Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract This study proposes a radial basis function neural network disturbance observer- (RBFNNDO) based anti-saturation backstepping controller for hypersonic vehicles with input saturations and multiple disturbances. Firstly, in response to the problem of ‘exploding complexity’ in backstepping controller, we adopt finite-time tracking differentiators (FTD), which realise higher tracking accuracy and tracking speed than those of the existing methods. Secondly, we develop multivariable neural network disturbance observers to estimate the lumped disturbances involving aerodynamic uncertainties and external disturbances, thereby improving the robustness of the proposed controller. Thirdly, in order to alleviate the input saturation and minimise the duration time, we use an adaptive fixed-time anti-saturation compensator (AFAC). The simulation results have proven that our proposed backstepping controller outperforms other existing methods in terms of control performance and saturation time.

Abstract Strategic trajectory planning (STP) is critical for improving flight efficiency and ensuring operational safety, particularly in large-scale flight operations. Given the long lead time of STP, accurately analysing wind forecast uncertainty is essential to enhancing the quality of planned trajectories. However, most existing research overlooks the time-variant nature of wind forecast uncertainty. This may lead to significant discrepancies between planned and actual flight trajectories, increasing operational costs and conflict risks. Therefore, this paper proposes a novel bilevel STP framework for large-scale flights that explicitly accounts for time-variant wind forecast uncertainty. The upper-level model optimises trajectories across multiple flights to minimise total flight time, based on the departure times determined by the lower-level model. The lower-level model mitigates potential conflicts by adjusting the departure times according to the trajectories selected by the upper level. To solve this problem efficiently, a time-variant A * algorithm (TVA * ) and a multi-objective cooperative co-evolution algorithm (MOCCEA) are developed, supported by static expectation (SE) and dynamic equilibrium grouping (DEG) strategies to accelerate computation. Experimental results confirm that the proposed method yields consistently dominant Pareto fronts, significantly enhancing flight efficiency while ensuring operational safety and fairness.