Fast Neural-Network Surrogate Model of the Updated Multi-Mode Anomalous Transport Module for NSTX-U

The multimode module (MMM) is a theory-based anomalous transport model that is used in integrated codes to predict the electron/ion temperature, electron/impurity density, and toroidal/poloidal rotation profiles of tokamak plasmas. It includes transport driven by a variety of electron and ion scale modes and accounts for the effects of collisions, fast-ion and impurity dilution, noncircular flux surfaces, finite beta, and Shafranov shift. Due to the large number of interconnected physical phenomena captured by MMM, it is a computationally intensive code, unsuitable for control applications that require from fast offline to real time or faster-than-real-time prediction speeds. Therefore, significant effort has been dedicated to the development of neural-network (NN)-based surrogate models for tokamaks like $\text {DIII-D}$ and EAST with the goal of reproducing a selected set of MMM’s output predictions at a much faster speed. In this work, a NN model for MMM9.1 is trained based on predictions within the National Spherical Torus Experiment Upgrade (NSTX-U) operating regime. This model, referred to as MMMNet, is capable of predicting the ion, electron, and impurity thermal diffusivities, electron particle diffusivity, and toroidal and poloidal momentum diffusivities. The newer MMM9.1 version improves the accuracy, consistency, speed, and physics basis of several components of MMM that are critical for NSTX-U as well as for the spherical-tokamak reactor concept. The MMM training data are based on NSTX experimental shots and adapted to suit the NSTX-U geometry and to cover the entirety of the NSTX-U parameter space. In simulation testing, MMMNet is shown to have a similar accuracy to MMM while running at a fraction of its computation time. This makes MMMNet suitable for real-time control applications.