Digital twin control for LHD plasmas based on data assimilation system ASTI

The operation of future fusion reactors requires a system that predicts and controls fusion plasma behavior under conditions of limited observation. To address this challenge, we have introduced a control approach based on data assimilation (DA), which integrates predictive model (digital twin) adaptation using real-time measurements and control estimation robust to model and observation uncertainties. The main part of the DA-based control system, ASTI, computes many integrated simulations in real-time to predict the probability distribution of future plasma states. In addition, the system estimates the optimal control input and the actual plasma state based on Bayes' theorem. The DA-based control system implemented in the Large Helical Device (LHD) was successfully applied to control the electron temperature using the electron cyclotron heating and the real-time Thomson scattering measurement. In the control experiment, it was demonstrated that the digital twin's predictive capability was improved by optimizing the model parameters using the real-time observations. The DA-based control system allows for the harmonic connection of measurement, heating, fueling, and simulation and can provide a flexible platform for digital twin control of future fusion reactors. In this talk, we will discuss the details of the control system and the demonstration experiments.