Prediction of Deformation Response of Target Plate in Underwater Explosion Based on Deep Learning Neural Network
Year: 2024
Type: article
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.
Authors: Zhiguo LI, Feng MA, Wei ZHU, Xiyu JIA, Yifan LI, Lei CHEN
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6903394020