High-resolution reconstruction of the ablative RT instability flow field via convolutional neural networks

Xia Zhiyang; Kuang Yuanyuan; Lu Yan; Yang Ming

doi:10.11884/HPLPB202436.240015

Volume 36 Issue 12

Nov. 2024

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Article Navigation > High Power Laser and Particle Beams > 2024 > 36(12): 122004

Xia Zhiyang, Kuang Yuanyuan, Lu Yan, et al. High-resolution reconstruction of the ablative RT instability flow field via convolutional neural networks[J]. High Power Laser and Particle Beams, 2024, 36: 122004. doi: 10.11884/HPLPB202436.240015

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Xia Zhiyang, Kuang Yuanyuan, Lu Yan, et al. High-resolution reconstruction of the ablative RT instability flow field via convolutional neural networks[J]. High Power Laser and Particle Beams, 2024, 36: 122004. doi: 10.11884/HPLPB202436.240015

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High-resolution reconstruction of the ablative RT instability flow field via convolutional neural networks

doi: 10.11884/HPLPB202436.240015

Xia Zhiyang^1
,,
Kuang Yuanyuan^{1, 2},
Lu Yan^{1
,
,},
Yang Ming^{1, 3
,
,}

1.
School of Physics and Optoelectronic Engineering, Anhui University, Hefei 230601, China
2.
School of Electronic and Information Engineering, Anhui University, Hefei 230601, China
3.
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China

Funds: National Natural Science Foundation of China (11805003; 11947102; 12004005); Natural Science Foundation of Anhui Province (2008085MA16; 2008085QA26); University Synergy Innovation Program of Anhui Province (GXXT-2022-039); State Key Laboratory of Advanced Electromagnetic Technology (Grant No. AET 2024KF006)

More Information

Author Bio:
Xia Zhiyang, 2571785114@qq.com
Corresponding author: Lu Yan, luyan2003@ahu.edu.cn;; Yang Ming, mingyang@ahu.edu.cn。
Received Date: 2024-02-25
Accepted Date: 2024-04-07
Rev Recd Date: 2024-09-20

Available Online: 2024-10-26

Publish Date: 2024-11-08

Abstract

Abstract

High-resolution flow field data has important applications in meteorology, aerospace engineering, high-energy physics and other fields. Experiments and numerical simulations are two main ways to obtain high-resolution flow field data, while the high experiment cost and computing resources for simulation hinder the specific analysis of flow field evolution. With the development of deep learning technology, convolutional neural networks are used to achieve high-resolution reconstruction of the flow field. In this paper, an ordinary convolutional neural network and a multi-time-path convolutional neural network are established for the ablative Rayleigh-Taylor instability. These two methods can reconstruct the high-resolution flow field in just a few seconds, and further greatly enrich the application of high-resolution reconstruction technology in fluid instability. Compared with the ordinary convolutional neural network, the multi-time-path convolutional neural network model has smaller error and can restore more details of the flow field. The influence of low-resolution flow field data obtained by the two pooling methods on the convolutional neural networks model is also discussed.
- convolutional neural networks,
- the ablative Rayleigh–Taylor instability,
- high-resolution reconstruction,
- multi-time-path,
- pooling

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References(26)

References

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