This paper presents an approach based on an intelligent fault-tolerant (FT) mechanism for enhancing a monopulse auto-tracking architecture through the integration of machine learning (ML) techniques. This approach is applied for the case of Oran ground station (GS) communicating in the S/X band frequency range to ensure system robustness during faulty events. The latter are due to the degradation or sudden failure of neuralgic radio frequency (RF) components, with an emphasis on the low-noise amplifier (LNA). To address these vulnerabilities, a probabilistic likelihood function is defined, and its statistical parameters are optimised using the grey wolf optimiser (GWO) in order to provide a means of automatic switchover to the ML tracking signal receiver (ML-TSR) control mode upon anomaly detection. Three ML algorithms, namely eXtreme Gradient Boosting (XGBoost), decision trees, and K-nearest neighbors (KNN), were implemented and evaluated using a database spanning eight years (2017–2024), including the operational parameters of the ALSAT-1B GS. The experimental results show that the KNN model achieves higher accuracy than the other approaches. A validation process was then conducted during an actual satellite pass encompassing a nominal phase followed by a GS LNA failure phase. During the nominal phase, a root mean square error (RMSE) of only 0.46° in azimuth and 0.64° in elevation was observed. Activation of our recovery mechanism during the second phase demonstrated that the robustness of the likelihood function, combined with the accuracy of the ML-TSR controller, ensures mission continuity without data loss, leading to four successful image downloads.

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.

Liquid hydrogen (LH2) is a promising energy carrier to decrease the climate impact of aviation. However, the inevitable formation of hydrogen boil-off gas (BOG) is a main drawback of LH2. As the venting of BOG reduces the overall efficiency and implies a safety risk at the airport, means for capturing and re-using should be implemented. Metal hydrides (MHs) offer promising approaches for BOG recovery, as they can directly absorb the BOG at ambient pressures and temperatures. Hence, this study elaborates a design concept for such an MH-based BOG recovery system at hydrogen-ready airports. The conceptual design involves the following process steps: identify the requirements, establish a functional structure, determine working principles and combine the working principles to generate a promising solution.

Engine health monitoring in the aeronautical domain is crucial. Aircraft engines operate under crucial conditions, and their failure can have major safety and operational consequences. The prognostic and health management (PHM) of engines plays an important role in keeping the operation of engines steady and secure. The main purpose of PHM is to predict future machinery faults by estimating their remaining useful life (RUL). To do so, PHM methods could apply machine learning methods using monitoring data to predict industrial machinery RUL. However, these predictions could differ from one source (predictor) to another, which is not totally reliable. In addition, this imperfection is due to data imprecision that needs to be managed. In this investigation, the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset is used, as engine health monitoring is highly critical and has a great impact on the maintenance of aeronautical systems. To tackle this challenge, we combine the process of studying sliding windows on time series data and taking advantage of the integration of learning algorithms. Specifically, we merge the convolutional neural network (CNN) with multi-layer perceptron, and CNN with long short-term memory. To further enhance the performance, we employ an ensemble learning approach to manage imprecision by applying fuzzy concepts embedded within the adaptive neuro-fuzzy inference system model and a decision tree as a meta-learner. Experimental errors are reduced compared to predictions offered by hard prediction models.

The effect of tab orientation on the mixing characteristics of a Mach 1.6 circular jet issuing from a convergent-divergent (C-D) nozzle is studied experimentally. The tabs used in this study are triangular tabs with sharp and truncated vertices positioned upright and in inverse orientations, along diametrically opposite locations at the nozzle exit. Mixing characteristics of the jet operated at nozzle pressure ratios (NPRs) 3 to 6, in step 1, were studied. For inverse triangular and inverse truncated triangular tabs, a maximum core length reduction of about 87% is achieved at NPR 3. The corresponding core length reductions caused by the upright triangular and truncated triangular, and rectangular tabs are 71, 81 and 84%, respectively. It is found that the jet mixing is strongly influenced by the combined effect of tab geometry, tip effectand the pressure gradient at the nozzle exit. The pressure distribution in the directions along and perpendicular to the tab, at different axial locations, was used to discern the evolution and spread of the jet. The pressure profiles for all tab geometries show that the jet spread perpendicular to the tab is more than that along the tab at all the NPRs studied. The shadowgraph images of the jets show that the waves present in the controlled jet are weaker than those in the uncontrolled jet.

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.

Unstable approaches contribute significantly to accidents during the critical approach and landing phases of flight, many of which could have been prevented by executing a go-around. This review investigates cognitive lockup, a tendency to adhere to task completion despite shifting priorities, and its role in aviation incidents. Specifically, we explore the psychological underpinnings of cognitive lockup, the influence on pilot decision-making and potential mitigation strategies. We examine factors such as task completion bias, framing effects and the perceived cost of task switching, and provide recommendations for training and policy modifications to reduce cognitive lockup. Aviation safety in critical flight phases can be improved through enhanced pilot training, mindfulness techniques, positive policy framing and AI-based alert systems.

A conventional 1D beam structural model from numerically obtained cross-sectional data was compared to a 2D/3D finite element (FE) model from geometry and material data for weakly fluid and structure-coupled rotor blade analysis. The commercial structural solver MSC NASTRAN was used to compare the static and dynamic structural properties of both approaches. Model blades with available experimental data were used to verify the employed computational framework. Piezoelectric actuator patches for active control were modelled with FEs, using a thermal analogy method. A framework for direct loads and deformation interpolation between structural and fluid flow solvers was used. This achieved a high-fidelity simulation of the aerodynamic, structural and servo-structural components. Degradation of actuator effectiveness under centrifugal force was demonstrated. Hovering rotor results for beam and FE method (FEM) models are shown, building towards an accurate simulation of periodic and non-periodic flight conditions with 3D piezoelectric structural models in the near future. FEM–CFD coupling will accelerate blade design by additionally considering the structural stresses in the simulation phase and the potential integration of on-blade actuators and sensors.

Aviation has a significant contribution to climate change, which is poised to increase in the coming years due to increasing demand in air travel. The A321 APPU aircraft could offer a significant improvement as it offers a synergistic combination of two interesting technologies – a fuel-flexible hydrogen combustion system combined with boundary layer ingestion, by introducing a hydrogen-powered auxiliary power and propulsion unit (APPU). This turboshaft engine is located in the tail cone and powers a boundary layer ingestion propulsor, producing approximately 15% of the thrust. To improve the efficiency of the APPU, the feasibility of the steam ijection and recovery (SIR) cycle is evaluated. This semi-closed water cycle can reduce fuel consumption and NOx emissions. Both the baseline and the SIR APPU are modelled in pyCycle, an open-source gas turbine parametric analysis tool. The baseline APPU engine was found to have a thermal efficiency of 45% and a mass of around 500 kg. The SIR cycle can reduce fuel consumption by up to 7% and decrease NOx emissions by approximately 33%, with an increase in engine mass of approximately 15%.

This paper provides a preliminary investigation into the use of a novel passive aircraft flight loads alleviation device called the Superelastic Monostable Spoiler. The work focuses primarily on understanding the behaviour of such a device and the related loads alleviation performance during dynamic gust events. A number of different design parameters are explored, such as the trigger condition and the activation speed. The main aim of the paper is to define the preliminary operational requirements of such a device in order to guide the future detailed design, which is not addressed here. It was found that the Superelastic Monostable Spoiler could potentially provide loads alleviation performance comparable with typical gust loads alleviation technologies currently used in modern civil aviation based on the use of ailerons and spoilers.

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 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.

The aeroelastic behaviour of a flapped-wing with freeplay and reduced stiffness is highly nonlinear. The optimal control of a nonlinear system is desired to optimise a given structural performance. In this paper, two novelties (a new method to solve state dependent Riccati equation, and inclusion of damage effects on aero-servo-elastic system) are developed to optimally control the nonlinear aeroelastic behaviour of a flapped-wing section including freeplay and reduced stiffness. To design the optimal controller, the State Dependent Riccati Equation (SDRE) is utilised based on a combination of the Hamiltonian matrix and the Schur method. A three degree of freedom (DoF) aeroelastic wing structure with a control surface is mathematically modelled, including freeplay in control surface, cubic nonlinear spring for description of the torsional stiffness and reduced stiffness factor in torsional spring due to damage. The effect of freeplay, reduced stiffness and concentrated nonlinearity in torsional spring are analysed on aeroelastic response. The system response is determined by time marching of the governing equations using a Matlab code. Various simulations results for multiple flow velocities and nonlinear parameters prove the effectiveness of this control method in flutter suppression. It is also shown that the control surface freeplay leads to limit cycle oscillation at speeds less than flutter speed. Furthermore, the simulation results show that the presence of a damage – which reduces the stiffness in torsional spring – leads to an increase in the oscillation amplitudes.

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.

A transition control methodology using hybrid laminar flow control (HLFC) for drag reduction for civil transport aircraft is described. An HLFC concept with a single suction chamber with varying porosity can be integrated into the leading-edge wing structure, with the wing ice protection system and high lift system. The resultant structural system is less complex than the multi-chamber concept, and with weight saving.

A turbulent type of pressure distribution has been applied to a civil transport retrofit HLFC wing design resulted with a higher aerodynamic performance than a favourable gradient type of pressure distribution. A turbulent type of pressure distribution also has the potential of being able to retain the performance of a conventional aircraft in the case of suction system failure.

An analysis of wind tunnel transition data with linear stability analysis method is described. Detailed measurements of the tunnel environment and fluctuations within the boundary layer are required to provide a better understanding of the physics of transition and data for non-linear methods.

A wind tunnel testing capability developed for measuring the effect of small features on transition in natural laminar flow could be extended to HLFC to investigate the effect of suction on the disturbances due to various degree of surface imperfections. A high Reynolds number wind tunnel simulation technique using surface suction to thin the turbulent boundary layer has been demonstrated theoretically and experimentally for a turbulent flow wing design. A theoretical investigation has shown that this technique could be extended to an aircraft with a HLFC wing design.

In flight safety monitoring, risk prediction demands high accuracy for rare but critical events such as engine failures and weather-related hazards. However, the limited sample size of such extreme cases within a single dataset poses a significant challenge to achieving precise predictions. Aggregating data across sources is a potential solution, but datasets are often not independently and identically distributed. Transfer learning can improve target performance by leveraging related sources, yet classical methods struggle with heavy-tailed data due to the lack of robust loss functions. This paper proposes a transfer learning algorithm based on composite quantile regression (CQR). It identifies and transfers information from similar sources and constructs pseudo response variables to linearise the CQR loss, enabling robust computation under heavy-tailed noise. Extensive simulation studies demonstrate the superior performance of the proposed method. The practical utility of the algorithm is further illustrated through its application to quick access recorder (QAR) flight data for hard landing risk prediction. This work aims to identify and quantify key controllable factors influencing landing safety through a data-driven approach. Our research provides quantitative analytical tools for multiple core safety aspects, including flight operation standardisation, aircraft condition monitoring, operational environment assessment and support resource allocation, thereby enabling more precise and proactive risk mitigation strategies.

Airbreathing waveriders often use the fuselage forebody as the pre-compression surface of the inlet, which tends to encounter complex internal-external flow coupling issues. First, the osculating cone method is employed, and a wide-speed-range airbreathing waverider is designed by partitioning it into the waverider forebody, elongated body and waverider aft body, achieving full waverider characteristics. Next, the configuration is optimised to address the internal-external flow coupling issue. The calculations show that the optimised configuration improves the lift-to-drag ratio by more than 20% and the total pressure recovery coefficient by more than 30% in both operating conditions compared to the baseline configuration. Finally, data mining techniques are applied to analyse the data from the optimisation process. It reveals the interdependent relationship between the vehicle’s internal and external flow performance, with the cone shock wave angle and wing extension line length having the most significant impact on aerodynamic performance, thereby generating design knowledge. The content of this paper covers configuration design, optimisation and data mining. The entire process is highly generalisable and can serve as a reference for other aircraft configuration design optimisation tasks. The resulting design knowledge can also provide valuable insights for researchers in future airbreathing waverider designs.

Area coverage optimisation is a hotspot in cooperative interception research for highly manoeuverable targets. In this paper, a geometric coverage-based fast approach is proposed to rapidly calculate cooperative interception regions for interceptors, achieving full coverage of the target predicted manoeuvering escape area. The method analytically derives both the required number of interceptors and the centre coordinates of each interceptable region. Simulation results verify the correctness and effectiveness of the proposed method. It demonstrates that for highly manoeuverable targets, this cooperative interception region calculation method can stably achieve full coverage of predicted manoeuvering escape areas while significantly reducing computation time, exhibiting excellent real-time performance.

The accuracy, robustness and affordability of localisation are fundamental to autonomous robotic inspection within aircraft maintenance, repair and overhaul (MRO) hangars. Hangars typically have high ceilings and are predominantly steel-framed structures with metal cladding. Because of this, they are regarded as GPS-denied environments, characterised by significant multipath effects and strict operational constraints, which together form a unique challenging setting. The lack of comparative techno-economic benchmarks for localisation technologies in such environments remains a critical gap. Addressing this, the paper presents the first techno-economic analysis that benchmarks motion capture (MoCap), ultra-wideband (UWB) and a ceiling-mounted camera (CMC) system across three operational scenarios: robot localisation, asset monitoring and surface defect detection within a single-bay hangar. A two-stage optimisation framework for camera selection and placement is introduced, which couples market-based camera-lens selection with an optimisation solver, producing camera layouts that minimise hardware while meeting accuracy and coverage targets. The consolidated blueprints provide quantification of the required equipment and its performance: 15 global-shutter GigE cameras are adequate for drone localisation, 9 cameras meet the requirements for on-bay monitoring and 49 high-resolution cameras facilitate defect mapping of the upper airframe surfaces for midsize defects. Across these scenarios, the study reports indicative performance and cost envelopes: a MoCap installation delivers submillimeter localisation at an estimated £190k per bay, UWB delivers centimetre-level tracking for around £49k and the proposed CMC system layouts achieve task-specific coverage with costs in the £9k–£77k range. The analysis equips MRO planners with an actionable method to balance accuracy, coverage and budget, demonstrating that an optimised CMC system can deliver robust and cost-effective sensing for next-generation smart hangars.

The conceptual design of mission-tailored aircraft is increasingly shifting towards system of systems (SoS) perspectives that account for system interactions using a holistic view. Agent-based modelling and simulation (ABMS) is a common approach for analysing an SoS, but the behaviour of its agents tends to be defined by rigid behaviour trees. The present work aims to evaluate the suitability of a prompt-engineered large language model (LLM) acting as the Incident Commander (IC), replacing the fixed behaviour trees that govern the agents’ decisions. The research contributes by developing a prompting framework for operational guidelines, constraints, and priorities to obtain an LLM commander within a wildfire suppression, SoS capable of replicating human decisions. By enabling agents in a simulation model with decision-making capabilities closer to those expected from humans, the commander’s decisions and potential emergent patterns can be translated into more defined requirements for aircraft conceptual design (ACD) (e.g., endurance, payload, sensors, communications, or turnaround requirements). Results showed that an LLM commander facilitated adaptive and context-aware decisions that can be analysed via decision logs. The results allow designers to derive aircraft requirements for their specific roles from operational outcomes rather than a priori assumptions, linking SoS mission needs and ACD parameters.