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超高速碰撞碎片云质量分布快速预测技术

周浩 李毅 兰胜威 刘海

周浩,李毅,兰胜威,等. 超高速碰撞碎片云质量分布快速预测技术[J]. 实验流体力学,2022,36(3):73-78 doi: 10.11729/syltlx20210125
引用本文: 周浩,李毅,兰胜威,等. 超高速碰撞碎片云质量分布快速预测技术[J]. 实验流体力学,2022,36(3):73-78 doi: 10.11729/syltlx20210125
ZHOU H,LI Y,LAN S W,et al. Predict the mass distribution of hypervelocity impact debris using deep learning[J]. Journal of Experiments in Fluid Mechanics, 2022,36(3):73-78. doi: 10.11729/syltlx20210125
Citation: ZHOU H,LI Y,LAN S W,et al. Predict the mass distribution of hypervelocity impact debris using deep learning[J]. Journal of Experiments in Fluid Mechanics, 2022,36(3):73-78. doi: 10.11729/syltlx20210125

超高速碰撞碎片云质量分布快速预测技术

doi: 10.11729/syltlx20210125
详细信息
    作者简介:

    周浩:(1986—),男,湖南常德人,工程师。研究方向:超高速碰撞数值模拟。通信地址:四川省绵阳市涪城区二环路南段6号(621000)。E-mail:499907834@qq.com

    通讯作者:

    E-mail:liyi@cardc.cn

  • 中图分类号: V19

Predict the mass distribution of hypervelocity impact debris using deep learning

  • 摘要: 在航天器防护构型设计中,快速预测空间碎片超高速碰撞防护屏产生碎片云的质量分布及其变化规律具有重要意义。本文初步探索了采用深度学习方法预测超高速碰撞碎片云的二维质量分布及其变化过程。训练数据来自约2000个弹丸(铝球)超高速正碰撞靶板(铝板)的光滑粒子流体动力学数值模拟结果,共考虑4个变量(弹丸速度范围3~8 km/s、弹丸半径范围2~8 mm、靶板厚度范围1~4 mm以及观测时间范围1~12 μs)。系统比较了反卷积模型和多层感知机两种模型的预测效果,重点考察了模型的外推能力(应用于训练参数范围之外)。研究结果表明:在训练参数范围内两种模型的预测精度都很高;反卷积模型能够捕捉到碎片云质量分布的颗粒特征,但外推能力较差;多层感知机模型将碎片云中的质量进行了局部均匀化处理,具有较强的外推能力;多层感知机模型通过学习1~12 μs的碎片云质量分布,能够以一定精度预测24 μs时刻的质量分布;反卷积模型的预测时间为毫秒量级,多层感知机模型的预测时间为秒量级。
  • 图  1  反卷积模型

    Figure  1.  De-convolutional neural networks architecture

    图  2  多层感知机模型

    Figure  2.  Multi-layer perceptron architecture

    图  3  数值模拟与模型预测结果对比(v=8 km/s,d=4 mm,r =2 mm)

    Figure  3.  Comparison of numerical simulations and model predictions(v=8 km/s, d=4 mm, r =2 mm)

    图  4  数值模拟与模型预测结果对比(v=8 km/s,d=1 mm,r =8 mm)

    Figure  4.  Comparison of numerical simulations and model predictions(v=8 km/s, d=1 mm, r =8 mm)

    图  5  模型内插能力(d=1.5 mm,r =7.5 mm,t =7.5 μs)

    Figure  5.  Interpolation capability in the velocity direction(d=1.5 mm, r =7.5 mm, t =7.5 μs)

    图  6  模型在弹丸速度上的外推能力

    Figure  6.  Model extrapolation capability in the velocity direction

    图  7  模型在弹丸半径上的外推能力

    Figure  7.  Model extrapolation capability in the impactor radius direction

    图  8  模型在靶板厚度上的外推能力

    Figure  8.  Model extrapolation capability in the target thickness direction

    图  9  模型在观测时间上的外推能力

    Figure  9.  Model extrapolation capability in the time direction

    图  10  外推算例的误差分布

    Figure  10.  Error distribution for model extrapolation cases

    表  1  外推算例参数设计

    Table  1.   Input parameters design for model extrapolation

    v/(km·s–1r/mmd/mmt/μs
    G18,91011815
    G253,6,912110
    G3552,4,6810
    G438112,162024
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-10-12
  • 录用日期:  2022-03-14
  • 修回日期:  2022-03-13
  • 网络出版日期:  2022-04-19
  • 刊出日期:  2022-06-25

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