Stochastic multiple mapping conditioning for a piloted, turbulent jet diffusion flame

Abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract: A stochastic implementation of the Multiple Mapping Conditioning (MMC) approach has been applied to a turbulent jet diffusion flame (Sandia Flame D). This implementation combines the advantages of the basic concepts of a mapping closure methodology with a probability density approach. A single reference variable has been chosen. Its evolution is described by a Markov process and then mapped to the mixture fraction space. Scalar micro-mixing is modelled by a modified ``interaction by exchange with the mean'' (IEM) mixing model where the particles mix with their -in reference space-conditionally averaged means. The formulation of the closure leads to localness of mixing in mixture fraction space and consequently improved localness in composition space. Results for mixture fraction and reactive species are in good agreement with the experimental data. The MMC methodology allows for the introduction of an additional ``minor dissipation time scale'' that controls the fluctuations around the conditional mean. A sensitivity analysis based on the conditional temperature fluctuations as a function of this time scale does not endorse earlier estimates for its modelling, but only relatively large dissipation time scales of the order of the integral turbulence time scale yield acceptable levels of conditional fluctuations that agree with experiments. With the choice of a suitable dissipation time scale, MMC-IEM thus provides a simple mixing model that is capable of capturing extinction phenomena, and it gives improved predictions over conventional PDF predictions using simple IEM mixing models. turing extinction phenomena, and it gives improved predictions over conventional PDF predictions using simple IEM mixing models. Permanent repository link Stochastic Multiple Mapping

Prediction of Global Extinction Conditions and Dynamics in Swirling Non-premixed Flames Using LES/CMC Modelling

“…The effects of mixing model on extinction prediction were assessed in both studies and it was found that in Reynolds Averaged Navier−Stokes (RANS) simulations the flame structures are more sensitive to the mixing model constant than in Large Eddy Simulations (LES). The Multiple Mapping Conditioning (MMC) model also demonstrates the ability in predicting the correct level of local extinction in Sandia flame series with the reasonable specifications of the respective mixing model constants [28][29][30]. Concerning the Conditional Moment Closure (CMC) model, its second-order and doubly conditioned variants with simple chemistry were developed to accurately capture the local extinction [31][32][33], but the application of simple chemical kinetics limits the analysis of local extinction.…”

A multiple mapping conditioning model for differential diffusion

“…The results presented in this work are for α = 0.02. D. 54 To match the decay rate of the second scalar we found that the best match was obtained with C = 1.24. Importantly the same set of model parameters are used for the two simulations and the model correctly predicts increasing scalar variance decay with decreasing Schmidt number.…”

Sensitivity analysis of LES–CMC predictions of piloted jet flames

“…The Sandia piloted jet flames (Barlow and Frank, 1998) Vogiatzaki et al (2011) in order to determine the value of modelling parameters which give the best agreement with conditional variance of temperature and various species mass fractions. Previous studies using transported PDF methods in RANS have produced good agreement with experiment (Lindstedt et al, 2000;Xu and Pope, 2000) for Flame F and also revealed the sensitivity of this Flame F to the chosen chemical mechanism (Cao and Pope, 2005).…”