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Flow, Turbulence and Combustion

, Volume 95, Issue 2–3, pp 501–517 | Cite as

Mixing Modelling Framework Based on Multiple Mapping Conditioning for the Prediction of Turbulent Flame Extinction

  • K. VogiatzakiEmail author
  • S. Navarro-Martinez
  • S. De
  • A. Kronenburg
Article

Abstract

A stochastic implementation of the Multiple Mapping Conditioning (MMC) approach has been applied to a turbulent piloted jet diffusion flame (Sandia flame F) that is close to extinction. Two classic mixing models (Curl’s and IEM) are introduced in the MMC context to model the turbulent mixing. The suggested model involves the use of a reference space (that is mapped to mixture fraction space) in order to define particle proximity. The addition of the MMC ideas to the IEM and Curl’s models, that is suggested in the current work, aspires to combine the simplicity of these two models with the enforced compositional locality without violating the linearity and independence principles. The formulation of the approach is discussed in detail and results are presented for the mixing field and reactive species. The predictions are compared with joint-scalar PDF simulations using the same mixing models and experimental data. Moreover, the sensitivity of the model to the particle number is examined. It is shown that MMC is less sensitive to the number of particles and can generally produce improved predictions of major and minor chemically reacting species with a lower number of particles.

Keywords

Multiple mapping conditioning Mixing models Probability density function approach Sandia flame F 

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • K. Vogiatzaki
    • 1
    Email author
  • S. Navarro-Martinez
    • 2
  • S. De
    • 3
  • A. Kronenburg
    • 3
  1. 1.School of Engineering and Mathematical SciencesCity UniversityLondonUK
  2. 2.Mechanical Engineering DepartmentImperial College LondonLondonUK
  3. 3.Institut für Technische Verbrennung, University of StuttgartStuttgartGermany

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