AI Revolutionizes Plasma Heating Prediction, Accelerating Nuclear Fusion Research
A new AI modelfor plasma heating pushes the boundaries of what was previously thought possible, achieving a 10million-fold speed increase while maintaining accuracy and even predicting plasma heating in scenarios where traditional numerical codes fail.
With our intelligence, we can train AI togo beyond the limitations of existing numerical models, says Álvaro Sánchez-Villar, a physicist and associate research fellow at the U.S. Department of Energy’s(DOE) Princeton Plasma Physics Laboratory (PPPL). Sánchez-Villar’s team has developed a real-time core ion cyclotron range of frequencies (ICRF) heating model for NSTX and WEST. This model is based on twononlinear regression algorithms: a random forest ensemble of decision trees and a multilayer perceptron neural network. The research was published in Nuclear Fusion on August 12, 2024, under the title Real-time capablemodeling of ICRF heating on NSTX and WEST via machine learning approaches.
Radio frequency (RF) wave heating systems are one of the key methods used to assist in heating magnetically confined fusion devices. Among them, ICRF heating plays a crucial role in the operation and stability of tokamaks. However, traditional numericalcodes used to predict plasma heating can be computationally expensive and time-consuming, limiting their real-time application.
The new AI model, developed by Sánchez-Villar’s team, tackles this limitation by leveraging machine learning techniques. The model was trained on a vast dataset of experimental data from NSTX and WEST,enabling it to learn the complex relationships between various plasma parameters and ICRF heating. This allows the model to predict plasma heating with remarkable accuracy and speed, achieving a 10 million-fold improvement over traditional numerical codes.
The implications of this breakthrough are significant for nuclear fusion research. Real-time prediction of plasma heatingallows researchers to optimize heating strategies, enhance plasma stability, and ultimately accelerate the development of fusion energy.
This AI model opens up new possibilities for real-time control and optimization of plasma heating, says Sánchez-Villar. It could lead to significant improvements in the efficiency and performance of fusion devices.
The researchteam is currently working to further refine the AI model and expand its capabilities. They aim to incorporate more complex plasma physics phenomena and develop models for other heating systems. This advancement in AI-powered plasma heating prediction is a testament to the transformative potential of artificial intelligence in accelerating the path towards clean and sustainable fusion energy.
References:
- Sánchez-Villar, A., et al. (2024). Real-time capable modeling of ICRF heating on NSTX and WEST via machine learning approaches. Nuclear Fusion, 64(8), 086027.
- https://www.pppl.gov/news/2024/08/ai-revolutionizes-plasma-heating-prediction-accelerating-nuclear-fusion-research
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