Progress in measuring plasma in nuclear fusion | Festina Lente - Your leading source of AI news | Turtles AI

 Recent developments in nuclear fusion show how AI, particularly neural networks, is greatly improving plasma measurement, making this process more efficient and accurate. These advances could have a significant impact on the search for clean and sustainable energy.

Key points:

•  Neural networks accelerate measurements of ion temperature and plasma rotational velocity.
•  The Deep Neural Network (DNN) model is over ten times faster than traditional methods.
•  Tests conducted on the EAST tokamak have confirmed the accuracy of the developed models.
•  These technologies can be adapted to various diagnostic systems in fusion research.

Amid growing interest in renewable energy sources, nuclear fusion research has seen remarkable progress, particularly with the integration of neural networks in plasma monitoring. The team of researchers at the Hefei Institute of Physical Sciences, led by Professor Lyu Bo, has applied these advanced technologies to analyze X-ray signals emitted by plasma, obtaining critical information such as ion temperature and rotational speed, which are essential for the stability of fusion reactors. Neural networks, trained on large datasets, can recognize complex patterns and perform real-time calculations, improving the speed and accuracy of measurements. In particular, two models have been developed: the Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), both of which are capable of providing accurate estimates of ion temperature profiles and plasma rotational velocities. Through rigorous tests on the EAST device, the researchers verified that the results obtained from the models significantly matched the collected data, highlighting the reliability of these technologies. One of the main findings of this study is the significant increase in processing speed, with DNNs outperforming traditional methods by more than 10 times, without compromising accuracy. This speed is critical for optimizing reactor operations and ensuring stable plasma confinement. In addition, CNNs have proven effective in predicting rotational velocity profiles and generating detailed maps of the ionic temperature within the plasma. The implications of this research are significant, as the models developed can be adapted to other diagnostics, making these technologies useful for broader applications in fusion research.

This work marks an important step forward in the development of methodologies that can contribute to the realization of clean energy through nuclear fusion.