Development and validation of a local time stepping-based PaSR solver for combustion and radiation modeling

In the current work, a local time stepping (LTS) solver for the modeling of combustion, radiative heat transfer and soot formation is developed and validated. This is achieved using an open source computational fluid dynamics code, OpenFOAM. Akin to the solver provided in default assembly i.e. reactingFoam, the solver developed here is also applicable for reacting flows but it utilizes the LTS approach which assists in reducing the computational runtime. Magnussen soot model is also incorporated into the code to simulate soot formation process. Besides this, the Weighted Sum of Gray Gases Model library in the edcSimpleFoam solver which was introduced during the 6th OpenFOAM workshop is modified and coupled with the current solver. One of the main amendments made is the integration of soot radiation submodel since this is significant in rich flames where soot particles are formed. The new solver is henceforth addressed as radiationReactingLTSFoam (rareLTSFoam). A performance benchmarking exercise is here carried out to evaluate the effect of each LTS parameter on calculation stability, results accuracy and computational runtime. The model validation uses two test cases. The first test case presents the modeling of a helium-stabilized, laminar premixed flame at rich condition in which soot formation is observed. Here, the solver is validated by comparing both the computed temperature and soot volume fraction along the axial direction against experimental measurements and simulation results generated by ANSYS CFX. As compared to the computational runtime required by the counterpart transient solver, a speedup of approximately fourteen-fold is obtained. In the second case, a turbulent non-premixed flame, namely Sandia Flame D is simulated. The computed and measured axial temperatures are compared. The rareLTSFoam solver has been proved to predict the temperature and soot volume fraction of both the flames reasonably well.