A Deep Learning-Based Solver for Underwater Explosion Shock Response Spectrum
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摘要: 由于冲击响应具有短时性和复杂性, 常使用冲击响应谱(SRS)作为分析冲击响应的工具。为克服传统冲击响应谱求解方法存在的计算速度与计算精度之间的矛盾, 文中提出基于深度学习的冲击响应谱快速求解器, 并根据冲击响应谱的特点设计自适应阈值选择机制, 提升求解器计算精度。对比求解器得到的冲击响应谱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。Abstract: Due to the short-duration and complexity of shock responses, Shock Response Spectrum(SRS) is commonly used as a tool for analyzing these responses. To address the trade-off between calculation speed and accuracy inherent in traditional SRS solving methods, this paper proposes a deep learning-based fast solver for shock response spectra. An adaptive threshold selection mechanism tailored to the characteristics of shock response spectra is designed to improve the solver's accuracy. A comparison between the SRS obtained by the proposed solver and the results calculated using traditional methods demonstrates a high degree of consistency, validating the effectiveness of the solver. Additionally, L2 regularization is introduced in the solution process, effectively preventing overfitting and further enhancing the robustness of the solver.
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Key words:
- ship /
- underwater explosion /
- shock response spectrum /
- deep learning
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表 1 自适应阈值选择机制对比实验
Table 1. Comparison experiment on adaptive threshold selection mechanism
是否使用
阈值选择机制RE/% RMSE 是 5.64 0.135 否 15.8 0.641 表 2 数值仿真工况表
Table 2. Table of numerical simulation conditions
序号 水深/m 爆距/m 装药/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 表 3 模型数据集信息
Table 3. Model Dataset Information
模型输入/输出数据信息 数据集划分信息 输入 冲击载荷时域加速度 训练集 80% 测试集 20% 输出 感兴趣点冲击响应谱 训练集 80% 测试集 20% 表 4 求解器与传统冲击谱计算方法性能对比
Table 4. Comparison between solver and traditional shock spectrum calculation methods
自然
频率指标 求解器 直接积
分法数字
滤波器龙格
库塔法100 核时 1.2 s 107 s 62 s 78 s RE 5.54 % − 15.48 % 8.29 % 500 核时 1.5 s 310 s 185 s 206 s RE 5.71 % − 15.93 % 8.54 % 1000 核时 1.6 s 539 s 249 s 320 s RE 6.18 % − 16.84 % 8.71 % -
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