• CN:11-2187/TH
  • ISSN:0577-6686

›› 2011, Vol. 47 ›› Issue (17): 29-36.

• 论文 • 上一篇    下一篇

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结构动力学模型确认问题的核密度估计方法

张保强;陈国平; 郭勤涛   

  1. 南京航空航天大学机械结构力学与控制国家重点实验室;南京航空航天大学机电学院
  • 发布日期:2011-09-05

Structural Dynamic Model Validation Problem Solution Using Kernel Density Estimation Method

ZHANG Baoqiang; CHEN Guoping; GUO Qintao   

  1. The State Key Laboratory of Mechanics and Control for Mechanical Structures,Nanjing University of Aeronautics and Astronautics College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics
  • Published:2011-09-05

摘要: 将核密度估计方法成功用于解决结构动力学模型确认的挑战问题,进一步明确模型确认在结构动力学中的实施过程。在美国圣地亚国家实验室提出的结构动力学模型确认挑战问题中,由于子结构与整体结构的弱非线性连接以及子结构个体差异引起的统计特性并不满足标准的概率分布,因此采用核密度估计方法建立子结构的概率模型,并使用核主元分析进行降维处理来提高核密度估计的计算效率;在子结构概率模型的基础上,使用校准试验数据对模型的准确度进行定性验证,同时使用确认试验数据对模型的精度进行定量评估;最后把确认过的子结构模型用于整体认证结构的评估以及最后目标模型的预测中,得到了与其他研究者相一致的结果。研究表明核密度估计方法是一种解决结构动力学模型确认问题的有效方法。

关键词: 不确定性分析, 核密度估计, 核主元分析, 结构动力学, 模型确认, 挑战问题

Abstract: The kernel density estimation method is successfully used to solve the structural dynamic model validation challenge problem to further clarify the implementation process of model validation in structural dynamics. Probability distribution properties of sub-structure due to the weakly nonlinear connections and individual differences is not normal distribution in structural dynamic model validation challenge problem presented by Sandia National Laboratories, so kernel density estimation method is used to establish the sub-structure probability model and dimension reduction utilizing kernel principal component analysis is used to improve the computational efficiency. The model accuracy is qualitative verified and quantitative confirmed using calibration and validation experimental data respectively based on the probability sub-structure model. Finally the accreditation structure assessment and the target structure prediction are performed using validated substructure model, which are consistent with the results of other researchers. The research indicates that the kernel density estimation is an effective approach to solve the model validation problem in structural dynamics.

Key words: Challenge problem, Kernel density estimation, Kernel principal comment analysis, Model validation, Structural dynamics, Uncertainty analysis

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