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.

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.