Dynamic parameterization approach for MMC modeling of coal volatile combustion

A sparse-Lagrangian particle implementation of the multiple mapping conditioning (MMC) model coupled to large eddy simulation (LES) for two-phase flows with reacting solid fuel particles is proposed. The MMC-LES model is developed and validated by means of carrier-phase direct numerical simulations (CP-DNS) of the devolatilization and volatile combustion from pulverized coal particles in statistically homogeneous isotropic turbulence (HIT). Two distinct Lagrangian particle clouds are introduced: The first cloud represents the inertial coal particles under going heat-up and pyrolysis, while the second cloud consists of stochastic MMC particles representing the reacting gas mixture of coal volatiles burning in air. To account for the heat and mass transfer between the two clouds, the one-to-one model for two-phase coupling in MMC is employed. To enhance model accuracy and practicality, a dynamic parameterization approach is proposed by deriving the governing MMC conditioning parameters, which are usually assumed to be constant, adaptively from the transient field of the volatile mixture fraction. Results show that conventional MMC predictions following best practice are in good agreement with the DNS for low fuel particle loadings, if suitable 𝑎-𝑝𝑟𝑖𝑜𝑟𝑖 information (ideally from DNS) is available to calibrate the model. ... mehrFor high fuel particle loadings stronger deviations are observed. Moreover, we find that in conventional MMC the nominal conditioning parameters in physical and mixture fraction space 𝑟𝑚 and 𝑓𝑚 cannot be realized as physical and mixture fraction distances 𝑑𝑥 and 𝑑𝑓 for the most part of the transient ignition and volatile combustion process as expected. Differently, the new dynamic approach does not need 𝑎-𝑝𝑟𝑖𝑜𝑟𝑖 calibration information from DNS, allows for a faithful match of the nominal and realized MMC conditioning parameters over time, and provides favorable predictions of the ignition and volatile combustion process. The sensitivity of the MMC results to the dynamic scaling factor 𝛼𝑓 and constant 𝐶𝜉 in the mixing time scale model is explored. A scaling factor 𝛼𝑓 = 0.03 is recommended, and while the standard value 𝐶𝜉 = 0.1 already yields reasonable predictions, improved results can be obtained for slightly smaller 𝐶𝜉.