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Quasi-DNS Dataset of a Piloted Flame with Inhomogeneous Inlet Conditions

  • Thorsten ZirwesEmail author
  • Feichi Zhang
  • Peter Habisreuther
  • Maximilian Hansinger
  • Henning Bockhorn
  • Michael Pfitzner
  • Dimosthenis Trimis
Article

Abstract

A quasi-DNS of the partially premixed turbulent Sydney flame in configuration FJ200-5GP-Lr75-57 has been conducted using detailed molecular diffusion for multi-component mixtures and complex reaction mechanisms. In order to study flame dynamics like regime transition in this flame for the development of new combustion models and to directly compare the quasi-DNS to different LES models, the simulation results are compiled into a data base. Because the simulation was performed with OpenFOAM, we demonstrate the quasi-DNS capabilities of OpenFOAM by performing canonical test cases. They attest that OpenFOAM’s cubic discretization has lower numerical diffusion compared to classical central difference schemes and can reach higher than second order convergence rate in some cases. The quasi-DNS of the Sydney flame is conducted with a self-developed reacting flow solver which is able to accurately compute molecular diffusion coefficients from kinetic gas theory and employs a fast implementation for detailed reaction mechanisms. The computational mesh is shown to be able to resolve the flow as well as the flame front sufficiently for the quasi-DNS. Comparisons with experimental data also show that the simulation can quantitatively reproduce measured time-mean and time-RMS statistics.

Keywords

Mixed-mode combustion Quasi-DNS Turbulent combustion OpenFOAM 

Notes

Acknowledgements

We thank Assaad Masri for providing valuable information about the burner setup and access to the experimental results, as well as for helpful discussions. This work utilizes resources from the national supercomputer Cray XC40 Hazel Hen at the High Performance Computing Center Stuttgart (HLRS) and the computational resource ForHLR II at KIT funded by the Ministry of Science, Research and the Arts Baden-Württemberg and DFG (“Deutsche Forschungsgemeinschaft”). The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. (http://www.gauss-centre.eu) for funding this project by providing computing time on the GCS Supercomputer JUWELS at Jülich Supercomputing Centre (JSC) and on the GCS Supercomputer HAZEL HEN at Höchstleistungsrechenzentrum Stuttgart (http://www.hlrs.de).

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Steinbuch Centre for ComputingKarlsruhe Institute of TechnologyEggenstein-LeopoldshafenGermany
  2. 2.Engler-Bunte-Institute, Division of Combustion TechnologyKarlsruhe Institute of TechnologyKarlsruheGermany
  3. 3.Institute for ThermodynamicsBundeswehr University MunichNeubibergGermany

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