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The Utilisation of Reduced Kinetics by Local Self-Similarity Tabulation Approach in 3D Turbulent Reactive Flow Simulation with LES and TPDF

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Abstract

Combustion simulations with high fidelity turbulence models and detailed chemistry may suffer from high computational power requirements due to the combined cost of time-scale dissipation and small integration steps. Such a limitation can be avoided by employing a hybrid reaction mechanism reduction method called local self-similarity tabulation (LS2T). LS2T directly solves several dominant species reactions and incorporates the effects of other species on dominant ones by data retrieval from pre-calculated tables. This paper demonstrates the application of LS2T method to a 3D combustion simulation of Sandia Flame-D. The combustion simulation uses large eddy simulation as turbulence solver and transported probability density function for species transport, to increase the accuracy of the simulation and avoid the use of any additional reaction model. The results show that by use of LS2T method, it is possible to maintain high accuracy and generate results similar to detailed chemistry of methane combustion while maintaining an acceptable computational effort.

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Availability of data and material

The results of CFD simulations done in Ansys Fluent, and 0D combustor results of Cantera in Matlab are readily available. The experimental data used is shared by the Engine Combustion Network and is open for access.

Code availability

The scripts that are developed are currently available and can be presented for review as needed.

Change history

Abbreviations

\(\varDelta _f\) :

Filter Size

\(\varGamma\) :

Molecular diffusivity

\(\mu\) :

Dynamic viscosity

\(\dot{\omega }_{ll}\) :

Chemical source term by only light species

\(\varOmega _m\) :

Frequency of mixing within the subgrid

\(\dot{\omega }_{lh}\) :

Chemical source term by light and heavy species

\(\phi\) :

Composition space variables

\(\phi _{f}\) :

Initial equivalence ratio

\({{\varvec{\psi }}}\) :

Composition space vector

\(S({{\varvec{\psi }}})\) :

Filtered reaction source term \(\widetilde{\dot{\omega }}\)

\(\rho\) :

Density

\(C_k\) :

SGS kinetic energy transport model constant

\(CV_i\) :

Linear interpolated value at each corner point

\(d_i\) :

Distance of corner i for the inverse distance weighing

F :

One-point, one-time joint filtered density function

\({\mathbf {f}}^{\mathbf {c}}\) :

\({\mathbf {g}}_{\mathbf {l}}^{\mathbf {nc}}\) functions computable part by light species

\(F_m\) :

Favre averaged mixture fraction

\({\mathbf {f}}^{\mathbf {nc}}\) :

\({\mathbf {g}}_{\mathbf {l}}^{\mathbf {nc}}\) functions non-computed part that is dependent on heavy species only

\({\mathbf {g}}_{\mathbf {l}}\) :

Function representing light species reaction rates and enthalpy change

\({\mathbf {g}}_{\mathbf {l}}^{\mathbf {c}}\) :

\({\mathbf {g}}_{\mathbf {l}}\) functions computable part by light species

\({\mathbf {g}}_{\mathbf {l}}^{\mathbf {nc}}\) :

\({\mathbf {g}}_{\mathbf {l}}\) functions non-computed part that is a function of both light and heavy species

\({h}^{h}_{\alpha }\) :

Partial molar enthalpy of heavy species

\({h}^{l}_{\alpha }\) :

Partial molar enthalpy of light species

IV :

Interpolated value by inverse distance weighing

k :

Initial condition vector \((p_0,T_0,\phi )\)

\(k_{sgs}\) :

subgrid scale kinetic energy

\(N_h\) :

Number of heavy species

\(N_l\) :

Number of major(light) species

\(p_0\) :

Initial pressure

\({T}_{0}\) :

Initial temperature

t :

Time

\(\mathbf {u}\) :

Velocity vector

W :

Molecular mass

\(WF_i\) :

Weight factor of corner i for the inverse distance weighing

\(y_d\) :

The dominant variable for LS2T, normalised temperature

\(\mathbf{y} _h\) :

State variable representing heavy species concentrations and temperature

\(Y_h\) :

Mass fraction of heavy species

\(\mathbf{y} _l\) :

State variable representing light species concentrations and temperature

\(Y_l\) :

Mass fraction of light species

z :

The power term of corner i for the inverse distance weighing

\(\mathbf{Wi}\) :

Wiener process

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Acknowledgements

Authors like to acknowledge the support from Bogazici University under grant number 11A06D1. Computing resources used in this work were provided by Turkish National Center for High-Performance Computing (UYBHM) under Grant Number 1001202011. Special thanks to Prof. Dr. Kunt Atalk and Prof. Dr. A. Erhan Aksoylu for their support and advice.

Funding

The study has been conducted with support from Bogazici University under grant number 11A06D1. Computing resources used in this work were provided by the National Center for High-Performance Computing of Turkey (UYBHM) under Grant Number 1001202011.

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  1. Mechanical Engineering Department, Bogazici University, Istanbul, Turkey

    Serdar Guryuva & Hasan Bedir

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  1. Serdar Guryuva
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Correspondence to Serdar Guryuva.

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The current study is based on the publications of Dr. Josette Bellan from Jet Propulsion Laboratory of California Institute of Technology. The authors declare that they have no conflict of interest.

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Guryuva, S., Bedir, H. The Utilisation of Reduced Kinetics by Local Self-Similarity Tabulation Approach in 3D Turbulent Reactive Flow Simulation with LES and TPDF. Flow Turbulence Combust 107, 1035–1063 (2021). https://doi.org/10.1007/s10494-021-00260-3

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