New Prediction Model Could Improve the Reliability of Fusion Power Plants

Fusion power promises a clean and virtually limitless source of energy by replicating the processes that power the sun. Achieving practical fusion energy, however, remains an enormous scientific and engineering challenge. One of the key obstacles involves safely managing the extreme conditions inside fusion reactors, particularly in devices called tokamaks, which use powerful magnetic fields to contain and heat plasma to temperatures hotter than the sun’s core.

A recent breakthrough from scientists at MIT may help overcome some of these challenges. The researchers developed a novel prediction model that combines physics-based plasma simulations with machine learning techniques to anticipate how the plasma will behave during critical shutdown phases, known as "rampdowns." These rampdowns are necessary to prevent unstable plasma conditions that can damage the reactor, but ironically, the rampdowns themselves can sometimes cause instabilities and physical damage.

The new model was trained and tested on data from the Swiss Plasma Center’s experimental tokamak, the TCV, using only hundreds of plasma pulses. By pairing neural networks with fundamental physics simulations, the model achieves high accuracy with relatively small data, which is significant given the high cost and scarcity of experimental runs. The prediction method can foresee plasma instabilities early and inform automated control adjustments to safely and reliably ramp down plasma currents—sometimes even faster and more safely than previous methods.

This approach addresses a critical reliability issue as fusion reactors scale up from experimental devices to grid-scale power plants. Reliable, routine control of plasma and its instabilities is necessary to make fusion energy practical for widespread use. MIT’s collaboration with Commonwealth Fusion Systems, an MIT spinout working on compact, net-energy-producing tokamaks like SPARC, aims to integrate these predictive tools to reduce disruptions and costly repairs in future fusion power plants.

In parallel, other advances are being made. At Lawrence Livermore National Laboratory, researchers developed a deep learning AI model that predicted with 74% confidence the success of a fusion experiment in 2022, the first to produce net energy gain. By training on over 150,000 simulations combined with experimental data through Bayesian inference, their AI accelerates design decisions by accurately forecasting experimental outcomes.

Together, these innovations in AI and machine learning applied with physics-based models represent major steps toward making fusion power a reliable and commercially viable clean energy source. As fusion research continues to advance, these predictive technologies could save time, reduce costs, and improve safety in the quest to harness the power of the stars here on Earth.