Stochasticity and Dynamics of High-Speed Reactive Flows

2011 ◽  
Author(s):  
E. S. Oran ◽  
Jiachun Li ◽  
Song Fu
Keyword(s):  
High Speed ◽  
Reactive Flows

A multigrid method for high speed reactive flows

1997 ◽  
Author(s):  
Scott Sheffer ◽  
Anthony Jameson ◽  
Luigi Martinelli ◽  
Scott Sheffer ◽  
Anthony Jameson ◽  
...  
Keyword(s):  
High Speed ◽  
Multigrid Method ◽  
Reactive Flows

scholarly journals A Numerical Investigation of Mixing Models in LES-FMDF for Compressible Reactive Flows

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 5180
Author(s):  
Wenwu Chen ◽  
Jianhan Liang ◽  
Lin Zhang ◽  
Qingdi Guan
Keyword(s):  
High Speed ◽  
Compressible Flows ◽  
Minimum Spanning Tree ◽  
Mass Density ◽  
Mixing Layer ◽  
Large Error ◽  
Mixing Model ◽  
Mixing Models ◽  
Reactive Flows ◽  
Model Coefficient

The filtered mass density function (FMDF) model has been employed for large-eddy simulations (LES) of compressible high-speed turbulent mixing and reacting flows. However, the mixing model remains a pressing challenge for FMDF methods, especially for compressible reactive flows. In this work, a temporal development mixing layer with two different convective Mach numbers, and , is used to investigate the mixing models. A simplified one-step reaction and a real hydrogen/air reaction are employed to study the mixing and turbulence-chemistry interaction. Two widely used mixing models, interaction by exchange with the mean (IEM) and Euclidean minimum spanning tree (EMST), are studied. Numerical results indicate that no difference is observed between the IEM and EMST models in simple reaction flows. However, for hydrogen/air reactions, the EMST model can predict the reaction more accurately in high-speed flow. For mixing models in compressible reactive flows, the requirement of localness preservation tends to be more essential as the convective Mach number increases. With the increase of compressibility, the sensitivity of the mixing model coefficient is reduced significantly. Therefore, the appropriate mixing model coefficient has a wider range. Results also indicate that a large error may result when using a fixed mixing model coefficient in compressible flows.

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