Parameters Uncertainty Updating Approach for Finite Elelment Model of Spot Welded Vehicle Structure
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摘要: 提出了针对参数不确定性的综合贝叶斯的有限元模型修正方法。即在贝叶斯中融入用以代替原模型的随机模型和基于吉布斯抽样的蒙特卡罗马尔科夫链抽样算法,选定较敏感参数进行仿真获取模型参数的后验分布动态统计特征,进而评估参数不确定度。实例验证了文中方法的有效性,表明随机响应面模型可极大提高吉布斯抽样计算效率,尤其适用于含多维不确定性参数的复杂结构有限元模型不确定性修正问题。Abstract: The paper proposed a new synthesized bayesianian approach for structure finite element model updatingby using parameters uncertainty estimation.It integrated stochastic models instead of the original model and markovchain Monte Carlo(MCMC)algorithm with gibbs sampling.By sensitivity analysis, sensitive parameters were used tosimulate and obtain parameters dynamic posterior statistics characteristics, and then the parameters uncertainty waseatimated.Numerical simulation indicates that the approach is effective, It evidently improves the computationalefficiency with SRSM, the method is suitable for finite element model updating of complex structure containing multi-parameters uncertainty.
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Key words:
- algorithms /
- computational efficiency /
- computer simulation /
- covariance matrix /
- efficiency
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