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  • 标题:Monte Carlo Simulation of Dynamic Problem Using Model Order Reduction Technique Highlighting on Tail Probability
  • 本地全文:下载
  • 作者:Naoki TAKANO ; Mitsuteru ASAI ; Kohta OKAMOTO
  • 期刊名称:Journal of Computational Science and Technology
  • 电子版ISSN:1881-6894
  • 出版年度:2012
  • 卷号:6
  • 期号:3
  • 页码:169-181
  • DOI:10.1299/jcst.6.169
  • 出版社:The Japan Society of Mechanical Engineers
  • 摘要:This paper aims at practical Monte Carlo (MC) simulation by finite element method for a dynamic problem where the load condition includes uncertainty factors. A new sampling scheme is proposed to predict an extreme case with high stress by unexpected combination of load parameters, which is involved in the tail probability, to be used in the fatigue life estimation of structures. The proposed scheme, named as stepwise limited sampling (SLS), can provide the expected value of the stress with moderate accuracy and provide reliable results with extremely high stress. In the demonstrative example, the proposed method was computationally cost-effective than usual MC simulation. In addition, a model order reduction (MOR) technique is employed to reduce both the computational cost and the memory requirement. The latter contributed to the fast MC simulation by parallel processing.
  • 关键词:Uncertainty;Sampling Scheme;Tail Probability;Dynamic Analysis;Monte Carlo Simulation;Model Order Reduction
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