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Neural networks guided by physics are creating new ways to observe the complexities of plasmas.
Fusion experiments take place under extreme conditions, with extremely high-temperature matter contained in specialized vacuum chambers. These conditions limit the ability of diagnostic tools to collect data on fusion plasmas. In addition, computer models of plasmas are very complex and have difficulty characterizing turbulent plasmas. This makes it difficult to compare models against measurements from experimental fusion devices.
Bridging Plasma Modeling and Experiments
In response, researchers have demonstrated a novel way to bridge
Challenges in Predictive Modeling
Predictive modeling of plasma turbulence in fusion experiments is challenging. This is due to the difficulty in modeling the conditions at the boundaries of these chaotic systems. Using a custom physics-informed approach to machine learning, researchers developed a framework able to directly solve for plasma properties that are usually not resolved in the boundary of experimental fusion devices. This allows scientists to predict how plasma fluctuations behave in experiments. It also allows them to test predictive models in ways consistent with theory. This sort of turbulence modeling was not previously practical.
Importance of Confinement in Fusion Plasmas
Adequate confinement of fusion plasmas is essential to reaching the goal of net fusion energy production. A key component in predicting confinement is understanding the ways plasma instabilities can cause cooling and loss of performance within the fusion device. Accordingly, the fusion community spent decades improving experiments’ measurement capabilities to refine predictive models. However, the extreme temperatures and vacuum conditions needed for fusion make it very difficult to deploy diagnostics within fusion devices. Researchers from the Massachusetts Institute of Technology recently published two papers addressing this challenge.
Innovative Research From MIT
In the first paper, the researchers demonstrated how DOI: 10.1063/5.0088216
“Deep Electric Field Predictions by Drift-Reduced Braginskii Theory with Plasma-Neutral Interactions Based on Experimental Images of Boundary Turbulence” by A. Mathews, J. W. Hughes, J. L. Terry and S. G. Baek, 2 December 2022, DOI: 10.1103/PhysRevLett.129.235002
Funding support came from the Natural Sciences and Engineering Research Council of Canada through the doctoral postgraduate scholarship, the Department of Energy Office of Science, Fusion Energy Sciences program, a Joseph P. Kearney Fellowship, and a Manson Benedict Fellowship from the Massachusetts Institute of Technology Department of Nuclear Science and Engineering.