Large eddy simulation of a lifted ethylene flame using a dynamic nonequilibrium model for subfilter scalar variance and dissipation rate

Kaul, Colleen M.; Raman, Venkat; Knudsen, Edward; Richardson, E.S.; Chen, Jacqueline H.

doi:10.1016/j.proci.2012.06.079

Cited by 30 publications

(24 citation statements)

References 36 publications

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“…Based on these detailed mechanisms, reduced mechanisms have also been developed, such as the 19-species reduced mechanism [69], 22-species reduced mechanism [70], etc. These reduced mechanisms have been successfully used [71,72] in several LES studies of ethylene flames. However, the present study simulates many LES cases and those mechanisms remain too expensive.…”

Section: Numerical Methods For Flame Simulationsmentioning

confidence: 99%

Prediction of combustion instability limit cycle oscillations by combining flame describing function simulations with a thermoacoustic network model

Han

Morgans

2015

Combustion and Flame

203

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a b s t r a c t Accurate prediction of limit cycle oscillations resulting from combustion instability has been a long-standing challenge. The present work uses a coupled approach to predict the limit cycle characteristics of a combustor, developed at Cambridge University, for which experimental data are available (Balachandran, Ph.D. thesis, 2005). The combustor flame is bluff-body stabilised, turbulent and partially-premixed. The coupled approach combines Large Eddy Simulation (LES) in order to characterise the weakly non-linear response of the flame to acoustic perturbations (the Flame Describing Function (FDF)), with a low order thermoacoustic network model for capturing the acoustic wave behaviour. The LES utilises the open source Computational Fluid Dynamics (CFD) toolbox, OpenFOAM, with a low Mach number approximation for the flow-field and combustion modelled using the PaSR (Partially Stirred Reactor) model with a global one-step chemical reaction mechanism for ethylene/air. LES has not previously been applied to this partially-premixed flame, to our knowledge. Code validation against experimental data for unreacting and partially-premixed reacting flows without and with inlet velocity perturbations confirmed that both the qualitative flame dynamics and the quantitative response of the heat release rate were captured with very reasonable accuracy. The LES was then used to obtain the full FDF at conditions corresponding to combustion instability, using harmonic velocity forcing across six frequencies and four forcing amplitudes. The low order thermoacoustic network modelling tool used was the open source OSCILOS (http://www.oscilos.com). Validation of its use for limit cycle prediction was performed for a well-documented experimental configuration, for which both experimental FDF data and limit cycle data were available. The FDF data from the LES for the present test case was then imported into the OSCILOS geometry network and limit cycle oscillations of frequency 342 Hz and normalised velocity amplitude of 0.26 were predicted. These were in good agreement with the experimental values of 348 Hz and 0.21 respectively. This work thus confirms that a coupled numerical prediction of limit cycle behaviour is possible using an entirely open source numerical framework.

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Section: Numerical Methods For Flame Simulationsmentioning

confidence: 99%

Prediction of combustion instability limit cycle oscillations by combining flame describing function simulations with a thermoacoustic network model

Han

Morgans

2015

Combustion and Flame

203

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“…The LES computations use a low-Mach number structured code with a staggered arrange- ment of variables [30]. Transport equations for velocity, mixture fraction and progress variable are solved, and the mixture fraction variance is obtained using an algebraic closure model with dynamic coefficient [31]. Similar to Ref.…”

Section: Les/flamelet Approach For Sandia D/e Flamesmentioning

confidence: 99%

Ensemble-LES analysis of perturbation response of turbulent partially-premixed flames

Hassanaly

Raman

2019

Proceedings of the Combustion Institute

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The response to small perturbations of the Sandia CH 4 /air flame series is studied using a dynamical systems formulation. Here, the Sandia D and E turbulent partially-premixed flames are computed using large eddy simulation (LES) with a flamelet/progress variable approach. Using 300 simultaneous LES computations of each flame, the partial Lyapunov spectrum of the flow is obtained, which provides a set of Lyapunov exponents (LEs) and Lyapunov vectors (LVs). Special numerical procedures to handle such large data sets and perform matrix manipulations on-the-fly are discussed. From the LEs, the dimension of the attractor for the flames was estimated, for the first time, to be at least 5000 (flame D) and 10500 (flame E). This is a significant result indicating that the chaoticity of the turbulent flame is confined to a volume of phase-space that is quantified. The growth of perturbations occurs at time-scales that are much larger than the smallest strain-rate based times scales, but smaller than the integral scales. Further, the Lyapunov vectors that determine the perturbation response show that the strongest perturbations are aligned with the jet breakdown region, while other LVs are aligned with the shear layer. It is found that both the extinction/re-ignition process and the turbulence-induced jet motion strongly contribute to the flame chaoticity but are not equally important for all the LVs. These results provide new insights into the perturbation response of turbulent flames. More broadly, this Lyapunov approach allows a full characterization of the chaotic dynamics associated with turbulent flames, which have applications for both modeling and control of such complex flows.

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“…A scale similarity model [8] was proposed, which assumes local equilibrium between production and dissipation of the variance at the small scales. However, this model presented accuracy limitations when applied to technically relevant flow configurations [3]. Non-equilibrium modelling can be achieved by constructing a variance transport equation (VTE).…”

Section: Introduction and Mathematical Formulationmentioning

confidence: 99%

“…However, both equations require a closure for the subfilter dissipation rate. Either through an algebraic expression using a turbulent time scale [2,3] or by using a transport equation for the scalar dissipation rate [4].…”

Section: Introduction and Mathematical Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of Subfilter Scalar Dissipation Rate and Mixture Fraction Variance Models

Ventosa-Molina

Lehmkuhl

Segarra

et al. 2016

Springer Proceedings in Physics

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The Flamelet/Progress-Variable (FPV) model is able to describe diffusion flames in the flamelet regime by parametrising the chemical problem as a function of a limited number of variables, namely the mixture fraction Z and the progressvariable c. The aim of the present paper is to study the effect of different mixture fraction variance Z v and subgrid scalar dissipation rate χ sgs models in the prediction of turbulent combustion phenomena, particularly for diffusion flames. Both parameters model scalar mixing at the subgrid level, which in turn controls the combustion process. Four different models are compared: an Equilibrium model, a mixture fraction Variance Transport Equation (VTE) model, a Transport Equation for the Second moment of the mixture fraction (STE), and a Scalar Dissipation Rate transport equation (SDR-TE).

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Large eddy simulation of a lifted ethylene flame using a dynamic nonequilibrium model for subfilter scalar variance and dissipation rate

Cited by 30 publications

References 36 publications

Prediction of combustion instability limit cycle oscillations by combining flame describing function simulations with a thermoacoustic network model

Prediction of combustion instability limit cycle oscillations by combining flame describing function simulations with a thermoacoustic network model

Ensemble-LES analysis of perturbation response of turbulent partially-premixed flames

Assessment of Subfilter Scalar Dissipation Rate and Mixture Fraction Variance Models

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