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PPPL-led STELLAR-AI to advance fusion research
Princeton Plasma Physics Laboratory is leading a new initiative with the goal of using AI technology to accelerate the development of fusion energy research through high-fidelity computer simulations. The project includes national laboratories, universities, technology companies, and other partners.
Simulation, Technology, and Experiment Leveraging Learning-Accelerated Research enabled by AI (STELLAR-AI) has been developed as part of the Department of Energy’s Genesis Mission, which was established by presidential executive order last year to speed up the application of AI in scientific research.
Marcos X. Navarro, Tom Rognlien, Marvin Rensink, Juri Romazanov, Andreas Kirschner, Oliver Schmitz
Fusion Science and Technology | Volume 79 | Number 3 | April 2023 | Pages 213-221
Technical Paper | doi.org/10.1080/15361055.2022.2148840
Articles are hosted by Taylor and Francis Online.
This study focuses on performing a multiphysics study using the ERO2.0 and UEDGE codes for two standard double null configurations for the Fusion Nuclear Science Facility: (a) 100% recycling and (b) 99% recycling. Results show that the main contributor to tungsten erosion along the divertor plates is impurities from the midplane waveguides. In addition, the standard high-recycling case (100% recycling) shows a significantly higher buildup of impurities along the divertor tiles during the startup phase, which can lead to a higher increase of energy loss in the plasma during steady-state operation. Last, for high recycling, anomalous diffusion can dominate over parallel field diffusion. The work performed in this study can be iteratively applied to a full operation scenario with additional physics such as those from neutrals, wall shaping, and additional external fields.