| A transported composition probability density function (PDF) method is developed for coal combustion, targeting high-temperature oxy-coal combustion for direct power extraction using magnetohydrodynamics. A consistent hybrid Lagrangian particle/Eulerian mesh algorithm is used to solve the modeled PDF transport equation for the gas phase, with finite-rate gas-phase chemistry. The model includes standard k -- epsilon turbulence, gradient transport for scalars, and a Euclidean minimum spanning tree (EMST) mixing model. A separate Lagrangian description is used to solve for the coal particle phase, including particle tracking, devolatilization and surface reaction models. Inter-phase coupling models are proposed for the couplings between the gas phase and the solid phase. A spectral photon Monte Carlo (PMC) method is built into the framework to account for the spectral radiative heat transfer for the gas phase. A systematic hierarchical approach has been pursued for model development. First, simulations were performed for laboratory-scale syngas (CO/H2/N2)-air jet flames where finite-rate chemistry is important. The next step was to simulate an oxy-natural gas furnace where the environment is as close as possible to that in an oxy-coal system, without the complications of a solid fuel. The model was then extended to include coal particles, and was tested both for a nonreacting particle-laden expansion flow and for two reacting air-coal jet flames. It has been found that turbulence-chemistry interactions are important in all the validation cases when species with slow chemistry are considered (e.g., CO, NO). Radiation dominates the heat-transfer characteristics in a high-temperature oxy-combustion environment, although the effects of turbulence-radiation interactions might not be significant. For coal combustion, finite-rate chemistry is important for correct flame structure predictions. The high-fidelity models constructed here have proven to be robust in different combustion environments, and have been exercised to calibrate simpler models and to test model assumptions that often are included in simpler models. |
