In modern aerospace engineering, the two major factors that define mission success are larger
payload capacity and fuel efficiency. This can be gained by reducing the structural weight
of the rocket fins. The need for ultralight yet structurally sound components has increased as
launch systems move toward greater speed regimes. In this endeavor, Additive Manufactur
ing (AM) has become a crucial facilitator, enabling the creation of complex lattice structures
with remarkable stiffness-to-weight ratios. However, these structures offer specific multi-physics
design problems, including structural mechanics, high-speed aerodynamics, and manufacturing
viability.

The review systematically examines the optimization of AM lattice fins under extreme aero
dynamic and thermal environments. It compares Truss Topology Optimization (TTO) with
density-based topology optimization, emphasizing that although TTO yields superior ultra
light designs, it frequently lacks precise formulations to guarantee kinematic compatibility in
indeterminate frames. The research also emphasizes the importance of Fluid–Thermal–Structure
Interaction (FTSI), pointing out that deflection and stability estimates might differ significantly
if thermal softening effects at supersonic and hypersonic speeds are ignored.

Manufacturing limitations are also discussed, which reveals that despite being simpler to con
struct, modular lattice layouts may result in mass penalties when compared to monolithic
topologies. The research presents a unified Design for Additive Manufacturing (DfAM) frame
work that integrates TTO with temperature-dependent material degradation models, explicit
buckling limitations, and aeroelastic tailoring approaches to overcome these trade-offs. This
all-encompassing strategy aims to create high-performance, manufacturable lattice fins that can
endure coupled aerodynamic and thermal stresses without sacrificing weight economy.
In the end, this research offers a roadmap toward next-generation lightweight fin topologies that
strike a balance between structural integrity, aerodynamic performance, and manufacturability,
opening the door for rockets that can go faster, further, and more effectively.

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.

Conceptual design of small UAVs intended for tactical ISR missions is often complicated by the disconnect between early-stage aerodynamic/mission sizing and the manufacturability and field-deployment constraints that emerge later in the design cycle. This work presents an additive-first conceptual design framework that embeds those constraints from the outset. Mission-driven sizing equations based on Raymer and Sadrey are coupled with a parametric OpenVSP model, while the sizing loop is constrained by practical limits imposed by low-cost fabrication: maximum printable part dimensions, PLA’s orientation-dependent stiffness, integration of carbon-tube spars, internal placement of thin subsystems, like servos, cables, and connectors, and the detachable interfaces required for hand-launch and backpackable operation. These constraints directly affect the admissible geometry, structural layout, and mass distribution and are therefore treated as first-order design drivers rather than downstream corrections.

Structural feasibility is evaluated through finite element analysis of printed PLA components and PLA–carbon hybrid substructures, providing stiffness and failure predictions that inform the conceptual iterations. A prototype vehicle is fabricated to assess deviations between analytical, numerical, and as-built characteristics, with emphasis on structural margins, assembly tolerances, and the practical penalties introduced by modularity. Results show that including printability and field-handling constraints at the conceptual stage leads to materially different optima in wing loading, aspect ratio, and internal structural layout when compared to traditional unconstrained sizing. By integrating mission analysis, aerodynamic estimation, and manufacturability into a unified early-stage process, the framework offers a fast, practical approach for the preliminary design of low-cost, rapidly deployable UAV systems.

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.