{"id":4640,"date":"2024-02-29T09:36:52","date_gmt":"2024-02-29T01:36:52","guid":{"rendered":"https:\/\/www.1ai.net\/?p=4640"},"modified":"2024-02-29T09:36:52","modified_gmt":"2024-02-29T01:36:52","slug":"ai-%e5%8a%a9%e5%8a%9b%e4%ba%ba%e9%80%a0%e5%a4%aa%e9%98%b3%e7%a8%b3%e5%ae%9a%e8%be%93%e5%87%ba%ef%bc%9a%e6%8f%90%e5%89%8d-300-%e6%af%ab%e7%a7%92%e9%a2%84%e6%b5%8b%e8%81%9a%e5%8f%98","status":"publish","type":"post","link":"https:\/\/www.1ai.net\/en\/4640.html","title":{"rendered":"AI helps stabilize the output of the "artificial sun": predicting plasma "tearing" in fusion 300 milliseconds in advance"},"content":{"rendered":"

the scientist<\/a>Over the past few decades, the dream of nuclear fusion power generation has been explored and pursued, but due to various challenges,These experimental reactors have not yet reached the point where they can replace fossil fuels.<\/strong><\/p>\n

\"7e5b3058-1562-4a09-86e3-95108ff48083\"<\/p>\n

One of the key challenges in making nuclear fusion generate stable electricity is the unpredictability of superheated plasma.However, the Princeton Plasma Physics Laboratory (PPPL) has made a major breakthrough in this regard.<\/strong><\/p>\n

The PPPL team designed and developed a new artificial intelligence that can predict plasma "tearing" in fusion 300 milliseconds in advance, thereby smoothing out irregularities and hopefully avoiding reactor restarts in the future.<\/p>\n

Fusion reactors use a ring structure, similar to a donut. Inside the tokamak reactor, magnetic fields control the plasma so that it does not break through the ring wall.<\/p>\n

But boiling plasma is difficult to control, and it can easily "tear apart" and escape the powerful magnetic fields that are used to contain it. Once the plasma escapes, the reactor needs to be shut down and reset.<\/p>\n

\"1d3504d7-8a17-4b06-99a3-f5ef8260a204\"<\/p>\n

The AI developed at PPPL has the potential to predict these instabilities long enough in advance so that they can be corrected, the tests were conducted at the Department of Energy\u2019s DIII-D national fusion facility in San Diego (see above).<\/p>\n

The A.I.A. algorithm does not give long warning times - the maximum forecast time is 300 ms. This is not enough time for humans to do anything, but the fusion response can be undermined only in a few milliseconds by the instability of the torn pattern\u3002<\/p>\n

THE TEAM WAS CONVINCED THAT ARTIFICIAL INTELLIGENCE CONTROLLERS COULD REDUCE THE VOLATILITY OF TORN PATTERNS IN DIII-D REACTORS. HOWEVER, THE NETWORK IS SPECIFICALLY DESIGNED FOR DIII-D TRAINING - IT CANNOT PREDICT OR STABILIZE THE INSTABILITY OF OTHER TOKAMAK TORN PATTERNS. RESEARCHERS WANT TO EVENTUALLY DEVELOP A MORE GENERAL ARTIFICIAL INTELLIGENCE, BUT THIS REQUIRES MORE TESTING\u3002<\/p>","protected":false},"excerpt":{"rendered":"

Scientists have spent the last few decades exploring and chasing the dream of generating electricity from nuclear fusion, though these experimental reactors have so far fallen short of replacing fossil fuels due to a variety of challenges. One of the key challenges in getting fusion to produce stable electricity is the unpredictability of superheated plasma, but the Princeton Plasma Physics Laboratory (PPPL) has made a major breakthrough in this area. The PPPL team has designed and developed a new artificial intelligence that can predict plasma \"tears\" in fusion up to 300 milliseconds in advance, smoothing out irregularities and hopefully avoiding reactor restarts in the future. Fusion reactors are ring-shaped, similar to a doughnut, and inside a tokamak, a magnetic field controls the plasma so that it doesn't break through the walls of the ring. No.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[146],"tags":[1413,1412],"collection":[],"class_list":["post-4640","post","type-post","status-publish","format-standard","hentry","category-news","tag-1413","tag-1412"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/4640","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/comments?post=4640"}],"version-history":[{"count":0,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/4640\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/media?parent=4640"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/categories?post=4640"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/tags?post=4640"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/collection?post=4640"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}