• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Physicists Discover What Happens Inside a Stable Vortex

Physicists Discover What Happens Inside a Stable Vortex

© iStock

Large vortices with characteristic spiral arms are often observed in the atmosphere and the ocean. Physicists from HSE University have explained how these structures form and why they retain their shape. The researchers found that velocities at points located along the same vortex arc remain correlated even over long distances. At the same time, this correlation weakens rapidly with increasing distance from the vortex centre. These differences help explain the formation of spiral arms and may improve models of atmospheric and oceanic currents. The findings have been published in Physical Review Fluids.

Turbulent flow is the movement of a liquid or gas, which is characterised by vortices, intense mixing, and sudden changes in velocity. It is impossible to trace the movement of each particle in such a stream, so researchers describe it through averaged characteristics. For example, using the paired correlation function, it is possible to understand how the fluid velocities are related at two flow points.

For fully developed turbulence, in which all directions are equivalent and there is no rotation, such correlations have already been studied in considerable detail. In a three-dimensional flow, the resulting vortices break up into smaller ones, and at large distances the velocities are practically independent of one another. In a two-dimensional flow, by contrast, vortices tend to merge into larger structures, and the correlations between velocities decay much more slowly with distance. 

In a rapidly rotating three-dimensional fluid, the structure of the flow changes: the mean flow, represented by a large vortex, becomes flatter, while small fluctuations within it retain a complex three-dimensional character. It is the interaction of these fluctuations that determines the statistical properties of the flow inside the vortex.

In their new paper, Prof. Sergey Vergeles and Associate Professor Leon Ogorodnikov from the Landau Institute for Theoretical Physics and the International Laboratory for Condensed Matter Physics at HSE University studied a rapidly rotating three-dimensional fluid in which a stable coherent vortex forms—a massive swirling flow that can arise in the ocean and atmosphere. In the atmosphere, such vortex structures are clearly visible in cyclones and anticyclones, where clouds gather in spiral arms, forming dense, extended regions. The authors investigated how velocity correlations within such a vortex behave both locally and at a distance. The researchers considered three components of velocity: radial (motion toward or away from the vortex axis), azimuthal (circular motion around the vortex axis), and vertical (motion along the vortex axis). 

It was found that correlations between velocities persist over long distances and decay slowly as the distance increases, but in different ways depending on direction: slower (logarithmically) along the circumference of the vortex, somewhat faster along its axis, and even faster (power-like) in radial direction. This effect arises from the spatial inhomogeneity of the medium’s rotation, in which fluid elements located at different distances from the rotation axis move with different angular velocities and therefore complete full rotations in different amounts of time. This slow decay of velocity correlations in the azimuthal direction, compared with the radial direction, is clearly manifested in spiral arms that are elongated along the direction of rotation of the fluid and compressed in the transverse direction. Similar spiral structures are observed in galaxies. Although these systems are governed by different physical processes, it is the inhomogeneous (differential) rotation that leads to the formation of arms of a similar shape.  

The authors also found that correlations between identical velocity components are largely independent of the specific way energy is injected into the system, whereas correlations between different components are sensitive to it.

Leon Ogorodnikov

'The long-range correlations between identical velocity components are largely independent of the statistical properties of the forcing that supplies energy to the system. In contrast, the cross-correlations between the radial and azimuthal velocity components behave differently: they are weaker, decay more rapidly with distance, and show a strong dependence on the forcing correlation function,' comments one of the study authors, Leon Ogorodnikov, Junior Research Fellow at HSE University's Laboratory for Condensed Matter Physics and the Landau Institute for Theoretical Physics.

The study findings can help better understand the internal structure of large coherent vortices that form in oceanic and atmospheric currents on Earth and other planets.

See also:

Scientists Train Neural Network to Generate Process Plans from 3D Models

Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

Biologists Discover 'Molecular Fingerprint' of Preeclampsia

Researchers at HSE University employed a new method to model hypoxia in placental cells during pregnancies complicated by preeclampsia and identified molecular markers of tissue hypoxia. Since hypoxia is one of the key mechanisms underlying preeclampsia, these findings are important for a more accurate and timely diagnosis of the disease and for the development of effective treatment methods. The paper has been published in Placenta.

‘Hedgehog’ Versus ‘Relatives’: Researchers Measure How the Brain Responds to Unexpected Words During Natural Speech

Russian neurophysiologists, including researchers from HSE University, have demonstrated the feasibility of using event-related fields (ERFs) to study brain activity during natural speech perception. The researchers showed that this approach can be applied not only to individual words but also to continuous speech. Their findings indicate that words whose meanings differ significantly from the preceding context require longer processing times. The study also reveals that the brain processes function words in two stages: first, it identifies their grammatical role and then uses this information to predict the next word. The study has been published in Frontiers in Human Neuroscience.

HSE Researchers Create New Corpus of Early Child Speech in Russian

Researchers at the HSE Centre for Language and Brain have presented RusLan-M, an open multimedia corpus that makes it possible to trace the development of early child speech in Russian from first words to the emergence of complex grammatical constructions. The database contains around 41 hours of video recordings and more than 35,000 child utterances. The new resource will help researchers study more precisely how children acquire Russian and, in the longer term, develop more reliable tools for assessing speech development. The study has been published in Language Resources and Evaluation.

Hybrid Intelligence: Competencies in the Age of AI Discussed at Technoprom-2026

Artificial intelligence is not creating new professions, but rather transforming the nature of existing ones. This was the conclusion reached by participants in the panel session ‘Hybrid Intelligence: Digital and Human Drivers of Development,’ organised by the Institute for Statistical Studies and Economics of Knowledge (ISSEK) at HSE University as part of the 13th International Forum of Technological Development (Technoprom-2026). The experts discussed how the nature of work is changing, which skills are becoming increasingly sought after, and what prevents companies from fully capitalising on new technologies.

Scientists Develop New Solution for 6G Communication Systems

A terahertz neuromorphic circuit developed by scientists at HSE University could make 6G communication systems both more accurate and energy-efficient. The circuit enables indoor tracking of mobile devices with an accuracy of up to 99%. The results were presented at PIERS 2026, an international symposium on Photonics and Electromagnetism held in China.

Scientists Develop Algorithm for More Reliable Processors in Data Centres

Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.

Researchers Rank Recommendation Algorithms Using Sports Tournament Model

Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed an approach for selecting recommendation algorithms more effectively. Their approach uses pairwise comparisons of algorithms to create a tournament table, with the overall ranking based on their performance across all datasets in the tournament. This can reduce the number of algorithms that need to be tested when developing new services, saving both time and money. The study was presented at the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026).

Researchers Develop Method for Direct Generation of Regulatory DNA

Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’

Researchers at HSE University and Sber Train Neural Networks to Better Predict User Preferences

The HSE FCS AI and Digital Science Institute and Sber have introduced a new architecture for recommendation systems that combines two classes of models, enabling algorithms to better predict users’ interests and needs. A preprint of the paper has been published on arxiv.org and presented at Urban ML.