基于深度学习的水下爆炸冲击响应谱求解器

王爽; 吕峰; 马峰; 陈思; 朱炜; 韩峰; 黄沁怡

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

基于深度学习的水下爆炸冲击响应谱求解器

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

王爽^1,,
吕峰^2,,
马峰^1, ,,
陈思^1,,
朱炜^1,,
韩峰^1,,
黄沁怡¹

1.
北京理工大学爆炸科学与技术国家重点实验室, 北京, 100081
2.
中国人民解放军32398部队, 北京, 100026

基金项目: 国家自然基金重点项目资助(U20A2071); 爆炸科学与安全防护全国重点实验室自主课题重点项目(ZDKT24-01).

详细信息

作者简介:
王爽：王　爽(1995-), 男, 在读博士, 主要研究方向为水中爆炸、冲击毁伤等

通讯作者:
马　峰(1973-), 男, 教授, 博士生导师, 主要研究方向为水中爆炸、冲击毁伤等.

中图分类号: TJ630; U674.7
计量
- 文章访问数: 127
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- PDF下载量: 22
- 被引次数: 0
出版历程
- 收稿日期: 2024-10-13
- 修回日期: 2024-11-22
- 录用日期: 2024-12-04
- 网络出版日期: 2024-12-27

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

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

摘要

摘要: 船舶冲击响应具有短时性和复杂性, 通常使用冲击响应谱(SRS)作为其分析工具。为克服传统SRS求解方法存在的计算速度与精度之间的矛盾, 文中提出一种基于深度学习的SRS快速求解器, 并根据SRS的特点设计自适应阈值选择机制, 提升求解器计算精度。对比求解器得到的SRS与采用传统方法计算的结果, 两者显示出高度一致性, 从而验证了求解器的有效性。此外, 文中在求解过程中引入L2正则化技术, 有效避免了过拟合现象的发生, 进一步增强了求解器的鲁棒性。
- 水下爆炸 /
- 冲击响应谱 /
- 深度学习
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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图 1 时、频域下的冲击信号

Figure 1. Shock response in time and frequency domains

下载: 全尺寸图片幻灯片

图 2 SRS计算示意图

Figure 2. Illustration of shock response spectrum calculation

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图 3 求解器模型架构

Figure 3. Solver model architecture

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图 4 自适应阈值选择机制对比实验

Figure 4. Comparison experiment on adaptive threshold selection mechanism

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图 5 不同方法结果对比

Figure 5. Comparison of results using different methods

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图 6 采用正则化方法对过拟合影响比较

Figure 6. Comparison of the impact of overfitting using regularization methods

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表 1 自适应阈值选择机制对比实验误差

Table 1. Comparative experimental error analysis of adaptive threshold selection mechanisms

是否使用阈值选择机制	I_RE/%	I_RMSE
是	5.64	0.135
否	15.8	0.641

下载: 导出CSV

表 2 数值仿真工况表

Table 2. Numerical simulation conditions

序号	水深/m	爆距/m	装药量/kg	方位
1	100	7	295	侧前方
2	100	7	295	正下方
3	100	7	295	侧后方
4	100	7	45	正下方

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表 3 模型数据集信息

Table 3. Dataset information of the model

数据类型	数据内容	数据集	占比/%
输入	冲击载荷时域加速度	训练集	80%
输入	冲击载荷时域加速度	测试集	20%
输出	感兴趣点SRS	训练集	80%
输出	感兴趣点SRS	测试集	20%

下载: 导出CSV

表 4 求解器与传统SRS计算方法性能对比

Table 4. Comparison between solver and traditional SRS calculation methods

方法	自然频率/Hz	核时/s	I_RE/%
求解器	100	1.2	5.54
	500	1.5	5.71
	1 000	1.6	6.18
直接积分法	100	107	—
	500	310	—
	1 000	529	—
数字滤波器	100	62	15.48
	500	185	15.93
	1 000	249	16.84
龙格库塔法	100	78	8.29
	500	206	8.54
	1 000	320	8.71

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