www.Autodiff.org - Publication: Application of Algorithmic Differentiation for Exact Jacobians to the Universal Laminar Flame Solver

Application of Algorithmic Differentiation for Exact Jacobians to the Universal Laminar Flame Solver

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Area
Computational Fluid Dynamics

Author(s)
Alexander Hück , Sebastian Kreutzer , Danny Messig , Arne Scholtissek , Christian Bischof , Christian Hasse

Published in
Computational Science -- ICCS 2018

Editor(s)
Yong Shi, Haohuan Fu, Yingjie Tian, Valeria V. Krzhizhanovskaya, Michael Harold Lees, Jack Dongarra, Peter M. A. Sloot

Year
2018

Publisher
Springer International Publishing

Abstract
We introduce algorithmic differentiation (ad) to the C++ Universal Laminar Flame (ULF) solver code. ULF is used for solving generic laminar flame configurations in the field of combustion engineering. We describe in detail the required code changes based on the operator overloading-based ad tool CoDiPack. In particular, we introduce a global alias for the scalar type in ULF and generic data structure using templates. To interface with external solvers, template-based functions which handle data conversion and type casts through specialization for the ad type are introduced. The differentiated ULF code is numerically verified and performance is measured by solving two canonical models in the field of chemically reacting flows, a homogeneous reactor and a freely propagating flame. The models stiff set of equations is solved with Newtons method. The required Jacobians, calculated with ad, are compared with the existing finite differences (FD) implementation. We observe improvements of ad over FD. The resulting code is more modular, can easily be adapted to new chemistry and transport models, and enables future sensitivity studies for arbitrary model parameters.

AD Tools
CoDiPack

BibTeX
@INPROCEEDINGS{
         Huck2018AoA,
       author = "H{\"u}ck, Alexander and Kreutzer, Sebastian and Messig, Danny and
         Scholtissek, Arne and Bischof, Christian and Hasse, Christian",
       editor = "Shi, Yong and Fu, Haohuan and Tian, Yingjie and Krzhizhanovskaya, Valeria V. and
         Lees, Michael Harold and Dongarra, Jack and Sloot, Peter M. A.",
       title = "Application of Algorithmic Differentiation for Exact {J}acobians to the Universal
         Laminar Flame Solver",
       booktitle = "Computational Science -- ICCS 2018",
       year = "2018",
       publisher = "Springer International Publishing",
       address = "Cham",
       pages = "480--486",
       abstract = "We introduce algorithmic differentiation (AD) to the C++ Universal Laminar Flame
         (ULF) solver code. ULF is used for solving generic laminar flame configurations in the field of
         combustion engineering. We describe in detail the required code changes based on the operator
         overloading-based AD tool CoDiPack. In particular, we introduce a global alias for the scalar type
         in ULF and generic data structure using templates. To interface with external solvers,
         template-based functions which handle data conversion and type casts through specialization for the
         AD type are introduced. The differentiated ULF code is numerically verified and performance is
         measured by solving two canonical models in the field of chemically reacting flows, a homogeneous
         reactor and a freely propagating flame. The models stiff set of equations is solved with Newtons
         method. The required Jacobians, calculated with AD, are compared with the existing finite
         differences (FD) implementation. We observe improvements of AD over FD. The resulting code is more
         modular, can easily be adapted to new chemistry and transport models, and enables future sensitivity
         studies for arbitrary model parameters.",
       isbn = "978-3-319-93713-7",
       doi = "10.1007/978-3-319-93713-7_43",
       ad_area = "Computational Fluid Dynamics",
       ad_tools = "CoDiPack"
}

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