Fusion energy is being developed as a solution to global energy problems. In particular, the magnetic confinement method, in which ultra-high temperature plasma is confined by a magnetic field, is the most advanced and is considered to be the most promising method for fusion reactors. By this method, the plasma is confined in the reactor in a high-temperature, high-density state by a magnetic field, and the energy released by the fusion reaction in the plasma is converted into electricity.
To realize this power generation method, it is essential to predict and control the complex behavior of fusion plasma. One possible control method is digital twin control, in which the fusion plasma is controlled based on the plasma reproduced in numerical space (digital twin). However, it is difficult to predict and analyze the plasma behavior with high accuracy using simulation models because the model must consider not only complex plasma flow, but also many other factors such as heating, fuel supply, impurities, and neutral particles. In addition, future fusion reactors will have limited measurement capabilities, which forces predictive control and plasma-state estimation under conditions of great uncertainty and lack of information.
A research group led by Assistant Professor Yuya Morishita, Professor Sadayoshi Murakami of the Graduate School of Engineering, Kyoto University, Japan, Assistant Professor Naoki Kenmochi, Assistant Professor Hisamichi Funaba, Professor Masayuki Yokoyama, Professor Masaki Osakabe of the National Institute for Fusion Science (NIFS), National Institutes of Natural Sciences (NINS), Japan, and Professor Genta Ueno of the Institute of Statistical Mathematics (ISM), Japan, and the Joint Support-Center for Data Science Research (RIOS-DS), Japan, has developed a new control system that can optimize the predictive model using real-time observations and estimate the optimal control based on the improved predictive model even under such highly uncertain conditions.
Source: Mirage News