Quantitative evaluation of grooves in fuel ice layers of ICF based on deep learning and x-ray phase retrieval

Quantitative evaluation of grooves in fuel ice layers of ICF based on deep learning and x-ray phase retrieval

In inertial confinement fusion, the grooves of the fuel ice layer of a capsule can lead to hydrodynamic instability growth during implosion. X-ray phase contrast (XPC) imaging is widely employed in the diagnosis of fuel layers. However, effective quantitative evaluation for grooves based on in-situ...

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Bibliographic Details
Main Authors: Kaijun Shi, Kai Wang, Xin Wang, Ji Yan, Baolu Yang, Cheng Yang, Mingtao Li, Mingxun Wang, Jie Xu, Fei Dai, Xing Zhang, Zhanshan Wang, Baozhong Mu
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Nuclear Fusion
Subjects:
inertial confinement fusion
x-ray phase contrast imaging
phase retrieval
deep learning
Online Access:https://doi.org/10.1088/1741-4326/adcfb2
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https://doi.org/10.1088/1741-4326/adcfb2

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