Researchers at the Princeton Plasma Physics Laboratory are harnessing artificial intelligence and machine learning to enhance fusion energy production, tackling the challenge of controlling plasma reactions. Their innovations include optimizing the design and operation of containment vessels and using AI to predict and manage instabilities, significantly improving the safety and efficiency of fusion reactions.
That’s why Churchill uses artificial intelligence to accelerate different codes and the optimization process itself. “We would really like to do higher-fidelity calculations but much faster so that we can optimize quickly,” he said.Similarly, Research Physicist Stefano Munaretto’s team is using artificial intelligence to accelerate a code called HEAT, which was originally developed by the DOE’s Oak Ridge National Laboratory and the University of Tennessee-Knoxville for PPPL’s tokamak NSTX-U.
“This would allow us to predict the heat fluxes that will appear in the next shot and to potentially reset the parameters for the next shot so the heat flux isn’t too intense for the divertor,” Munaretto said. “This work could also help us design future fusion power plants.” Plasma has different properties, such as density, pressure, temperature, and the intensity of the magnetic field. These properties change how the waves interact with the plasma particles and determine the waves’ paths and areas where the waves will heat the plasma. Quantifying these effects is crucial to controlling the radio frequency heating of the plasma so that researchers can ensure the waves move efficiently through the plasma to heat it in the right areas.
The project focuses on trying different kinds of machine learning to speed up a widely used physics code. Sánchez Villar and his team showed multiple accelerated versions of the code for different fusion devices and types of heating. The models can find answers in microseconds instead of minutes with minimal impact on theof the results. Sánchez Villar and his team were also able to use machine learning to eliminate challenging scenarios with the optimized code.
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