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    Employing Sensitivity Derivatives for Robust Optimization under Uncertainty in CFD (2004)
    In:  CASI
    Details
    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
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  • 2
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    Employing Sensitivity Derivatives to Estimate Uncertainty Propagation in CFD (2004)
    In:  CASI
    Details
    Publication Date: 2019-07-13
    Description: Two methods that exploit the availability of sensitivity derivatives are successfully employed to predict uncertainty propagation through Computational Fluid Dynamics (CFD) code for an inviscid airfoil problem. An approximate statistical second-moment method and a Sensitivity Derivative Enhanced Monte Carlo (SDEMC) method are successfully demonstrated on a two-dimensional problem. First- and second-order sensitivity derivatives of code output with respect to code input are obtained through an efficient incremental iterative approach. Given uncertainties in statistically independent, random, normally distributed flow parameters (input variables); these sensitivity derivatives enable one to formulate first- and second-order Taylor Series approximations for the mean and variance of CFD output quantities. Additionally, incorporation of the first-order sensitivity derivatives into the data reduction phase of a conventional Monte Carlo (MC) simulation allows for improved accuracy in determining the first moment of the CFD output. Both methods are compared to results generated using a conventional MC method. The methods that exploit the availability of sensitivity derivatives are found to be valid when considering small deviations from input mean values.
    Keywords: Statistics and Probability
    Type: 9th ASCE EMD/SEI/GU/AD Joint Specialty Conference on Probabilistic Mechanics and Structural Reliability; Jul 26, 2004 - Jul 28, 2004; Albuquerque, NM; United States
    Format: text
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