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The Efficient Sensitivity Analysis on Statistical Moments and Probability Constraints in Robust Optimal Design

강건 최적설계에서 통계적 모멘트와 확률 제한조건에 대한 효율적인 민감도 해석

  • 허재성 (한국항공우주연구원 KHP 개방실 엔진팀) ;
  • 곽병만 (한국과학기술원 기계공학과)
  • Published : 2008.01.01

Abstract

The efforts of reflecting the system's uncertainties in design step have been made and robust optimization or reliability-based design optimization are examples of the most famous methodologies. In their formulation, the mean and standard deviation of a performance function and constraints expressed by probability conditions are involved. Therefore, it is essential to effectively and accurately calculate them and, in addition, the sensitivity results are required to obtain when the nonlinear programming is utilized during optimization process. We aim to obtain the new and efficient sensitivity formulation, which is based on integral form, on statistical moments such as the mean and standard deviation, and probability constraints. It does not require the additional functional calculation when statistical moments and failure or satisfaction probabilities are already obtained at a design point. Moreover, some numerical examples have been calculated and compared with the exact solution or the results of Monte Carlo Simulation method. The results seem to be very satisfactory.

Keywords

Sensitivity Analysis;Statistical Moment;Probability Constraint;Moment Method;Robust Optimal Design

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Cited by

  1. Study on Feasibility of Applying Function Approximation Moment Method to Achieve Reliability-Based Design Optimization vol.35, pp.2, 2011, https://doi.org/10.3795/KSME-A.2011.35.2.163