Regularized deconvolution method for turbulent combustion modeling
The modeling of filtered chemical source terms in large-eddy simulation (LES) of turbulent reacting flows remains a challenge. Deconvolution methods are an attractive technique for representing these unclosed terms. With this technique, filtered scalars are reconstructed through deconvolution. The chemical source terms that are computed directly from the deconvolved scalars are filtered explicitly to represent the turbulence-chemistry interaction. However, the approximate deconvolution method (ADM), frequently employed for non-reacting flows, exhibits shortcomings for reacting scalars. This is because ADM does not ensure essential conservation and boundedness conditions. So to address this issue, we propose a regularized deconvolution method (RDM) based on an optimization procedure. We conduct a priori and a posteriori analyses to examine RDM as a closure in LES. These investigations are performed in the context of explicit filtering. By showing that RDM is accurate and stable with respect to both the filter width and time, we conclude that the new deconvolution method shows promise in application to combustion LES.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Stanford Univ., CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC); National Aeronautics and Space Administration (NASA)
- Grant/Contract Number:
- AC02-05CH11231; NNX15AV04A
- OSTI ID:
- 1543526
- Alternate ID(s):
- OSTI ID: 1410734
- Journal Information:
- Combustion and Flame, Vol. 176, Issue C; ISSN 0010-2180
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
| Unsmoothed Particle Hydrodynamics | preprint | January 2018 |
| Assessment of deconvolution-based flamelet methods for progress variable rate modeling | preprint | January 2018 |
| Modelling turbulent premixed flames using convolutional neural networks: application to sub-grid scale variance and filtered reaction rate | preprint | January 2018 |
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