Neural network model of neutral beam injection in the EAST tokamak to enable fast transport simulations

Wang, Zibo; Morosohk, Shira M.; Rafiq, Tariq; Schuster, Eugenio; Boyer, Mark Dan; Choi, W.

doi:10.1016/j.fusengdes.2023.113514

Title: Neural network model of neutral beam injection in the EAST tokamak to enable fast transport simulations

Journal Article · Mon Feb 13 00:00:00 EST 2023 · Fusion Engineering and Design

DOI:https://doi.org/10.1016/j.fusengdes.2023.113514· OSTI ID:1974424

^[1]; Morosohk, Shira M. ^[1]; Rafiq, Tariq ^[1]; Schuster, Eugenio ^[1];

^[2]; Choi, W. ^[3]

Lehigh Univ., Bethlehem, PA (United States)
Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
General Atomics, San Diego, CA (United States)

The neutral beam injection (NBI) system in EAST produces energetic neutral particles, which collide with electrons and ions in tokamak plasmas and heat the plasmas through Coulomb collisions. Moreover, it drives a non-inductive source of current, due to the charge-exchange collision between neutral particles and ions, and injects toroidal torque, which generates a toroidal rotation of the plasma. The effect caused by the NBI system, such as plasma heating, current drive, total neutron rate, momentum transfer, and shine-through, are modeled by a comprehensive module called NUBEAM. However, NUBEAM is computationally intensive since it relies on Monte Carlo methods. In this work, a neural network model has been developed as a surrogate model for NUBEAM in EAST. The database for neural-network model training, validation and testing is generated by running TRANSP for experimental discharges from recent EAST campaigns (after the latest NBI upgrade) while using the NUBEAM module. Simulation results illustrate that the trained neural network has the capability of replicating the predictions made by NUBEAM while demanding a significantly shorter execution time. Finally, these results indicate that surrogate models like the one proposed in this work could enable fast transport simulations for EAST after integrating them into a control-oriented predictive code such as COTSIM.

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Research Organization:: Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)

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

Grant/Contract Number:: SC0010537

OSTI ID:: 1974424

Alternate ID(s):: OSTI ID: 1924812

Journal Information:: Fusion Engineering and Design, Vol. 191; ISSN 0920-3796

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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