Title: Constructing a new predictive scaling formula for ITER's divertor heat-load width informed by a simulation-anchored machine learning

Abstract

Understanding and predicting divertor heat-load width λq is a critically important problem for an easier and more robust operation of ITER with high fusion gain. Previous predictive simulation data for λ_q using the extreme-scale edge gyrokinetic code XGC1 [S. Ku et al., Phys. Plasmas 25, 056107 (2018)] in the electrostatic limit under attached divertor plasma conditions in three major US tokamaks [C. S. Chang et al., Nucl. Fusion 57, 116023 (2017)] reproduced the Eich and Goldston attached-divertor formula results [formula #14 in T. Eich et al., Nucl. Fusion 53, 093031 (2013) and R. J. Goldston, Nucl. Fusion 52, 013009 (2012)] and furthermore predicted over six times wider λ_q than the maximal Eich and Goldston formula predictions on a full-power (Q = 10) scenario ITER plasma. After adding data from further predictive simulations on a highest current JET and highest-current Alcator C-Mod, a machine learning program is used to identify a new scaling formula for λ_q as a simple modification to the Eich formula #14, which reproduces the Eich scaling formula for the present tokamaks and which embraces the wide λ_q^XGC for the full-current Q = 10 ITER plasma. Additionally, the new formula is then successfully tested on three more ITERmore » plasmas: two corresponding to long burning scenarios with Q = 5 and one at low plasma current to be explored in the initial phases of ITER operation. The new physics that gives rise to the wider λ_q^XGC is identified to be the weakly collisional, trapped-electron-mode turbulence across the magnetic separatrix, which is known to be an efficient transporter of the electron heat and mass. Electromagnetic turbulence and high-collisionality effects on the new formula are the next study topics for XGC1.« less

Authors:

Chang, C. S.  ^[1];

• Search DOE PAGES for author "Chang, C. S."
• Search DOE PAGES for ORCID "0000-0002-3346-5731"

Ku, S.  ^[1];

• Search DOE PAGES for author "Ku, S."
• Search DOE PAGES for ORCID "0000-0002-9964-1208"

Hager, R.  ^[1];

• Search DOE PAGES for author "Hager, R."
• Search DOE PAGES for ORCID "0000-0002-4624-3150"

Churchill, R. M.  ^[1];

• Search DOE PAGES for author "Churchill, R. M."
• Search DOE PAGES for ORCID "0000-0001-5711-746X"

Hughes, J.  ^[2];

• Search DOE PAGES for author "Hughes, J."
• Search DOE PAGES for ORCID "0000-0003-4802-4944"

Köchl, F. ^[3]; Loarte, A.  ^[4];

• Search DOE PAGES for author "Loarte, A."
• Search DOE PAGES for ORCID "0000-0001-9592-1117"

Parail, V. ^[5]; Pitts, R. A.  ^[4]

• Search DOE PAGES for author "Pitts, R. A."
• Search DOE PAGES for ORCID "0000-0001-9455-2698"

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+ Show Author Affiliations

1. Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
2. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Plasma Science and Fusion Center
3. Technische Univ. Wien, Vienna (Austria). Atominstitut
4. ITER Organization, St. Paul Lez Durance (France)
5. Culham Science Centre, Abingdon (United Kingdom). Culham Centre for Fusion Energy (CCFE), EURATOM/UKAEA Fusion Association

Publication Date:

Mon Feb 01 00:00:00 EST 2021

Research Org.:

Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)

Sponsoring Org.:

USDOE Office of Science (SC)

OSTI Identifier:

1780827

Alternate Identifier(s):

OSTI ID: 1763840

Grant/Contract Number:  

AC02-09CH11466; DEAC02-09CH11466

Resource Type:

Accepted Manuscript

Journal Name:

Physics of Plasmas

Additional Journal Information:

Journal Volume: 28; Journal Issue: 2; Journal ID: ISSN 1070-664X

Publisher:

American Institute of Physics (AIP)

Country of Publication:

United States

Language:

English

Subject:

70 PLASMA PHYSICS AND FUSION TECHNOLOGY; Plasma turbulence particle-in-cell method; plasma confinement; machine learning; plasma instabilities; computer simulation; tokamaks