Supervised machine learning-based multivariate regression of parallel closures for a high-collisionality deuterium-carbon plasma

Lee, Min Uk; Ji, Jeong-Young; Lee, Hae June

doi:10.1063/5.0167904

Supervised machine learning-based multivariate regression of parallel closures for a high-collisionality deuterium-carbon plasma

Journal Article · Thu Nov 02 00:00:00 EDT 2023 · Physics of Plasmas

DOI:https://doi.org/10.1063/5.0167904· OSTI ID:2369609

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Utah State University, Logan, UT (United States); Pusan National University, Busan (South Korea); Utah State University
Utah State University, Logan, UT (United States)
Pusan National University, Busan (South Korea)

Many plasmas of interest in laboratory experiments and space consist of multiple ion species. In tokamak edge plasmas, for instance, ionized impurities expelled from the vessel wall influence plasma transport. When describing multi-species plasmas using fluid equations, we need accurate closure relations to close the set of fluid equations. In this study, we introduce the development of fitting formulas for parallel closures using supervised machine learning, in conjunction with the recent closure theory, considering multi-ion collisions and arbitrary ion temperatures. We apply this approach to a high-collisionality deuterium-carbon plasma and demonstrate its effectiveness. As a result, the machine learning-based method for developing practical and accurate closures can be extended to a wider range of plasmas.

View Accepted Manuscript (DOE)

Research Organization:: Utah State University, Logan, UT (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Fusion Energy Sciences (FES)

Grant/Contract Number:: FG02-04ER54746; SC0022048

OSTI ID:: 2369609

Journal Information:: Physics of Plasmas, Journal Name: Physics of Plasmas Journal Issue: 11 Vol. 30; ISSN 1070-664X

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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70 PLASMA PHYSICS AND FUSION TECHNOLOGY
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Supervised machine learning-based multivariate regression of parallel closures for a high-collisionality deuterium-carbon plasma

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