Work

Damage prediction of hull structure under near-field underwater explosion based on machine learning

Year: 2024

Type: article

Abstract: • The UNDEX loads are closely related to standoff distance. • ML provides an effective approach for predicting structural damage. • The structural deformation exhibits a multi-peak phenomenon. Underwater explosions generate diverse loads, encompassing shock waves and bubble pulsation, and pose a substantial threat to naval vessels. In this study, finite element simulations were carried out to evaluate the response of the hull to different underwater explosion cases, including varying TNT masses and standoff distances. With the increase of the standoff distance, the deformation of the bottom plate was observed to exhibit a multi-peak phenomenon with a sharp decrease followed by a slight increase and subsequent reduction. Based on a large hull damage dataset obtained from the finite element simulations, a deep neural network (DNN) model was trained using machine learning (ML) algorithms. The deformation values predicted by the model were found to closely match the simulation results. This method provides an approach for the rapid prediction of the deformation of hull structures under near-field underwater explosions.

Source: Applied Ocean Research

DOI: 10.1016/j.apor.2024.104329

Authors: Zhenhong He, Xiaoqi Chen, Xiaoqiang Zhang, Yongbo Jiang, Xianben Ren, Ying Li

Institutions: Beijing Institute of Technology, State Key Laboratory of Explosion Science and Safety Protection, Wuhan University of Technology

Language: English

Topic: Structural Response to Dynamic Loads

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,080

Funders: National Natural Science Foundation of China, National Science Fund for Distinguished Young Scholars