Research on Prediction of Damage Deformation Response of Explosion Target Plate in Water Based on Deep Learning

LI Zhiguo; MA Feng; ZHU Wei; JIA Xiyu; LI Yifan; CHEN Lei

doi:10.11993/j.issn.2096-3920.2024-0069

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LI Zhiguo, MA Feng, ZHU Wei, JIA Xiyu, LI Yifan, CHEN Lei. Research on Prediction of Damage Deformation Response of Explosion Target Plate in Water Based on Deep Learning[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0069

Citation:

LI Zhiguo, MA Feng, ZHU Wei, JIA Xiyu, LI Yifan, CHEN Lei. Research on Prediction of Damage Deformation Response of Explosion Target Plate in Water Based on Deep Learning[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0069

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Research on Prediction of Damage Deformation Response of Explosion Target Plate in Water Based on Deep Learning

doi: 10.11993/j.issn.2096-3920.2024-0069

State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing 100081, China

Received Date: 2024-04-12
Accepted Date: 2024-06-04
Rev Recd Date: 2024-05-24

Available Online: 2024-11-07

Abstract

Abstract

The deformation of the target plate in underwater explosion manifests as a complex nonlinear coupling interaction between the structure and the fluid under the impact of shock waves. In this paper, a deep learning neural network is designed and optimized to predict the dynamic deformation displacement data of the target plate under different conditions of target plate thickness, shock factor, explosive dosage, and explosion distance. The R² coefficient and accuracy rate of the test set reach 0.99 and 0.95. Compared with 25 simulation conditions, the explosion deformation response analysis graph formed by 9, 261 working conditions based on the prediction model can cover a more detailed range of characteristic parameters and the trend of maximum deformation variation, providing important reference for underwater weapon design and underwater protection applications.
- underwater explosion,
- deep learning,
- deformation response,
- neural network

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References(18)

References

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