Hück, Alexander ; Kreutzer, Sebastian ; Messig, Danny ; Scholtissek, Arne ; Bischof, Christian ; Hasse, Christian
Shi, Y. ; Fu, H. ; Tian, Y. ; Krzhizhanovskaya, V. V. ; Lees, M. H. ; Sloot, P. M. A. (eds.) (2018):
Application of Algorithmic Differentiation for Exact Jacobians to the Universal Laminar Flame Solver.
In: Lecture Notes in Computer Science, 10862, In: Computational Science – ICCS 2018, pp. 480-486, Springer International Publishing, ISBN 978-3-319-93713-7,
DOI: 10.1007/978-3-319-93713-7_43,
[Book Section]
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.
Item Type: | Book Section |
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Erschienen: | 2018 |
Editors: | Shi, Y. ; Fu, H. ; Tian, Y. ; Krzhizhanovskaya, V. V. ; Lees, M. H. ; Sloot, P. M. A. |
Creators: | Hück, Alexander ; Kreutzer, Sebastian ; Messig, Danny ; Scholtissek, Arne ; Bischof, Christian ; Hasse, Christian |
Title: | Application of Algorithmic Differentiation for Exact Jacobians to the Universal Laminar Flame Solver |
Language: | English |
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. |
Book Title: | Computational Science – ICCS 2018 |
Series: | Lecture Notes in Computer Science |
Series Volume: | 10862 |
Publisher: | Springer International Publishing |
ISBN: | 978-3-319-93713-7 |
Uncontrolled Keywords: | Combustion engineering; Flamelet simulation; Algorithmic differentiation; Exact Jacobians; Newton method; C++ |
Divisions: | 16 Department of Mechanical Engineering 16 Department of Mechanical Engineering > Simulation of reactive Thermo-Fluid Systems (STFS) 20 Department of Computer Science 20 Department of Computer Science > Scientific Computing Zentrale Einrichtungen Zentrale Einrichtungen > University IT-Service and Computing Centre (HRZ) Zentrale Einrichtungen > University IT-Service and Computing Centre (HRZ) > Hochleistungsrechner |
Event Title: | Computational Science — ICCS 2018 |
Date Deposited: | 19 Jun 2018 07:42 |
DOI: | 10.1007/978-3-319-93713-7_43 |
URL / URN: | https://doi.org/10.1007/978-3-319-93713-7_43 |
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