基于深度学习神经网络的水中爆炸靶板变形响应预测研究

Translated title of the contribution: Prediction of Deformation Response of Target Plate in Underwater Explosion Based on Deep Learning Neural Network

Zhiguo Li, Feng Ma, Wei Zhu, Xiyu Jia*, Yifan Li, Lei Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The deformation of a target plate in underwater explosion is manifested 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 coefficient of determination and accuracy of prediction on the test set reach 0.99 and 0.95, respectively. 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.

Translated title of the contributionPrediction of Deformation Response of Target Plate in Underwater Explosion Based on Deep Learning Neural Network
Original languageChinese (Traditional)
Pages (from-to)1045-1052 and 1062
JournalJournal of Unmanned Undersea Systems
Volume32
Issue number6
DOIs
Publication statusPublished - Dec 2024

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Li, Z., Ma, F., Zhu, W., Jia, X., Li, Y., & Chen, L. (2024). 基于深度学习神经网络的水中爆炸靶板变形响应预测研究. Journal of Unmanned Undersea Systems, 32(6), 1045-1052 and 1062. https://doi.org/10.11993/j.issn.2096-3920.2024-0069