Identifying divertor detachment using a machine learning model trained on divertor camera images from DIII-D.

This paper describes the application of a machine learning (ML) algorithm using a convolution neural network, first developed in Boyer et al. ["Classification and prediction of detachment in DIII-D using neural networks trained on C III imaging," Nucl. Fusion (submitted) (2024)], to detect divertor detachment in DIII-D. Detachment detection is based on images from tangentially viewing upper and lower filtered divertor cameras that measure CIII emission at 465 nm. Separate ML models are developed for lower single null and upper single null configurations with mostly closed divertor shapes. Due to the viewing angle and divertor geometry, camera images of the upper divertor show a stark contrast in CIII emission between attached and detached conditions and the model identified detachment with 100% accuracy in the test dataset. For the lower divertor images, the contrast between attached and detached conditions is lower and the model identifies detachment with 96% accuracy. This ML model will be applied to the image data after each shot to provide a rapid assessment of divertor detachment to aid operation of DIII-D with the potential extension to other devices in the future.