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.

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.