper makes use of a validated OpenFOAM® solver to underscore the aerodynamic impact of shifting the side-lobes in upstream, downstream, upward and downward directions with respect to the middle-lobe while retaining the same volume. These tri-lobed airship hull variants called as skewed tri-lobed hulls, have been comprehensively investigated through the usage of numerical solver (Reynolds-averaged Navier-Stokes) at high Reynolds number flow across various angles of attack. The investigations delineate significant impact of skewed side-lobes on the overall aerodynamics of the tri-lobed airships. Skewing the side-lobes in fore direction leads to drag mitigation at the expense of degraded aerodynamic efficiency owing to lift reduction. Contrarily, aft-skewness amounts to an aerodynamic efficiency enhancement of $ \approx 17{\rm{\% }}$ and improved pitch stability while marginally increasing the pressure drag liability. Aerodynamic efficiency enhancement is attributed to increased lifting force. Skewed upward and downward variants present an overall aerodynamic efficiency reduction. The paper further made use of detailed flow-field visualistion as well as pressure-coefficient distribution plots to underscore the underlying flow-physics related to aforementioned aerodynamic trends. These investigations emphasised the presence of varying three-dimensional relieving effect, intermixing between the three lobes as well as diverse flow separation characteristics downstream of the maximum diameter region leading to the aerodynamic variations thereof. The paper enhances aerodynamic understanding related to tri-lobed geometry that will be crucial in implementing future design changes to the baseline model for improved aerodynamic performance. Amalgamation of these inferences with an optimisation scheme could be implemented in future aerodynamic investigations to optimise tri-lobed geometry for enhanced aerodynamic utility.