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.

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.

A method is proposed for high-resolution neutron spectrum regulation across the entire energy domain, which helps to determine the optimal neutron spectrum for transuranic isotope production and a regulation scheme to establish this optimal neutron spectrum within the irradiation channels. The state-of-the-art production schemes for ²⁵²Cf and ²³⁸Pu in the High Flux Isotope Reactor were optimized, improving the yield of ²⁵²Cf by 12.16% and that of ²³⁸Pu by 7.53% to 25.84%.

Today, Insilico Medicine (“Insilico”), the clinical-stage generative artificial intelligence (AI)-driven drug discovery company, announces its upcoming Summer Pharma.ai Updates webinar, scheduled for 10 a.m. ET on July 10. During the webinar, Founder and CEO Alex Zhavoronkov, PhD, will provide the latest company developments and introduce new features and applications of Insilico’s AI software suite, with a particular focus on the latest advancements in Biology42 and Chemistry42 through product demos, in-depth explanations, and real-world use cases. Ready for the future of Pharma.ai? Register here.