Accelerated AI technique enhances computation for safeguarding fusion reactor heat dissipation
In a groundbreaking development, a new artificial intelligence (AI) system called HEAT-ML is set to transform the future of fusion energy. This innovative technology generates 3D maps, known as 'shadow masks', which show protected areas within fusion reactors, safeguarding them from direct plasma contact.
The AI method, HEAT-ML, overcomes the bottleneck of traditional methods by significantly reducing computation times. Instead of the 30+ minutes taken for detailed 3D simulations of magnetic field line interactions, HEAT-ML can predict "magnetic shadows"—areas within a fusion reactor shielded from the extreme plasma heat—in milliseconds.
Trained on data from about 1,000 prior simulations, HEAT-ML has the ability to quickly recognise patterns and accurately identify these critical safe zones. The benefits of this rapid, AI-driven approach are manifold.
Firstly, it significantly accelerates simulation times, allowing for quicker and more efficient reactor design and operation adjustments. Secondly, it improves the protection of reactor components by accurately locating areas shielded from intense plasma heat, preventing damage and costly interruptions. Thirdly, it facilitates safer plasma conditions by providing better insight into heat distribution, aiding the optimization of materials and reactor geometry.
Moreover, HEAT-ML contributes to advancing fusion energy development by addressing one of the major technical hurdles—managing extreme plasma heat inside fusion reactors. If successfully harnessed, fusion could supply Earth with carbon-free energy. Locating these regions quickly and accurately is crucial for ensuring the long-term operation of fusion systems.
Initially, testing focused on 15 tiles near the bottom of SPARC's exhaust system. The team behind HEAT-ML aims to expand its capabilities to handle exhaust systems of any shape or size, as well as other plasma-facing components within a tokamak. The AI system could also support informed decision-making during operations by adjusting the plasma.
The AI system HEAT-ML, developed by Commonwealth Fusion Systems, DOE's Princeton Plasma Physics Laboratory, and Oak Ridge National Laboratory, is designed specifically for the SPARC tokamak under construction by CFS in Massachusetts. The plasma heat in a tokamak exceeds the temperature of the sun's core, making the management of this heat a significant challenge.
In summary, HEAT-ML provides a rapid, AI-driven approach to map plasma heat protection zones inside fusion reactors, offering both speed and precision that enhance reactor safety and development efficiency. This new technology could lay the foundation for software that significantly speeds up the design of future fusion systems, bringing practical fusion power closer to reality.
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