| タイトル | Employing Sensitivity Derivatives to Estimate Uncertainty Propagation in CFD |
| 著者（英） | Putko, Michele M.; Taylor, Arthur C., III; Newman, Perry A. |
| 著者所属（英） | NASA Langley Research Center |
| 発行日 | 2004-01-01 |
| 言語 | eng |
| 内容記述 | 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. |
| NASA分類 | Statistics and Probability |
| 権利 | No Copyright |
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