Neural networks for estimation of divertor conditions in DIII-D using C III imaging
Year: 2024
Type: article
Abstract: Deep learning approaches have been applied to images of C III emission in the lower divertor of DIII-D to develop models for estimating the level of detachment and magnetic configuration (X-point location and strike point radial location). The poloidal distance from the target to the C III emission front is used to represent the level of detachment. The models perform well on a test dataset not used in training, achieving F 1 scores as high as 0.99 for detachment state classification and root mean squared error (RMSE) as low as 2 cm for front location regression. Predictions for shots with intermittent reattachment are studied, with class activation mapping used to aid in interpretation of the model predictions. Based on the success of these models, a third model was trained to predict the X-point location and strike point radial position from C III images. Though the dataset covers only a small range of possible magnetic configurations, the model shows promising results, achieving RMSE around 1 cm for the test data.
Source: Nuclear Fusion
Authors: M.D. Boyer, F. Scotti, V. Gajaraj
Institutions: Princeton Plasma Physics Laboratory, Cornell University
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,470
OpenAlex ID: W4401749275