In a significant advancement for nuclear fusion technology, researchers have developed a novel method for predicting and controlling Vertical Displacement Events (VDEs) using machine learning techniques. This breakthrough, spearheaded by S. Inoue and his team at the National Institutes for Quantum and Radiological Science and Technology in Japan, focuses on utilizing magnetic diagnostics to enhance the stability of fusion reactors, particularly during the commissioning phase of the JT-60SA project.
VDEs pose a serious risk to fusion devices, leading to asymmetric heat loads on the first walls and substantial electromagnetic stresses on conducting materials. These events can compromise the integrity of fusion reactors, making effective prediction and control crucial for the future of energy generation through fusion. “Our approach allows us to not only predict these events but also to guide them in a safer direction, significantly reducing potential damage,” Inoue stated.
The researchers employed a Support Vector Machine (SVM) to analyze magnetic data, creating a redundant logic system that can forecast VDEs with impressive accuracy. By setting the control voltage to zero upon detection of a VDE, the team successfully directed these events, mitigating the risks associated with uncontrolled occurrences. This innovative technique represents a substantial leap forward in the realm of equilibrium control for fusion reactors.
Looking ahead, the team plans to refine their predictive model further by integrating more detailed data from the equilibrium controller, including the control values of each PID component. “By enhancing our data inputs, we can improve the prediction accuracy even more, which is critical as we move towards operational fusion reactors,” Inoue explained. This development could pave the way for more stable and efficient fusion energy production, an area with immense commercial potential as the world seeks sustainable energy solutions.
The implications of this research extend beyond the laboratory. As fusion technology matures, the ability to manage VDEs effectively could lead to more reliable and economically viable fusion power plants. This could ultimately contribute to a cleaner energy landscape, aligning with global efforts to transition away from fossil fuels.
Published in the journal ‘Nuclear Fusion’ (translated from Japanese to English), this research underscores the vital intersection of machine learning and nuclear physics, showcasing how advanced computational techniques can solve complex challenges in energy production. For more information about the research and its implications, you can visit the National Institutes for Quantum and Radiological Science and Technology.
