Shadow masks predictions in SPARC tokamak plasma-facing components using HEAT code and machine learning methods

Corona, Domenica [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:0000000212911552); d’Abusco, Manuel Scotto [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:0009000174249242); Churchill, Michael [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:000000015711746X); Munaretto, Stefano [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:0000000314650971); Kleiner, Andreas [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:0000000258008027); Wingen, Andreas [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000188551349); Looby, Tom [Commonwealth Fusion Systems, Devens, MA (United States)]

doi:10.1016/j.fusengdes.2025.115010

DOE OSTI · 2566826

Shadow masks predictions in SPARC tokamak plasma-facing components using HEAT code and machine learning methods

Corona, Domenica [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:0000000212911552)·d’Abusco, Manuel Scotto [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:0009000174249242)·Churchill, Michael [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:000000015711746X)·Munaretto, Stefano [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:0000000314650971)·Kleiner, Andreas [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:0000000258008027)·Wingen, Andreas [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000188551349)·Looby, Tom [Commonwealth Fusion Systems, Devens, MA (United States)]

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Abstract

Here, this work uses machine learning (ML) to complement HEAT (Heat flux Engineering Analysis Toolkit) by developing 3-D footprint surrogate models for fast and accurate heat load calculations in the divertor of the SPARC tokamak. The focus is on shadowed regions, or magnetic shadows, caused by the 3-D geometry of plasma-facing components (PFCs). ML classifiers are employed to create a surrogate model for HEAT generated shadow masks, predicting these shadow masks and divertor heat flux profiles based on a diverse range of equilibria and only the plasma current, safety factor(q95) at the edge, and magnetic flux angles as input parameters. The ultimate goal is to integrate the model for real-time control and future operational decisions.

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70 PLASMA PHYSICS AND FUSION TECHNOLOGY CAD equilibrium heat flux neuronal network plasma facing component shadow mask surrogate model

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Corona, Domenica [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:0000000212911552), d’Abusco, Manuel Scotto [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:0009000174249242), Churchill, Michael [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:000000015711746X), Munaretto, Stefano [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:0000000314650971), Kleiner, Andreas [Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)] (ORCID:0000000258008027), Wingen, Andreas [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000188551349), Looby, Tom [Commonwealth Fusion Systems, Devens, MA (United States)]. 2025-05-02. Shadow masks predictions in SPARC tokamak plasma-facing components using HEAT code and machine learning methods. https://doi.org/10.1016/j.fusengdes.2025.115010

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