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

WANG Shuang; LÜ Feng; MA Feng; CHEN Si; ZHU Wei; HAN Feng; HUANG Qinyi

doi:10.11993/j.issn.2096-3920.2024-0144

Volume 33 Issue 3

Jun 2025

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Article Navigation > Journal of Unmanned Undersea Systems > 2025 > 33(3): 545-551

WANG Shuang, LÜ Feng, MA Feng, CHEN Si, ZHU Wei, HAN Feng, HUANG Qinyi. A Deep Learning-Based Solver for Underwater Explosion Shock Response Spectrum[J]. Journal of Unmanned Undersea Systems, 2025, 33(3): 545-551. doi: 10.11993/j.issn.2096-3920.2024-0144

Citation:

WANG Shuang, LÜ Feng, MA Feng, CHEN Si, ZHU Wei, HAN Feng, HUANG Qinyi. A Deep Learning-Based Solver for Underwater Explosion Shock Response Spectrum[J]. Journal of Unmanned Undersea Systems, 2025, 33(3): 545-551. doi: 10.11993/j.issn.2096-3920.2024-0144

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

doi: 10.11993/j.issn.2096-3920.2024-0144

WANG Shuang^1
,,
LÜ Feng^2
,,
MA Feng^{1
,
,},
CHEN Si^1
,,
ZHU Wei^1
,,
HAN Feng^1
,,
HUANG Qinyi¹

1.
State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China
2.
Unit 32398^th, the Liberation Army of China, Beijing 100026, China

Received Date: 2024-10-13
Accepted Date: 2024-12-04
Rev Recd Date: 2024-11-22

Available Online: 2024-12-27

Abstract

Abstract

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.
- underwater explosion,
- shock response spectrum,
- deep learning

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References(15)

References

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