Chinese scientists have made an important advance in fusion research by improving the speed of plasma measurement by ten times. AI technologies have enhanced the efficiency. And accuracy of key parameters, such as plasma measurement, marking a major milestone toward stable fusion energy. This new technique uses neural networks for the devices that x-ray plasma. And determine ion temperature and rotation speed more rapidly.
Constraints in Tracking Plasma Behavior Dynamically AI
Fusion reactors aim at replicating the sun’s energy-creating mechanisms on earth. And therefore are very complicated machines that contain. And control plasma- a superheated gas undergoing reaction which is key in the fusion process. As the stability of the fusion process is concerned, it is important to control and monitor plasma behavior continuously.
The ion temperature and rotation velocity of plasma are two of the crucial operational parameters that will affect the stability and performance of a reactor. Still, they have regretted that measuring these parameters in real time has been problematic among fusion researchers.
How Neural Networks Help To Solve Problems AI
The research team in the Hefei Institutes of Physical Science, under the leadership of Professor Lyu Bo, proposed an approach integrating the use of neural networks and x-ray crystal spectroscopy. Recognized as a form of artificial intelligence (AI), neural networks can recognize patterns and make intricate calculations. The team ‘trained’ these networks on huge datasets for interpreting X-ray signals from plasma to help in the rapid assessment of ion temperature and rotation speed.
In this study, the research group created two models which are: Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN). Both models were able to calculate the ion temperature and rotation velocity profiles in real-time.
Successful Testing on the EAST Fusion Device
They applied these neural network models to the latest EAST device in China (In the EAST Device, Neural RT simulated and trained) to test their algorithms. All models were verified with high accuracy to experimental data and model predictions standing in good agreement with data.
This brings transformational possibilities for increasing the efficiency of monitoring the plasma and controlling fusion reactions. Such an innovative approach integrates the consideration of the plasma physics and the engineering design of the Tokamak which makes achieving a burning plasma very near.
