Optimization Strategies for Model-based Scenario Planning in Tokamaks Based on Different Types of Equilibrium Solvers

E. Schuster, B. Leard, T. Rafiq

50th European Physical Society (EPS) Conference on Plasma Physics (CPP)

Salamanca, Spain, July 8-12, 2024

Model-based optimization approaches to scenario planning, as proposed in [1], have been proven effective without requiring the level of resources needed in “human-in-the-loop” experimental or numerical approaches, where the actuator trajectory waveforms are determined through a substantial number of trial-and-error attempts. Not only does this more systematic approach via model-based optimization provide a truly optimized result, but also automates the process, saving many hours of work. Model-based scenario optimization has been enabled by the development of fast predictive transport codes such as COTSIM (Control-Oriented Transport SIMulator) since the optimizer must repetitively call the prediction code during the optimization process. The modular configuration of COTSIM makes adding or removing physics complexity as needed extremely simple. It can be configured to run by choosing transport and source models from a library of models ranging from empirical scalings, semi- empirical analytical models, and machine-learning-based models. COTSIM is based on Matlab/Simulink®, which makes it control-design friendly and capable of both running closed- loop simulations and being wrapped by an external optimizer. The Multi-Mode Module (MMM), a physics-based model designed for multi-species, multi-fluid, and multi-mode anomalous transport calculations in tokamak discharge, is a distinctive feature of COTSIM. Machine-learning surrogate models recently developed for transport and sources are leveraged to solve the trade-off between high prediction accuracy (needed to make the optimization useful) and low computational cost (needed to make the optimization tractable). The transport solver in COTSIM has been recently coupled with both fixed-boundary and free-boundary equilibrium solvers. Different whole-device time-evolving optimization strategies are discussed in this work as a function of the type (fixed-boundary vs. free-boundary) and the operation mode (direct vs. indirect) of the equilibrium solver. Demonstration of these approaches is focused on NSTX-U.

[1] Y. Ou, C. Xu, E. Schuster et al., Plasma Phys. Control., 50 (2008) 115001.
[2] T. Rafiq et al., Phys. Plasmas 20, 032506 (2013).

*Supported by the U.S. DoE under DE-SC0010661, DE-SC0010537, and DE-SC0021385.