Capturing NOx Emissions from a Hydrogen RQL Burner using the Incompletely Stirred Reactor Network Approach

Abstract

NO formation from a range of flow-split conditions in a hydrogen RQL-like combustor is investigated using an Incompletely Stirred Reactor Network (ISRN) framework anchored to a single reference CFD solution. The refer-ence flow field is obtained from Large Eddy Simulation (LES) coupled with Conditional Moment Closure (CMC), and the ISRN equations are then solved by post-processing this reference solution. The present work extends the single-reference ISRN strategy to operating conditions that significantly modify the reference flow and mixing fields, demonstrated here through the flow split variations in a hydrogen RQL combustor. To estimate NO at dif-ferent dilution ratios without additional LES-CMC simulations, the reference mixture fraction, velocity, density and scalar dissipation rate (SDR) fields are scaled in a zone-dependent manner to represent the first-order effect of changing the amount of air entering through the primary stream and side dilution jets. Both LES-CMC and ISRN use detailed H2 chemistry. The ISRN reproduces the main spatial features of NO predicted by LES-CMC, includ-ing high-NO regions in the primary zone and lower NO levels downstream of the dilution jets. Increasing dilution decreases NO, consistent with the experimentally observed trend, although the absolute EINO values are overpre-dicted. Conditional mass-fraction distributions show that regions with higher SDR have lower conditional peak NO, and that the reduction in conditional NO becomes weaker at higher dilution ratios (30-50% flow split). A sensitivity study with different reactor counts indicates limited sensitivity of the predicted temperature and key species fields. The use of both low- and high-dilution reference cases shows that the decreasing EINO trend is preserved, although the absolute level remains affected by the approximate scaling. The present results therefore support the use of scaled single-reference ISRN as a rapid trend-prediction tool for parametric studies.