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

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

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

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

Citation:

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

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Multi-Degree-of-Freedom Equipment Shock Response Model 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 address the challenge of analyzing the response of multi-degree-of-freedom naval equipment under explosive shock loads, this study proposes a deep learning-based shock response prediction model. Traditional single-degree-of-freedom models cannot effectively analyze the complex shock responses of multi-degree-of-freedom systems. Leveraging deep learning technology, particularly the data feature extraction and nonlinear modeling capabilities of neural networks, this model learns the relationship between the shock spectrum and input shock loads from numerical simulation data, achieving efficient and accurate calculation of shock response spectra at critical points within naval structures. This approach fills the gaps of existing models in handling multi-degree-of-freedom equipment and meets the demand for rapid, accurate analysis of complex system shock responses. Experimental results demonstrate 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 overcoming the limitations of traditional models in multi-degree-of-freedom system analysis.
- explosion shock,
- shock response spectrum,
- deep learning,
- multi-degree-of-freedom equipment

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References(13)

References

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