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Numerical simulation of turbulent premixed flames with conditional source-term estimation Salehi, Mohammad Mahdi


Conditional Source-term Estimation (CSE) is a closure model for turbulence-chemistry interactions. This model is based on the conditional moment closure hypothesis for the chemical reaction source terms. The conditional scalar field is estimated by solving an integral equation using inverse methods. CSE was originally developed for - and has been used extensively in - non-premixed combustion. This work is the first application of this combustion model to predictive simulations of turbulent premixed flames. The underlying inverse problem is diagnosed with rigorous mathematical tools. CSE is coupled with a Trajectory Generated Low-Dimensional Manifold (TGLDM) model for chemistry. The CSE-TGLDM combustion model is used with both Reynolds-Averaged Navier-Stokes (RANS) and Large-Eddy Simulation (LES) turbulence models to simulate two different turbulent premixed flames. Also in this work, the Presumed Conditional Moment (PCM) turbulent combustion model is employed. This is a simple flamelet model which is used with the Flame Prolongation of ILDM (FPI) chemistry reduction technique. The PCM-FPI approach requires a presumption for the shape of the probability density function of reaction progress variable. Two shapes have been examined: the widely used beta-function and the Modified Laminar Flamelet PDF (MLF-PDF). This model is used in both RANS and large-eddy simulation of a turbulent premixed Bunsen burner. Radial distributions of the calculated temperature field, axial velocity and chemical species mass fraction have been compared with experimental data. This comparison shows that using the MLF-PDF leads to predictions that are similar, and often superior to those obtained using the beta-PDF. Given that the new PDF is based on the actual chemistry - as opposed to the ad hoc nature of the beta-PDF - these results suggest that it is a better choice for the statistical description of the reaction progress variable.

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