KI in der Kernfusionsplasmakontrolle

A tokamak uses magnetic fields to confine charged plasma and control its position and shape within a vessel. Confinement and stability are demanding engineering problems, but a temperature value should not be treated as identical for every device or experiment. Magnetic Control of Tokamak Plasmas through Deep Reinforcement Learning reports a 2022 collaboration on the Variable Configuration Tokamak at the Swiss Plasma Center. The learned policy maps available observations and control goals to voltage commands for the magnetic coils. The official report describes coordinating TCV’s nineteen coils with one learned control system. Training uses a simulated environment, providing trial-and-error experience before the studied hardware tests. A simulator can reduce the need for uncontrolled exploration on a real device, but its fidelity and the transfer procedure still need validation. The demonstrated shapes include elongated configurations and a droplet containing two plasmas in the vessel at the same time. These are controlled experimental configurations, not a claim that every possible reactor geometry has been solved. An official description reports ten thousand control interactions per second; a uniform cadence of that rate corresponds to one interaction every 0.1 milliseconds. Fast inference is necessary for the reported feedback loop, while learning in simulation is a separate stage. A disruption is a degradation or loss of plasma confinement that can impose large thermal and mechanical loads, as ITER’s glossary explains. Predicting or avoiding such events is a distinct research question. Successfully shaping plasma does not establish that a controller prevents all disruptions, produces net electricity, or transfers unchanged to a future power plant.