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Regulation Compliant AI for Fusion: Real-Time Image Analysis-Based Control of Divertor Detachment in Tokamaks

Nathaniel Chen
Cheolsik Byun
Azarakash Jalalvand
Sangkyeun Kim
Andrew Rothstein
Filippo Scotti
Steve Allen
David Eldon
Keith Erickson
Egemen Kolemen
Main:9 Pages
12 Figures
Bibliography:3 Pages
3 Tables
Abstract

While artificial intelligence (AI) has been promising for fusion control, its inherent black-box nature will make compliant implementation in regulatory environments a challenge. This study implements and validates a real-time AI enabled linear and interpretable control system for successful divertor detachment control with the DIII-D lower divertor camera. Using D2 gas, we demonstrate feedback divertor detachment control with a mean absolute difference of 2% from the target for both detachment and reattachment. This automatic training and linear processing framework can be extended to any image based diagnostic for regulatory compliant controller necessary for future fusion reactors.

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