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基于CVAE的超高速碰撞碎片云运动过程的快速预测技术

周浩 李毅 张浩 陈鸿 任磊生

周 浩,李 毅,张 浩,等. 基于CVAE的超高速碰撞碎片云运动过程的快速预测技术[J]. 实验流体力学,2021,35(5):40-46 doi: 10.11729/syltlx20200058
引用本文: 周 浩,李 毅,张 浩,等. 基于CVAE的超高速碰撞碎片云运动过程的快速预测技术[J]. 实验流体力学,2021,35(5):40-46 doi: 10.11729/syltlx20200058
ZHOU H,LI Y,ZHANG H,et al. Evolution prediction of HVI debris based on CVAE model[J]. Journal of Experiments in Fluid Mechanics, 2021,35(5):40-46. doi: 10.11729/syltlx20200058
Citation: ZHOU H,LI Y,ZHANG H,et al. Evolution prediction of HVI debris based on CVAE model[J]. Journal of Experiments in Fluid Mechanics, 2021,35(5):40-46. doi: 10.11729/syltlx20200058

基于CVAE的超高速碰撞碎片云运动过程的快速预测技术

doi: 10.11729/syltlx20200058
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

Evolution prediction of HVI debris based on CVAE model

  • 摘要: 在航天器防护构型设计中,需要快速、精确预测空间碎片超高速撞击防护屏产生碎片云的质量分布及其运动过程。采用深度学习方法,基于条件变分自编码器(CVAE)模型和大量铝球超高速正撞击铝板的光滑粒子流体动力学(SPH)方法的数值模拟结果,初步构建了碎片云空间质量分布与运动特征的快速预测模型。数值模拟中把铝球速度(3.00~8.00 km/s)、铝球半径(2.00~8.00 mm)、铝板厚度(1.000~4.000 mm)以及观测时间(1.0~12.0 μs) 4个变量作为输入控制参数,生成大量格式统一的训练集数据。模型隐藏层采用200个特征数据来描述碎片云质量分布,训练集参数范围内平均误差在0.6%以内,生成一个碎片云质量分布的平均时间小于7 ms。
  • 图  1  典型超高速碰撞碎片云工程模型

    Figure  1.  Typical HVI debris engineering model

    图  2  锌弹丸超高速撞击锌板

    Figure  2.  Zinc projectile impact Zinc plate

    图  3  铜弹丸超高速撞击铝板

    Figure  3.  Copper projectile impact aluminum plate

    图  4  典型超高速碰撞碎片云发展过程试验(左)与SPH数值模拟(右)结果对比

    Figure  4.  Comparison of experiment and SPH simulation for a typical HVI impact

    图  5  CVAE模型

    Figure  5.  Structure of the CVAE model

    图  6  CVAE模型训练过程

    Figure  6.  Training of the CVAE model

    图  7  碎片云发展过程的CVAE模型预测与数值模拟结果对比(v = 8.00 km/s,d = 4.000 mm,r = 2.00 mm)

    Figure  7.  Comparison of the CVAE model prediction and numerical simula-tion of the evolution of debris(v = 8.00 km/s,d = 4.000 mm,r =2.00 mm)

    图  8  碎片云发展过程的CVAE模型预测与数值模拟结果对比(v = 8.00 km/s,d = 1.000 mm,r = 8.00 mm)

    Figure  8.  Comparison of the CVAE model prediction and numerical simu-lation of the evolution of debris(v = 8 km/s,d = 1 mm,r = 8 mm)

    图  9  训练集中2016个数据的平均误差

    Figure  9.  Average error of 2016 data on the training set

    图  10  模型在速度上的内插能力(d = 1.000 mm,r = 8.00 mm,t = 12.0 μs)

    Figure  10.  Interpolation capability of the model at the velocity direction(d = 1.000 mm, r = 8.00 mm, t = 12.0 μs)

    图  11  测试集中81个数据的平均误差

    Figure  11.  Average error of 81 data on the testing set

    图  12  模型在速度上的外插能力

    Figure  12.  Extrapolation capability of the model at the velocity direction

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出版历程
  • 收稿日期:  2020-04-17
  • 修回日期:  2020-09-13
  • 网络出版日期:  2021-11-15
  • 刊出日期:  2021-10-25

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