{"id":67792,"date":"2025-08-14T01:04:08","date_gmt":"2025-08-14T01:04:08","guid":{"rendered":"https:\/\/www.newsbeep.com\/ca\/67792\/"},"modified":"2025-08-14T01:04:08","modified_gmt":"2025-08-14T01:04:08","slug":"finding-the-shadows-in-a-fusion-system-faster-with-ai","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ca\/67792\/","title":{"rendered":"Finding the shadows in a fusion system faster with AI"},"content":{"rendered":"

\"Finding<\/p>\n

An artist’s interpretation of the inside of a fusion vessel, where some of the inner surfaces are directly exposed to the plasma. Some regions lie in the “magnetic shadow” of other components and are therefore magnetically shielded from the intense heat of the plasma. Credit: Kyle Palmer \/ PPPL Communications Department<\/p>\n

A public\u2011private partnership between Commonwealth Fusion Systems (CFS), the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) and Oak Ridge National Laboratory has led to a new artificial intelligence (AI) approach that is faster at finding what’s known as “magnetic shadows” in a fusion vessel: safe havens protected from the intense heat of the plasma.<\/p>\n

Known as HEAT\u2011ML, the new AI could lay the foundation for software that significantly speeds up the design of future fusion<\/a> systems. Such software could also enable good decision\u2011making during fusion operations by adjusting the plasma so that potential problems are thwarted before they start.<\/p>\n

“This research shows that you can take an existing code and create an AI surrogate that will speed up your ability to get useful answers, and it opens up interesting avenues in terms of control and scenario planning,” said Michael Churchill, co\u2011author of a paper<\/a> in Fusion Engineering and Design about HEAT\u2011ML and head of digital engineering at PPPL.<\/p>\n

Fusion, the reaction that fuels the sun and stars, could provide potentially limitless amounts of electricity on Earth. To harness it, researchers need to overcome key scientific and engineering challenges. One such challenge is handling the intense heat coming from the plasma, which reaches temperatures hotter than the sun’s core when confined using magnetic fields in a fusion vessel known as a tokamak. Speeding up the calculations that predict where this heat will hit and what parts of the tokamak will be safe in the shadows of other parts is key to bringing fusion power to the grid.<\/p>\n

“The plasma\u2011facing components of the tokamak might come in contact with the plasma, which is very hot and can melt or damage these elements,” said Dom\u00e9nica Corona Rivera, an associate research physicist at PPPL and first author on the paper on HEAT\u2011ML. “The worst thing that can happen is that you would have to stop operations.”<\/p>\n

\"Finding<\/p>\n

A visual sequence illustrating the divertor structure and simulation results. The first image shows a 20-degree section CAD model of the divertor. The second image provides a 360-degree overview of the entire divertor, displaying the region known as “Tile-4.” The third image zooms in on a specific region of interest, focusing on 15 tiles of the divertor in the “Tile-4” region. The fourth image presents the shadowmask output from the HEAT code, showing the carrier behavior for the selected tiles, orange color corresponds to a value of 1 or shadowed and blue color to a value of 0 or non-shadowed. Credit: Fusion Engineering and Design (2025). DOI: 10.1016\/j.fusengdes.2025.115010<\/p>\n

\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tPPPL amplifies its impact through public\u2011private partnership<\/p>\n

HEAT\u2011ML was specifically made to simulate a small part of SPARC: a tokamak currently under construction by CFS. The Massachusetts company hopes to demonstrate net energy gain by 2027, meaning SPARC would generate more energy than it consumes.<\/p>\n

Simulating how heat impacts SPARC’s interior is central to this goal and a big computing challenge. To break down the challenge into something manageable, the team focused on a section of SPARC where the most intense plasma heat exhaust intersects with the material wall. This particular part of the tokamak, representing 15 tiles near the bottom of the machine, is the part of the machine’s exhaust system that will be subjected to the most heat.<\/p>\n

To create such a simulation, researchers generate what they call shadow masks. Shadow masks are 3D maps of magnetic shadows, which are specific areas on the surfaces of a fusion system’s internal components<\/a> that are shielded from direct heat. The location of these shadows depends on the shape of the parts inside the tokamak and how they interact with the magnetic field lines<\/a> that confine the plasma.<\/p>\n

Creating simulations to optimize the way fusion systems operate<\/p>\n

Originally, an open\u2011source computer program called HEAT, or the Heat flux Engineering Analysis Toolkit, calculated these shadow masks. HEAT was created by CFS Manager Tom Looby during his doctoral work with Matt Reinke, now leader of the SPARC Diagnostic Team, and was first applied on the exhaust system for PPPL’s National Spherical Torus Experiment\u2011Upgrade machine.<\/p>\n

\n Discover the latest in science, tech, and space with over 100,000 subscribers who rely on Phys.org for daily insights.
\n Sign up for our free newsletter<\/a> and get updates on breakthroughs,
\n innovations, and research that matter\u2014daily or weekly.\n <\/p>\n

HEAT\u2011ML traces magnetic field<\/a> lines from the surface of a component to see if the line intersects other internal parts of the tokamak. If it does, that region is marked as “shadowed.” However, tracing these lines and finding where they intersect the detailed 3D machine geometry was a significant bottleneck in the process. It could take around 30 minutes for a single simulation and even longer for some complex geometries.<\/p>\n

HEAT\u2011ML overcomes this bottleneck, accelerating the calculations to a few milliseconds. It uses a deep neural network: a type of AI that has hidden layers of mathematical operations and parameters that it applies to the data to learn how to do a specific task by looking for patterns. HEAT\u2011ML’s deep neural network<\/a> was trained using a database of approximately 1,000 SPARC simulations from HEAT to learn how to calculate shadow masks.<\/p>\n

HEAT\u2011ML is currently tied to the specific design of SPARC’s exhaust system; it only works for that small part of that particular tokamak and is an optional setting in the HEAT code. However, the research team hopes to expand its capabilities to generalize the calculation of shadow masks for exhaust systems of any shape and size, as well as the rest of the plasma<\/a>\u2011facing components inside a tokamak<\/a>.<\/p>\n

More information:
\n\t\t\t\t\t\t\t\t\t\t\t\tD. Corona et al, Shadow masks predictions in SPARC tokamak plasma-facing components using HEAT code and machine learning methods, Fusion Engineering and Design (2025). DOI: 10.1016\/j.fusengdes.2025.115010<\/a><\/p>\n

\n\t\t\t\t\t\t\t\t\t\t\t\t\tProvided by
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tPrinceton University<\/a>
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/p>\n

\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/p>\n

\n\t\t\t\t\t\t\t\t\t\t\t\tCitation:
\n\t\t\t\t\t\t\t\t\t\t\t\tFinding the shadows in a fusion system faster with AI (2025, August 13)
\n\t\t\t\t\t\t\t\t\t\t\t\tretrieved 13 August 2025
\n\t\t\t\t\t\t\t\t\t\t\t\tfrom https:\/\/phys.org\/news\/2025-08-shadows-fusion-faster-ai.html\n\t\t\t\t\t\t\t\t\t\t\t <\/p>\n

\n\t\t\t\t\t\t\t\t\t\t\t This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
\n\t\t\t\t\t\t\t\t\t\t\t part may be reproduced without the written permission. The content is provided for information purposes only.\n\t\t\t\t\t\t\t\t\t\t\t <\/p>\n","protected":false},"excerpt":{"rendered":"An artist’s interpretation of the inside of a fusion vessel, where some of the inner surfaces are directly…\n","protected":false},"author":2,"featured_media":67793,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[24],"tags":[49,48,999,1000,314,996,66,997,61,998],"class_list":{"0":"post-67792","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-physics","8":"tag-ca","9":"tag-canada","10":"tag-materials","11":"tag-nanotech","12":"tag-physics","13":"tag-physics-news","14":"tag-science","15":"tag-science-news","16":"tag-technology","17":"tag-technology-news"},"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/67792","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/comments?post=67792"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/67792\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media\/67793"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media?parent=67792"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/categories?post=67792"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/tags?post=67792"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}