首页|University of Lorraine Reports Findings in Machine Learning (Mutual interactions between plasma filaments in a tokamak evidenced by fast imaging and machine lea rning)
University of Lorraine Reports Findings in Machine Learning (Mutual interactions between plasma filaments in a tokamak evidenced by fast imaging and machine lea rning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Nancy, France, by News Rx correspondents, research stated, "Magnetically confined fusion plasmas are su bject to various instabilities that cause turbulent transport of particles and h eat across the magnetic field. In the edge plasma region, this transport takes t he form of long filaments stretched along the magnetic field lines." Our news journalists obtained a quote from the research from the University of L orraine, "Understanding the dynamics of these filaments, referred to as blobs, i s crucial for predicting and controlling their impact on reactor performance. To achieve this, highly resolved passive fast camera measurements have been conduc ted on the COMPASS tokamak. These measurements are analyzed using both conventio nal tracking methods and a custom-developed machine-learning approach designed t o characterize more particularly the mutual interactions between filaments. Our findings demonstrate that up to 18% of blobs exhibit mutual intera ctions in the investigated area close to the separatrix, at the border between c onfined and nonconfined plasma. Notably, we present direct observations and radi al dependence of blob coalescence and splitting, rapid reversals in the propagat ion direction of the blob, as well as their dependence on the radial position."
NETL
NSTL
