A Neural Network Version of Multi-Mode Model for Control-oriented Fast Simulations in DIII-D
S. Morosohk, E. Schuster, T. Rafiq
Division of Plasma Physics (DPP) Annual Meeting of the American Physical Society (APS)
Fort Lauderdale, FL, USA, October 21-25, 2019
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Abstract
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Multi-Mode Model (MMM) is a physics-oriented model that is used to
predict thermal, particle and poloidal/toroidal momentum transport in
tokamak plasmas. It includes a model for ion temperature gradient,
trapped electron, kinetic ballooning, peeling, collisionless and
collision dominated magnetohydrodynamics modes as well as model for
electron temperature gradient modes, and a model for drift resistive
inertial ballooning modes [1]. While MMM is a relatively accurate model,
it is too computationally intense for control design, which demands a
model capable of producing similar predictions with a significantly
faster run time. Neural networks offer the potential to replicate
complicated calculations with a high level of accuracy while
simultaneously producing results with a run time orders of magnitude
faster than that of MMM. In this work, a database of predictive TRANSP
runs for DIII-D was built using MMM. This database was used to train
and test a neural network (MMMnet). The trained network is shown to
match the results of MMM with high accuracy while producing results in
a run time on the order of microseconds. This allows MMMnet to be used
for model-based optimization and control applications with the potential
of running in real time.
[1] T. Rafiq et al., Phys. Plasmas 20, 032506 (2013).
