التفاصيل البيبلوغرافية
| العنوان: |
Enhancing SRF cavity stability and minimizing detuning with data-driven resonance control based on dynamic mode decomposition |
| المؤلفون: |
Faya Wang |
| المصدر: |
AIP Advances, Vol 13, Iss 7, Pp 075104-075104-6 (2023) |
| بيانات النشر: |
AIP Publishing LLC, 2023. |
| سنة النشر: |
2023 |
| المجموعة: |
LCC:Physics |
| مصطلحات موضوعية: |
Physics, QC1-999 |
| الوصف: |
Effective resonance control of superconducting radio frequency (SRF) cavities is critical for large machines like LCLS-II, as failure to achieve proper control can result in increased RF power consumption, higher cryogenic heat loads, and increased costs. To address this challenge, we have developed a machine learning (ML) model based on the dynamic mode decomposition method to represent the forced cavity dynamics. Using this model, we designed a model predictive controller (MPC) and demonstrated through simulation that the MPC can effectively stabilize the amplitude and phase of SRF cavities using only a frequency actuator, even in the presence of multiple mechanical modes. The lightweight and explicit ML model makes the controller suitable for direct implementation on field-programmable gate arrays, unlocking the full potential of SRF linacs like LCLS-II, enabling higher beam power and energy, and also serving as an advanced motion controller for various applications, such as photon beamlines and storage rings. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2158-3226 |
| Relation: |
https://doaj.org/toc/2158-3226 |
| DOI: |
10.1063/5.0154213 |
| URL الوصول: |
https://doaj.org/article/279cb2a1e4fb468cb9b012ec1ca172d1 |
| رقم الانضمام: |
edsdoj.279cb2a1e4fb468cb9b012ec1ca172d1 |
| قاعدة البيانات: |
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