Publication Date:
2019-07-13
Description:
A robust optimization is demonstrated on a two-dimensional inviscid airfoil problem in subsonic flow. Given uncertainties in statistically independent, random, normally distributed flow parameters (input variables), an approximate first-order statistical moment method is employed to represent the Computational Fluid Dynamics (CFD) code outputs as expected values with variances. These output quantities are used to form the objective function and constraints. The constraints are cast in probabilistic terms; that is, the probability that a constraint is satisfied is greater than or equal to some desired target probability. Gradient-based robust optimization of this stochastic problem is accomplished through use of both first and second-order sensitivity derivatives. For each robust optimization, the effect of increasing both input standard deviations and target probability of constraint satisfaction are demonstrated. This method provides a means for incorporating uncertainty when considering small deviations from input mean values.
Keywords:
Statistics and Probability
Type:
PMC2004
,
9th ASCE Engrg. Mechanics Div., Structural Engrg. Inst, Geotechnical Inst. Aerospace Div. and the Sandia National Labs. Joint Specialty Conference on Probabilistic Mechanics and Structural Reliability; Jul 26, 2004 - Jul 28, 2004; Albuquerque, NM; United States
Format:
application/pdf
