Accelerated predictive models based on TRANSP for scenario optimization and control of NSTX-U

Model-based control and scenario development for fusion devices rely on a hierarchy of models of varying fidelity and speed. Integrated modeling codes, like TRANSP, can provide high-fidelity simulation capability, but are not well-suited for real-time implementation. Data-driven reduced modeling based on higher fidelity models provides a path for developing accelerated models for these tasks. Several such models have been developed for NSTX-U including a real-time capable neural network beam model, NubeamNet, that calculates heating, torque, and current drive profiles from equilibrium parameters and measured profiles. Models have also been developed for real-time evaluation of plasma conductivity, bootstrap current, and flux surface averaged geometric quantities for use in current profile control algorithms. Approaches to hyper parameter tuning have been studied to enable optimization of generalization and complexity. Hardware-in-the-loop simulations in the NSTX-U plasma control system show suitability of the models for real-time applications. Initial applications, including estimation of anomalous fast ion diffusivity to match measured neutron rates, will be presented.