Artificial intelligence and human ingenuity are merging at the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL) to address the challenge of generating clean and reliable energy from fusing plasma. Unlike traditional computer code, machine learning software can analyze data, infer relationships, and adapt based on new knowledge. PPPL researchers are leveraging machine learning to improve their control over fusion reactions, optimize vessel design, heating methods, and maintain stable control over the reaction for extended periods.
In a significant breakthrough, PPPL researchers successfully used machine learning to avoid magnetic disruptions in fusion plasma. This achievement was accomplished on two different tokamaks, DIII-D and KSTAR, which are donut-shaped devices that use magnetic fields to hold plasma. The ability to predict disruptions and adjust settings in real time allows for stable control of the plasma and maximizes fusion energy generation. This work is a critical step in managing fusion reactions and advancing the field towards commercial power generation.
Artificial intelligence has been instrumental in taming instabilities in fusion reactors, with PPPL being at the forefront of harnessing this technology. Researchers have demonstrated the effectiveness of machine learning models in preventing disruptions on tokamaks, paving the way for real-time decision-making and control of plasma reactions. PPPL Principal Research Physicist William Tang and his team have developed models for dealing with disruptive events well in advance, opening up new possibilities for managing fusion processes.
PPPL’s artificial intelligence projects extend beyond tokamaks to include stellarators, another type of fusion reactor with a more complex design. Machine learning is used to enhance the design process by optimizing different codes and accelerating simulations. PPPL’s Head of Digital Engineering, Michael Churchill, is leveraging artificial intelligence to improve the validation of stellarator designs, balancing the level of detail in calculations with speed. The goal is to develop a “digital twin” system that integrates simulated models with real-world data to improve predictive accuracy.
Researchers at PPPL are focusing on optimizing codes like HEAT and using machine learning to accelerate complex plasma simulations for fusion reactors. By training machine learning models on key input parameters, they can streamline codes and run simulations faster without compromising accuracy. This approach allows for faster decision-making and optimization between plasma shots, ultimately aiding in the design and operation of future fusion power plants.
Efforts are also underway to optimize radio frequency heating of plasma ions using artificial intelligence. By quantifying plasma properties and simulating radio wave interactions, researchers aim to control plasma heating more effectively. Machine learning models have been developed to accelerate physics codes that simulate these interactions, enabling real-time corrections and predictions to enhance plasma control. This research is essential for improving understanding of plasma behavior and optimizing heating techniques in fusion reactors.
