梁潇帝, 刘寅东. 基于PCA-BOA-KNN模型的水下爆炸舰船结构破损评估[J]. 中国舰船研究, 2024, 19(3): 1–8. doi: 10.19693/j.issn.1673-3185.03470
引用本文: 梁潇帝, 刘寅东. 基于PCA-BOA-KNN模型的水下爆炸舰船结构破损评估[J]. 中国舰船研究, 2024, 19(3): 1–8. doi: 10.19693/j.issn.1673-3185.03470
LIANG X D, LIU Y D. Breakage assessment of ship structures based on PCA-BOA-KNN model underwater explosions[J]. Chinese Journal of Ship Research, 2024, 19(3): 1–8 (in Chinese). doi: 10.19693/j.issn.1673-3185.03470
Citation: LIANG X D, LIU Y D. Breakage assessment of ship structures based on PCA-BOA-KNN model underwater explosions[J]. Chinese Journal of Ship Research, 2024, 19(3): 1–8 (in Chinese). doi: 10.19693/j.issn.1673-3185.03470

基于PCA-BOA-KNN模型的水下爆炸舰船结构破损评估

Breakage assessment of ship structures based on PCA-BOA-KNN model underwater explosions

  • 摘要:
    目的 为解决水下爆炸作用下舰船结构破口损伤评估问题,建立一种基于PCA-BOA-KNN模型的破口预报方法。
    方法 首先,分别建立五舱段和七舱段有限元模型,对21组水下爆炸工况进行爆炸仿真分析;然后,基于主成分分析(PCA)法,对加速度峰值、速度峰值、位移峰值、应力峰值和超压峰值进行降维处理,得到2个本征特征量;最后,将由主成分分析法得到的结果代入贝叶斯网络优化(BOA)的KNN模型, 通过建立的破口预报模型,预测一组工况下舰船不同剖面处的破口情况。
    结果 结果显示,通过主成分分析法提取的前2个因子的累计贡献率为85.165%,这2个因子可代表5个特征量的主要信息;基于PCA-BOA-KNN模型的破口预报结果与仿真结果基本一致。
    结论 所提的预报模型方法对舰船结构破口预报有效,对于不同主尺度船体结构破口预报有一定的参考价值。

     

    Abstract:
    Objective To address the issue of assessing structural breach damage in ships under underwater explosion, an breach prediction method based on the PCA-BOA-KNN model is established.
    Methods First, finite element models for five-compartment and seven-compartment segments are constructed, and explosion simulation analysis is carried out for 21 sets of underwater explosion conditions. Subsequently, principal component analysis (PCA) is employed to reduce the dimensionality of the peak acceleration, peak velocity, peak displacement, peak stress and peak overpressure values, resulting in two principal features. Finally, the PCA results are integrated into a Bayesian optimization algorithm (BOA) K-Nearest Neighbors (KNN) model. The established breach prediction model is used to predict the breach conditions at different ship cross-sections under a set of conditions.
    Results The results show that by using PCA to extract the first two factors, the cumulative contribution rate is 85.165%. Therefore, the first two factors can represent the primary information of the five features. The results obtained using the PCA-BOA-KNN breach prediction model are generally consistent with the simulation results.
    Conclusion The proposed prediction model approach is effective for predicting ship structural breaches and has reference value for predicting breachs in ship structures with different principal dimensions.

     

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