Publication: Sensitivity analysis of turbulence models using automatic differentiation
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Sensitivity analysis of turbulence models using automatic differentiation

- Article in a journal -
 

Area
Computational Fluid Dynamics

Author(s)
C. H. Bischof , H. M. Bücker , A. Rasch

Published in
SIAM Journal on Scientific Computing

Year
2005

Abstract
Turbulence models are an example of computer simulations that parameterize complicated phenomena and depend on artificial model parameters heuristically justified from empirical information and experimental data. To assess the confidence in the results of such a turbulence simulation, a derivative-based sensitivity analysis is carried out. The sensitivities of the flow over a backward-facing step with respect to parameters of the turbulence model are investigated. The standard k-ε model and the renormalization group (RNG) k-ε model are compared. Both turbulence models are implemented in the FLUENT code to which automatic differentiation is applied using the ADIFOR system. In our case studies, none of the two turbulence models turns out to be the least sensitive with respect to all turbulence parameters.

AD Tools
ADIFOR

BibTeX
@ARTICLE{
         Bischof2005Sao,
       author = "C. H.~Bischof and H. M. B{\"u}cker and A.~Rasch",
       title = "Sensitivity analysis of turbulence models using automatic differentiation",
       journal = "SIAM Journal on Scientific Computing",
       pages = "510--522",
       doi = "10.1137/S1064827503426723",
       abstract = "Turbulence models are an example of computer simulations that parameterize
         complicated phenomena and depend on artificial model parameters heuristically justified from
         empirical information and experimental data. To assess the confidence in the results of such a
         turbulence simulation, a derivative-based sensitivity analysis is carried out. The sensitivities of
         the flow over a backward-facing step with respect to parameters of the turbulence model are
         investigated. The standard $k$-$\varepsilon$ model and the renormalization group (RNG)
         $k$-$\varepsilon$ model are compared. Both turbulence models are implemented in the FLUENT code
         to which automatic differentiation is applied using the ADIFOR system. In our case studies, none of
         the two turbulence models turns out to be the least sensitive with respect to all turbulence
         parameters.",
       year = "2005",
       volume = "26",
       number = "2",
       ad_area = "Computational Fluid Dynamics",
       ad_tools = "ADIFOR"
}


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