Assessment of the presumed mapping function approach for the stationary laminar flamelet modelling of reacting double scalar mixing layers

This paper assesses the Presumed Mapping Function (PMF) approach in the context of the Stationary Laminar Flamelet Modelling (SLFM) of a reacting Double Scalar Mixing Layer (DSML). Starting from a prescribed Gaussian reference field, the PMF approach provides a presumed Probability Density Function (PDF) for the mixture fraction that is subsequently employed to close the Conditional Scalar Dissipation Rate (CSDR) upon doubly-integrating the homogeneous PDF transport equation. The PMF approach is unique in its ability to yield PDF and CSDR distributions that capture the effect of multiple fuel injections of different composition. This distinct feature overcomes the shortcomings of the classical SLFM closures (the β-distribution for the PDF and the counterflow solution for the CSDR). The current study analyses the impact of the binary (two-stream) and trinary (three-stream) PMF approaches on the structure of laminar flamelets in a DSML formed by the mixing of a fuel stream and an oxidiser stream separated by a pilot. The conditions of a partially-premixed methane/air piloted jet flame are considered. A parametric assessment is performed by varying the local mixing statistics and the findings are compared to those of the classical SLFM approach. Further, the influence of the PMF approach on flamelet extinction and transport by means of differential diffusion is thoroughly investigated. It is shown that the trinary PMF approach captures the influence of the pilot stream as it is capable of yielding bimodal CSDR and trimodal PDF distributions. It is further demonstrated that, when the influence of the pilot is significant, flamelets generated using the trinary CSDR closure extinguish at higher strain levels compared to flamelets generated using the binary and counterflow closures. Lastly, it is shown that the trinary PMF approach can be critical for accurate SLFM computations of DSMLs when differential diffusion effects are important.