Pei Zhu

Urban drone logistics face significant challenges in long-distance delivery due to limited drone endurance, making reasonable vertiport siting and air-route planning a critical research problem. Existing studies typically treat facility siting and route configuration as independent or sequential decisions, leading to suboptimal network efficiency and empty-haul wastage. This study proposes an integrated bi-layer optimization framework that integrates vertiport location and air-route planning for urban UAV logistics networks. The upper-level model optimizes vertiport placement and tiered capacity sizing using an improved genetic algorithm (GA) with Halton sequence initialization and a memory bank mechanism, while the lower-level path planning model designs delivery routes via a Greedy-Embedded Ant Colony Optimization (GACO) algorithm. Unlike conventional approaches, the proposed framework establishes a closed-loop iterative coordination mechanism that feeds routing costs back into siting decisions, introduces a hierarchical vertiport deployment scheme dynamically matching facility capacity with regional demand density, and implements a semi-open routing policy that allows cross-station landings to reduce empty-haul flights. Numerical experiments based on real operational data from Shanghai demonstrate that the proposed framework achieves an 8.75% total cost reduction over the traditional sequential location-then-routing strategy. In addition, the hierarchical vertiport construction strategy contributes 4.49% cost savings compared to non-hierarchical schemes, while maintaining strong robustness, with facility overcapacity strictly below 5% under routine demand disturbances up to 10%. These results confirm the effectiveness of the proposed framework in improving the economic efficiency and operational adaptability of urban UAV delivery networks.

High-rise building fires pose substantial challenges to conventional firefighting operations due to restricted rescue space and the difficulty of delivering suppression resources rapidly. To improve response efficiency, this study proposes a two-stage mission planning framework for multi-station UAV-based firefighting. The proposed methodology simultaneously accounts for environmental wind, building obstacles, fire evolution, and UAV payload constraints. In the first stage, an improved particle swarm optimization (PSO) algorithm is employed to generate time-optimal flight paths satisfying both spatial obstacle-avoidance and wind-field constraints. In the second stage, based on the actual flight times derived from the first stage, the multi-UAV resource scheduling problem is formulated as a mixed-integer linear programming (MILP) model to minimize the total fire suppression mission duration. Additionally, an isochrone-based firefighting coverage circle is introduced to optimize the layout of additional fire stations. Simulation results indicate that while optimized paths remain geometrically similar under varying wind conditions, wind-induced flight time variations significantly affect UAV arrival sequences and flight times. In the scheduling stage, differences in station layouts and fire scales alter projectile release timing; under unfavorable conditions, such temporal differences can increase the total mission duration by more than 28%. Notably, the optimized addition of fire stations effectively enhances response redundancy in high-rise clusters, reducing fire suppression time in adjacent scenarios by approximately 50%. The proposed method provides theoretical support and methodological guidance for cooperative UAV firefighting and emergency resource optimization in urban environments.

A comprehensive three-dimensional numerical study is conducted to investigate thermal Marangoni convection in a shallow rectangular cavity, focusing on the fundamental differences between the single-free-surface and two-free-surface configurations. Simulations are performed over a range of thermal Marangoni numbers (Ma) and two aspect ratios (As = 1 and ∞). The results reveal that the flow structure is significantly altered by the presence of a second free surface. At a lower Ma, the two-free-surface system exhibits a symmetric and double-layered counter-circulation pattern driven by Marangoni stresses on both interfaces, distinct from the single recirculation roll observed in the single-free-surface case. This configuration reduces the effective vertical confinement and attenuates local temperature gradients near sidewalls. Once Ma exceeds a critical value, the flow transitions to a three-dimensional oscillatory state characterized by hydrothermal waves (HTWs) and stationary longitudinal rolls. The two-free-surface system shows higher oscillation frequencies but lower amplitudes compared to its single-free-surface counterpart under identical conditions, attributed to the underlying interplay between effective fluid inertia and spatial constraint in the two configurations. Furthermore, longitudinal wall confinement (As = 1) significantly influences wave pattern development, promoting standing-wave-type oscillations, while removing longitudinal confinement (As = ∞) facilitates the direct formation of oblique HTW. A universal transition sequence from periodic oscillation to quasi-periodic oscillation, and finally to chaos, is observed with increasing Ma for all configurations. The present study would provide more insights into the critical role of geometric confinement and interfacial conditions in governing the stability and pattern formation of thermal Marangoni convection.

The Civil Aviation Administration of China mandates comprehensive evaluations of emergency response effectiveness. However, existing studies predominantly focus on evaluating response capabilities rather than actual effectiveness. This leads to evaluation results deviating from reality. Other studies evaluating response effectiveness are mostly limited by incomplete indicator systems and flawed algorithms. To address the questions, this study takes runway unsafe events as the study subject, focuses on evaluating response effectiveness, examines common evaluation methodologies, identifies critical gaps in indicator systems, and discusses traditional algorithmic vulnerabilities. Utilizing the Delphi method and fuzzy Analytic Hierarchy Process, this study establishes a four-tier indicator system encompassing evaluation objectives, phases, processes, and elements and proposes an optimized model incorporating scoring criteria, indicator weights, and correction coefficients designed to mitigate inherent algorithmic vulnerabilities prevalent in traditional methodologies. Finally, two simulation verifications based on real incidents demonstrate that the model of emergency response effectiveness at civil airports has a notable improvement in evaluation accuracy.

To investigate the fire suppression effectiveness of perfluorohexanone in low-pressure environments, a self-built fire suppression experimental platform was utilized to analyze the influence of ambient pressure and heat release rate on its performance. The results demonstrate that under normal-pressure conditions, the extinguishing time increases with the heat release rate of the fire source, whereas under low-pressure conditions, the extinguishing time decreases as the heat release rate increases. Specifically, under normal pressure, the extinguishing times for Fire Pan A (10 cm × 10 cm × 10 cm), Fire Pan B (15 cm × 15 cm × 10 cm), and Fire Pan C (20 cm × 20 cm × 10 cm) were 5.03 s, 8.15 s, and 9.63 s, respectively. In contrast, under low pressure, the extinguishing times were significantly shorter, with reductions of 2.8 s, 6.59 s, and 8.45 s, respectively. In terms of temperature reduction, the flame temperature decreased by approximately 300 °C under normal pressure, while under low pressure, it decreased by only about 100 °C. The concentration of hydrogen fluoride (HF) produced after extinguishment was relatively low, indicating limited toxicity. The HF concentration under normal pressure was, on average, approximately 59.2% higher than that under low-pressure conditions. Based on parameters such as the mass of the extinguishing agent, temperature changes, and hydrogen fluoride content, a fire suppression effectiveness model was established. The results show that the weight coefficient for chemical inhibition intensity is as high as 38.81, significantly exceeding other factors, demonstrating that perfluorohexanone primarily relies on chemical inhibition to interrupt the combustion chain reaction. This conclusion provides an important theoretical basis for the design and optimization of fire suppression systems in low-pressure environments such as aviation and high-altitude areas.

The aviation industry needs to develop sustainable, fire-safe cabin interior materials. Although wood is eco-friendly, its high flammability makes it challenging to meet flame retardant standards. Enhancing wood fire safety requires the creation of an environmentally friendly and flame retardant coating. In this study, a new type of intumescent flame retardant (IFR) coating was applied to the wood surface using the layer-by-layer (LBL) technique, with fully bio-based chitosan (CS), agar, and phytic acid (PA) as key components. The coated wood demonstrated improved durability, flame resistance, and thermal stability. Particularly, the Wood-2 sample achieved a vertical burning test (UL-94) V-0 rate and a limiting oxygen index (LOI) of 53.1%, which exceeded most previous reported flame retardant coatings. Cone calorimeter test and infrared thermography analysis confirmed that a thick layer of intumescent char formed when the coating was exposed to heat, effectively hindering heat transfer and oxygen supply. This flame retardant effect is attributed to a synergistic mechanism involving nitrogen/phosphorus (N/P) elements. This study offers an environmentally friendly solution for wood flame retardancy and lays an experimental and theoretical foundation for the development of green aviation interior materials.

The frequent occurrence of forest fires in mountainous regions has posed severe threats to both the ecological environment and human activities. This study proposed a multi-target firefighting task planning method of forest fires by multiple UAVs (Unmanned Aerial Vehicles) integrating task allocation and path planning. The forest fire environment factors such high temperatures, dense smoke, and signal shielding zones were considered as the threats. The multi-UAVs task allocation and path planning model was established with the minimum of flight time, flight angle, altitude variance, and environmental threats. In this process, the study considers only the use of fire-extinguishing balls as the fire suppressant for the UAVs. The improved multi-population grey wolf optimization (MP–GWO) algorithm and non-Dominated sorting genetic algorithm II (NSGA-II) were designed to solve the path planning and task allocation models, respectively. Both algorithms were validated compared with traditional algorithms through simulation experiments, and the sensitivity analysis of different scenarios were conducted. Results from benchmark tests and case studies indicate that the improved MP–GWO algorithm outperforms the grey wolf optimizer (GWO), pelican optimizer (POA), Harris hawks optimizer (HHO), coyote optimizer (CPO), and particle swarm optimizer (PSO) in solving more complex optimization problems, providing better average results, greater stability, and effectively reducing flight time and path cost. At the same scenario and benchmark tests, the improved NSGA-II demonstrates advantages in both solution quality and coverage compared to the original algorithm. Sensitivity analysis revealed that with the increase in UAV speed, the flight time in the completion of firefighting mission decreases, but the average number of remaining fire-extinguishing balls per UAV initially decreases and then rises with a minimum of 1.9 at 35 km/h. The increase in UAV load capacity results in a higher average of remaining fire-extinguishing balls per UAV. For example, a 20% increase in UAV load capacity can reduce the number of UAVs from 11 to 9 to complete firefighting tasks. Additionally, as the number of fire points increases, both the required number of UAVs and the total remaining fire-extinguishing balls increase. Therefore, the results in the current study can offer an effective solution for multiple UAVs firefighting task planning in forest fire scenarios.

To address the demands of efficient forest fire detection and suppression, an adaptive multiple UAV swarm collaborative firefighting strategy considering dynamic forest fire spread and resource constraints was proposed in this paper. The multiple UAV swarm adaptive information-driven collaborative search (MUSAIDCS) algorithm and the resource-limited firefighting model were established. A temperature change-driven adaptive step-length search strategy is proposed to improve the accuracy of the search and detection of fire spots. The critical water flow rate required for fire suppression is calculated to evaluate the firefight performance, and an emergency bidding algorithm is applied to enable multiple UAV swarms collaborative firefighting under limited resources, including different payloads per UAV and swarm number. The comparative simulations for four search strategies indicate that the MUSAIDCS search strategy can significantly reduce task completion time and improve firefighting efficiency compared with the other traditional search strategies. Increasing payload quantity per UAV and swarm number can further enhance task completion efficiency and firefighting effectiveness. This study demonstrates that a resource-constrained collaborative firefighting strategy enables the dynamic allocation of UAV swarm resources under limited conditions and then optimizes firefighting performance within constraints.

Aircraft cargo compartment fires are one of the main threats to the safety of civil aircraft. In this study, a series of numerical simulations on the fire suppression performance of water mist in cargo compartments was carried out to examine the effects of the spray pattern, droplet size, and nozzle layout. The fire dynamics simulator (FDS) code was used to construct a fire suppression scenario in a full-scale aircraft cargo compartment. The results show that the extinguishment time of a corner fire was longer compared with center and sidewall fires due to the relatively larger distance from the nozzle and, therefore, a lower effective number of droplets reaching the flame area. Solid and hollow spray patterns showed significant differences in the spray coverage area. For a fixed flow rate, the hollow spray showed better fire suppression performance than solid spray. When the droplet size varied from 50 to 400 µm, the fire extinguishment time first increased and then decreased, corresponding to the dominant mechanism of the smothering effect of small droplets and the cooling effect of large droplets. In addition, the nozzle layout affected the water coverage on the ground of the cargo compartment. With an increase in nozzle number, the water mist flux was more evenly distributed and the fire extinguishment effect also increased.

For air traffic controllers, the extent of their cognitive workload can significantly impact their cognitive function and response time, consequently influencing their operational efficiency or even resulting in safety incidents. In order to enhance the accuracy and efficiency in determining the cognitive workload of air traffic controllers, a cognitive workload detection method for air traffic controllers based on mRMR and fewer EEG channels was proposed in this study. First of all, a set of features related to gamma waves was initially proposed; subsequently, an EEG feature evaluation method based on the mRMR algorithm was employed to pinpoint the most relevant indicators for the detection of the cognitive workload. Consequently, a model for the detection of the cognitive workload of controllers was developed, and it was optimized by filtering out channel combinations that exhibited higher sensitivity to the workload using the mRMR algorithm. The results demonstrate that the enhanced model achieves the accuracy and stability required for practical applications. Notably, in this study, only three EEG channels were employed to achieve the highly precise detection of the cognitive workload of controllers. This approach markedly increases the practicality of employing EEG equipment for the detection of the cognitive workload and streamlines the detection process.