Zone-adaptive modeling of turbulent flames with multiple chemical mechanisms

Abstract

A novel zone-adaptive modeling method (AdaCM) with multiple chemical mechanisms is proposed for turbulent flames to achieve high-fidelity, yet computationally efficient, predictions. Specifically, a computational economical species transport model, e.g., the well-mixed model, via finite volume algorithm is employed with a simple mechanism as the base model for the whole computational domain, while the advanced transported probability density function (TPDF) method via Lagrangian particle tracking is employed with a detailed mechanism only for spatial regions with intense turbulence-chemistry interaction (TCI), denoted as the “PDF regions”. The PDF regions are dynamically identified based on local flow and flame characteristics and may evolve with time. A two-way particle/finite volume submodel coupling is formulated to ensure the composition consistency between submodels in the PDF regions and impose the correct interface conditions for composition and mass flow rate on the boundary of the PDF regions. With regard to transformation between different species representations in the mechanisms, a species reconstruction/reduction approach based on constrained chemical equilibrium is proposed to ensure element conservation and an adequate specification of unrepresented species at the model interface. The proposed adaptive modeling method has been applied to the well-known Sandia Flame D, in which the well-mixed combustion model with a 6-species, two-step global mechanism is employed as a base model and the high-fidelity TPDF with a 25-species skeletal mechanism is employed for regions with intense TCI. Results demonstrate the consistency in PDF regions between submodels with two distinct mechanisms. The predictions from the adaptive modeling are almost identical to those of TPDF and agree well with experimental measurements, illustrating the preservation of prediction accuracy in the adaptive method. In addition, the total number of computational particles in AdaCM for Flame D is only 18% of that for the stand-alone TPDF, and the recorded computational speedup is about 2.8.