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Bayesian Parameter Estimation for Prognosis of Crack Growth under Variable Amplitude Loading

변동진폭하중 하에서 균열성장예지를 위한 베이지안 모델변수 추정법

  • Leem, Sang-Hyuck (School of Aerospace & Mechanical Engineering, Korea Aerospace Univ.) ;
  • An, Da-Wn (School of Aerospace & Mechanical Engineering, Korea Aerospace Univ.) ;
  • Choi, Joo-Ho (School of Aerospace & Mechanical Engineering, Korea Aerospace Univ.)
  • 임상혁 (한국항공대학교 항공우주 및 기계공학부) ;
  • 안다운 (한국항공대학교 항공우주 및 기계공학부) ;
  • 최주호 (한국항공대학교 항공우주 및 기계공학부)
  • Received : 2011.05.18
  • Accepted : 2011.08.16
  • Published : 2011.10.01

Abstract

In this study, crack-growth model parameters subjected to variable amplitude loading are estimated in the form of a probability distribution using the method of Bayesian parameter estimation. Huang's model is employed to describe the retardation and acceleration of the crack growth during the loadings. The Markov Chain Monte Carlo (MCMC) method is used to obtain samples of the parameters following the probability distribution. As the conventional MCMC method often fails to converge to the equilibrium distribution because of the increased complexity of the model under variable amplitude loading, an improved MCMC method is introduced to overcome this shortcoming, in which a marginal (PDF) is employed as a proposal density function. The model parameters are estimated on the basis of the data from several test specimens subjected to constant amplitude loading. The prediction is then made under variable amplitude loading for the same specimen, and validated by the ground-truth data using the estimated parameters.

본 연구에서는 측정된 균열 데이터를 토대로 변동하중 하에서의 균열성장모델 변수들을 베이지안 모델변수 추정 방법을 통해서 확률적인 분포로 구하는 방법을 제시하였다. 모델변수의 확률분포를 구하기 위해 Markov Chain Monte Carlo (MCMC) 샘플링 방법을 이용하였다. 변동하중 하에서는 균열성장 모델이 더욱 복잡해 짐에 따라 기존의 MCMC 기법으로는 확률분포를 잘 구하지 못하므로 주변확률밀도분포를 제안함수로 사용하는 MCMC 기법을 새롭게 제안하였다. 모델변수의 추정을 위해 여러 크기의 일정 진폭 하중 하에서 시편시험을 수행하여 얻은 균열성장 데이터를 이용하였다. 추정된 변수들을 사용하여 변동하중 하에서의 시편에 대해 균열성장 예측을 수행하였고, 이를 실제 시험 데이터를 통해서 검증하였다.

Keywords

References

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