基于随机抽样与距离判别的GARTEUR模型修正与确认研究

doi:10.7527/S1000-6893.2013.0327

固体力学与飞行器总体设计

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基于随机抽样与距离判别的GARTEUR模型修正与确认研究

毕司峰, 邓忠民

北京航空航天大学宇航学院, 北京 100191

收稿日期:2013-03-22 修回日期:2013-06-17 出版日期:2013-12-25 发布日期:2013-07-12
通讯作者: 邓忠民，Tel.：010-82339526E-mail：dengzhongmin@buaa.edu.cn E-mail:dengzhongmin@buaa.edu.cn
作者简介:毕司峰男，博士研究生。主要研究方向：飞行器结构动力学分析，结构有限元模型确认。Tel：010-82338724E-mail：sifeng.bi@sa.buaa.edu.cn；邓忠民男，博士，教授，博士生导师。主要研究方向：空间飞行器结构分析与设计，空间飞行器轨道分析与设计，计算结构力学。Tel：010-82339526E-mail：dengzhongmin@buaa.edu.cn
基金资助:
国家自然科学基金（10972019）；北航博士创新基金

Stochastic Model Updating and Validation of the GARTEUR Structure Based on Random Sampling and Distance Discrimination

BI Sifeng, DENG Zhongmin

School of Astronautics, Beihang University, Beijing 100191, China

Received:2013-03-22 Revised:2013-06-17 Online:2013-12-25 Published:2013-07-12
Supported by:
National Natural Science Foundation of China (10972019); Innovation Foundation of BUAA for PhD Graduates

摘要/Abstract

摘要：

提出了一种基于随机抽样技术与距离判别分析的结构有限元随机模型修正（SMU）方法，并将其应用到GARTEUR飞机模型的有限元模型修正过程中。传统的模型修正方法以灵敏度分析及优化分析方法为核心，对有限元模型的输入参数进行修正。而本文的随机模型方法充分考虑了有限元建模过程与试验测量中普遍存在的不确定性，利用Monte Carlo抽样方法进行大量的随机抽样实验，完成不确定性从输入参数向输出特征的传递分析；在参数修正过程中，利用距离判别分析计算试验与仿真两个数据集之间的统计学差异，并通过迭代程序逐步修正输入参数使仿真数据逐步收敛于测量数据；利用径向基函数，在修正过程中引入代理模型，在保证精度的同时大大降低了随机模型修正的计算量。利用MCS.Patran的二次开发语言PCL开发了随机抽样实验的相关程序，并可以自动收集数据用于参数修正的迭代运算。通过普遍认可的三级确认准则对GARTEUR有限元模型可靠性进行了确认分析，结果表明提出的随机模型修正方法具有可行性和工程应用价值。

关键词: 模型修正, 模型确认, 不确定性, Monte Carlo法, 距离判别分析, GARTEUR

Abstract:

A stochastic model updating (SMU) method using distance discrimination analysis and random sampling technique is proposed and subsequently applied to the updating process of the GARTEUR benchmark structure. In contrast to the traditional deterministic model updating procedure in which parameters are calibrated by sensitivity and optimization analysis, the proposed SMU method takes into consideration uncertainties which are general in the modeling as well as test processes. Uncertainty propagation is performed by Monte Carlo sampling method in which a large scale stochastic sampling process is proposed to describe uncertainties from parameters to features. Distance discrimination analysis is presented to quantify the degree of similarity and dissimilarity between analytical and test data. Input parameters are calibrated to the test data through an iterative procedure integrating the above uncertainty propagation and quantification methods. In order to reduce calculation cost, a metamodel is constructed using radial basis function with an acceptable precision. The relative PCL program of MSC.Patran is employed to submit multiple finite element (FE) analyses and to extract information for subsequent analysis. An application is performed on the GARTEUR structure and the updating results are assessed by the widely accepted 3-steps validation criteria. The updating and validation results show the proposed SMU method is valid and effective in engineering application.

Key words: model updating, model validation, uncertainty, Monte Carlo method, distance discrimination analysis, GARTEUR

中图分类号:

V214
TB12

毕司峰, 邓忠民. 基于随机抽样与距离判别的GARTEUR模型修正与确认研究[J]. 航空学报, 2013, 34(12): 2757-2767.

BI Sifeng, DENG Zhongmin. Stochastic Model Updating and Validation of the GARTEUR Structure Based on Random Sampling and Distance Discrimination[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013, 34(12): 2757-2767.

参考文献

[1] Mares C, Mottershead J E, Friswell M I. Stochastic model updating: Part 1－theory and simulated example. Mechanical Systems and Signal Processing, 2006, 20(7): 1674-1695.

[2] Zhang L M. Computer simulation & model validation with application to srtength and environment engineering. Structure & Environment Engineering, 2002, 29(2): 42-47. (in Chinese) 张令弥. 计算仿真与模型确认及在结构环境与强度中的应用. 强度与环境, 2002, 29(2): 42-47.

[3] Ding J F, Ma X R, Han Z Y, et al. Three-step model updating method in structure dynamics and its application. Acta Aeronautica et Astronautica Sinica, 2010, 31(3): 546-552. (in Chinese) 丁继峰, 马兴瑞, 韩增尧, 等. 结构动力学模型修正的三步策略及其实践. 航空学报, 2010, 31(3): 546-552.

[4] Mottershead J E, Link M, Friswell M I. The sensitivity method in finite element model updating: a tutorial. Mechanical Systems and Signal Processing, 2011, 25(7): 2275-2296.

[5] Calvi A. Uncertainty-based loads analysis for spacecraft: finite element model validation and dynamic responses. Computers & Structures, 2005, 83(14): 1103-1112.

[6] Xia R W. Engineering optimization theory and algorithm. Beijing: Beihang University Press, 2003. (in Chinese) 夏人伟. 工程优化理论与算法. 北京: 北京航空航天大学出版社, 2003.

[7] Bi S, Deng Z, Chen Z. Stochastic validation of structural FE-models based on hierarchical cluster analysis and advanced Monte Carlo simulation. Finite Elements in Analysis and Design, 2013, 67: 22-33.

[8] Doebling S W, Hemez F M, Schultze J F, et al. Overview of structural dynamics model validation activities at Los Alamos National Laboratory. 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2002.

[9] Thomas L, Red-Horse J. Structural dynamics challenge problem: summary. Computer Methods in Applied Mechanics and Engineering, 2008, 197(29): 2660-2665.

[10] Zang C, Schwingshackl C W, Ewins D J. Model validation for structural dynamic analysis: an approach to the Sandia structural dynamics challenge. Computer Methods in Applied Mechanics and Engineering, 2008, 197(30): 2645-2659.

[11] Link M, Friswell M I. Working Group 1: generation of validated structural dynamiv models: results of a benchmark study utilising the GARTEUR SM-AG19 test-bed. Mechanical Systems and Signal Processing, 2003, 17(1): 9-21.

[12] Szekely G S, Pradlwarter H J, Schueller G I, et al. A detailed scatter analysis of the structural response of spacecraft. European Conference on Spacecraft Structures, Materials & Mechanical Testing, 2001: 605-610.

[13] Hurtado J E, Barbat A H. Monte Carlo techniques in computational stochastic mechanics. Archives of Computational Methods in Engineering, 1998, 5(1): 3-29.

[14] Mei C L, Zhou J L. Applied statistical methods. Beijing: Science Press, 2002: 218-245. (in Chinese) 梅长林, 周家良. 实用统计方法. 北京: 科学出版社, 2002: 218-245.

[15] Dou Y F, Liu F, Zhang W H. Research on comparative analysis of response surface methods. Journal of Engineering Design, 2007, 14(5): 359-363. (in Chinese) 窦毅芳, 刘飞, 张为华. 响应面建模方法的比较分析. 工程设计学报, 2007, 14(5): 359-363.

[16] Ni L. Research on classified validation of complex structure model. Nanjing: College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, 2009. (in Chinese) 倪磊. 复杂结构有限元模型分级确认研究. 南京: 南京航空航天大学航空宇航学院, 2009.

[17] Jiang Y Q. Study on the RBF implicit surface and its application. Chengdu: School of Information Science & Technology, Southwest Jiaotong University, 2011. (in Chinese) 江永全. 径向基函数隐式曲面的研究及应用. 成都: 西南交通大学信息科学与技术学院, 2011.

[18] Degener M, Hermes M. Ground vibration test and finite element analysis of the GARTEUR SM-AG19 testbed. DLR Report, IB 232-96 J08, 1996.

E-mail：hkxb@buaa.edu.cn

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基于随机抽样与距离判别的GARTEUR模型修正与确认研究

Stochastic Model Updating and Validation of the GARTEUR Structure Based on Random Sampling and Distance Discrimination

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