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

Xia Zhiyang Kuang Yuanyuan Lu Yan Yang Ming

夏治洋, 旷圆圆, 卢艳, 等. 基于卷积神经网络的烧蚀瑞利-泰勒不稳定性流场高分辨率重建[J]. 强激光与粒子束, 2024, 36: 122004. doi: 10.11884/HPLPB202436.240015
引用本文: 夏治洋, 旷圆圆, 卢艳, 等. 基于卷积神经网络的烧蚀瑞利-泰勒不稳定性流场高分辨率重建[J]. 强激光与粒子束, 2024, 36: 122004. doi: 10.11884/HPLPB202436.240015
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
Citation: 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

基于卷积神经网络的烧蚀瑞利-泰勒不稳定性流场高分辨率重建

doi: 10.11884/HPLPB202436.240015
详细信息
  • 中图分类号: O357;

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

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
  • 摘要:

    高分辨率流场数据在气象学、航空航天工程、高能物理等领域有着重要的应用价值。实验和数值模拟是两种获取高分辨率流场数据的主要途径。但是高昂的实验成本和仿真计算资源阻碍了研究者对流场演化的具体分析。随着深度学习技术的发展,卷积神经网络被用来实现流场的高分辨率重建。针对烧蚀瑞利-泰勒不稳定性流场重建提出了普通卷积神经网络模型和多重时间路径卷积神经网络模型。这两个模型可以在很短的时间内对流场进行高分辨率重建,极大地丰富了高分辨率重建技术在流体不稳定性研究中的应用。与普通卷积神经网络相比,多重时间路径卷积神经网络模型的误差较小,可以还原流场的更多细节。此外,还讨论了用于获取低分辨率流场的不同池化方法对卷积神经网络模型性能的影响。

  • Figure  1.  Schematic diagram of ordinary CNN structure

    Figure  2.  Schematic diagram of the structure of a multi-time-path CNN

    Figure  3.  Error maps of two convolutional neural network models

    Figure  4.  Comparison of reconstructed results with average pooling (r=4)

    Figure  5.  Comparison of reconstructed results with maximum pooling (r=4)

    Figure  6.  Comparison of the high-resolution reconstructed density data (weak nonlinear stage with ablation, disturbance wavelength=12 μm)

    Figure  7.  Comparison of the high-resolution reconstructed density data (classical linear stage, disturbance wavelength=12 μm)

    Figure  8.  Comparison of the high-resolution reconstructed density data (nonlinear stage with ablation, disturbance wavelength=12 μm)

    Figure  9.  Comparison of the high-resolution reconstructed density data (weak nonlinear stage with ablation, disturbance wavelength=30 μm)

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出版历程
  • 收稿日期:  2024-02-25
  • 修回日期:  2024-09-20
  • 录用日期:  2024-04-07
  • 网络出版日期:  2024-10-26
  • 刊出日期:  2024-11-08

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