基于BP神经网络的爆炸用激波管峰值压力预测方法

陈梓薇 王仲琦 曾令辉

陈梓薇, 王仲琦, 曾令辉. 基于BP神经网络的爆炸用激波管峰值压力预测方法[J]. 爆炸与冲击, 2024, 44(5): 054101. doi: 10.11883/bzycj-2023-0187
引用本文: 陈梓薇, 王仲琦, 曾令辉. 基于BP神经网络的爆炸用激波管峰值压力预测方法[J]. 爆炸与冲击, 2024, 44(5): 054101. doi: 10.11883/bzycj-2023-0187
CHEN Ziwei, WANG Zhongqi, ZENG Linghui. A method for predicting peak pressure in an explosion shock tube based on BP neural network[J]. Explosion And Shock Waves, 2024, 44(5): 054101. doi: 10.11883/bzycj-2023-0187
Citation: CHEN Ziwei, WANG Zhongqi, ZENG Linghui. A method for predicting peak pressure in an explosion shock tube based on BP neural network[J]. Explosion And Shock Waves, 2024, 44(5): 054101. doi: 10.11883/bzycj-2023-0187

基于BP神经网络的爆炸用激波管峰值压力预测方法

doi: 10.11883/bzycj-2023-0187
基金项目: 国家重点研发计划 (2021YFC3001204)
详细信息
    作者简介:

    陈梓薇(1998- ),女,硕士研究生,czw_bit@163.com

    通讯作者:

    王仲琦(1972- ),男,博士,副教授,czqwang@bit.edu.cn

  • 中图分类号: O383

A method for predicting peak pressure in an explosion shock tube based on BP neural network

  • 摘要: 针对爆炸用激波管缺乏相应的经验公式和数值模拟时效性差的问题,同时为了快速得到激波管内的峰值压力,建立预测爆炸用激波管试验段峰值压力的四层反向传播(back propagation,BP)神经网络。采用数值模拟方法计算激波管试验段峰值压力,计算结果与激波管爆炸试验结果进行对比,平均相对误差为2.69%。证明激波管数值模型的准确性后,将数值模拟得到的195组激波管测得的峰值压力作为输出层,激波管驱动段TNT的药量、药柱的长径比以及爆炸比例距离作为神经网络的输入层。为了加快神经网络迭代速度和提高预测精度,使用自适应矩估计(adaptive moment estimation,ADAM)算法作为神经网络误差梯度下降的优化算法。结果表明,训练好的神经网络得到的预测结果与模拟值基本吻合,预测结果与数值模拟结果的平均相对误差为3.26%。BP神经网络模型能够反映激波管爆炸的峰值压力与影响因素之间的映射关系,采用BP 神经网络模型计算时比数值模拟节约了大量运算时间。
  • 图  1  爆炸用激波管示意图

    Figure  1.  Schematic diagram of the explosion shock tube

    图  2  激波管数值模型

    Figure  2.  Simulation model of the shock tube

    图  3  峰值压力与网格独立性验证的比较

    Figure  3.  Comparison of peak pressure and grid independence verification

    图  4  激波管试验装置示意图及测点布设图

    Figure  4.  Schematic diagram of the shock tube and the location of the measurement points of the shock tube

    图  5  不同工况下的压力时程曲线

    Figure  5.  Pressure-time curves for different cases

    图  6  预测激波管峰值压力的神经网络拓扑结构示意图

    Figure  6.  Schematic diagram of neural network topology for predicting peak pressure in a shock tube

    图  7  预测峰值压力与数值峰值压力对比图

    Figure  7.  Comparison of predicted and simulated peak pressure

    图  8  BP神经网络预测拟合图

    Figure  8.  BP neural network prediction fitting diagram

    表  1  材料参数

    Table  1.   Material parameters

    材料密度/(kg·m−3)定压比热/(J·kg−1·K−1)抗拉强度/MPa
    4340钢7830477818
    TNT1630
    空气1.225717.6
    下载: 导出CSV

    表  2  部分激波管爆炸数值模拟数据

    Table  2.   The partial data of shock tube explosion by numerical simulation

    药量/g 长径比 爆炸比例距离/(mm·g−1/3) 峰值压力模拟值/kPa
    10.4 2.00 3394.74 147.84
    70.1 0.50 1185.72 182.17
    70.1 4.00 899.78 848.38
    166.2 0.50 343.76 997.61
    186.9 1.33 505.42 902.55
    249.3 0.75 1110.63 238.39
    280.5 0.25 456.77 467.08
    473.2 1.00 499.19 1352.11
    584.3 0.90 387.55 1298.94
    下载: 导出CSV

    表  3  不同工况下测点测量的峰值超压

    Table  3.   Different cases of peak overpressure obtained from measurement points

    测点峰值压力/MPa
    5 g TNT10 g TNT20 g TNT25 g TNT30 g TNT
    10.2500.4300.7401.0400.857
    20.2250.3580.5340.5970.588
    30.1890.2200.3890.4170.442
    40.0880.1360.1450.1790.192
    50.0810.1310.1720.2020.209
    下载: 导出CSV

    表  4  工况2中测点的试验数据与数值模拟结果对比

    Table  4.   Comparison of experimental data and numerical simulation results at measurement points in the case 2

    测点测点距原点距离/m峰值压力/MPa
    试验数值模拟相对误差/%
    11.8890.4300.4163.32
    22.7720.3580.3463.33
    34.3890.2200.2142.50
    45.0000.1360.141−3.64
    57.9620.1310.1300.67
    下载: 导出CSV

    表  5  压力峰值试验数据与预测结果对比

    Table  5.   Comparison of peak pressure between test date and predicted result

    TNT药量/g测点离原点
    位置/m
    峰值压力/kPa
    试验预测相对误差/%
    52.772227247.734.72
    102.772358349.51−2.37
    106.481140134.74−3.75
    201.889740763.843.22
    203.555410416.601.61
    251.88910401076.293.49
    302.772588594.281.06
    309.443186177.91−4.34
    下载: 导出CSV

    表  6  预测评价指标

    Table  6.   Prediction evaluation indicators

    预测模型MAERMSEMAPE%R2
    BP神经网络25.443.33.470.99
    多元线性回归480597.785.80.58
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-05-18
  • 修回日期:  2024-03-15
  • 网络出版日期:  2024-03-26
  • 刊出日期:  2024-05-08

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