Towards the Development of a Neural-Network Version of NUBEAM for Scenario Optimization and Control in KSTAR

S. Morosohk, M.D. Boyer, E. Schuster

KSTAR Conference

Seoul, South Korea, February 20-22, 2019

NUBEAM is a high-accuracy simulation code developed to calculate the effects of neutral beams on the plasma. However, the code is computationally demanding due to its Monte Carlo nature. This makes it unrealistic to use in real-time control or in situations where a large number of simulation runs are required, such as in model-based scenario optimization for instance. A neural network model (NubeamNet) has been originally developed to closely approximate NUBEAM results for NSTX-U at drastically reduced run time [1]. More recently, a database of NUBEAM runs has been developed for DIII-D and used to train a neural network for model-based applications requiring a faster run time. The trained neural network can match the NUBEAM data with high accuracy, and requires only a fraction of a second to compute the outputs for a given set of inputs. The trained model is suitable for integration into real-time control systems and for use in model-based scenario planning. Simulation results are presented to illustrate the potential of the trained neural-network version of NUBEAM in DIII-D. Since the same neural network can be retrained for different machines, present and planned work toward extending this technique to KSTAR will be discussed. It is anticipated that this machine-learning-based model for the neutral-beam-generated heating, torque, and current drive may play an important role in the development of algorithms for scenario optimization and control in KSTAR.

[1] M.D. Boyer et al., “Real-time capable neural network approximation of NUBEAM for use in the NSTX-U control system,” 45th EPS Conference on Plasma Physics, 2018.