Analysis of Multi-Degree-of-Freedom Equipment Shock Response  Based on Deep Learning

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

doi:10.11993/j.issn.2096-3920.2024-0143

Volume 33 Issue 4

Aug 2025

Turn off MathJax

Article Contents

Article Navigation > Journal of Unmanned Undersea Systems > 2025 > 33(4): 623-629

HUANG Qinyi, ZHU Wei, MA Feng, CHEN Si, WANG Shuang. Analysis of Multi-Degree-of-Freedom Equipment Shock Response Based on Deep Learning[J]. Journal of Unmanned Undersea Systems, 2025, 33(4): 623-629. doi: 10.11993/j.issn.2096-3920.2024-0143

Citation:

HUANG Qinyi, ZHU Wei, MA Feng, CHEN Si, WANG Shuang. Analysis of Multi-Degree-of-Freedom Equipment Shock Response Based on Deep Learning[J]. Journal of Unmanned Undersea Systems, 2025, 33(4): 623-629. doi: 10.11993/j.issn.2096-3920.2024-0143

Citation:

PDF( 1809 KB)

Analysis of Multi-Degree-of-Freedom Equipment Shock Response Based on Deep Learning

doi: 10.11993/j.issn.2096-3920.2024-0143

School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China

Received Date: 2024-10-10
Accepted Date: 2024-11-14
Rev Recd Date: 2024-11-04

Available Online: 2024-12-09

Abstract

Abstract

To analyze the response of multi-degree-of-freedom ship equipment under explosive shock loads, this study proposed a deep learning-based shock response prediction model. Traditional single-degree-of-freedom models struggle to efficiently analyze the complex shock responses of multi-degree-of-freedom systems. By leveraging deep learning technology, especially the data feature extraction and nonlinear modeling capabilities of neural networks, this model learned the correlation between shock spectra and input shock loads from numerical simulation data, enabling efficient and accurate calculation of shock response spectra at critical points in ship structures. This approach overcame the limitations of existing models in handling multi-degree-of-freedom equipment and met the demand for rapid and precise analysis of complex system shock responses. Experimental results show that the model can accurately predict the shock response spectra of multi-degree-of-freedom equipment, with a relative error of less than 8% compared to simulation data, effectively resolving the bottlenecks of traditional models in multi-degree-of-freedom system analysis.
- explosive shock,
- shock response,
- deep learning,
- multi-degree-of-freedom equipment

FullText(HTML)

References(13)

References

[1]	刘永明, 孙焕新, 杨裕根. 多自由度系统冲击谱研究[J]. 上海铁道大学学报(自然科学版), 1998, 23(6): 52-57. LIU Y M, SUN H X, YANG Y G. A study on shock spectrum of multiple degrees of freedom system[J]. Journal of Shanghai Tiedao University(Natural Science Edition), 1998, 23(6): 52-57.
[2]	姜涛, 周方毅, 张可玉, 等. 水面舰艇甲板设备抗冲击设计谱分析[J]. 工程爆破, 2009, 15(4): 8-12. JIANG T, ZHOU F Y, ZHANG K Y, et al. Analysis on design shock-resistant spectrum of equipment fixed on deck[J]. Engineering Blasting, 2009, 15(4): 8-12.
[3]	OH J S, LEE TH, CHOI S B. Design and analysis of a new magnetorheological damper for generation of tunable shock-wave profiles[J]. Shock and Vibration, 2018: 8963491.
[4]	TRUONG D D, JANG B S, JU H B. Development of simplified method for prediction of structural response of stiffened plates under explosion loads[J]. Marine Structures, 2021, 79: 103039. doi: 10.1016/j.marstruc.2021.103039
[5]	LIU Y, REN S F, ZHAO P F. Application of the deep neural network to predict dynamic responses of stiffened plates subjected to near-field underwater explosion[J]. Ocean Engineering, 2022, 247: 110537. doi: 10.1016/j.oceaneng.2022.110537
[6]	BRUNTON S L, KUTZ J N. Data-driven science and engineering: Machine learning, dynamical systems, and control[M]. 2nd ed. Cambridge: Cambridge University Press, 2022.
[7]	钱丽娜. 基于神经网络的水下加筋圆柱壳冲击环境预报[D]. 哈尔滨: 哈尔滨工程大学, 2021.
[8]	冯麟涵, 杨俊杰, 焦立启. 基于RBF神经网络的船舶冲击谱速度数据挖掘与预报[J]. 振动与冲击, 2022, 41(13): 189-194, 210. FENG L H, YANG J J, JIAO L Q. Data mining and prediction of ship shock spectral velocity based on RBF neural network[J]. Journal of Vibration and Shock, 2022, 41(13): 189-194, 210.
[9]	ZHANG M, DRIKAKIS D, LI L, et al. Machine-learning prediction of underwater shock loading on structures[J]. Computation, 2019, 7(4): 58. doi: 10.3390/computation7040058
[10]	HEMATI M S, SAPSIS T P. Physics-informed machine learning for predicting extreme events in complex systems[J]. Physical Review Letters, 2017, 118: 084501. doi: 10.1103/PhysRevLett.118.084501
[11]	YU Y, YAO H, LIU Y. Structural dynamics simulation using a novel physics-guided machine learning method[J]. Engineering Applications of Artificial Intelligence, 2020, 96: 103947. doi: 10.1016/j.engappai.2020.103947
[12]	MCKAY M D, BECKMAN R J, CONOVER W J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code[J]. Technimetrics, 1979, 21(2): 239-245.
[13]	GUO J, GU C, YANG J, et al. Data mining and application of ship impact spectrum acceleration based on PNN neural network[J]. Ocean Engineering, 2020, 203: 107193. doi: 10.1016/j.oceaneng.2020.107193