A Deep Learning-Based Solver for Underwater Explosion Shock Response Spectrum

Due to the short duration and complexity of ship shock responses, the shock response spectrum(SRS) is commonly used as a tool for analyzing these responses. To address the conflict between calculation speed and accuracy inherent in traditional SRS solving methods, this paper proposed a deep learning...

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Bibliographic Details
Published in水下无人系统学报 Vol. 33; no. 3; pp. 545 - 551
Main Authors Shuang WANG, Feng LÜ, Feng MA, Si CHEN, Wei ZHU, Feng HAN, Qinyi HUANG
Format Journal Article
LanguageChinese
Published Science Press (China) 01.06.2025
Subjects
deep learning
shock response spectrum
underwater explosion
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Summary:Due to the short duration and complexity of ship shock responses, the shock response spectrum(SRS) is commonly used as a tool for analyzing these responses. To address the conflict between calculation speed and accuracy inherent in traditional SRS solving methods, this paper proposed a deep learning-based fast solver for the SRS. An adaptive threshold selection mechanism tailored to the characteristics of the SRS was designed to improve the solver’s calculation accuracy. A comparison between the SRS obtained by the proposed solver and the results calculated using traditional methods demonstrated a high degree of consistency, validating the effectiveness of the solver. Additionally, L2 regularization was introduced in the solution process, effectively preventing overfitting and further enhancing the robustness of the solver.
ISSN:2096-3920
DOI:10.11993/j.issn.2096-3920.2024-0144