Regulation Compliant AI for Fusion: Real-Time Image Analysis-Based Control of Divertor Detachment in Tokamaks
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866914259601981440 |
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| author | Chen, Nathaniel Byun, Cheolsik Jalalvand, Azarakash Kim, Sangkyeun Rothstein, Andrew Scotti, Filippo Allen, Steve Eldon, David Erickson, Keith Kolemen, Egemen |
| author_facet | Chen, Nathaniel Byun, Cheolsik Jalalvand, Azarakash Kim, Sangkyeun Rothstein, Andrew Scotti, Filippo Allen, Steve Eldon, David Erickson, Keith Kolemen, Egemen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02897 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Regulation Compliant AI for Fusion: Real-Time Image Analysis-Based Control of Divertor Detachment in Tokamaks Chen, Nathaniel Byun, Cheolsik Jalalvand, Azarakash Kim, Sangkyeun Rothstein, Andrew Scotti, Filippo Allen, Steve Eldon, David Erickson, Keith Kolemen, Egemen Machine Learning Computer Vision and Pattern Recognition Systems and Control Plasma Physics 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. |
| title | Regulation Compliant AI for Fusion: Real-Time Image Analysis-Based Control of Divertor Detachment in Tokamaks |
| topic | Machine Learning Computer Vision and Pattern Recognition Systems and Control Plasma Physics |
| url | https://arxiv.org/abs/2507.02897 |