基于BP神经网络的水中双爆源爆炸冲击波峰值压力预测模型研究

Prediction Model of Two Underwater Explosion Sources’ Explosion Shock Wave Peak Pressure Based on BP Neural Network

  • 摘要: 为了获得水中等质量两爆源同步爆炸时冲击波耦合中心的峰值压力计算模型,利用Autodyn计算得到不同药量和爆距下的峰值压力数据. 一方面根据量纲分析确定的函数形式拟合数据从而获得峰值压力的计算公式;另一方面对药量、爆距及峰值压力三类数据进行对数变换和归一化,并将其分为训练集和测试集,然后将训练集代入BP神经网络进行训练,得到结构相对简单、均方误差最小的BP神经网络预测模型. 结果表明:公式计算结果和BP神经网络模型计算得到的峰值压力与实际值吻合较好,公式计算值与实际值的平均相对误差为1.08%,BP神经网络预测值与实际值的平均相对误差为0.52%,与公式计算相比,BP神经网络能够以更少的数据样本容量实现更高的精度预测.

     

    Abstract: In order to obtain the calculation model of peak pressure at shock wave coupling center when two explosion sources with equal mass exploded simultaneously in water, Autodyn was used to compute peak pressure data under different charge amounts and detonation distances. On the one hand, calculation formula of peak pressure was obtained by fitting the data in the function form specified by dimensional analysis. On the other hand, logarithmic transformation and normalization were performed on three types of data: total charge, detonation distance, and peak pressure, which were divided into training set and test set. The training set was then fed into the BP neural network for training, yielding a BP neural network prediction model with a relatively simple structure and a lowest mean square error. The findings reveal that the peak pressure predicted by the formula calculation model and the BP neural network model agrees well with the actual value. The average relative error between the calculated formula value and the actual value is 1.08%, while the average relative error between the projected BP neural network value and the actual value is 0.52%. It means that BP neural network can achieve higher accuracy predictions with a smaller data sample size compared with formula calculations.

     

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