In the 21st century, the desire for improved fuel efficiency of engines, lower fuel prices, and the need to reduce greenhouse gas emissions such as CO2 and NOx are leading the aviation industry to seek hybrid-electric jet engines for commercial aircraft. These aircraft will have greater maintenance challenges due to additional components requiring more reliable materials for the engine’s parts, such as turbine blades. Turbine blades must be composed of materials that have enhanced fatigue performance. Resistance to dynamic loads and high strength will be needed to ensure modern gas turbine blades are as reliable as possible. This review paper examines hybrid-electric engine turbine blades and subsequently introduces additive manufacturing (AM) and multi-principal element alloys (MPEAs) with a focus on laser powder bed fusion (LPBF), high-entropy alloys (HEAs), and medium-entropy alloys (MEAs). The tensile properties of LPBF HEAs range from 5 to 47% elongation and a UTS of 572–1640 MPa, while LPBF MEAs range from 8 to 73.9% and a UTS of 573–1382 MPa. This study focused on dynamic and fatigue properties while acknowledging gaps in high-temperature testing. The combination of mechanical properties with the ability to control internal geometry makes these AM alloys an attractive option for the next generation of gas turbine blades.

Utah State University has developed a high-performance “green” hybrid propulsion technology based on the unique electrical breakdown properties of 3D-printed acrylonitrile butadiene styrene. Using 3D-printed ABS as fuel, typical startup sequences require approximately 5–15 joules and, once started, the system can be sequentially fired with no additional energy inputs required. The number of possible ignitions is limited only by the amount of fuel. The most technologically mature version uses gaseous oxygen (GOX) as oxidizer and 3D-printed ABS as fuel. While GOX is mass-efficient, it lacks volumetric efficiency unless highly pressurized. Nytrox, a blend of GOX and nitrous oxide, improves propellant density and volumetric efficiency, while maintaining acceptable levels of mass efficiency (specific impulse). Nytrox can safely self-pressurize, eliminating the need for a separate oxidizer pressurization system and reducing overall complexity. However, employing Nytrox as a direct substitute for GOX results in reduced ignition reliability and considerably increases cold-start ignition latency. This paper quantifies the latency, explores its sources, and analyzes expected behaviors. Solutions include raising combustion and storage pressures to boost oxygen content in Nitrox’s liquid phase and increasing combustion chamber pressure to reduce ignition delays.

Satellite constellations are increasingly employed in regional remote sensing applications such as environmental monitoring and disaster management. However, achieving efficient and timely coverage for discontinuous regions with high revisit frequency remains a significant challenge. This study first compares low Earth circular and elliptical orbit constellations in terms of coverage performance, economic efficiency, and orbital lifetime. Based on this comparison, a dedicated design methodology for elliptical orbit constellations aimed at discontinuous regional coverage is developed. A critical Sun-synchronous repeating elliptical orbit is selected as the baseline configuration, and its key orbital parameters including the semi-major axis, eccentricity, and argument of perigee are analytically derived. Furthermore, a flexible constellation configuration model is proposed, introducing a modified Walker-inspired kn/kn/k pattern. This model establishes direct mathematical relationships between the constellation’s repetition factor, phasing parameters, and temporal coverage metrics to systematically guide the overall design process. A case study on wildfire monitoring in China’s Qinling Mountains demonstrates the feasibility and effectiveness of the proposed approach, achieving a one-hour revisit time over the target region with a 24-satellite constellation. The results indicate that the proposed methodology provides a cost-effective and adaptable framework for satellite constellation design in remote sensing applications, particularly suited to dynamic environmental monitoring and emergency response missions.

The precise identification of terminal area arrival procedures is crucial for airspace planning, traffic management, and safety analysis. Traditional methods are limited in automatically detecting specific procedural maneuvers from large amounts of trajectory data. This paper proposes a methodology with knowledge-based filtering to automatically identify three common air traffic control arrival procedures, namely Point Merge System, Vector for Space, and Trombone, from historical trajectory data. After clustering the landing trajectories in the terminal area, we identify the predominant flight patterns. Then, a knowledge-based filtering algorithm, designed based on knowledge of the procedure and geometry criteria, is employed to precisely extract trajectories with different procedure patterns. Experimental results demonstrate that this method effectively identifies the distinct procedural trajectories. An in-depth analysis of the extracted trajectories reveals significant characteristics and differences in their spatial distribution, trajectory structure, and operational efficiency. This work provides data-driven decision support for evaluating terminal area operational performance and arrival procedures.

The time-optimal rendezvous problem is crucial for efficiently executing on-orbit servicing (OOS) missions in the future. To fulfill the detection requirement during rendezvous process, it is an essential issue that the maneuvering spacecraft flies over the designated waypoint. This paper presents an innovative methodology for planning the time-optimal spacecraft rendezvous trajectory, involving the constraints related to a flyover waypoint and being forced by a constant thrust. The method is specifically designed to handle the optimal problems with the shortest and unspecified flyover time and terminal rendezvous time. First, this article outlines the scenarios for a time-optimal rendezvous that incorporates the constraints of a flyover waypoint. Second, a time-normalized relative dynamic model for maneuvering spacecraft is derived using the Clohessy–Wiltshire (CW) equation. Third, the time-optimal control output under the constant thrust is provided leveraging Pontryagin’s minimum principle (PMP). Meanwhile, an indirect solution equation is established with the constraints of relative position and velocity for the flyover waypoint during the rendezvous process. Finally, a computational methodology for solving this time-optimal problem is proposed, integrating the initial guess for the unspecified time, multi-objective particle swarm optimization using multiple search strategies (MMOPSO) and Newton–Raphson method (NRM). Simulation results demonstrate that the method can effectively and practically solve the time-optimal rendezvous trajectory planning under a constant thrust, while satisfying the constraints of the flyover waypoint. Moreover, Monte Carlo simulations are performed, the results of which indicate that the proposed methodology exhibits strong robustness and fidelity.

The rapid growth of low-altitude air traffic demands airspace evaluation frameworks that are scalable, flexible, and efficient. However, existing airspace partitioning strategies, primarily designed for sparse, long-distance civil aviation, are ill-suited to the dense and complex low-altitude environment. Moreover, the heterogeneous nature of low-altitude conditions cannot be adequately captured. To address this challenge, we propose a novel low-altitude airspace evaluation framework centered on a hierarchical voxel-based partitioning strategy. This strategy explicitly accommodates the diverse operational requirements of drones across different airspace layers. We couple this with an efficient multi-resolution airspace unit encoding mechanism that dynamically aggregates and evaluates airspace availability. To demonstrate the practical utility of our framework, we further develop an energy-aware, multi-scale route-planning algorithm that operates seamlessly across the hierarchical representation. Simulation results show that our method significantly improves computational efficiency in airspace evaluation, while the proposed planner achieves higher energy efficiency compared to conventional approaches like A*.

The present study demonstrates a step-by-step method for optimizing the outflow valve geometry and maximizing thrust generation. In this system, the skin-mounted OutFlow Valve (OFV) acts as a convergent–divergent nozzle and, as such, the De Laval nozzle equations are considered as guidance for the shape optimization. The performance of the skin-mounted flapped OFV optimized designs is assessed with a combination of analytical equations and Computational Fluid Dynamics (CFD) methods. The three-dimensional Reynolds-Averaged Navier–Stokes (RANS) yield reliable thrust recovery estimates and reveal key aspects of the aerodynamic flow behaviour through the valve, highlighting the interaction between the skin-mounted flapped OFV components. The results compare well with the analytical approach, providing a basis upon which a skin-mounted flapped OFV can be tailored for a specific mission.

The proliferation of Resident Space Objects (RSOs), including satellites, rocket bodies, and debris, poses escalating challenges for Space Situational Awareness (SSA). Optical light curves capture temporal brightness variations influenced by factors such as attitude variation, viewing geometry, and surface properties. When appropriately processed and analyzed, these data can support RSO characterization and classification. This paper presents a scoping review of machine learning (ML) and deep learning (DL) methods for RSO classification using light-curve data. From 297 peer-reviewed studies published between 2014 and 2025, a screened subset of 29 works is selected for detailed methodological comparison. We trace the methodological evolution from handcrafted feature engineering toward convolutional, recurrent, and self-supervised models that learn representations directly from photometric time series. An analysis of three publicly accessible databases, Mini Mega TORTORA, Space Debris Light-Curve Database, and Ukrainian Database, reveals pronounced class imbalance, with payloads comprising over 80% of observations. While models trained on simulated data routinely achieve 95 to 99% accuracy, performance on measured light curves degrades to 75 to 92%, exposing a persistent gap between simulation and observation. We further identify data scarcity, repeated observations of the same objects, and inconsistent evaluation protocols as key barriers to reproducible benchmarking. Future progress will require benchmark-ready, sensor-aware datasets spanning diverse orbital regimes and viewing geometries, alongside physics-informed and transfer-learning approaches that improve robustness across sensors and between synthetic and observational domains.

This paper proposes a data-driven model for predicting rotor fluid-structure interaction (FSI) load with efficient aeroelastic analysis. Unsteady flow-field snapshots obtained from computational fluid dynamics (CFD) simulations are first processed using Proper Orthogonal Decomposition (POD) to reduce the dimensionality of the flow data and extract the dominant modal time coefficients. Based on these reduced-order representations, the Dynamic Mode Decomposition with control (DMDc) method is used to identify a time-domain state-space model of the aerodynamic system. The identified data-driven aerodynamic model is coupled with the structural dynamic equations, which allows time-domain reconstruction and prediction of unsteady aerodynamic forces and structural loads under aeroelastic interactions. Hence, an efficient reduced-order model for aerodynamic load is established. The proposed approach is first validated using a two-dimensional airfoil subjected to different motion inputs, where the reduced-order aerodynamic predictions are compared with high-fidelity CFD results. Then, a three-dimensional sectional reduced-order model for a rotor is developed based on blade element theory, and aeroelastic coupled simulations are conducted for the SA349 rotor. The results demonstrate that the proposed method can accurately capture unsteady aerodynamic loads and aeroelastic responses, while significantly improving computational efficiency compared to high-fidelity simulations.

Civil aviation VHF communication is safety-critical, yet operational links are routinely disturbed by atmospheric effects, aging hardware, and electromagnetic interference. The resulting anomalies are typically weak, intermittent, and extremely rare, which makes real-time detection difficult under strong temporal dependence and severe class imbalance. We propose an end-to-end framework that couples (i) a learnable kernel projection for adaptive nonlinear feature extraction, (ii) a differentiable relevance–redundancy objective for feature refinement, and (iii) conditional temporal generation to augment minority anomaly patterns. A lightweight CNN–LSTM head is used for streaming inference. Training uses a mixture of operational anomalies and simulated degradation scenarios, while evaluation is conducted using operational data only. Experiments on 1.2 million VHF frames collected from real flight operations and ground station monitoring achieve an F1-score of 0.947, ROC-AUC of 0.972, and PR-AUC of 0.968, with an average inference latency of 34.7 ms.