Reliability assessment in complex technical systems often involves capturing the interdependency between multiple subsystems and the gradual loss of their functional effectiveness. This challenge becomes particularly critical in the context of unmanned aerial vehicles (UAVs), where sustained operational capability is essential for the safe execution of autonomous missions. This work presents a state-based methodology to estimate the future mission capability of UAVs subject to progressive component degradation. To generate a representative dataset, numerous simulated flight missions are conducted with randomly varying degradation profiles across multiple actuators. A hidden semi-Markov model (HSMM) is trained on this data to characterize the progressive reduction in system performance over time. To improve model tractability and generalization, raw flight data is first reduced to a concise set of performance-related parameters, from which critical sensor signals—such as roll, pitch, yaw, horizontal and vertical airspeeds, and current consumption—are estimated. The proposed approach enables the identification of system-wide dependencies between degradation patterns and mission-relevant behavior. By linking current operational states to the likelihood of meeting future performance requirements, it offers a quantitative basis for predictive reliability assessment. Looking ahead, this framework may support extensions toward forecasting sensor trends and in-flight anomaly detection, contributing to more anticipatory and resilient UAV operations.

The safe and accurate measurement of liquid hydrogen (LH₂) tank fill levels is a critical enabling technology for the adoption of hydrogen as a sustainable aviation fuel. Although LH₂ level measurement techniques have been applied in industrial, automotive, and space applications, no integrated system has yet been validated at the scale, robustness, and precision required for modern aircraft Fuel Quantity Indication Systems (FQISs).

Differential pressure sensors are commonly employed in industrial cryogenic systems and hydrogen refueling stations; however, their accuracy is strongly influenced by dynamic effects such as filling transients and liquid sloshing, rendering them unsuitable for aviation-grade FQIS requirements. While simulations and analytical studies propose alternative LH₂ level sensing concepts, experimental validation and direct comparative assessments of different sensor architectures remain scarce. Furthermore, although several manufacturers offer LH₂ level sensors, their stated measurement accuracies have not been independently verified, highlighting the need for systematic experimental investigation under representative operating conditions.

In this work, five liquid level sensing concepts based on measurements of dielectric constant, thermal capacity, and optical absorption are experimentally evaluated. Cryogenic tests are conducted using liquid nitrogen as a representative surrogate for liquid hydrogen. The results demonstrate that optical absorption-based approaches are unsuitable for reliable cryogenic liquid level measurement. In contrast, capacitive probes and discrete resistive thermal sensors exhibit robust and repeatable performance under cryogenic conditions, achieving measurement accuracies better than ±1.5 mm. These findings provide experimentally grounded guidance for the development of future LH₂-compatible FQIS architectures for aviation applications.

Leading-edge slats play a critical role in aircraft lift generation. However, their mobility and comparatively small dimensions make the integration of efficient de-icing systems particularly challenging. This study presents an integration concept for an electro-impulse de-icing system based on copper coils. The coils generate an inductive pulse that produces a localized deflection of the outer skin and initiates a wave propagating in both the spanwise and chordwise directions. The resulting shear stresses at the ice–skin interface cause the ice to detach.

The system is first implemented in a simplified demonstrator featuring constant curvature and good accessibility. Initial test campaigns focus on optimizing key parameters such as doubler thickness, capacitor voltage, and coil spacing. A high-speed digital image correlation system is employed to analyze the spatial propagation of the induced wave. Additionally, a limited fatigue test is conducted to assess the system’s operational reliability.

In the subsequent phase, the electro-impulse de-icing system is integrated into a scaled slat. To ensure optimal pulse transmission, the copper coils are shaped to match the wing contour. De-icing tests are performed using the recommended values for liquid water content (LWC) and droplet diameter (MVD) specified in NACA Technical Report 1855. The results show extensive de-icing along the leading-edge region. The de-iced surface areas are weighted according to their contribution to the lift distribution to evaluate the effectiveness of different de-icing scenarios.

The findings demonstrate that the proposed electro-impulse de-icing system is highly effective and operates significantly more efficiently than conventional electrothermal or bleed-air de-icing systems.

Ice accretion on high-lift systems reduces lift, increases drag and can lead to unsafe operating conditions. In conventional aircraft, ice protection is usually provided by hot bleed air from the engines. However, this method is energetically costly and not feasible in more-electric or fully electric aircraft. Therefore, ice protection systems for more-electric and fully-electric aircraft must work with bleedless systems and limited power available. In this context, hybrid de-icing concepts that combine electrothermal heating with electromechanical excitation are a promising option.
This work presents a simulation strategy for a hybrid de-icing system for a slat that incorporates an electro-impulse de-icing (EIDI) system alongside structural electrothermal heating. In this framework, ice shedding is modelled using a temperature-dependent cohesive zone formulation at the ice–structure interface combined with a brittle cracking model in the ice, representing crack initiation and fragmentation. Embedded resistance heaters are represented by prescribed heat input in defined zones, generating transient temperature fields. These fields, together with temperature-dependent material properties, link the thermal and mechanical analyses.
The simulations demonstrate that the hybrid concept resolves the conflict between thermal and mechanical de-icing methods. Modest, localized electrothermal heating reduces ice adhesion, enabling EIDI to achieve near-thermal shedding performance using less energy than thermal-only operation. Further improvements in efficiency are achieved through the selective activation of heater zones, which reduces the required impulse levels and structural loading while avoiding the very high energy input of fully thermal systems and the limited de-icing of purely mechanical approaches. Overall, the framework provides a basis for designing and optimizing hybrid ice protection systems in terms of energy demand and de-icing efficiency. This supports the integration of effective ice protection systems into more- and fully electric aircraft.

Composite aircraft panels play a critical role in the structural efficiency of modern airframes, particularly during early design stages where laminate selection decisions have long-term implications. While stiffness requirements are typically evaluated under individual load cases, aircraft panels are in practice subjected to combined mechanical and thermal loads, making single-load assessments insufficient for informed structural design decisions.

In this work, a design-oriented numerical study is presented to evaluate the stiffness response of a flat composite aircraft panel under multiple representative loading conditions, including in-plane compression, transverse pressure and uniform thermal loading. A set of symmetric laminate layups commonly employed in aerospace structures is examined using finite element analysis (FEA). For each load case, displacement-based stiffness metrics are formulated using normalized reference quantities, enabling consistent comparison between different layup configurations independently of the applied load magnitudes.

The individual stiffness responses are subsequently combined within a multi-objective framework to identify trade-offs between competing structural requirements. Pareto fronts are constructed to highlight laminate configurations that offer balanced stiffness performance across multiple loading scenarios, supporting rational layup selection from an aircraft structural design perspective. The results indicate that laminate designs optimised for a dominant load case may exhibit unfavourable behaviour when assessed under combined loading, underscoring the importance of multi-load considerations at the preliminary design stage.

The proposed approach provides a transparent and computationally efficient methodology for comparative assessment of composite aircraft panels and is well-suited for early structural design studies. The framework can be readily extended to more complex configurations, such as stiffened panels or coupled buckling analyses, in future work.

Borescope inspection of aircraft engine high-pressure compressors (HPCs) is safety-critical yet remains highly manual because inspection footage is noisy (blur, glare, occlusion), viewpoints vary across blade regions, and decisions must be traceable for audit. While deep learning has been applied to aero-engine defect detection, most studies focus on curated single-frame “snapshot” evaluation and accuracy, with limited attention to workflow-level decisions, evidence traceability (frames/timestamps), shortcut learning from on-screen UI overlays, and generalisation under real inspection view shift. This research proposes an audit-friendly decision support approach for HPC borescope inspection that outputs stage/segment-level decisions (AUTO_PASS/ NEEDS_REVIEW/AUTO_FLAG) together with evidence frames and timestamps. Using borescope videos from seven engines, covering early, mid, and late segments of the HPC, we construct a real-workflow dataset, exclude frames with unmaskable overlay states, and evaluate generalisation with engine-holdout, stage/segment-holdout, and view-holdout protocols. As a baseline, we train a ResNet-18 classifier using stage-level defect labels, then develop an event-window evidence model that learns from temporally localised defect windows and aggregates Top-K supporting frames (with timestamps) to form stage/segment-level decisions. UI overlays are neutralised to reduce shortcut learning, and leakage control is verified via UI-only “cheat tests.” Across engine-holdout folds, the proposed evidence-frame approach is expected to deliver high defect sensitivity with improved specificity compared with stage-label baseline, by leveraging temporally localised event learning and Top-K evidence aggregation to suppress UI- and noise-driven false positives. Robustness will be assessed by the stability of stage/segment-level decisions under realistic degradations, with low flip rates anticipated at the chosen operating point. In view-holdout transfer (Root-to-Platform vs. LE/Upper-Blade-to-Tip) and stage/segment-holdout, a measurable performance shift is anticipated, motivating explicit viewpoint validation when claiming inspection coverage. Overall, this work reframes borescope AI from image classification to audit-ready decision support aligned with real inspection workflows.

As Unmanned Aerial Vehicles (UAVs) are increasingly deployed in complex, cluttered, and dynamically changing environments, conventional Euclidean control architectures based on Euler-angle parameterizations exhibit fundamental limitations. These include representation singularities, gimbal lock, and degraded control performance during aggressive maneuvers such as high-speed turns, rapid attitude changes, and near-hover transitions under actuator saturation. To address these challenges, this paper proposes a unified navigation and control framework that models the multi-rotor UAV configuration directly on the Special Euclidean group SE(3). By exploiting the intrinsic geometric structure of the configuration manifold, the proposed formulation provides a singularity-free representation of coupled translational and rotational dynamics, thereby avoiding the drawbacks associated with local attitude parameterizations and under-actuation-induced singularities. Building upon the SE(3) modeling framework, an SE(3)-consistent Model Predictive Control (MPC) scheme is developed for trajectory tracking and obstacle-aware navigation. The proposed MPC explicitly incorporates physical and operational constraints, including actuator saturation limits, vehicle dynamics, and safety margins with respect to static and dynamic obstacles. This allows the controller to generate dynamically feasible control inputs while maintaining robustness in challenging flight regimes. The overall framework is implemented and evaluated using high-fidelity simulations in the MATLAB/Simulink UAV Toolbox, enabling realistic modeling of aerodynamics, sensing, and actuation. Simulation results obtained across aggressive, high-curvature flight scenarios demonstrate that the proposed geometric MPC approach achieves improved tracking accuracy, smoother control inputs, and enhanced robustness compared to conventional cascaded position–attitude control architectures. Furthermore, the computational performance remains suitable for real-time receding-horizon execution. These results indicate that geometric manifold-based control provides a principled and effective foundation for constraint-aware autonomy and reliable sim-to-real transfer in advanced multi-rotor UAV applications.

Hall effect thrusters (HETs) exhibit electron cross-field mobility 10–100 times above classical predictions, yet no single mechanism consistently explains performance across the 1–100 kW range. This work presents a fully predictive, parameter-free phenomenological transport model that unifies three dominant anomalous channels into a single effective collision frequency:

ν_eff = α_B/(16B) + ν_NWC + ν_trans

The Bohm term employs a theoretically derived α_B ≈ 1/32 arising from quasi-linear saturation of azimuthal E×B drift waves damped by ion-sound waves in the near-plume, eliminating empirical 1/16–1/100 fudge factors. Near-wall conductivity ν_NWC is computed self-consistently using velvet-regime secondary electron emission yields (γ_SEE ≈ 0.55 at 150–300 eV for BN) and exact channel geometry. The novel plasma-wall transition term ν_trans accounts for pre-sheath expansion via a generalized Boltzmann relation incorporating κ-distributed non-Maxwellian tails (κ ≈ 5–8), which naturally limits axial conductivity when the Debye length approaches channel half-width.

The resulting closed-form mobility reproduces the breathing-mode frequency (within 8%), discharge current oscillations (amplitude and shape), and thrust efficiency of the SPT-100 (1.35 kW), NASA-300M (9 kW), and nested-channel X3 (up to 102 kW) with errors below 3% across three orders of discharge power. Most importantly, the model predicts—without adjustment—the observed abrupt efficiency collapse above 70 % channel utilization in high-power nested designs, directly linking it to pre-sheath choking of NWC. This framework delivers transparent scaling laws and design guidelines for next-generation >50 kW HETs required for lunar gateways and Mars cargo missions.

Improving the efficiency of drone propulsion is crucial in extending flight times and cutting down on energy losses during dynamic maneuvers. Traditional Brushless DC (BLDC) motor controllers depend on mathematical models and reactive adjustments, which often struggle to keep up with sudden changes in load, wind disturbances, and fluctuations in battery voltage. To tackle these challenges, this study presents an innovative control strategy that merges Model-Free Adaptive Control (MFAC) with a bio-inspired hybrid optimization technique inspired by Eel Foraging and Gooseneck Barnacle behaviors. Unlike model-based methods, MFAC continuously fine-tunes its control actions using real-time sensor data, eliminating the need for motor parameters and making it robust against unpredictable operating conditions. The hybrid optimization algorithm enhances this adaptability by swiftly identifying the most energy-efficient control inputs that ensure necessary thrust while minimizing switching losses, torque ripple, and power consumption. Experimental tests with a 1000 KV BLDC motor show significant improvements in torque–speed stability, rapid convergence in controller tuning, and a marked reduction in power demand during critical drone flight modes like hovering and ascending. These findings suggest that combining MFAC with bio-inspired optimization paves the way for developing high-efficiency UAV propulsion systems that can achieve longer endurance, reliable real-time control, and less reliance on complex motor models.

Hail impact poses a critical threat to the structural integrity of aircraft composite skins, with its severity influenced by changing climatic conditions and increasing aircraft operating speeds. In this study, a combined experimental and numerical investigation is undertaken to examine the impact response of Carbon Fibre-Reinforced Polymer (CFRP) laminates subjected to hail-ice impact, with the objective of improving the understanding of damage behaviour in aerospace composite structures. Symmetric CFRP laminates of approximately 3 mm in thickness, representative of typical aircraft skin configurations, are considered for the investigation. Controlled hail-ice impact tests are conducted at a specified impact velocity to simulate realistic hail strike conditions, and repeated impact events are considered to evaluate the influence of cumulative damage.

Post-impact damage assessment is carried out using ultrasonic C-scan inspection and optical microscopy to identify internal damage modes such as delamination, matrix cracking, and fibre failure. In parallel, numerical simulations are performed using the explicit finite element solver LS-DYNA to replicate the experimental impact conditions. The numerical model enables the evaluation of deformation behaviour, stress distribution, and energy transfer during the impact event, thereby providing insight into the progressive damage mechanisms of the CFRP laminate. The combined experimental and numerical framework presented in this work aims to support the development of improved composite layup designs and validated predictive methodologies for assessing hail impact tolerance in future aircraft applications.