A team of researchers at the Massachusetts Institute of Technology (MIT) has made significant progress in the quest for large-scale nuclear fusion, potentially paving the way for a clean and virtually limitless energy source. Their research, published on October 16, 2023, in the journal Nature Communications, focuses on predicting the behavior of plasma within a tokamak reactor, an essential step toward making fusion energy reliable and practical.
Nuclear fusion mimics the processes that power stars, offering a promising alternative to fossil fuels. The tokamak, a donut-shaped device that employs strong magnets to confine plasma, has been a focal point in fusion research. While it has shown considerable potential, scientists face challenges in controlling fusion reactions, particularly when it comes to safely managing the plasma once a reaction has begun.
Researchers have identified that when a tokamak operates at full capacity, its plasma can move at speeds of up to 62 miles per hour (approximately 100 kilometers per hour) and reach temperatures of around 180 million degrees Fahrenheit (or 100 million degrees Celsius). These extreme conditions necessitate careful management. If the reactor needs to be shut down, operators must gradually reduce the plasma current, a process known as “ramp down.” However, this procedure can be problematic, leading to minor damage inside the reactor, which requires significant time and resources to repair.
Allen Wang, the lead author of the study and a graduate student at MIT, explained, “For fusion to be a useful energy source, it’s going to have to be reliable. To be reliable, we need to get good at managing our plasmas.” Uncontrolled plasma terminations can generate intense heat fluxes, posing risks to the reactor’s internal walls. Wang noted that particularly with high-performance plasmas, ramp-down procedures could push the plasma closer to instability limits, making it a delicate balancing act.
Given the high costs associated with running fusion reactors—most of which operate only a few times a year due to efficiency challenges—researchers often face obstacles in testing their theories. To address this, the MIT team leveraged a combination of physics and machine learning to enhance their predictive capabilities. They utilized a neural network model paired with a physical model that describes plasma dynamics, training it on data collected from the TCV, a small experimental fusion device located in Switzerland.
The dataset used by the researchers included variations in the plasma’s initial temperature and energy levels, as well as data from the duration and conclusion of each experimental run. By applying an algorithm to generate “trajectories,” the team was able to predict how the plasma would likely behave as the fusion reaction progressed. When they tested this model on actual TCV runs, following the trajectories resulted in successful ramp-down procedures, confirming their approach’s effectiveness.
“We did it a number of times, and we did things much better across the board. So, we had statistical confidence that we made things better,” Wang stated. He emphasized the importance of addressing the scientific challenges required to make fusion energy a routine and practical option. “What we’ve done here is the start of what is still a long journey. But I think we’ve made some nice progress.”
The advancements made at MIT represent a promising step forward in the pursuit of nuclear fusion, with the potential to revolutionize energy production in the future. As researchers continue to innovate and refine their techniques, the dream of a clean, safe, and abundant energy source may one day become a reality.