Integration of MMMNet for NSTX-U into COTSIM to Enable Fast and Accurate Predictions for Scenario Planning and Control Applications

The Multi-Mode-Model (MMM) is a theory-based turbulent transport code that calculates the electron and ion thermal, electron particle, impurity, toroidal and poloidal momentum anomalous diffusivities. MMM encompasses models for ion/electron gradient modes, trapped electron modes, peeling, kinetic ballooning, microtearing, and high mode number MHD modes. Due to its extended computation times, MMM is less suited for control applications that require from very fast to real-time or faster-than-real-time prediction speeds. To overcome this issue, a neural-network-based surrogate model has been developed to reproduce the predictions by MMM at a fraction of the computational cost. This neural network was trained on TRANSP shots derived from experimental data from the 2016 NSTX-U run campaign with diffusivities calculated using MMM9.1. MMMNet for NSTX-U has been shown to capture the majority of the behavior of MMM while drastically reducing computation time [1]. In this work, MMMNet for NSTX-U has been integrated in the Control Oriented Transport SIMulator (COTSIM). COTSIM captures the dominant physics of the nonlinear plasma dynamics during a tokamak discharge. Profile evolution comparisons are made between TRANSP, COTSIM+MMMNet, and COTSIM+Coppi-Tang, demonstrating how MMMNet significantly improves the prediction capabilities of COTSIM for NSTX-U.



[1] B. Leard, S. M. Morosohk, Z. Wang, T. Rafiq, E. Schuster, “Fast Neural-Network Surrogate Model of the Updated Multi-Mode Anomalous Transport Module for NSTX-U,” IEEE SOFE, Oxford, UK, July 9-13, 2023.