Assessing the Multi-Regime Capability of the Super-Grid Linear Eddy Model (SG-LEM) Using the Darmstadt Multi-Regime Burner

Artikel i vetenskaplig tidskrift, 2024

Recent advances in combustion modelling for Large Eddy Simulation (LES) have increasingly utilised lower-dimensional manifolds, such as Flamelet Generated Manifolds and Flamelet/Progress Variable methods, due to their computational efficiency. These methods typically rely on one-dimensional representations of flame structures, often assuming premixed or non-premixed configurations. However, practical combustion devices frequently operate under partially-premixed conditions and present challenges due to mixture inhomogeneities and complex flow features. The Linear Eddy Model (LEM) offers an alternative by directly simulating turbulence-chemistry interactions without presuming specific flame structures. However, traditional LES-LEM approaches are computationally quite expensive due to the need for resolved LEM domains to be embedded in every LES cell.The authors developed the Super-Grid LEM (SG-LEM) method (Comb. Theor. Model. 28, 2024) to address these computational challenges by coarse-graining the LES mesh and embedding individual LEM domains within clusters of LES cells. This study evaluates SG-LEM in the context of the Multi-Regime Burner (MRB) introduced by Butz et al. (Combust. Flame, 210, 2019), which features both premixed and non-premixed flame characteristics. SG-LEM simulations of the MRB case demonstrate the method’s sensitivity to clustering parameters, with flow-aligned clusters significantly improving flame stability. LEM domains on the super-grid were able to represent the MRB flame topology while LES radial profiles including velocity, mixture fraction, temperature, and CO mass fraction, were validated against experimental data and also reference simulations using standard combustion closures. The work also investigates discrepancies in CO profiles using conditional statistics and stand-alone LEM simulations. Finally, the work identifies areas of improvement for the SG-LEM framework, in particular relating to cluster generation, and (advective and diffusive) mass exchange between neighbouring LEM domains, as well as possible solutions for future SG-LEM implementations which could improve the model’s predictive capability.