Static mesh adaptation for reliable large eddy simulation of turbulent reacting flows


Article in journal




AIP Physics of Fluids


P. W. Agostinelli, B. Rochette, D. Laera, J. Dombard, B. Cuenot, and L. Gicquel, "Static mesh adaptation for reliable large eddy simulation of turbulent reacting flows", Physics of Fluids 33, 035141 (2021)


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.


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