Tokamaks are experimental fusion devices designed to confine plasma at extremely high temperatures to produce clean fusion energy. A critical challenge is managing the rampdown phase when the plasma current is safely turned off to avoid instabilities that can damage the reactor’s interior. MIT researchers combined machine learning with a physics-based plasma model to simulate plasma behavior during rampdown, training their approach on hundreds of plasma pulses from Switzerland’s TCV tokamak.
This hybrid model efficiently learns from limited data, a significant advantage since each fusion experiment is costly and quality data is scarce. The researchers also developed an algorithm translating predictions into control instructions for tokamak operation, enabling safer and sometimes faster plasma shutdowns without disruptions. As fusion devices scale toward grid-scale energy production, controlling plasma instabilities during all operational phases becomes crucial for device reliability and longevity.
The model’s ability to predict and manage plasma behavior can reduce the risk of damages that require extensive repairs and downtime. The project, supported by Commonwealth Fusion Systems and the EUROfusion Consortium, represents a vital step toward making fusion energy a reliable power source. However, the journey remains long, and continuous development is necessary to tackle the complex science of fusion plasma control.
👉 Pročitaj original: MIT AI News
