Development of real-time density profile inversion with deep neural network model for multi-bands X-mode polarization reflectometer on EAST
Abstract Microwave reflectometry will serve as the primary diagnostic tool for measuring plasma density in ITER. The real-time inversion of high spatiotemporal resolution density profiles is a critical research focus for plasma fueling and control in both current and future fusion devices. A fast density profile inversion algorithm based on a deep neural network model has been proposed for the microwave reflectometry profile inversion database under various discharge parameters on EAST. Firstly, the model directly takes the high-sampling-rate raw time-domain <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>I</mml:mi> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:mi>Q</mml:mi> </mml:mrow> </mml:math> data from the multi-bands (Q-, V- and W-bands) microwave reflectometer as input and demonstrates rapid and effective feature extraction capabilities. Secondly, the model outputs a two-dimensional vector containing both the positions of the microwave cut-off layers and the density information. Notably, the model exhibits excellent adaptability across different discharge parameters on EAST, including variations in magnetic field strength ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>B</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">t</mml:mi> <mml:mn>0</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> = 1.57–2.30 T), different plasma confinement such as L-mode and H-mode, and during phases such as current ramp-up and steady-state discharge. The next step involves integrating this data-driven real-time density profile algorithm directly into the deployed profile reflectometer data acquisition system on EAST to enable parallel computation of data acquisition and processing. This integration aims to provide real-time density profile distribution, particularly during density feedback control experiments, such as gas or pellet injection experiments.
Authors
- Xiang Gao (ORCID: https://orcid.org/0000-0003-1885-2538)
- Kaixuan Ye
- Jia Huang (ORCID: https://orcid.org/0000-0001-6289-1812)
- Gongshun Li (ORCID: https://orcid.org/0000-0002-5542-1238)
- Chenguang Wan (ORCID: https://orcid.org/0000-0002-6005-4460)
- Yan Feng (ORCID: https://orcid.org/0000-0002-4974-7004)
- Zijie Liu (ORCID: https://orcid.org/0000-0002-4123-6445)
- Kangning Geng (ORCID: https://orcid.org/0000-0001-7808-0192)
- Fei Wen (ORCID: https://orcid.org/0000-0001-7929-5005)
- Guoqiang Li (ORCID: https://orcid.org/0000-0003-0792-4348)
- Tao Zhang (ORCID: https://orcid.org/0000-0002-1555-6226)
- Zhen Zhou (ORCID: https://orcid.org/0009-0000-8261-7837)
Institutions
- University of Science and Technology of China (CN)
- Forschungszentrum Jülich (DE)
- Nanyang Technological University (SG)
- Chinese Academy of Sciences (CN)
- Institute of Plasma Physics (CN)
Publication Details
- Journal
- Plasma Physics and Controlled Fusion
- Published
- 2025-04-03
- DOI
- https://doi.org/10.1088/1361-6587/adc830
- Citations
- 5
- Primary Topic
- Soil Moisture and Remote Sensing
- Type
- article
- Field-Weighted Citation Impact
- 4.00
Funders
- National Natural Science Foundation of China
- Chinese Academy of Sciences
- China Postdoctoral Science Foundation
- Hefei Institutes of Physical Science, Chinese Academy of Sciences
- National Postdoctoral Program for Innovative Talents
- National Key Research and Development Program of China
- Institute of Plasma Physics, Chinese Academy of Sciences