Search Results (1,683)

Turbofan engine performance is critically sensitive to operating conditions, yet comprehensive frameworks that simultaneously assess exergo-economic, exergo-environmental, and lifecycle cost performance across realistic flight envelopes remain limited, particularly for Gulf-region climates. In this study, we present a comprehensive analysis of the exergo-economic, exergo-environmental, and lifecycle costings of five different configurations of two-spool and triple-spool turbofan engines. The analysis was carried out for a wide range of four operating conditions, namely ambient temperature, flight altitude, Mach number, and % relative humidity, with emphasis on the climate conditions likely to be found in the Gulf region. The computational models developed were validated against published data to confirm their reliability. It was found that fuel consumption was the most significant contributor to total lifecycle ownership cost, between 60 and 75% of hourly operating cost over a 20-year service period. Ambient temperature, Mach number, and Cruise altitude represented the most significant drivers of long-term economic performance, with % relative humidity having little effect. Exergo-economic analysis showed that the major cost mechanisms changed dramatically with operating conditions. Exergy destruction and component inefficiencies determined the costs at Takeoff, with capital investment being the dominant factor when cruising. Increase in both or either ambient temperature and altitude was shown to reduce cost rates but simultaneously reduced thermo-economic efficiency via higher specific exergy costs. However, increase in Mach number enhances both exergy output and cost-effectiveness, confirming that specific exergy cost is a more reliable indicator of true system performance than cost rate alone. The two-spool configurations show superior specific CO₂ emissions, with Case 3 recording the lowest emissions at Takeoff and Case 2 at Cruise. For exergy-based environmental indicators, Case 3 performs best at both Takeoff and Cruise, achieving the lowest environmental destruction coefficient and index, as well as the highest environmental benign index among all five configurations. These findings provide actionable guidance for engine selection, operational optimization, and sustainable propulsion system design. Full article

Multiple unmanned aerial vehicles (UAVs) performing cooperative missions in complex environments face challenges such as difficult cooperative decision-making, stringent spatiotemporal consistency constraints, and environmental uncertainty. The cooperative mission considered in this paper aims to enable multiple UAVs to simultaneously arrive at multiple constant-velocity moving targets. To address these challenges, this paper proposes a multi-agent guided soft actor–critic (MAGSAC) deep reinforcement learning algorithm. Under the centralized training with decentralized execution (CTDE) framework, a Guider network is introduced to guide the local actor network in learning coordinated strategies, thereby alleviating the non-stationarity of multi-agent decision-making under uncertain environments. An estimated time of arrival (ETA)-based spatiotemporal coordination reward function is designed to promote synchronized arrival. To address sparse rewards, a hindsight experience replay (HER) mechanism based on backward trajectory reconstruction is developed, and a delayed collision-constraint activation mechanism is incorporated to improve convergence while maintaining flight safety. Simulation results show that MAGSAC outperforms existing mainstream algorithms in synchronization success rate, temporal synchronization accuracy, and safety. Full article

Surface congestion and taxiing delays at high-density airports increasingly constrain aviation sustainability, as ground-phase fuel consumption and emissions constitute a significant share of total airport emissions. Existing studies typically decouple air traffic flow management from ground resource scheduling, hindering coordinated optimization of punctuality, environmental benefits, and resource utilization. This paper proposes a multi-objective optimization method for runway-taxiway systems oriented toward air–ground collaborative decision-making, integrating Calculated Take-Off Time (CTOT) compliance constraints. A tri-objective mixed-integer programming model is formulated to minimize CTOT deviation, total taxiing time, and runway workload imbalance. A hybrid intelligent algorithm, SSA-SCA-NSGA-II, is designed with a bidirectional elite feedback mechanism to address this NP-hard problem. Validation uses real operational data of 58 departure flights during a peak period at Beijing Daxing International Airport. The results demonstrate that the proposed method achieves effective trade-offs on the Pareto front: CTOT compliance rate increased from 77.6% to 89.7–96.6%; total taxiing time decreased from 692 min to 551–635 min; and dual-runway utilization imbalance declined from 5.2% to 1.7–3.8%. These improvements translate into quantifiable sustainability gains: fuel consumption is reduced by 1425–3525 kg and CO₂ emissions by 4503–11,139 kg per peak hour, alongside a 19-percentage point improvement in punctuality that lowers passenger delay costs and reduces controller coordination workload. By simultaneously advancing environmental sustainability (carbon footprint reduction), economic sustainability (fuel and operational cost savings), and social sustainability (service punctuality and labor efficiency), the framework provides a measurable, monitorable, and policy-relevant decision-support tool for green airport surface operations aligned with sustainable development goals (SDGs). Full article

This work presents a nonlinear dynamic model and an energy-based control strategy for a coaxial vertical take-off and landing Unmanned Aerial Vehicle (UAV) equipped with independently tilting propulsion units. The proposed model captures the full six-degree-of-freedom motion of the vehicle and explicitly incorporates the forces and moments produced by the coaxial thrust-vectoring propulsion system, as well as the additional force components induced by the two-degree-of-freedom thrust vectoring mechanism. To regulate the vehicle during hover, cruise, and transition maneuvers, a passivity-based control framework formulated in terms of unit quaternions is developed. The control law simultaneously stabilizes the translational and rotational subsystems without relying on model linearization. In order to map the virtual control forces and torques into physically realizable actuator commands, a nonlinear control allocation procedure is introduced. This allocation scheme enables independent angular positioning of the propulsion units while computing the corresponding motor angular velocities. The effectiveness of the proposed modeling and control framework is assessed through three-dimensional dynamic simulations and numerical experiments, demonstrating accurate trajectory tracking, autonomous UAV landing capabilities, and smooth transitions between flight regimes for thrust-vectored UAV platforms. Full article

With the continuous expansion of unmanned aerial vehicle (UAV) applications, generating near-optimal paths and achieving effective obstacle avoidance in complex environments remain highly challenging tasks. To address the problems of multi-objective path planning and obstacle detection for UAV flight missions in orchard environments, this paper proposes a novel hybrid algorithmic framework named NeuroJPS-A. The main scientific contribution is the synergistic integration of neural combinatorial optimization, 3D-JPS, and adaptive APF, enabling task-aware obstacle avoidance and closed-loop trajectory adjustment. This method introduces neural combinatorial optimization from the TSP into the 3D-JPS algorithm, optimizing the search mechanism of the traditional JPS and further shortening the UAV’s globally planned path length. In addition, this study integrates the proposed algorithm with the APF to solve the local dynamic obstacle avoidance problem. Quantitative results show that NeuroJPS-A reduces path length by 10% and the number of turns by 47.8% in 2D, and achieves a 24.9% shorter path and 22% of A*’s computation time in 3D. To verify the performance of the proposed method, comprehensive simulation experiments were conducted. The experimental results demonstrate that the NeuroJPS-A algorithm enables UAVs to quickly and effectively generate optimal planned routes, ensuring safe navigation in complex orchard environments and preventing collisions during flight missions. Full article

To improve the optimization performance of the original Bounty Hunter Optimizer (BHO) in complex search environments, this paper proposes a quantum computing and adaptive mechanism-based BHO, named QCAMBHO. The proposed algorithm integrates three complementary strategies: quantum-computing-enhanced initialization, adaptive Lévy flight, and an adaptive differential operator. These mechanisms are designed to improve population diversity, strengthen global exploration, and enhance later-stage exploitation. The performance of QCAMBHO is evaluated on the CEC2017 and CEC2022 benchmark test suites. Experimental results show that QCAMBHO achieves competitive or superior optimization performance compared with several advanced algorithms in terms of convergence accuracy, stability, and robustness. Ablation experiments further confirm the positive contribution of each strategy and the synergistic effect of their integration. To examine its practical applicability, QCAMBHO is further used to optimize the key parameters of Kernel Extreme Learning Machine (KELM), and a QCAMBHO-KELM model is constructed for enterprise bankruptcy prediction. The results show that QCAMBHO-KELM achieves better overall classification performance than BHO-KELM and other comparison models across multiple evaluation metrics, including accuracy, Matthews correlation coefficient, sensitivity, specificity, precision, recall, and F1-score. These findings indicate that QCAMBHO not only provides an effective optimizer for complex numerical problems but also offers a promising decision-support tool for improving the accuracy and reliability of enterprise bankruptcy early warning. Full article

Under the background of the green transformation and energy conservation in the aviation field, hydrogen-fueled aero-engines are the primary direction for achieving sustainable aviation power development. However, the unique thermophysical properties of hydrogen fuel induce extreme thermal load challenges to engine thermal management. Based on the requirements of energy conservation and digital-intelligent technologies, this paper reviewed the recent research progress, important challenges, and future development directions in the thermal management field for hydrogen-fueled aero-engines, and filled the gaps in existing related reviews. (1) As for the liquid hydrogen thermal properties and thermal management requirements, the unique thermal physical properties of liquid hydrogen can easily cause fluctuations in heat load, large temperature differences, and material compatibility issues such as hydrogen embrittlement during storage, transportation, and combustion. The application of thermal barrier coatings, the design of targeted cooling structures, and the regulation of heat loss in the pipeline of the hydrogen supply system require particular attention. (2) As for the technical architecture and optimization of thermal management, the optimization of the high-pressure side manifolds in the cooled cooling air heat exchanger increases the flow uniformity by 18.8% and reduces the weight by 22.5%. The intercooled recuperated engine with the optimum area ratio reduces specific fuel consumption by 5.3% compared to the baseline engine in cruise. However, the system-level optimization research of the above widely recognized solutions is relatively limited in terms of coordinating the energy flow of engines. The baseline engine employed the method of system integration optimization to achieve a 2.99% increase in thrust and a 6.78% reduction in fuel consumption. (3) As for the thermal management modeling and simulation, the intelligent optimization method based on computational fluid dynamics reduces the pressure loss coefficient of the vane-integrated heat exchanger by 36%. Nevertheless, the multiphysics coupling model confronts a contradiction between computational cost and accuracy. (4) As for the comprehensive evaluation method, the advanced configuration of the hydrogen-fueled aero-engine can approximately reduce specific fuel consumption by 68.5% and NOx emission by 12.7% under the same maximum thrust condition. The hydrogen consumption of the proton exchange membrane fuel cells system model compared with the baseline system, optimized by the multi-objective optimization algorithm, has decreased by 15%, while the thermal uniformity has improved by 20–30%. However, the current evaluation system mostly focuses on a single dimension, lacking the analysis of nonlinear coupling among multiple factors and a closed-loop mechanism for evaluation, optimization, and verification. Future research should focus on the matching model of liquid hydrogen’s thermophysical properties and full flight conditions, global multi-energy flows optimization methods, multidimensional collaborative numerical simulation, multiphysics coupling models, and multidimensional comprehensive evaluation systems, to provide closed-loop theoretical support for the efficient, intelligent, and reliable thermal management system for hydrogen-fueled aero-engines. Full article

In multi-UAV bearing-only active tracking, the estimation and control performance is fundamentally determined by the target-centered bearing geometry, as passive angle-of-arrival measurements provide directional information without direct range information. To overcome this limitation, this paper formulates the problem as prescribed-shell bearing-geometry self-organization, in which the radial sensing scale is regulated to an admissible shell while the angular bearing distribution is actively reshaped on that shell. A shell-compatible moment–volume bearing-enclosure potential is first constructed directly on unit bearing directions, decoupling angular geometry improvement from unsafe range reduction and encoding directed balance, angular isotropy, and noncoplanar enclosure. To realize the resulting direction-space descent via physical UAV motion, a radius-normalized tangential lifting mechanism is derived from bearing-direction kinematics, eliminating radius-dependent angular-rate bias. The nominal radial–tangential command is then executed through a bearing-geometry-preserving ECBF-QP that embeds a predefined-time radial shell-reaching constraint, enforces target standoff, inter-UAV separation, and input constraints, and preserves shell reaching and tangential geometry improvement whenever feasible. Closed-loop analysis establishes shell reaching, practical bearing-geometry descent, safety forward invariance, and signal boundedness. Finally, improved bearing geometry, prescribed-shell convergence, preserved safety margins, reduced disruption from safety filtering, and real-world implementability are demonstrated via simulations, ablation studies, ROS-based validation, and a real-world flight experiment, which provide a promising approach for multi-UAV bearing-only active tracking. Full article

This study presents an unsteady numerical investigation of multi-port lateral jet interaction phenomena on a hypersonic vehicle configuration. An unsteady RANS approach with Menter’s SST k-ω model is implemented to investigate the transient interference mechanisms among single-, triple-, and quintuple-port arrangements, focusing on jet initiation and termination transients. Upstream jets establish bow shocks and a separation zone that progressively degrade the effective pressure ratio for downstream ports. This aerodynamic shielding manifests as nonlinear escalation in coupling intensity, with the quintuple-port configuration exhibiting complex multi-level shock systems distinct from simple superposition of single-port effects. Flow field development completes within approximately 0.5 ms, yet jet-induced vortical structures exhibit pronounced temporal hysteresis during the decay phase, with the high-pressure zone dissipating progressively from upstream to downstream regions. Under steady-state conditions, the quintuple-port arrangement attains a normal force amplification coefficient of 1.044 alongside a pitching moment amplification coefficient of 4.387, illustrating substantial moment augmentation potential inherent to multi-port interference effects. These findings furnish theoretical foundations for Reaction Control System (RCS) port layout optimization and control strategy development in hypersonic flight vehicles. Full article

Coordinating multiple unmanned aerial vehicle (UAV) systems under strict energy and temporal constraints remains a complex scheduling problem. Existing reinforcement learning methods typically rely on fixed-time-step modeling, which struggles to accommodate flight actions of varying durations and often leads to temporal mismatches between task planning and physical execution. To address this limitation, we propose an Adaptive Hierarchical Semi-Markov Decision Process (AH-SMDP) framework. This architecture decouples task allocation from execution by modeling variable-length actions via an SMDP. An event-driven synchronization mechanism is introduced to align the swarm’s decision-making rhythm with actual task completion times. Additionally, a state-aware reward formulation and a dynamic action space pruning strategy are designed to help UAVs balance energy efficiency with deadline compliance. Simulation results in multi-constraint environments demonstrate that the AH-SMDP framework effectively improves scheduling performance compared to standard MAPPO and PPO algorithms. Under the evaluated experimental settings, the proposed method yields improvements of approximately 30% in average task completion rate, 40% in energy reduction, and 60% in convergence stability. Ablation studies further suggest that this integrated framework offers a viable and effective approach for multi-UAV scheduling. Full article

To address the challenges of wind–solar output correlation, renewable-output uncertainty, transient stability, and economic optimization, this paper proposes a transient stability-constrained optimal power flow (TSCOPF) model considering wind–solar correlation. First, kernel density estimation (KDE) is employed to establish the marginal probability density functions of wind and photovoltaic outputs, and a Frank-Copula function is used to characterize the wind–solar correlation and construct a joint probability distribution model. A Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is then used to generate wind–solar output scenarios, which are further reduced by K-means++ clustering. Second, a transient stability assessment method combining a graph convolutional network with attention mechanism (GCN-Attention) and conditional mutual information (CMI)-based feature selection is developed to extract key stability features, and a TSCOPF model considering renewable-energy integration is constructed. Third, an improved Coati Optimization Algorithm (ICOA) integrating refraction-based opposition learning, Levy flight, and spiral search strategies is proposed to enhance global optimization performance. Simulations on the modified Institute of Electrical and Electronics Engineers (IEEE) 39-bus system and the IEEE 118-bus system demonstrate the accuracy, effectiveness, and scalability of the proposed method. Full article

To address the strong constraints, multiple objectives, and high dynamism of space telemetry, tracking, and command (TT&C) resource scheduling in flight-type launch scenarios, this study proposes a dynamic scheduling approach that integrates priority rules with adaptive multi-task evolution. First, a mixed-integer programming model is developed to capture fixed and mobile equipment, diverse mission requirements, and spatiotemporal coupling constraints, with the optimization objectives of minimizing the total mobile distance, the total number of deployed devices, and the number of mobile devices deployed. Second, a priority-based dynamic rescheduling mechanism is designed to support rolling insertion of emergency tasks and, when necessary, adjust conflicting tasks according to priority rules. Third, an improved NSGA-II algorithm is introduced, incorporating adaptive population adjustment, early stopping, and decoding caching to enhance multi-task search efficiency and convergence stability. Finally, a simulation experiment is constructed based on a typical commercial launch scenario, targeting three types of static task scenarios of different scales; the proposed Adaptive-NSGA-II consistently yields feasible schedules, while reducing the solution time by 70.4%, 62.7%, and 61.0%, respectively, compared with standard NSGA-II. In the dynamic emergency task insertion scenarios, the proposed priority-rule-based rolling rescheduling strategy successfully completes insertion in all three cases, achieves zero additional mobile distance in low-conflict scenarios, and remains significantly superior to the fixed-time direct insertion strategy even under high-conflict conditions. The experimental results demonstrate the effectiveness, robustness, and engineering applicability of the proposed method for TT&C resource scheduling in flight-type launch operations. Full article

The Vortex Ring State (VRS) is a critical aerodynamic hazard for rotorcraft, characterized by highly unsteady fluid–structure interactions and severe low-frequency vibrations. While data-driven deep learning models have shown promise in aviation state monitoring, their inherent “black-box” nature fundamentally contradicts the stringent interpretability requirements of airworthiness certification. To address this, we propose an “AI for Science” paradigm, investigating whether advanced Vision Transformers (ViT) can autonomously discover underlying aerodynamic mechanisms without human physical priors. First, to ensure absolute data fidelity, flight test datasets of a coaxial unmanned aerial vehicle were rigorously labeled using cross-validation from high-fidelity Computational Fluid Dynamics (CFD) simulations and wind tunnel tests. One-dimensional vibration signals were then transformed into two-dimensional Continuous Wavelet Transform (CWT) spectrograms. By employing Target-Layer Gradient Adaptation (Grad-CAM) techniques, we conducted a systematic comparison between traditional Convolutional Neural Networks (ResNet50) and ViT. The results demonstrate that while CNNs suffer from diffuse attention caused by high-frequency noise, the frozen-backbone ViT model achieves a physically interpretable accuracy of $93.24\%$, while autonomously locking its global attention onto a perfectly horizontal feature band centered at $41.7$ Hz. Crucially, this autonomously discovered feature precisely aligns with the theoretically derived once-per-revolution (1P) fundamental frequency of the rotor’s flap-lag coupling response under VRS aerodynamic turbulence. This research provides direct visual evidence bridging black-box AI decisions with classical fluid mechanics, proposing a “Mechanism-Guided Verification” framework that offers a trustworthy pathway for the future certification of AI in safety-critical aerospace systems. Full article

Agaricus bisporus is highly valued for its sensory and nutritional aspects, but it is highly perishable because of its intense postharvest metabolism. In this study, we looked at how white button mushrooms responded to three thermal regimes including constant refrigeration (4 °C), ambient storage (20 °C), and repeated temperature stress (RTS). The investigations were combined with an innovative smart packaging called Store Box (SB). Using an integrated volatilomics approach combining both gas chromatography–mass spectrometry (GC-MS) and proton transfer reaction time of flight mass spectrometry (PTR-ToF-MS), we tracked volatile compounds from intact mushrooms and internal tissues and linked these with weight loss and CO₂ production. According to the results, the SB stabilizes the internal microenvironment by reducing moisture loss and modulating gas exchange through controlled CO₂ buildup. This mechanism attenuated the respiratory surge and significantly delayed the rise of spoilage-related markers like methanethiol, acetaldehyde, and hexanal. At the same time, SB preserved freshness indicators such as 1-octen-3-one and benzaldehyde, which normally fade as mushrooms age. The protective effect was especially clear during thermal fluctuations, where SB acted as a metabolic buffer. Overall, this work offers new insights into the volatilome dynamics of Agaricus bisporus and confirms that smart packaging can help offset the damage caused by temperature instability along the supply chain. Full article

Multi-UAV cooperative persistent tracking in complex obstacle environments requires agents to approach dynamic targets while ensuring obstacle avoidance and flight safety; however, standard multi-agent reinforcement learning (MARL) methods typically rely on a single policy to implicitly handle both objectives, making it difficult to balance task performance and risk control. To address this issue, this paper proposes a Target-Aware Safety-Residual Pursuit Reinforcement Learning (TASRP) framework for constrained three-dimensional environments. A continuous-control 3D tracking environment is constructed in IsaacLab, where two multirotor UAVs cooperatively track a dynamic target under random, target-blocking, and gate-like obstacle layouts, boundary constraints, and inter-agent collision risks, with each UAV producing a four-dimensional action composed of normalized thrust and body-frame torques. TASRP adopts a dual-head residual policy in which a pursuit branch generates nominal actions, and a safety branch predicts corrective residuals, together with a risk-aware gating mechanism, a target-guided teacher for obstacle detouring, and a dual-critic safety-constrained optimization scheme. Under clean observations, TASRP achieves task success rates of 75–79%, obstacle crash rates of 13–15%, and boundary crash rates of 1–2% across three representative scenarios. Under noisy observations, TASRP achieves 72.1% task success, 20.3% obstacle crash, and 2.8% boundary crash, outperforming MAPPO (61.2%, 61.2%, 5.6%) and HAPPO (58.1%, 73.5%, 4.1%). These results indicate that explicitly decoupling target-oriented control and safety correction enables a more effective and robust performance–safety trade-off under both clean and moderately noisy observations. Full article