Neoclassical toroidal viscosity torque prediction via deep learning

Clement, Mitchell D.; Logan, Nikolas; Boyer, Mark D.

doi:10.1088/1741-4326/ac3e83

Title: Neoclassical toroidal viscosity torque prediction via deep learning

Journal Article · Thu Dec 23 00:00:00 EST 2021 · Nuclear Fusion

DOI:https://doi.org/10.1088/1741-4326/ac3e83· OSTI ID:1836829

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Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

GPECnet is a densely connected neural network that has been trained on GPEC data, to predict the plasma stability, neoclassical toroidal viscosity (NTV) torque, and optimized 3D coil current distributions for desired NTV torque profiles. Using NTV torque, driven by non-axisymmetric field perturbations in a tokamak, can be vital in optimizing pedestal performance by controlling the rotation profile in both the core, to ensure tearing stability, and the edge, to avoid edge localized modes (ELMs). The generalized perturbed equilibrium code (GPEC) software package can be used to calculate the plasma stability to 3D perturbations and the NTV torque profile generated by applied 3D magnetic fields. These calculations, however, involve complex integrations over space and energy distributions, which takes time to compute. Initially, GPECnet has been trained solely on data representative of the quiescent H-mode (QH) scenario, in which neutral beams are often balanced and toroidal rotation is low across the plasma profile. Lastly, this work provides the foundation for active control of the rotation shear using a combination of beams and 3D fields for robust and high performance QH mode operation.

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Research Organization:: General Atomics, San Diego, CA (United States)

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

Grant/Contract Number:: FC02-04ER54698; AC52-07NA27344; AC02-09CH11466

OSTI ID:: 1836829

Journal Information:: Nuclear Fusion, Vol. 62, Issue 2; ISSN 0029-5515

Publisher:: IOP ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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