10.5281/zenodo.6450226
https://zenodo.org/records/6450226
oai:zenodo.org:6450226
P. W. Agostinelli,1, 2, a) B. Rochette,2 D. Laera,2 J. Dombard,2 B. Cuenot,2 and L. Gicquel2
P. W. Agostinelli,1, 2, a) B. Rochette,2 D. Laera,2 J. Dombard,2 B. Cuenot,2 and L. Gicquel2
1)Safran Helicopter Engines, Avenue Joseph Szydlowski, 64510 Bordes, France 2)CERFACS, 42 Avenue Gaspard Coriolis, 31057 Toulouse, France
Static mesh adaptation for reliable Large Eddy Simulation of turbulent reacting flows
Zenodo
2021
2021-01-25
10.5281/zenodo.6450225
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
The design challenge of reliable lean combustors needed to decrease pollutant emissions has clearly progressed with
the common use of experiments as well as Large Eddy Simulation (LES) because of its ability to predict the interactions
between turbulent flows, sprays, acoustics and flames. However, the accuracy of such numerical predictions depends
very often on the user’s experience to choose the most appropriate flow modeling and, more importantly, the proper
spatial discretization for a given computational domain. The present work focuses on the last issue and proposes a static
mesh refinement strategy based on flow physical quantities. To do so, a combination of sensors based on the dissipation
and production of kinetic energy coupled to the flame-position probability is proposed to detect the regions of interest
where flow physics happens and grid adaptation is recommended for good LES predictions. Thanks to such measures a
local mesh resolution can be achieved in these zones improving the LES overall accuracy while, eventually, coarsening
everywhere else in the domain to reduce the computational cost. The proposed mesh refinement strategy is detailed and
validated on two reacting-flow problems: a fully premixed bluff-body stabilized flame, i.e. the VOLVO test case, and a
partially premixed swirled flame, i.e. the PRECCINSTA burner, which is closer to industrial configurations. For both
cases, comparisons of the results with experimental data underline the fact that the predictions of the flame stabilization,
and hence the computed velocity and temperature fields, are strongly influenced by the mesh quality and significant
improvement can be obtained by applying the proposed strategy.
European Commission
10.13039/501100000780
766264
Machine learning for Advanced Gas turbine Injection SysTems to Enhance combustoR performance.