Neural-Network Model of the Multi-Mode Anomalous Transport Module
Real-time-capable Machine-learning-based Accelerated Model of MMM for Transport Simulations

S. Morosohk, T. Rafiq, E. Schuster

28th IAEA Fusion Energy Conference

Nice, France, October 12-17, 2020 -> May 10-15, 2021 (Remote)

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. The current version of MMMnet uses a simple artificial neural network structure to predict the electron thermal, ion thermal, impurity particle, and toroidal momentum diffusivities while reproducing MMM data with 86% accuracy and keeping the calculation time as a fraction of that associated with MMM. A new version of MMMnet is being trained to add ion particle and poloidal momentum diffusivities as additional outputs. Model-based control techniques require models with fast calculation times, making many existing physics-oriented predictive codes unsuitable. The control-oriented predictive code COTSIM (Control-Oriented Transport Simulator) has been developed to calculate 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 relies on scaling laws and control-level models, often leading to a lower range of validity across different scenarios and less accurate predictions. 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.