Tuesday, November 08, 2022

Machine learning facilitates “turbulence tracking” in Tokomak fusion reactors

Update of 11/9/2022: Forgot to add, I believe those instabilities (e.g. turbulences) and the containment of the plasma that have plagued successful energy generation from nuclear fusion.
 
Recommendable! Amazing stuff! Will artificial intelligence/machine learning finally facilitate the use of nuclear fusion as a new main energy source? It has been in the making for about seven decades!

Perhaps too many of our limited resources have been wasted on such foolish endeavors like unreliable renewable energy (e.g. wind mills)!

"Fusion, which promises practically unlimited, carbon-free energy using the same processes that power the sun, is at the heart of a worldwide research effort that could help mitigate climate change. ...
When the researchers tested the trained models using real video clips, the models could identify blobs with high accuracy — more than 80 percent in some cases. The models were also able to effectively estimate the size of blobs and the speeds at which they moved. ...
However, blobs appear like filaments falling out of the plasma at the very edge, between the plasma and the reactor walls. These random, turbulent structures affect how energy flows between the plasma and the reactor. ..."

From the abstract:
"The analysis of turbulence in plasmas is fundamental in fusion research. Despite extensive progress in theoretical modeling in the past 15 years, we still lack a complete and consistent understanding of turbulence in magnetic confinement devices, such as tokamaks. Experimental studies are challenging due to the diverse processes that drive the high-speed dynamics of turbulent phenomena. This work presents a novel application of motion tracking to identify and track turbulent filaments in fusion plasmas, called blobs, in a high-frequency video obtained from Gas Puff Imaging diagnostics. We compare four baseline methods ... trained on synthetic data and then test on synthetic and real-world data obtained from plasmas in the Tokamak à Configuration Variable (TCV). The blob regime identified from an analysis of blob trajectories agrees with state-of-the-art conditional averaging methods for each of the baseline methods employed, giving confidence in the accuracy of these techniques. By making a dataset and benchmark publicly available, we aim to lower the entry barrier to tokamak plasma research ..."

Machine learning facilitates “turbulence tracking” in fusion reactors | MIT News | Massachusetts Institute of Technology A new approach sheds light on the behavior of turbulent structures that can affect the energy generated during fusion reactions, with implications for reactor design.


The schematic of the Tokamak à Configuration Variable (TCV) (left) and its interior (right).


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