Computational Models for Turbulent Reacting Flows: References

The role of temperature, mixture fraction, and scalar dissipation rate on transient methane injection and auto-ignition in a jet in hot coflow burner

Scale Adaptive Simulation of a thermoacoustic instability in a partially premixed lean swirl combustor

This paper presents time-resolved numerical simulations of a well-characterized sooting swirl flame at elevated pressure. Recently published unsteady Reynolds averaged Navier–Stokes simulations (URANS) are compared here to newly performed large eddy simulations (LES). Finite-rate chemistry, where transport equations are solved for each chemical species, is employed for the gas phase, a sectional approach for polycyclic aromatic hydrocarbons (PAHs), and a two-equation model for soot particles. Feedback effects such as the consumption of gaseous soot precursors by growth of soot and PAHs are inherently captured accurately by a coupled solution of the set of governing equations. The numerical results (velocity components, temperature, and soot volume fraction) compare well with experimental data. No significant differences between URANS and LES are observed for time-averaged temperatures and velocity components, while the prediction of soot is significantly improved by LES. It will be shown that an a...

/pdf/toward-finite-rate-chemistry-large-eddy-simulations-of-12mwl5aqun.pdf

Toward finite-rate chemistry large-eddy simulations of sooting swirl flames

Numerical study of a pulsed auto-igniting jet flame with detailed tabulated chemistry

pdf/an-implicit-high-order-k-exact-finite-volume-approach-on-3ee251xv1q.pdf

An implicit high-order k-exact finite-volume approach on vertex-centered unstructured grids for incompressible flows

Anwendung eines Transportgleichungs-PDF-Verfahrens zur Berechnung der Selbstzündung eines Methan-Freistrahles

Di usive mass and heat uxes cause unclosed terms in transported probability density function (TPDF) methods which need to be modeled accurately, in order to account for the e ects as local extinction or stabilization of turbulent ames. The modeling of ame stabilization is addressed in this work with the example a non-piloted, non-premixed hydrogen-air ame. Experimental data suggest that di erential di usion plays a major role at the root of this ame. To account for this e ect, a di usion model recently introduced for TPDF/LES (Large-Eddy-Simulation) methods is modi ed and applied in RANS (ReynoldsAveraged-Navier Stokes) calculations. Several computations employing this model with varying particle-numbers and grid-resolutions demonstrate the capability of the di usion model to take di erential di usion into account and thus improve ame stabilization.

Transported PDF Calculations of a Turbulent, Non-Premixed, Non-Piloted, Hydrogen-Air Flame with Differential Diffusion

Development and Application of a Transported Probability Density Function Method on Unstructured Three-Dimensional Grids for the Prediction of Nitric Oxides

Finite-rate chemistry combustion simulations require the computationally expensive direct integration of the chemical source term. In order to reduce the calculation time of such simulations, an efficient artificial neural network approach is developed. The artificial neural network consists of a classification methodology to subdivide the thermochemical state space into smaller clusters and a subsequent multi-layer perceptron for each of the clusters. Due to the subdivision approach, the multi-layer perceptron size can be kept small, which guarantees a computationally efficient artificial neural network. For the classification two different approaches are investigated, namely a self-organizing map and a k-means binary decision tree. Both methods are tested with respect to their clustering quality and their performance. Chemical reactors are used to generate training and validation data. The multi-layer perceptron prediction includes all species of the original chemical mechanism. A priori comparison with direct integration proved the ability of both methods to give accurate results even for minor species. A posteriori calculations of ignition delay are conducted over an initial temperature range of 1100 to 1700 K and an equivalence ratio range of φ = 0.7 to φ = 1.4. While species concentrations and temperature profiles are reproduced well for most of the initial conditions, the prediction quality of the artificial neural networks decreases for a few calculations starting at low temperatures. Performance benchmarks confirmed that the artificial neural network approaches are superior to direct source term integration in terms of computational costs. The benchmarks also revealed that the k-means binary decision tree-based approach is three times faster than the self-organizing map approach.

/pdf/application-of-artificial-neural-networks-for-the-simulation-41jbyjxgav.pdf

Application of Artificial Neural Networks for the Simulation of a Perfectly Premixed Chemical Reactor

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