Application of machine learning for detecting and tracking turbulent structures in plasma fusion devices using ultra fast imaging

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61389021%3A_____%2F24%3A00616628" target="_blank" >RIV/61389021:_____/24:00616628 - isvavai.cz</a>

Result on the web

<a href="https://www.nature.com/articles/s41598-024-79251-z" target="_blank" >https://www.nature.com/articles/s41598-024-79251-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-024-79251-z" target="_blank" >10.1038/s41598-024-79251-z</a>

Result language

angličtina

Original language name

Application of machine learning for detecting and tracking turbulent structures in plasma fusion devices using ultra fast imaging

Original language description

This study explores the application of machine learning techniques for detecting and tracking plasma filaments around the boundary of magnetically confined tokamak plasmas. Plasma filaments, also called blobs, are responsible for enhanced turbulent transport across magnetic field lines, and their accurate characterization is crucial for optimizing the performance of magnetic fusion devices. We present a novel approach that combines machine learning methods applied to data obtained from ultra-fast cameras, including YOLO (You Only Look Once) for object detection, semantic segmentation, and specific tracking methods. This approach enables fast and accurate detection and tracking of filaments while overcoming the limitations of conventional methods, which are time-consuming and prone to human subjectivity. A significant advance in our study lies in the development of a method for automatically labeling a large batch of data, which greatly facilitates the training of supervised machine learning algorithms. Using these techniques, we obtained promising results demonstrating a significant improvement over conventional tracking methods, achieving a detection accuracy of up to 98.8%, while reducing the inference time per frame by 15% to 31% compared to conventional Kalman filter tracking. These results open up new perspectives for investigating turbulent phenomena in tokamaks, and could have important implications for the development of controlled nuclear fusion.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10305 - Fluids and plasma physics (including surface physics)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Scientific Reports

ISSN

2045-2322

e-ISSN

2045-2322

Volume of the periodical

14

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

16

Pages from-to

27965

UT code for WoS article

001355873800036

EID of the result in the Scopus database

2-s2.0-85209119161