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.

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.

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.

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.

It is well known that the amount of damage caused by lightning strikes to protected composite airframe structures depends on the paint characteristics, often applied on the surface of composite structures to protect from environmental effects and to personalise a product. In this work, physically based models of the mechanical loads induced by lightning strikes are employed in the generation of the mechanical overpressure fields due to a simulated lightning strike, while accounting for the paint thickness. These fields are then implemented into a three-dimensional finite element framework and combined with a damage model to predict the effect of paint thickness on the mechanical damage in composite structures subjected to this type of events. These models accurately predict the increase of damage extent with the increase of paint thickness, which is corroborated by experimental observations from industry and by the experimentally observed trends reported in literature.

Maintenance procedures are critically important for preserving the structural integrity, maintaining the functionality and ensuring the operational safety of aircraft. Traditional inspection techniques used in aircraft are often costly, time-consuming and prone to human mistake. Today, the opportunities provided by digitalisation and automation in aircraft maintenance and inspection processes are paving the way for innovative approaches. In this context, the use of inspection systems supported by image processing technologies has the potential to bring about a significant transformation in aircraft maintenance. Visual inspection methods integrated with unmanned aerial vehicles (UAVs) enable the rapid, accurate and repeatable detection of defects such as corrosion and cracks on the external surfaces of aircraft. This study focuses on the automatic detection and classification of defects on the external surfaces of aircraft, based on tests and analyses carried out by artificial intelligence algorithms using high-resolution data. The model developed in this study was implemented in Python in the Google Colab environment and supported by AI algorithms trained on visual data. The main objective is to investigate the feasibility of UAV-based systems for aircraft visual inspection and to provide concrete evidence of their practical applicability. In this regard, the UAV platform selected for image acquisition is intended to comprehensively scan the target areas and capture images with sufficient resolution for processing by artificial intelligence algorithms. A review of the literature reveals that UAV- and AI-based integrated approaches have been explored in only a limited number of studies related to aircraft maintenance. In this context, the present study proposes a system that enables the rapid and accurate detection of structural defects such as corrosion and cracks on the external surfaces of aircraft.

This study introduces a mission-centric design optimisation framework for unmanned aerial vehicles (UAVs) to enhance mission performance across diverse operational scenarios. The proposed framework integrates multidisciplinary design optimisation with a wargaming-based simulation environment and leverages deep neural network-based surrogate models to balance key performance metrics, such as aerodynamic efficiency, radar cross section, structural weight and payload capacity. By incorporating automated task assignment, path planning and a probabilistic combat model, the framework evaluates UAV configurations in multi-domain, multi-asset scenarios. The algorithm identifies optimal solutions that maximise mission success while managing trade-offs among survivability, lethality and cost. Simulation results illustrate the framework’s functionality through representative mission scenarios, highlighting how design variables can influence operational effectiveness relative to baseline configurations. Furthermore, the modular design approach enables rapid UAV reconfiguration for evolving mission needs, offering scalable and adaptable solutions. These findings highlight the importance of integrating mission simulation tools with advanced optimisation techniques to address challenges in dynamic, high-threat environments, providing a robust methodology for UAV and fleet design.

Hard landings are a perennial issue for airlines, resulting in lost aircraft utilisation, ground delays and landing gear damage. With the Boeing 787 series in widespread use with airlines globally, this study aims to quantify the influence of several flight parameters on the vertical load factor at touchdown for the Boeing 787 using data from the aircraft’s quick access recorder (QAR). A hierarchical regression analysis was performed on 13 variables that were grouped into three sets: (A) Aircraft and Environmental Conditions, (B) Flare Parameters and (C) Final Manoeuvres. These sets were entered sequentially to predict touchdown load factor in Gs. The final model was statistically significant (p < 0.001), explaining 14% of the variance in touchdown G. Final Manoeuvres (Set C) was the largest unique contributor, accounting for 5% of the variance. Three flight parameters were found to be significant predictors: windspeed, vertical speed at 20ft AGL and stick pitch (forward). For the latter, pitch-down control input resulted in an average increase of 0.08G compared to a stick-neutral input.

Renewed interest in supersonic air travel has prompted researchers to reconsider the design and operation of supersonic transport aircraft. Previously, such aircraft were restricted to overwater routes due to the disturbances caused by their sonic booms. Now, however, low-boom designs and overland flight at marginally supersonic Mach numbers are seen as potential enablers for widespread supersonic air travel. As a result, the trajectories that next-generation supersonic transports may fly are likely to be less constrained than for previous types, and in the last decade there has been a noticeable increase in research focusing on trajectory planning for such aircraft. This paper reviews the different methods that have been used to generate and optimise the flight paths of past and future supersonic transports. The challenges associated with optimising trajectories for aircraft that do not yet exist are discussed, and suggestions for future research activity are presented. Climate-optimal trajectory planning and development of detailed, non-proprietary supersonic aircraft performance models are identified as two key areas for future work.

This study presents a surrogate-model-assisted Quasi-Newton optimisation framework for simultaneously improving the aerodynamic performance and radar stealth characteristics of an unmanned aerial vehicle (UAV). High-fidelity computational fluid dynamics (CFD) and computational electromagnetics (CEM) simulations are integrated through surrogate models generated via a face-centred central composite design within a design of experiments framework. Quadratic polynomial response surface equations are constructed for key aerodynamic and radar cross-section (RCS) metrics, enabling analytical gradient evaluation. A gradient-based quasi-Newton method with Broyden–Fletcher–Goldfarb–Shanno Hessian updates is employed to minimise a scalarised objective function combining normalised maximum lift coefficient, overall RCS and frontal RCS. Constraints are imposed on the lift-to-drag ratio ($L/D \geq 10$) and static longitudinal stability (${C_{m0}} \geq 0$). Analytical derivatives from the response surface equations (RSEs) eliminate the need for direct numerical differentiation of CFD/CEM outputs, reducing computational cost and eliminating simulation noise. An interior-point sequential quadratic programming strategy is used to ensure satisfaction of nonlinear constraints during the optimisation process. The optimised UAV design demonstrates a $12{\rm{\% }}$ increase in maximum lift coefficient and a $30{\rm{\% }}$ reduction in both overall and frontal RCS compared to the baseline configuration. The results are confirmed through high-fidelity CFD and RCS simulations and are further validated experimentally in an anechoic chamber, with close agreement across all measured frequencies. The proposed methodology provides an efficient and experimentally verified approach for integrated aerodynamic and stealth optimisation in UAV design.

This paper presents a novel artificial intelligence-based autopilot control system designed for the Cessna Citation X (CCX) aircraft longitudinal motion during cruise. In this control methodology, the unknown aircraft dynamics in the state-space representations of each vertical speed (VS) mode and altitude hold (AH) mode were approximated by two multiplayer fuzzy recurrent neural networks (MFRNNs) trained online using a novel approach based on particle swarm optimisation and backpropagation algorithms. These MFRNNs were used with two sliding mode controllers to guarantee the robustness of both VS and AH modes. In addition, a novel fuzzy logic-based transition algorithm was proposed to efficiently switch the controller between these autopilot modes. The performance of the controllers was evaluated with a nonlinear simulation platform developed for the CCX based on data from a Level D research aircraft flight simulator certified by the FAA. The system stability and robustness were proved by the Lyapunov theorem. The simulation, tested under 925 flight conditions, demonstrated the controllers exceptional tracking capability in a variety of uncertainties, including turbulent and non-turbulent flight scenarios. In addition, the design ensured that the smoothness of the control input signals was maintained in order to preserve the mechanical integrity of the elevator actuation system.

In this paper, a sliding mode guidance law for impact angle control without violating a seeker’s field-of-view limits is proposed against targets with various motions and unknown acceleration, including stationary, constant-velocity moving and manoeuvring targets. To develop the guidance law, the kinematic conditions for engagement geometry are defined, and the sliding mode control is applied to satisfy the homing constraint and impact angle control. Then, the relation between look angle, the desired line-of-sight angle and desired impact angle is established to guarantee the target in the field of the missile’s view. The stability of the proposed approach is analysed using Lyapunov theory. Furthermore, the look angle is examined to verify the field-of-view constraint, and a capturability analysis is conducted. To evaluate the performance of the proposed law, numerical simulations demonstrate that the proposed approach achieves satisfactory miss distance and impact angle error while adhering to the field-of-view limit.

Hydrogen is a leading candidate for zero-emission propulsion in aviation, particularly when stored and utilised in its liquid form. However, key components such as composite cryogenic pressure vessels remain at low Technology Readiness Levels (TRL), requiring further investigation into their structural performance under realistic operational conditions. The present work aims to provide a validated numerical methodology for simulating the thermomechanical behaviour and the progressive damage evolution of composite cryogenic hydrogen tanks. The finite element framework incorporates ply-level failure criteria and stiffness degradation laws to capture intra-laminar damage mechanisms under combined pressure and temperature loads. The modelling approach is validated against experimental data from coupon-level open-hole tension tests and subcomponent-scale composite pipes burst tests, demonstrating strong correlation in terms of failure onset and progression.

The validated methodology is subsequently applied to a demonstrator, comprising a composite liquid hydrogen tank, subjected to three representative loading scenarios: internal pressure, cryogenic temperature and combined cryogenic-mechanical loading. Results reveal that matrix-dominated damage initiates near the cylinder – dome interfaces of the tank and propagates across the laminate, while fibre failure is not observed in the investigated load cases. This suggests that potential hydrogen leakage is the initial critical failure condition that occurs before any other important structural damage of the tank, highlighting the need for appropriate tank design. The performed study contributes to the understanding of structural integrity of composite cryogenic tanks and offers a computational basis for future design and certification efforts in hydrogen aviation systems.