New research utilising data from NASA’s Parker Solar Probe has provided the first direct evidence of a phenomenon known as the “helicity barrier” in the solar wind. This discovery, published in Physical Review X by Queen Mary University of London researchers, offers a significant step towards understanding two long-standing mysteries: how the Sun’s atmosphere is heated to millions of degrees and how the supersonic solar wind is generated.

University of Maryland astronomers discovered that an unexpected blast of space rocks ejected during DART mission carried three times more momentum than the spacecraft itself, leading to new insights for future planetary defense missions.

Thanks to NASA’s James Webb Space Telescope, researchers at Embry-Riddle Aeronautical University have observed four additional Wolf-Rayet systems. Each has several visible dust shells around it, similar to the one found around Wolf-Rayet star WR-140.

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.

“Space ice” contains tiny crystals and is not, as previously assumed, a completely disordered material like liquid water, according to a new study by scientists at UCL (University College London) and the University of Cambridge.

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.