As urban drone logistics becomes a practical reality, balancing economic cost, ground safety risk, and noise impact poses a systemic challenge. A recent study proposes a novel approach to design urban low-altitude logistics networks, incorporating noise constraints into a multi-objective optimization framework. By combining a layered network model with a dual-population co-evolutionary algorithm, the research provides a new direction for the low-altitude logistics infrastructure of future cities.

A recent study investigates the contrasting patterns of symbiotic nitrogen fixation (SNF) and asymbiotic nitrogen fixation (ANF) along altitudinal gradients in subtropical forests. The research found that SNF rates declined with increasing altitude due to higher soil nitrogen availability and lower air temperatures, while ANF rates showed a hump-shaped pattern, influenced by soil properties at lower altitudes and climatic factors at higher altitudes. The study underscores the importance of distinguishing between SNF and ANF in ecological studies and Earth system models, providing valuable insights for improving global BNF estimates and refining model predictions.

The Vortex Particle Method (VPM) is a meshless vortex flow simulation approach gaining traction for its efficient simulation of unsteady vortex wakes evolution. However, traditional VPM has huge challenge on accurately simulating complex flows due to its poor numerical stability. Recently, a team of aviation researchers led by Min Chang from Northwestern Polytechnical University in China have developed a Stability-enhanced VPM (SEVPM). These advancements enable stable, high-fidelity simulations of complex flows. The researchers demonstrated that their SEVPM can accurately and stably simulate high Reynolds number flows and shear turbulence. The researchers plan to further validate and refine the Stability-enhanced VPM by applying it to more complex and realistic flow scenarios.

Aircraft conceptual design is a highly complex process involving multidisciplinary trade-offs and creative thinking. Recent advances in generative artificial intelligence (AI) provide promising opportunities to automate and augment this process. A new study, recently published in the Chinese Journal of Aeronautics, presents an AI-driven framework capable of generating aircraft configuration schemes based on design requirements, integrating aerodynamic knowledge and system constraints. This research fills a key gap in intelligent design methodology, offering a new tool to revolutionize the early stages of aircraft development.

High-resolution flow field data are critical for accurately evaluating the aerodynamic performance of aircraft. However, acquiring such data through large-scale numerical simulations or wind tunnel experiments is highly resource-intensive. Flow field super-resolution techniques offer an efficient alternative by reconstructing high-resolution data from low-resolution inputs. While existing super-resolution methods can recover the global structure of the flow, they often struggle to capture fine local details, especially shock waves. To address this limitation, this research proposes the FlowViT-Diff framework that integrates Vision Transformers (ViT) with an enhanced denoising diffusion probabilistic model to simultaneously capture global coherence and local flow features with high fidelity.

Maritime recovery of spacecraft is critical for crewed missions, offering advantages such as reduced impact forces and enhanced safety. While airbag cushioning systems have been widely adopted to mitigate landing impacts, prior studies predominantly focused on land or calm-water scenarios, leaving the complex interactions between airbags, reentry capsules, and ocean waves poorly understood. This study published in the Chinese Journal of Aeronautics on June6, 2025, addresses this gap by employing a Fluid-Structure Interaction (FSI) model to analyze water-landing characteristics under wave conditions, revealing key mechanisms such as wave-phase-dependent impact forces and horizontal velocity thresholds for stability. The findings provide essential insights for optimizing recovery systems, ensuring safer and more reliable maritime operations for reusable spacecraft.

Deflagration-to-Detonation Transition (DDT) process is the most common technique for obtaining stable detonation propagation. Although the detonation initiation appearances are different, the essential physical characteristic is the same: the local hot spot created by the energy focus. One or more bow shocks created by Mach reflection remain as strong transverse shocks after the detonation front. The corresponding numerical simulations show that the strong transverse shock propagation behavior strongly depends on the location where the hot spot forms. This work provides some fresh new insights into the DDT process, which may improve the understanding of DDT formation mechanisms.

For multi-vehicle networks, Cooperative Positioning (CP) technique has become a promising way to enhance vehicle positioning accuracy. Especially, the CP performance could be further improved by introducing Sensor-Rich Vehicles (SRVs) into CP networks, which is called SRV-aided CP. However, the SRV-aided CP system may split into several sub-clusters that cannot be connected with each other in dense urban environments, in which the sub-clusters with few SRVs will suffer from degradation of CP performance. In this work, a new locally-centralized CP method based on the clustering optimization strategy, aiming to fully utilize potential available information from high precision node, has been proposed.

Safe and feasible path planning is crucial for achieving autonomous navigation of fixed-wing Unmanned Aerial Vehicles (UAVs) in complex environments. However, due to the high-speed flight and complex control requirements of fixed-wing UAVs, ensuring the feasibility and safety of planned paths in complex environments remains challenging. Researchers at Beihang University have developed a feasible path planning algorithm named Closed-loop Radial Ray A* (CL-RaA*). The core components of the CL-RaA* include an adaptive variable-step-size path search and a just-in-time expansion primitive. By integrating these two components and conducting safety checks on the trajectories to be expanded, the CL-RaA* can rapidly generate safe and feasible paths that satisfy the differential constraints of fixed-wing UAVs.

Modern flight control demands faster response, greater adaptability, and resilience against unknowns—challenges traditional control systems struggle to meet. Incremental Nonlinear Dynamic Inversion (INDI) has emerged as a compelling solution, shifting control logic away from models toward real-time measurements. In a sweeping two-part review, researchers chart the path of INDI from its mathematical roots to its growing role inapplications. With its modular structure and built-in robustness, INDI is no longer just an academic concept.