基于贝叶斯深度学习的复杂结构爆炸载荷的快速估计

潘美霖 彭卫文 冷春江 邱玖禄 钟巍

潘美霖, 彭卫文, 冷春江, 邱玖禄, 钟巍. 基于贝叶斯深度学习的复杂结构爆炸载荷的快速估计[J]. 爆炸与冲击. doi: 10.11883/bzycj-2024-0191
引用本文: 潘美霖, 彭卫文, 冷春江, 邱玖禄, 钟巍. 基于贝叶斯深度学习的复杂结构爆炸载荷的快速估计[J]. 爆炸与冲击. doi: 10.11883/bzycj-2024-0191
PAN Meilin, PENG Weiwen, LENG Chunjiang, QIU Jiulu, ZHONG Wei. Fast estimation of blast loading of complex structures based on Bayesian deep learning[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2024-0191
Citation: PAN Meilin, PENG Weiwen, LENG Chunjiang, QIU Jiulu, ZHONG Wei. Fast estimation of blast loading of complex structures based on Bayesian deep learning[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2024-0191

基于贝叶斯深度学习的复杂结构爆炸载荷的快速估计

doi: 10.11883/bzycj-2024-0191
详细信息
    作者简介:

    潘美霖(1999- ),女,硕士研究生,panmlin@mail2.sysu.edu.cn

    通讯作者:

    钟 巍(1986- ),男,博士,副研究员,zhongwei@nint.ac.cn

  • 中图分类号: O382.1

Fast estimation of blast loading of complex structures based on Bayesian deep learning

  • 摘要: 对于复杂结构的爆炸载荷估计,传统数值模拟方法计算耗时长,而基于神经网络的快速估计仅能进行点估计却无法给出结果的置信度。为此,结合贝叶斯理论和深度学习,构建了复杂结构爆炸载荷快速估计的贝叶斯深度学习方法。通过开源数值模拟软件,计算了爆炸当量、位置、速度等参数大范围变化下复杂结构的爆炸载荷数据,基于贝叶斯理论将深度学习模型参数视为随机变量,利用变分贝叶斯推断高效训练模型,在保证爆炸载荷快速估计精度的同时,赋予模型不确定性量化的能力。结果表明,该方法在训练数据以外的爆炸载荷快速估计的误差约为12.2%,置信区间涵盖真实值的百分比超过81.6%,单点爆炸载荷估计时间不超过20 ms。该方法是实现复杂结构爆炸载荷快速、可信估计的新方法。
  • 图  1  复杂结构模型及压力监测点分布

    Figure  1.  Complex structure model and distribution of pressure monitoring points

    图  2  网格尺寸不同时数值模拟与经验公式计算的结果对比

    Figure  2.  Comparison of numerical simulation results with empirical formulas under different grid settings

    图  3  模型训练过程中损失与训练迭代次数的关系

    Figure  3.  Relationship between iteration times and loss during model training

    图  4  超压峰值的估计结果

    Figure  4.  The estimated result of the peak overpressure

    图  5  超压峰值的真实值与估计值

    Figure  5.  The true value and estimated value of the peak overpressure

    图  6  范围不同时超压峰值的真实值与估计值

    Figure  6.  The true value and estimated value of the peak overpressure for different ranges

    表  1  爆源参数的范围

    Table  1.   The range of explosion source parameter

    爆源相关参数 符号 单位 参数范围
    当量 Q kg (200, 2000)
    爆心距 R m (40, 100)
    起爆速度 U m/s (0, 1020)
    下载: 导出CSV

    表  2  经验常数的取值

    Table  2.   The value of empirical constant

    A/TPa B/TPa R1 R2 λ e/(TJ·m−3) ρ0/(kg·m−3)
    609.77 12.95 4.50 0.9 0.25 9.0 1 630
    下载: 导出CSV

    表  3  不同背景网格设置下数值模拟所需的计算时间

    Table  3.   Calculation time required for numerical simulation with different background grid settings

    背景网格尺寸/m 5.0 7.5 10.0
    计算时间/h 10 2 1
    下载: 导出CSV

    表  4  待调节的超参数及其取值范围

    Table  4.   The hyperparameter to be adjusted and the corresponding range

    神经网络层数 每层神经元数 激活函数 学习率
    2~8 16, 32, 64, 128, 256 ReLU, Leaky ReLU 0.001, 0.005, 0.010
    下载: 导出CSV

    表  5  模拟平台的参数

    Table  5.   Parameters of the simulation platform

    平台处理器内存操作系统
    天河二号超级计算中心2×12 Intel Xeon E5-2692 v2/单节点128 GB/单节点Linux lon26 3.10.0-514.el7.x86_64
    本地台式机AMD Ryzen7 3700x8-Core八核32 GBWindows10
    下载: 导出CSV

    表  6  超压峰值的估计精度和不确定性

    Table  6.   The estimation accuracy and uncertainty of the peak overpressure

    估计精度 不确定性
    δmape R2 ppicp wnmpiw
    训练集 测试集 训练集 测试集 训练集 测试集 训练集 测试集
    0.104 0.122 0.834 0.830 0.921 0.816 0.041 0.057
    下载: 导出CSV

    表  7  基于目标范围的误差分解

    Table  7.   Error decomposition based on target range

    超压峰值/kPa 样本数量 δmape/% φ/%
    0 < δmape ≤ 10 10 < δmape ≤ 20 20 < δmape ≤ 30 30 < δmape ≤ 50 δmape ≤ 50
    Δp0 ≤ 50 1927 10.173 66.27 21.33 7.16 3.68 98.44
    50 < Δp0 ≤ 100 282 20.492 37.94 24.82 15.60 16.67 95.03
    100 < Δp0 ≤ 150 124 17.714 41.13 20.97 18.55 14.52 95.15
    150 < Δp0 ≤ 400 83 25.008 18.07 18.07 26.51 32.53 95.18
    下载: 导出CSV

    表  8  基于比例距离的误差分解

    Table  8.   Error decomposition based on scaled distance

    比例距离/$ (m{\cdot kg}^{-\frac{1}{3}}) $ 样本数量 δmape/% φd/%
    0 < δmape ≤ 10 10 < δmape ≤ 20 20 < δmape ≤ 30 30 < δmape ≤ 50 δmape ≤ 50
    3 < d ≤ 5 388 21.114 34.79 23.97 16.75 18.04 93.55
    5 < d ≤ 7 963 13.091 57.22 23.88 8.72 7.79 97.61
    7 < d ≤ 10 949 8.389 71.97 18.12 6.43 2.85 99.37
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-06-18
  • 修回日期:  2024-10-09
  • 网络出版日期:  2024-11-05

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