Neural-Network Model of the Multi-Mode Anomalous for Accelerated Transport Simulations
S. Morosohk. A. Pajares, T. Rafiq, E. Schuster
Nuclear Fusion 61 (2021) 106040 (10pp).
Abstract
|
|
A neural network version of the multi-mode anomalous transport module,
known as MMMnet, has been developed to calculate plasma turbulent
diffusivities in DIII-D with a calculation time suitable for control
applications. MMMnet uses a simple artificial neural network structure
to predict the ion thermal, electron thermal, and toroidal momentum
diffusivities while reproducing Multi-Mode Model (MMM) data with good
accuracy and keeping the calculation time as a fraction of that associated
with MMM. Model-based control techniques require models with fast
calculation times, making many existing physics-oriented predictive codes
unsuitable. The control-oriented predictive code Control Oriented Transport
SIMulator (COTSIM) calculates the most significant plasma dynamics in
response to the different actuators while running at a speed useful for
control design. In order to achieve this calculation speed, COTSIM often
relies on scaling laws and control-level models. Replacing some of these
scaling laws and control-level models with neural network versions of
more complex physics-level models has the potential of increasing the
range of validity and the level of accuracy of COTSIM without compromising
its computational speed. In this work, MMMnet is integrated into COTSIM
to improve the turbulent diffusivity predictions, which will in turn
improve the prediction accuracy associated with the dynamics of many
plasma properties.