Chinese Fusion Reactor Achieves Plasma Density Previously Thought to Be Impossible · Solving the mystery that could help fusion reactors survive
Plasma stability concerns the stability properties of a plasma in equilibrium and its behavior under small perturbations.
Researchers are unraveling the influence of energetic particles on the stability of plasmas in fusion reactors.
“Not only did we show our approach was capable of maintaining a high-performing plasma without instabilities, but we also showed that it can work at two different facilities,” said research leader Egemen Kolemen, associate professor of mechanical and aerospace engineering and the Andlinger Center for Energy and the Environment. Now, a team of fusion researchers led by engineers at Princeton and the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL) have successfully deployed machine learning methods to suppress these harmful edge instabilities — without sacrificing plasma performance. With their approach, which optimizes the system’s suppression response in real-time, the research team demonstrated the highest fusion performance without the presence of edge bursts at two different fusion facilities — each with its own set of operating parameters. “With our machine learning surrogate model, we reduced the calculation time of a code that we wanted to use by orders of magnitude,” said co-first author Ricardo Shousha, a postdoctoral researcher at PPPL and former graduate student in Kolemen’s group.
Table 1 Two sets of MEPC reactor parameters Parameter Dolan LTS design [10] New HTS design B-field in ring cusps Ba [T] 6 16 Applied voltage /a [kV] 400 500 Anode gap spacing d [mm] 4 4 Plasma electron density ne [m3] 1:1 1020 2:9 1020 Plasma temperature Te Ti [keV] 20 25 Coil/cusp radius R [m] 3.5 0.83 Plasma length L [m] 36 10 Dolan scaling relation gain Q 10 10 5 Page 4 of 12 Journal of Fusion Energy (2022) 41:5 123 Assuming a first-generation reactor design might have R 3rp as shown in Fig. 5, the volume Vs of the plasma section is: Vs ¼ Cv pR2l; ð4Þ where Cv ¼ 4 9ð2c þ 1Þ þ 4 9ðc þ 1Þ þ 1 9 ð5Þ The surface area Ss of the plasma section is: Ss ¼ Cs 2pRl; ð6Þ where Cs ¼ 2 3ðc þ 1Þ þ 1 3 : ð7Þ Since the total plasma length L ¼ Nc l (from Table 1) with number of ring cusps Nc 1, the total plasma volume and surface area is approximately: V ¼Cv pR2L ð8Þ S ¼Cs 2pRL: ð9Þ Note the plasma volume for the LTS design is 280 m3, one-third that of the ITER tokamak [23].
For feedback stabilization, the plasma equilibrium remains the same and the time-varying suppression signal interacts only with the unstable part of the plasma.
The shaping of a plasma can have a stabilising effect, enabling higher-performance plasmas which have higher pressure and better confinement.
Therefore, it is necessary to actively control the tokamak based on the observed plasma state, to manoeuvre high-pressure plasma while avoiding tearing instability, the leading cause of disruptions. By specifically designing the reward function, we can train the actor model to actively control the tokamak to pursue a high-pressure plasma while keeping the tearing possibility low. In this work, we develop an AI controller that adaptively controls actuators to pursue high plasma pressure while maintaining low tearability, based on observed plasma profiles. **Fig. 2: Illustration of the tokamak control by the AI tearing-avoidance system and the plasma responses.**. The AI-based tearing-avoidance system actively controls the beam power and the plasma triangularity to maintain the possibility of future tearing-instability occurrence at a low level. During the RL training process, the dynamic model predicts future *β*N and tearability from the given plasma conditions and actuator values determined by the AI controller.