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

CHEN Ziwei; WANG Zhongqi; ZENG Linghui

doi:10.11883/bzycj-2023-0187

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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. 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. 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. 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. doi: 10.11883/bzycj-2023-0187

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

doi: 10.11883/bzycj-2023-0187

State Key Laboratory of Explosive Science and Technology, Beijing Institute of Technology, Beijing 100081, China

Received Date: 2023-05-18
Rev Recd Date: 2024-03-15

Available Online: 2024-03-26

Abstract

Abstract

In response to the problems of the lack of corresponding empirical formulas and the poor timeliness of simulation for the explosive shock tube, and to quickly obtain the peak pressure of the shock tube used in explosions, a four-layer back propagation (BP) neural network was established to predict the peak pressure in the experimental section of the shock tube. After verifying the grid independence, numerical simulation was used to calculate the peak pressure of the test section of the shock tube, and the simulation data were compared with the experimental data of the shock tube explosion, and the average relative error is 2.49%. After proving the accuracy of the numerical simulation values, the 195 sets of peak pressure obtained from the numerical simulation in the shock tube test section were used as the output layer, and the TNT dosage in the shock tube driving section, aspect ratio of the charge column, and explosion proportional distance were used as the input layer for BP neural network training. To speed up the neural network iterations and increase the prediction accuracy, Adam's algorithm was used as an optimization algorithm for neural network error gradient descent. The results show that the predicted results obtained through the trained neural network are basically consistent with the simulated values, and the average relative error between the predicted results and the numerical values is 3.26%. In contrast to the evaluation metrics obtained using multiple regression analysis (mean absolute error (MAE) of 480 and coefficient of determination (R²) of 0.58), the four-layer BP neural network obtains a MAE of 25.4 and an R² of 0.99 for the validation set. The BP neural network model can reflect the mapping relationship between the peak pressure of the shock tube explosion and the influencing factors, and improve several times compared with the time required for numerical simulation, so it has the value of practical engineering applications.
- BP neural network,
- shock tube,
- peak pressure,
- adaptive moment estimation

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References(18)

References

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