Abstract: The module division scheme of commercial aircraft and other complex system products has a significant impact on the functionality, performance, and cost of the aircraft. To obtain a scientifically sound and rational module division scheme for commercial aircraft, this paper establishes a comprehensive evaluation method for the module division scheme of commercial aircraft based on the Analytic Hierarchy Process (AHP) and Grey Fuzzy Evaluation Theory. A model combining AHP and Grey Fuzzy Comprehensive Evaluation is developed. This method is applied to assess the module division scheme of the forward fuselage structure of a commercial aircraft. The results indicate that the AHP-Grey Fuzzy Comprehensive Evaluation method can yield an optimal module division scheme for the forward fuselage structure, thereby validating the scientific and rational nature of the module division evaluation method for commercial aircraft. The research findings reveal a scientific approach to the module division of complex system products and offer new insights into the module division schemes of such products. Although there are limitations, the results of this study hold significant implications for both theory and practice.

Abstract: This paper presents a neural network-based path planning method for fixed-wing UAVs under terminal roll angle constraints. The nonlinear optimal path planning problem is first formulated as an optimal control problem. The necessary conditions derived from Pontryagin’s Maximum Principle are then established to convert extremal trajectories as the solutions of a parameterized system. Additionally, a sufficient condition is proposed to guarantee that the obtained solution is at least locally optimal. By simply propagating the parameterized system, a training dataset comprising at least locally optimal trajectories can be constructed. A neural network is then trained to generate the nonlinear optimal control command in real time. Finally, numerical examples demonstrate that the proposed method robustly ensures the generation of at least local optimal trajectories in real time while satisfying the prescribed terminal roll angle constraint.

Abstract: Liquid hydrogen (LH2) and 100% synthetic paraffinic kerosene (SPK), or sustainable aviation fuel (SAF), offer promising low-emission alternatives to conventional Jet-A fuel for long-distance flights—assuming they are produced through processes that support net-zero well-to-wake (WTWa) emissions. This study assesses the WTWa performance, including non-CO₂ effects, of a blended wing body aircraft designed to carry 300 passengers over a range of 13,890 km, using either LH2 or 100% SPK. The analysis quantifies emissions during the operational phase and evaluates fuel production impacts using the GREET model. Results from over 100 fuel production pathways show that LH2 can deliver net-zero or even negative WTWa CO₂-equivalent emissions when derived from biomass or produced via integrated fermentation combined with carbon capture and storage. Non-CO₂ emissions are found to be a major contributor to overall climate impacts. Using miscanthus as a feedstock, 100% SPK reduces WTWa CO₂-equivalent emissions by 70–85% relative to Jet-A. A high-level supply assessment suggests that by 2050, production of SAF and hydrogen could satisfy the energy needs of long-haul aviation, assuming a 4% annual increase in air traffic and widespread adoption of these alternative fuels. These findings offer valuable direction for future research, cost assessments, and policy-making aimed at enabling sustainable long-haul air travel.

Abstract: Liquid hydrogen (LH2) and 100% synthetic paraffinic kerosene (SPK), or sustainable aviation fuel (SAF), represent viable alternatives to conventional Jet-A for long-haul aviation, provided they are produced via pathways enabling net-zero well-to-wake (WTWa) emissions. This study evaluates the WTWa performance, including non-CO2 emissions, of a blended wing body aircraft (300 passengers, 13,890 km range) powered by either LH2 or 100% SPK. Use-phase emissions are quantified, and fuel production impacts are assessed using the GREET model. Analysis of over 100 production pathways reveals that LH2 can achieve net-zero or negative WTWa CO2-equivalent emissions when produced from biomass or integrated fermentation with carbon sequestration. Non-CO2 emissions are shown to contribute significantly to WTWa impacts. When miscanthus is used as a feedstock, 100% SPK reduces WTWa CO2-equivalent emissions by 70–85% compared to Jet-A. A high-level supply analysis indicates that SAF and hydrogen production in 2050 could meet the energy demands of long-haul aviation, assuming a 4% annual traffic growth rate and full adoption of these fuels. These findings provide critical insights to guide R&D investments, fuel cost analyses, and aviation policy development for sustainable long-haul aviation.

Abstract: Corrosion in combination to fatigue cycling loading is inevitable and becomes a challenging problem even though selection of inherently corrosion protected materials has been applied based on established in house experience. Aero engine mount structures are exposed to dust and salt environmental conditions both during operational and non-operational periods. Predictions of remaining useful corrosion fatigue life are becoming tough due to the materials strength degradation5 from the service condition and hence rationalized approach is being currently used to assess their structural integrity. This paper brings a novel approach to predict the corrosion fatigue by proposing random parameter model in combination to experimental data. The two random parameter model with probability method is employed here to determine the time-independent corrosion fatigue life of magnesium structural casting used heavily in mount structures. The same is also correlated with experiments data from literature for validating the proposed stochastic corrosion fatigue model to address the technical variance occurred during in service.

Abstract: Existing stellar map simulators lack color temperature information, have a complex system structure, and cannot independently control the color temperature of stars. Therefore, this study developed an OLED-based semi-physical simulation method and a simulation algorithm for the stellar map with color temperature information to realize a semi-physical simulation of the stellar map close to the real situation in space. The study also aimed to independently control the color temperature of each star. The simulation effect of the stellar map with color temperature information was verified using four stellar maps. The developed simulator achieved independent and controllable color temperature information for each star in the stellar map.

Abstract: High-Test Peroxide (HTP) monopropellant thrusters are being considered for spacecraft lander missions due to their simplicity and reduced toxicity compared to traditional propellants. Pulse-Width Modulation (PWM) throttling is a key technique for precise thrust control in such systems. However, PWM throttling can lead to pressure surges and oscillations in the propellant feed system, potentially compromising system reliability. This study investigates the influence of PWM parameters, specifically duty cycle and frequency, on pressure surges and oscillations in a 50 N-class HTP monopropellant thruster. The objective is to identify stable operating conditions that mitigate these effects, thereby enhancing the reliability of PWM throttling for lander applications. An experimental setup was developed, including a 50 N-class thruster with a MnO$_2$/La/Al$_2$O$_3$ catalyst and a solenoid valve for PWM control. Cold flow tests using water characterized valve response and water hammer effects, while hot fire tests with 90 wt.\% HTP evaluated thruster performance under steady-state and PWM conditions. Analytical methods, including Joukowsky's equation and power spectral density analysis, were used to interpret the data and understand the underlying mechanisms. The results showed that while surge pressures generally aligned with steady-state values, specific PWM conditions led to amplified surges, particularly at low duty cycles. Additionally, high duty cycles induced chugging instability. The natural frequencies of the feed system were found to play a crucial role in these phenomena. Stable operating conditions were identified by avoiding duty cycles that cause constructive interference of pressure waves. This research demonstrates that by carefully selecting PWM parameters based on the feed system's dynamic characteristics, pressure surges and oscillations can be minimized, ensuring reliable operation of HTP monopropellant thrusters in PWM throttling mode. These findings contribute to the development of more efficient and safer propulsion systems for spacecraft landers.

Abstract: To meet the requirements of the high spatiotemporal three-dimensional (3D) airflow field within the glide path corridor during carrier-based aircraft / unmanned aerial vehicle (UAV) landings, this paper proposes a prediction method for high spatiotemporal resolution 3D ship airwake along the glide path by integrating computational fluid dynamics (CFD), backpropagation (BP) neural network, and Doppler wind lidar (DWL). Firstly, taking the conceptual design aircraft carrier model as the research object, CFD numerical simulation of the ship airwake within the glide path region is carried out by using the Poly-Hexcore grid and the detached eddy simulation (DES) / the Reynolds-averaged Navier-Stokes (RANS) turbulence models. Then, using the high spatial resolution ship airwake along the glide path obtained from steady RANS computations under different headwind conditions as sample dataset, the BP neural network prediction models were trained and optimized. Along the ideal glide path within 200 m behind the stern, the correlation coefficients between the predicted results of the BP neural network and the headwind, crosswind, and vertical wind of the testing samples exceeded 0.95, 0.91, 0.82, respectively. Finally, using the inflow speed and direction with high temporal resolution from the bow direction obtained by the shipborne DWL as input, the BP prediction models can achieve accurate prediction of the 3D ship airwake along the glide path with high spatiotemporal resolution (3 m, 3 Hz).

Abstract: As designers push engine efficiency closer to thermodynamic limits, the analysis of flow instabilities developed in High-Pressure Turbine (HPT) is crucial to minimizing aerodynamic losses and optimizing secondary air systems. Purge flow, while essential for protecting turbine components from thermal stress, significantly impacts the overall efficiency of the engine and is strictly connected to cavity modes and rim-seal instabilities. This paper presents an experimental investigation of these instabilities in an HPT stage, tested at engine-representative flow conditions in the short-duration turbine rig of the von Karman Institute. As operating conditions significantly influence instability behavior, this study provides valuable insight for future turbine design. Fast-response pressure measurements reveal asynchronous flow instabilities linked to ingress-egress mechanisms, with intensities modulated by the Purge Rate (PR). The maximum strength is reached at PR=1.0%, with comparable intensities persisting for higher rates. For lower PRs, the instability diminishes as the cavity becomes unsealed. An analysis based on the cross-power spectral density is applied to quantify the characteristics of the rotating instabilities. The speed of the asynchronous structures exhibits minimal sensitivity to the PR, approximately 65% of the rotor speed. In contrast, the structures length scale shows considerable variation, ranging from 11-12 lobes at PR=1.0%, to 14 lobes for PR=1.74%. The frequency domain analysis reveals a complex modulation of these instabilities and suggests a potential correlation with low engine-order fluctuations.

Abstract: The characteristics of aeroengine components at sub-idle conditions are difficult to obtain directly through experiments and must be extrapolated from the characteristics above idle. However, existing extrapolation methods face issues such as incomplete utilization of available data, infeasibility under limited available data, and inability to completely cap-ture the unique operating modes of the components. To address these challenges, this pa-per proposes an extrapolation method for sub-idle component characteristics of aeroen-gines based on curve and surface fitting. In consideration of the smooth and continuous nature of engine component characteristics, the method performs curve and surface fitting on the above-idle characteristics and extrapolates sub-idle characteristics from the fitting results. The method is applied to extrapolate the compressor and turbine characteristics of a micro turbojet engine and validate the results through ground-start simulations under different inlet conditions. The results demonstrate that the method effectively overcomes the limitations of existing extrapolation methods and meets the requirements for simulat-ing the ground start process of aeroengines under various inlet conditions.

Abstract: Unmanned Aerial Vehicles (UAVs) require rigorous structural inspections to ensure safety and integrity throughout their lifecycle. Traditional visual inspection methods are labor-intensive, subjective, and inadequate for real-time fault detection. This study presents an integrated software application that enables vibration-based structural health monitoring within a closed-loop Product Lifecycle Management (PLM) framework. The system collects time-domain vibration data from UAV components during the pre-flight phase and applies deep learning architectures, including Gated Recurrent Units (GRUs), Long Short-Term Memory networks (LSTMs), and Convolutional Neural Networks (CNNs) for accurate fault classification. Communication with the UAV is handled through the DroneKit-Python API, while RESTful APIs interface with the Aras Innovator PLM platform to automate data exchange and support predictive maintenance. Upon detecting anomalies, the application triggers safety protocols, such as UAV disarming and automatic maintenance request generation. Experimental validation shows that the proposed system achieves high fault detection accuracy, confirming the feasibility of the closed-loop PLM approach. The system enhances reliability and traceability, and supports data-driven decision-making by enabling continuous feedback across the UAV lifecycle.

Abstract: Due to the nature of composites, the ability to accurately locate low-energy impacts is crucial for Structural Health Monitoring (SHM) in the aerospace sector. For this purpose, several techniques have been developed in the past and, among them, Artificial Intelligence (AI) has demonstrated promising results with high performance. The non-linear behaviour of AI-based solutions has made them able to withstand scenarios where complex structures and different impact configuration have been introduced; making accurate location predictions. However, the black-box nature of AI poses a challenge in the aerospace field, where reliability, trustworthiness, and validation capability are paramount. To overcome this problem, eXplainable Artificial Intelligence (XAI) techniques emerge as a solution, enhancing model transparency, trust, and validation. This research places a previously trained Impact-Locator-AI under the spotlight, revealing whether it is truly reliable and worthy of application in aerospace industry.

Abstract: As fuel efficiency and operational costs become critical concerns in the growing general aviation sector, drag reduction technologies for small aircraft have gained paramount importance. This review critically examines the current state of nanosecond laser etching technology for microgroove-based drag reduction on aircraft skin materials. We synthesize advancements in numerical simulations and experimental approaches while addressing key challenges such as precision control of micrometer-level morphology, thermal-induced microcracks, and material fatigue. A multidisciplinary framework integrating multi-physics modeling and fatigue life prediction is proposed to bridge the gap between laboratory research and industrial implementation. Our analysis highlights that optimized microgrooves can reduce aerodynamic drag in controlled experiments, yet scalability and long-term durability remain critical barriers. This work provides actionable insights for advancing nanosecond laser etching toward certification-ready solutions in small aircraft manufacturing.

Abstract: Nitrous oxide is a highly suitable oxidizer for hybrid rockets due to its self-pressurizing properties, moderate cost, and high accessibility. However, its vapor pressure and density are highly dependent on ambient temperature, requiring careful consideration of temperature variations in real applications. To mitigate this issue, an oxidizer called Nytrox was produced by simply adding a small fraction of oxygen to bulk nitrous oxide. This modification enables the hybrid rocket propulsion system to maintain a nearly constant average thrust and total impulse across a wide range of ambient temperatures. A series of 7-second hot-fire tests of a small Nytrox/ polypropylene hybrid rocket engine operating at ~60 barA of running tank pressure demonstrated a consistent average thrust of 45.3 ± 0.7 kgf and a total impulse of 307.6 ± 3.9 kgf·s within an N₂O temperature range of 5.9-22.6°C, compared to highly varying values of a N₂O/polypropylene one within an N₂O temperature range of 10.8-29.8°C. Furthermore, the specific impulse of the Nytrox hybrid rocket engine increases mildly with decreasing temperature because of the increasing amount of added oxygen that benefits the combustion for generating the thrust.

Abstract: The frequent occurrence of space debris collision incidents has made research on autonomous satellite avoidance necessary. Against this backdrop, the paper presents a short-term autonomous space debris avoidance algorithm based on the Equivalent Linear Velocity Obstacle (ELVO) paradigm, which addresses the challenges of multiple debris scenarios and real-time decision-making. Error analysis and compensating terms are provided to enhance the algorithm’s accuracy. Simulations are proposed to validate the algorithm, and the simplified design reduces the online computational load, demonstrating its feasibility for future on-orbit usage.

Abstract: Silicon Photomultipliers (SiPMs) are optical sensors widely used in space applications due to their high photon detection efficiency, low power consumption, and robustness. However, in Low Earth Orbit (LEO), their performance degrades over time due to prolonged exposure to ionizing radiation, primarily from trapped protons and electrons. The dominant radiation-induced effect in SiPMs is an increase in dark current and dark count rate, which can compromise detector sensitivity. This study investigates the potential of thermal annealing as a mitigation strategy for radiation damage in SiPMs. We designed and tested PCB-integrated heaters to selectively heat irradiated SiPMs and induce recovery processes. A PID-controlled system was developed to stabilize the temperature at 100 °C, and a remote-controlled experimental setup was implemented to operate under irradiation conditions. Two SiPMs were simultaneously irradiated with protons at the EDRA facility, with one undergoing thermal annealing between irradiation cycles and the other serving as a control. Throughout the experiment, dark current was continuously monitored using a source measure unit, and I–V curves were recorded before and after irradiation. The results show that thermal annealing effectively reduces dark current, supporting its feasibility as a low-complexity strategy to mitigate radiation damage in space-based SiPM applications.

Abstract: This study investigates the application of advanced deep learning models for the classification of aviation safety incidents, focusing on four models: Simple Recurrent Neural Network (sRNN), Gated Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (BLSTM), and DistilBERT. The models were evaluated based on key performance metrics, including accuracy, precision, recall, and F1-score. DistilBERT achieved perfect performance with an accuracy of 1.00 across all metrics, while BLSTM demonstrated the highest performance among the deep learning models, with an accuracy of 0.9896, followed by GRU (0.9893) and sRNN (0.9887). Class-wise evaluations revealed that DistilBERT excelled across all injury categories, with BLSTM outperforming the other deep learning models, particularly in detecting fatal injuries, achieving a precision of 0.8684 and an F1-score of 0.7952. The study also addressed the challenges of class imbalance by applying class weighting, although the use of more sophisticated techniques, such as focal loss, is recommended for future work. This research highlights the potential of transformer-based models for aviation safety classification and provides a foundation for future research to improve model interpretability and generalizability across diverse datasets. These findings contribute to the growing body of research on applying deep learning techniques to aviation safety and underscore opportunities for further exploration in NLP-driven risk assessment and incident classification.

Abstract: This paper presents a multi-fidelity optimization procedure for aircraft wing design, implemented in the early stages of the aircraft design process. Since wing shape is a key factor influencing aerodynamic performance, having an accurate estimate of its efficiency at the conceptual design phase is highly beneficial for aircraft designers. This study introduces a comprehensive optimization framework for designing the wing of a Class I fixed-wing mini-UAV with electric propulsion, focusing on maximizing aerodynamic efficiency and operational performance. Utilizing Class Shape Transformation (CST) in combination with Surrogate-Based Optimization (SBO) techniques, the research first optimizes the airfoil shape to identify the most suitable airfoil for the UAV wing. Subsequently, SBO techniques are applied to generate wing geometries with varying characteristics, including aspect ratio (AR), taper ratio (λ), quarter-chord sweep angle (Λ0.25), and tip twist angle (ε). These geometries are then evaluated using both low- and high-fidelity aerodynamic simulations. The integration of SBO techniques enables an efficient exploration of the design space while minimizing computational costs associated with iterative simulations. Specifically, the proposed SBO framework enhances the wing’s aerodynamic characteristics by optimizing the lift-to-drag ratio and reducing drag.

Abstract: In this paper, we propose and proof an innovative concept of radar antennas suited for Collision Avoidance (CA) systems installed on board small Unmanned Aircraft (UA). The proposed architecture provides 360º monopulse coverage around the host platform enabling the detection and accurate position estimation of airborne, non-cooperative hazards using lightweight, low-profile antennas. These antennas can be manufactured using low-cost 3D printing techniques and are easily integrable in the UAs airframe without degrading their airworthiness. In the paper, we sketch a Detect and Avoid (DAA) concept of operations (ConOps) built on the ConOps for separation management developed by the SESAR 2020 project BUBBLES in line with the SESAR U-space ConOps Ed. 4. In that ConOps, Remain Well Clear (RWC) and Collision Avoidance functions are provided separately (namely, the responsibility for providing the RWC function lies with ground-based U-space services whereas the CA function is considered an airborne safety net provided by on-board equipment). From the ConOps, we define operation-centric design requirements and describe the proposed architecture. We prove the concept by a combination of simulations and measurements in anechoic chamber using a prototype at 24 GHz.

Abstract: Current flight procedure design primarily relies on expert experience, lacking a systematic approach to comprehensively balance safety, route simplification, and environmental impact. To address this challenge, this paper proposes a reinforcement learning-based method that leverages carefully crafted reward engineering to achieve an optimized flight procedure design, effectively considering safety, route simplicity, and environmental friendliness. To further enhance performance by tackling the low sampling efficiency in the Replay Buffer, we introduce a multi-objective sampling strategy based on the Pareto frontier, integrated with the Soft Actor-Critic (SAC) algorithm. Experimental results demonstrate that the proposed method generates executable flight procedures in the BlueSky open-source flight simulator, successfully balancing these three conflicting objectives, while achieving a 28.6% increase in convergence speed and a 4% improvement in comprehensive performance across safety, route simplification, and environmental impact compared to the baseline algorithm. This study offers an efficient and validated solution for the intelligent design of flight procedures.