Performance of the Eddy Dissipation Concept (EDC) in the regime of Moderate and Intense Low-oxygen Dilution (MILD) combustion is investigated. The special MILD features, where chemical and turbulence time scales are comparable (Damköhler number close to unity), have led several researchers to suggest modifications of EDC, mainly by changing model constants. EDC with standard and modified constants are compared, and the importance of each effect is outlined. Different fine-structure reactor models and their inflow/initial conditions are discussed and found to play a significant role. The reacting fraction of fine structures, which in virtually all other numerical studies is set to unity, is also discussed and found to be important. We observe better agreement with experiment when the reacting fraction is reduced below unity, which is also described by the original EDC. The results obtained with the variable reacting fraction are found to improve both the temperature distributions and the lift-off height predictions. The calculations are carried out with the use of open source software OpenFOAM. The main test case was the Delft Jet-in-Hot-Coflow burner emulating MILD regime at three different flow conditions (jet Reynolds numbers of 2500, 4100 and 8800).

No abstract

The current paper focuses on the numerical simulation of the Delft jet in hot co-flow (DJHC) burner, fed with natural gas and biogas, using the eddy dissipation concept (EDC) model with dynamic chemistry reduction and tabulation, i.e., tabulated dynamic adaptive chemistry (TDAC). The central processing unit (CPU) time saving provided by TDAC is evaluated for various EDC model constants and chemical mechanisms of increasing complexity, using a number of chemistry reduction approaches. Results show that the TDAC method provides speed-up factors of 1.4–2.0 and more than 10 when using a skeletal mechanism (DRM19) and a comprehensive kinetic mechanism (POLIMIC1C3HT), respectively. The directed relation graph with error propagation (DRGEP), dynamic adaptive chemistry (DAC), and elementary flux analysis (EFA) reduction models show superior performances when compared to other approaches, such as directed relation graph (DRG) and path flux analysis (PFA). All of the reduction models have been adapted for run-time reduction. Furthermore, the contribution of tabulation is more important with small mechanisms, while reduction plays a major role with large ones.