Search Results (588)

Current research in commercial aviation is exploring numerous propulsion and aircraft technologies to mitigate its environmental impact. While purely hydrogen-powered aircraft face manifold challenges, combining hydrogen and conventional fuel may facilitate the introduction of hydrogen-based flight. This study dives into the conceptual design of a parallel-hybrid dual-fuel regional aircraft retrofit based on the D328eco. The assessed retrofit approach aims to extend airframe service life and reduce emissions by incorporating a novel propulsion system. By integrating high-temperature fuel cells (FCs) to assist conventional turboshaft engines, the powertrain reduces fuel consumption. Utilizing the Bauhaus Luftfahrt Aircraft Design Environment, various aircraft-level sensitivities and hybridization strategies were assessed. The fuel/payload ratio was identified as a key metric, and enabling FC support during diversion climb while minimizing that ratio shifted the corresponding hybridization degree from 20.3% to 37.2%. Retaining the reference turboshaft-engine for reduced retrofit development costs, a hybridization degree of 20.2% was attainable while the minimum allowable payload was carried aboard. Subsequent off-design mission analysis revealed a decrease in transport efficiency for reduced mission ranges, underlining the importance of market-tailored aircraft designs. The main studies were complemented by a higher-level emission and climate impact assessment to set the basis for more generalized retrofit statements. Full article

Reliable electric load forecasting is an important engineering problem for power-system planning, grid stability, and mission-critical energy management. This paper presents a symmetry-aware simulation and modeling framework for medium-range electric load forecasting under noisy and uncertain operating conditions. The proposed approach combines a Multi-Model Partitioning Filter (MMPF) with Nonlinear Autoregressive Exogenous (NARX) submodels, while two adaptive optimization strategies, genetic algorithm-based resource allocation (GARA) and Particle Swarm Optimization (PSO), are used to optimize the contribution weights of the parallel predictors. The modeling process uses real commercial power-system data and evaluates the forecasting framework over April–September 2025. To simulate realistic engineering disturbances, correlated symmetric Gaussian noise is injected into the testing phase under moderate and heavy noise scenarios. The cyclic symmetry of temporal variables, such as hours and months, is preserved through unit-circle encoding, while the symmetry and asymmetry of residual error symmetric distributions are examined through scatter plot analysis. As for the context of forecasting residuals as diagnostic signals, it is important to transfer symmetry properties that can be used to evaluate the behavior of optimized predictors, along with the cyclic encoding of inputs. This means that by implementing residual-symmetry analysis, the conclusion that GARA and PSO produce concentrated, balanced, and biased errors under moderate noise and heavily correlated noise conditions can be achieved. Finally, our results show that both GARA and PSO improve the robustness of the hybrid MMPF-NARX model, but PSO consistently achieves lower MAPE values, smoother convergence, and lower computational burden. The optimal configuration is obtained with nine NARX submodels, beyond which additional model complexity offers no meaningful performance gain. Overall, the study shows that symmetry-aware modeling, adaptive optimization, and noise-based simulation can support more reliable forecasting in modern power-system engineering applications. Full article

To address the strong coupling among engine geometry, propulsion performance, aerodynamic response, flight trajectory, and manufacturing cost, this study establishes an integrated propulsion–aerodynamics–trajectory–cost design framework for high-speed, long-range rocket-boosted vehicles. Six chamber and nozzle geometric parameters are selected as design variables. An engine performance model is first used to calculate propulsion responses, including thrust, chamber pressure, and specific impulse. A mass and configuration update model then transfers the effects of engine parameter variations to the overall vehicle characteristics, while aerodynamic data and a two-dimensional point-mass trajectory model are introduced to obtain mission-level indicators, including maximum velocity, maximum altitude, and range. An existing manufacturing cost decomposition model for solid rocket motors is extended by coupling the cost response with component masses, geometric dimensions, and parameter-update relationships, thereby enabling the simultaneous evaluation of mission performance and manufacturing cost within the integrated computational chain. Kriging surrogate models are constructed for rapid prediction of the coupled system responses, entropy-weighted TOPSIS is used to screen feasible candidates, and SQP is employed for continuous constrained refinement. Compared with the baseline design, the comprehensive evaluation index increases from 0.4861 to 0.6110. The maximum velocity, maximum altitude, and range increase by 8.29%, 26.81%, and 21.24%, respectively, while the manufacturing cost increases by only 0.48%. The evaluation index is also 15.68% higher than that of the engine-level optimized design. These results demonstrate that the integrated consideration of propulsion, aerodynamics, trajectory, and manufacturing cost improves mission-level performance–cost trade-offs and provides a system-level design approach for mission-oriented and cost-aware solid rocket motor development. Full article

Flight data-driven modeling has become an important approach for trajectory prediction, anomaly detection, and risk assessment in unmanned aerial vehicles and other aerial systems. Such data are usually high-dimensional, nonlinear, multirate, and non-stationary, especially during maneuvering flight, environmental disturbance, and mission-phase transitions. Traditional physics-based methods and shallow machine learning models often have limited adaptability in these conditions, while Long Short-Term Memory (LSTM) networks and their variants have shown strong potential for learning temporal dependencies from complex flight sequences. This paper reviews the development and application of LSTM-family models for flight data analysis, with attention to both methodological performance and resource-aware deployment. The reviewed models include basic LSTM, BiLSTM, CNN-LSTM, ConvLSTM, LSTM autoencoder, attention-enhanced LSTM, graph-based LSTM, and uncertainty-aware LSTM. Their applications are discussed in three main areas: flight trajectory prediction, anomaly detection, and risk assessment. Beyond prediction accuracy, this review also examines robustness to distribution shift, physical consistency, interpretability of anomalies, uncertainty estimation, and onboard implementation. A four-layer and ten-dimensional evaluation framework is presented across data characteristics, model performance, physical-mechanism consistency, and system-engineering constraints. The evidence shows that model size, runtime memory, computational cost, inference latency, energy consumption, and target hardware are often insufficiently reported, while direct quantitative onboard validation remains scarce. This gap highlights the need for standardized deployment reporting. Full article

Tail rotor failure represents a critical emergency in helicopter flight operations. Conventional recovery mandates an immediate engine shutdown and transition to autorotation, significantly compromising both mission survivability and operational flexibility. To achieve stable powered flight, this paper establishes a yaw stability strategy integrating vertical tail side-force control with active main rotor torque suppression. This strategy employs controlled sideslip to generate a yaw-restoring moment from the vertical tail and an increased descent rate to reduce rotor power requirement. These two effects act in concert to counteract the rotor torque. Trim analysis of a representative helicopter in a tail rotor failure condition validates the strategy. Two yaw control architectures are developed for the failure operation: (1) a cascade loop comprising yaw angle, yaw rate, lateral velocity, and roll angle, and (2) the latter omitting lateral velocity. Comparative simulations demonstrate that although Loop (1) yields slower yaw convergence than Loop (2), it delivers enhanced stability. Furthermore, an emergency control trajectory tailored for moderate forward velocity is proposed. The trajectory initiates with a controlled descent-rate increase to arrest yaw divergence. The forward velocity is then augmented to mitigate sideslip, and the descent rate is gradually reduced, ensuring adequate altitude clearance over the landing zone. This study provides a strategy for enabling powered flight under tail rotor failure conditions. Full article

University campuses with native vegetation function as living botanical gardens, biodiversity laboratories and curated plant collections, sharing core missions with formal botanical institutions: floristic inventory, in situ and ex situ plant conservation, scientific interpretation, environmental education and biodiversity stewardship. Against this backdrop, limited plant awareness—the tendency to overlook and undervalue plants in one’s surroundings—remains a structural challenge in botany teaching. This article proposes the floristic inventory of the Tulcán campus (Universidad del Cauca, Popayán, Colombia) as the core of a pedagogical strategy that operates the campus as a non-formal botanical educational institution. A sequential explanatory mixed-methods design was applied: (1) ecological inventory across 25 systematic field trips during 2025; (2) educational design with faculty validation; and (3) a pilot with 142 undergraduate students of Biology and Environmental Engineering. We recorded 160 species in 65 families (83% native, including endemic and threatened taxa); designed a four-component strategy (cooperative field research, biocultural connection, applied plant ecology, interpretive trails); and observed a descriptive performance gradient favouring field-immersive courses, together with qualitative patterns consistent with limited plant awareness. The Tulcán campus can be intentionally managed as a living botanical garden delivering inventory, conservation, interpretation and education functions. Full article

The modern multi-domain battlespace increasingly demands cost-effective, persistent platforms for Forward Air Control (Airborne) (FAC(A)) and Close Air Support (CAS) missions. While utilizing Commercial Derivative Aircraft (CDA) presents a viable alternative to high-maintenance military assets, the specific aerodynamic, ergonomic, and cognitive workload limitations encountered during the transition across the civil–military “airworthiness seam” remain a significantly underexplored gap in contemporary aerospace literature. To address this gap, a comprehensive empirical flight test campaign was executed on the LX7-135 turboprop to evaluate its tactical mission suitability. Employing the Cooper–Harper Handling Qualities Rating and Bedford Workload scales, and guided by military specifications (MIL-HDBK-516C, MIL-F-8785C), the study systematically assessed the aircraft’s pure performance, unaugmented flight dynamics, and human–machine interface during simulated combat scenarios, including dynamic 9-Line briefings and kinetic “Box Pattern” delivery profiles. Flight test data indicated 12 “SATISFACTORY” parameters, highlighting climb rates, extended endurance, and heavily damped short-period and Dutch roll modes optimal for target tracking. Conversely, the evaluation identified 4 “UNSATISFACTORY” and 10 “TOLERABLE” deficiencies—chiefly a single-door egress bottleneck, restricted stick clearance, degraded longitudinal static stability, and the absence of secure tactical communications. Although these constraints elevated pilot cognitive workload during multi-axis tasks, the LX7-135 demonstrates potential suitability for further development for the FAC(A) role, subject to the successful implementation and subsequent flight-test verification of the identified critical engineering modifications and specialized training syllabi. Full article

The black soil region of Northeast China is a key grain-production area where cropland water erosion threatens soil fertility and sustainability. We diagnosed cropland soil loss across six diagnostic years/time slices (2001, 2005, 2010, 2015, 2020, and 2024) using a Revised Universal Soil Loss Equation (RUSLE)-based remote-sensing workflow implemented in Google Earth Engine (GEE). Rainfall erosivity was derived from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily precipitation, soil erodibility from SoilGrids, topography from the Shuttle Radar Topography Mission digital elevation model (SRTM DEM), vegetation cover from the Landsat normalized difference vegetation index (NDVI), and cropland extent from ESA WorldCover; alternative rainfall sources, cropland masks, and P-factor settings were used for sensitivity analyses. Under the slope-graded P-factor scenario, mean annual soil loss ranged from 1.60 to 3.07 t ha⁻¹ yr⁻¹, and the proportion of cropland exceeding T = 2 t ha⁻¹ yr⁻¹ ranged from 25.2% to 52.9%. Soil loss fluctuated among years because rainfall erosivity and cover-management effects partly counteracted each other. Risk was concentrated in sloping piedmont and hilly cropland, whereas broad plains were dominated by very slight and slight erosion. P-factor parameterization represented the largest structural uncertainty. The workflow provides regional screening evidence for field verification and conservation-practice assessment, rather than direct site-specific engineering prescriptions. Full article

Hybrid rocket engines (HREs), which pair a fluid oxidizer with a solid fuel, offer safety and handling advantages, can reduce environmental impact relative to selected legacy systems, and are capable of deep throttling and restart, making them strong candidates for “green” in-space propulsion applications. However, until very recently, there has not been any flight heritage of an HRE used in a spaceflight mission. Over the past decade, the Propulsion Research Laboratory at Utah State University (PRL-USU) has matured a portfolio of HRE technologies—low-energy arc ignition, digital throttling, additively manufactured sustainable fuels, the Nytrox green oxidizer, and electroplated thruster assemblies—that together address the historical barriers to spaceflight adoption. This paper summarizes that progress, describes two flight systems built upon it, and presents a roadmap for future applications. Full article

Perovskite solar cells (PSCs) have emerged as a disruptive photovoltaic technology for aerospace and extreme environment applications, driven by their substantial power-to-weight ratio and mechanical flexibility. However, continuous operation under harsh conditions, characterized by the AM0 spectrum, deep vacuum, extreme thermal cycling, and ionizing radiation, exposes the fundamental thermodynamic instability of traditional organic–inorganic hybrid perovskites. This comprehensive review systematically synthesizes 131 recent studies to provide a holistic framework for designing ultrastable, radiation-hardened PSCs. We critically examine the underlying degradation mechanisms, including vacuum-induced volatile desorption, UV-triggered halide segregation, and thermomechanical fracture at buried interfaces. To overcome these critical barriers, we highlight advanced engineering strategies: the transition to all-inorganic CsPbX₃ and lead-free double/chalcogenide perovskites (e.g., Cs₂SnI₆, CaHfS₃), the implementation of dopant-free inorganic transport layers coupled with self-assembled monolayers (SAMs) for cascade band alignment, and the integration of polymeric scaffolds for fracture energy toughening. Furthermore, we emphasize the imperative shift toward solvent-free vacuum deposition techniques (ALD, PLD). A distinctive focus of this review is the integration of Artificial Intelligence; specifically, we evaluate Deep Learning architectures, such as Long Short-Term Memory (LSTM) networks, for predictive State of Health (SOH) monitoring, underscoring the vital transition from simulated to empirical datasets. Finally, coupled with Material Flow Cost Accounting (MFCA), this review outlines a strategic roadmap for the commercialization and deployment of autonomous, self-diagnosing photovoltaic platforms in next-generation satellite and deep-space missions. Full article

The rapid expansion of Unmanned Aerial Vehicle (UAV) applications in both civilian and military sectors has intensified the demand for propulsion systems capable of delivering higher speed, increased endurance, and improved payload capacity. While battery-electric propulsion remains dominant for small UAV platforms, its limited energy density restricts operational range and mission flexibility. As a result, micro gas engines have emerged as a viable alternative for applications requiring high power-to-weight ratios and sustained high-speed operation. This review examines the fundamentals, scaling effects, and classification of micro gas turbine propulsion systems used in UAV applications, with emphasis on micro turbojets and related hybrid configurations. The paper discusses the thermodynamic principles governing micro gas engines and analyzes the aerodynamic, thermal, and combustion challenges associated with miniaturization, including low Reynolds number effects, tip leakage losses, thermal management limitations, and combustion instability. Furthermore, the study reviews the operational characteristics and mission suitability of different propulsion architectures for reconnaissance UAVs, high-speed UAVs, including reconnaissance and loitering platforms, target drones, and hybrid-electric aerial platforms. Recent developments involving additive manufacturing, advanced control systems, recuperated cycles, and hybrid-electric integration are also evaluated as enabling technologies for next-generation UAV propulsion. The findings demonstrate that although micro gas turbines continue to face important efficiency and manufacturing challenges at reduced scales, they remain essential for mission profiles that exceed the capabilities of purely electric propulsion systems. Full article

Space inertial actuators, represented by reaction wheels, momentum wheels, flywheels, and control moment gyroscopes, are indispensable angular-momentum exchange devices in spacecraft attitude determination and control systems. Owing to their non-consumable operation, high reliability, and precise torque-generation capability, these actuators are widely employed for attitude stabilization, rapid attitude maneuvering, payload disturbance mitigation, and precision pointing in modern satellites and space vehicles. With the rapid growth of high-resolution Earth observation missions, deep-space exploration programs, and large-scale commercial satellite constellations, the performance requirements imposed on inertial actuators are becoming increasingly stringent. This paper systematically reviews the current state of the art in space inertial actuator technologies by integrating peer-reviewed research with publicly available industrial product information, and summarizes recent research progress, representative products, and development strategies in the United States, Europe, Russia, and China. By comparing the technical characteristics and evolutionary pathways of mainstream reaction wheel, momentum wheel, flywheel, and control moment gyroscope systems, the study identifies the principal technological drivers shaping future development. The analysis reveals five dominant trends: extended operational lifetime, higher control accuracy, greater torque and angular-momentum density, reduced micro-vibration disturbance, and scalable, cost-effective manufacturing. These findings provide a systematic technical reference for future research, engineering design, and industrial development of advanced space inertial actuator systems in China and internationally, particularly for next-generation spacecraft requiring long service life, high pointing accuracy, low disturbance, and scalable production. Full article

Piston engines are the core power units of large unmanned aerial vehicles (UAVs), and the characteristics of their thermal-fluid working processes directly affect the power performance and operational reliability of UAVs. To address the difficulty of simultaneously ensuring simulation accuracy and computational efficiency in conventional piston engine models, a lightweight simulation method for the thermal-fluid modeling of piston engines is proposed. A Rotax 915 piston engine for large UAVs was selected as the target engine. A one-dimensional thermal-fluid working process model was first established in GT-Power, and a Kriging surrogate model was then constructed to develop a lightweight simulation framework, enabling the fast and accurate prediction of key engine response parameters. The proposed framework enables the rapid prediction of engine performance, propeller load, and lubrication responses under UAV mission-related operating conditions. The surrogate model delivered favorable prediction accuracy against the GT-Power model (R² > 0.95, MAPE < 5%); flight data verification (excluding cold start) presented a MAPE of 11% and 27% for exhaust gas temperature and lubricating oil pressure, respectively. The proposed lightweight model was validated against actual UAV flight data, and the results show that it can effectively capture the variation trends of exhaust gas temperature and lubricating oil pressure. Further simulation results indicate that with increasing altitude, engine power decreases whereas brake specific fuel consumption (BSFC) increases. With increasing engine speed, engine power increases, BSFC decreases, and lubricating oil pressure declines. Under different flight phases, the takeoff phase shows the highest engine power and BSFC, the cruise phase corresponds to the maximum propeller torque and thrust, and the descent phase exhibits the lowest overall load level. The results provide a useful basis for performance evaluation, condition monitoring, and digital-twin-oriented modeling of piston engines for large UAVs. Full article

Flying Ad-Hoc Networks (FANETs), Internet of Things (IoT), and Internet of Vehicles (IoV) are critical enablers of intelligent transportation and smart city ecosystems. Their reliance on shared wireless channels, however, exposes them to diverse jamming attacks that threaten communication reliability, mission effectiveness, and safety. This paper presents a comprehensive study of jamming threats in integrated FANET–IoT–IoV environments and analyzes conventional and advanced anti-jamming techniques across physical, link/MAC, spectral, spatial, temporal, and hybrid domains. To address the challenges posed by heterogeneous and dynamic network conditions, we propose a cross-layer anti-jamming framework that integrates Cognitive Radio (CR) for dynamic spectrum access and Multi-Agent Reinforcement Learning (MARL) for cooperative, adaptive decision-making. The framework employs a Perception Engine for local anomaly detection, a Cognitive Engine for constructing a collaborative jamming map, and a Decision and Action Engine for multi-agent DRL-based mitigation. Simulation results demonstrate that the proposed CR-MARL framework significantly improves packet delivery ratio, reduces latency, and adapts efficiently to varying jamming strategies, while maintaining low energy and computational overhead, making it suitable for resource-constrained UAVs, vehicles, and IoT sensors. Full article

Traffic engineering aims to control infrastructure and population behaviour to achieve optimal usage of road networks. Fairness is fundamental to stimulate cooperation in large populations, and plays an important role in traffic engineering, as it increases the well-being of users, improves driving safety by rule adherence, and overcomes public resistance at legislative implementation. Despite the importance of fairness, only a few works have translated fairness into the transportation domain, with a focus on transportation planning rather than traffic engineering. Moreover, existing works in traffic engineering discuss fairness superficially and insufficiently, focussing on only a few definitions. The mission of this work is (i) to challenge narrowly specified, efficiency-oriented engineering formulations in traffic engineering, (ii) to establish a link to modern fairness theories, and (iii) to highlight the importance of fairness when allocating scarce, public good, mobility resources between road users. To achieve this, a mode-agnostic, distributive fairness framework for mobility resource allocation is proposed. It serves when designing traffic engineering solutions, and convinces in public debates with a useful, argumentative tool-set to confront equity considerations. Ultimately, this enables systematic research and design of fairness-informed control systems, demonstrated by three case studies on signalized intersection management and static road pricing. Full article