Sensitivity derivatives for advanced CFD algorithm and viscous modelling parameters via automatic differentiation

Green, Lawrence L.; Newman, Perry A.; Haigler, Kara J.

doi:10.2514/6.1993-3321

Cited by 15 publications

(3 citation statements)

References 2 publications

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“…Sensitivity derivatives can also aid in this assessment (Ref. 19) since the adequacy of an internal model's (i.e., algorithm, turbulence, etc.) prediction capability generally depends, to some extent, on the modeling parameter values specified as input.…”

Section: Introductionmentioning

confidence: 99%

Approach for uncertainty propagation and robust design in CFD using sensitivity derivatives

Putko

Newman

Taylor

et al. 2001

15th AIAA Computational Fluid Dynamics Conference

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106

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This paper presents an implementation of the approximate statistical moment method for uncertainty propagation and robust optimization for a quasi 1-D Euler CFD code. Given uncertainties in statistically independent, random, normally distributed input variables, a first-and second-order statistical moment matching procedure is performed to approximate the uncertainty in the CFD output. Efficient calculation of both first-and second-order sensitivity derivatives is required. In order to assess the validity of the approximations, the moments are compared with statistical moments generated through Monte Carlo simulations. The uncertainties in the CFD input variables are also incorporated into a robust optimization procedure. For this optimization, statistical moments involving firstorder sensitivity derivatives appear in the objective function and system constraints. Second-order sensitivity derivatives are used in a gradient-based search to successfully execute a robust optimization. The approximate methods used throughout the analyses are found to be valid when considering robustness about input parameter mean values.

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Section: Introductionmentioning

confidence: 99%

Approach for uncertainty propagation and robust design in CFD using sensitivity derivatives

Putko

Newman

Taylor

et al. 2001

15th AIAA Computational Fluid Dynamics Conference

Self Cite

106

View full text Add to dashboard Cite

show abstract

“…Uncertainty quantification [9][10][11][12][13][14] (UQ) can be used to both improve and clarify the evaluations of the probability of an occurrence, and of the consequence of an occurrence within a risk assessment. Uncertainty quantification begins in steps 1 and 2 of the 17 common technical processes above, with the examination of requirements for a given problem to identify "fuzzy" statements, those that cannot be definitively fulfilled.…”

Section: A Uncertainty Quantificationmentioning

confidence: 99%

Decision Support Methods and Tools

Green¹,

Alexandrov²,

Brown³

et al. 2006

11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

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This paper is one of a set of papers, developed simultaneously and presented within a single conference session, that are intended to highlight systems analysis and design capabilities within the Systems Analysis and Concepts Directorate (SACD) of the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC). This paper focuses on the specific capabilities of uncertainty/risk analysis, quantification, propagation, decomposition, and management, robust/reliability design methods, and extensions of these capabilities into decision analysis methods within SACD. These disciplines are discussed together herein under the name of Decision Support Methods and Tools. Several examples are discussed which highlight the application of these methods within current or recent aerospace research at the NASA LaRC. Where applicable, commercially available, or government developed software tools are also discussed.

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“…Furthermore, the SD calculation scheme must be efficient, because this operation represents a large portion of the total optimization time. Several recent research efforts [1][2][3][4][5] have demonstrated the potential for using automatic differentiation (AD) to compute the SD required for MDO from a variety of codes.…”

Section: Introductionmentioning

confidence: 99%

Parallel calculation of sensitivity derivatives for aircraft design using automatic differentiation

Bischof

Green

Haigler

et al. 1994

5th Symposium on Multidisciplinary Analysis and Optimization

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Sensitivity derivative (SD) calculation via automatic differentiation (AD) typical of that required for the aerodynamic design of a transport-type aircraft is considered. Two ways of computing SD via code generated by the ADIFOR automatic differentiation tool are compared for efficiency and applicability to problems involving large numbers of design variables.A vector implementation on a Cray Y-MP computer is compared with a coarse-grained parallel implementation on an IBM SP1 computer, employing a Fortran M wrapper.The SD are computed for a swept transport wing in turbulent, transonic flow; the number of geometric design variables varies from 1 to 60 with coupling between a wing grid generation program and a state-of-the-art, 3-D computational fluid dynamics program, both augmented for derivative computation via AD. For a small number of design variables, the Cray Y-MP implementation is much faster.As the number of design variables grows, however, the IBM SP1 becomes an attractive alternative in terms of compute speed, job turnaround time, and total memory available for solutions with large numbers of design variables. The coarse-grained parallel implementa-

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Sensitivity derivatives for advanced CFD algorithm and viscous modelling parameters via automatic differentiation

Cited by 15 publications

References 2 publications

Approach for uncertainty propagation and robust design in CFD using sensitivity derivatives

Approach for uncertainty propagation and robust design in CFD using sensitivity derivatives

Decision Support Methods and Tools

Parallel calculation of sensitivity derivatives for aircraft design using automatic differentiation

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