Reconstruction subgrid models for nonpremixed combustion

Large-eddy simulation of combustion problems involves highly nonlinear terms that, when filtered, result in a contribution from subgrid fluctuations of scalars, Z, to the dynamics of the filtered value. This subgrid contribution requires modeling. Reconstruction models try to recover as much information as possible from the resolved field Z, based on a deconvolution procedure to obtain an intermediate field ZM. The approximate reconstruction using moments (ARM) method combines approximate reconstruction, a purely mathematical procedure, with additional physics-based information required to match specific scalar moments, in the simplest case, the Reynolds-averaged value of the subgrid variance. Here, results from the analysis of the ARM model in the case of a spatially evolving turbulent plane jet are presented. A priori and a posteriori evaluations using data from direct numerical simulation are carried out. The nonlinearities considered are representative of reacting flows: power functions, the dependence of the density on the mixture fraction (relevant for conserved scalar approaches) and the Arrhenius nonlinearity (very localized in Z space). Comparisons are made against the more popular beta probability density function (PDF) approach in the a priori analysis, trying to define ranges of validity for each approach. The results show that the ARM model is able to capture the subgrid part of the variance accurately over a wide range of filter sizes and performs well for the different nonlinearities, giving uniformly better predictions than the beta PDF for the polynomial case. In the case of the density and Arrhenius nonlinearities, the relative performance of the ARM and traditional PDF approaches depends on the size of the subgrid variance with respect to a characteristic scale of each function. Furthermore, the sources of error associated with the ARM method are considered and analytical bounds on that error are obtained

Modèles de flammelette en combustion turbulente avec extinction et réallumage : étude asymptotique et numérique, estimation d’erreur a posteriori et modélisation adaptative

On s’intéresse ici aux erreurs de modélisation liées à l’usage de modèles de flammelette sous-maille en combustion turbulente non prémélangée. Le but de cette thèse est de développer une stratégie d’estimation d’erreur a posteriori pour déterminer le meilleur modèle parmi une hiérarchie, à un coût numérique similaire à l’utilisation de ces mêmes modèles. Dans un premier temps, une stratégie faisant appel à un estimateur basé sur les résidus pondérés est développée et testée sur un système d’équations d’advection-diffusion-réaction. Dans un deuxième temps, on teste la méthodologie d’estimation d’erreur sur un autre système d’équations, où des effets d’extinction et de réallumage sont ajoutés. Lorsqu’il n’y a pas d’advection, une analyse asymptotique rigoureuse montre l’existence de plusieurs régimes de combustion déjà observés dans les simulations numériques. Nous obtenons une approximation des paramètres de réallumage et d’extinction avec la courbe en «S», un graphe de la température maximale de la flamme en fonction du nombre de Damköhler, composée de trois branches et d’une double courbure. En ajoutant des effets advectifs, on obtient également une courbe en «S» correspondant aux régimes de combustion déjà identifiés. Nous comparons les erreurs de modélisation liées aux approximations asymptotiques dans les deux régimes stables et établissons une nouvelle hiérarchie des modèles en fonction du régime de combustion. Ces erreurs sont comparées aux estimations données par la stratégie d’estimation d’erreur. Si un seul régime stable de combustion existe, l’estimateur d’erreur l’identifie correctement ; si plus d’un régime est possible, on obtient une fac˛on systématique de choisir un régime. Pour les régimes où plus d’un modèle est approprié, la hiérarchie prédite par l’estimateur est correcte.We are interested here in the modeling errors of subgrid flamelet models in nonpremixed turbulent combustion. The goal of this thesis is to develop an a posteriori error estimation strategy to determine the best model within a hierarchy, with a numerical cost at most that of using the models in the first place. Firstly, we develop and test a dual-weighted residual estimator strategy on a system of advection-diffusion-reaction equations. Secondly, we test that methodology on another system of equations, where quenching and ignition effects are added. In the absence of advection, a rigorous asymptotic analysis shows the existence of many combustion regimes already observed in numerical simulations. We obtain approximations of the quenching and ignition parameters, alongside the S-shaped curve, a plot of the maximal flame temperature as a function of the Damköhler number, consisting of three branches and two bends. When advection effects are added, we still obtain a S-shaped curve corresponding to the known combustion regimes. We compare the modeling errors of the asymptotic approximations in the two stable regimes and establish new model hierarchies for each combustion regime. These errors are compared with the estimations obtained by using the error estimation strategy. When only one stable combustion regime exists, the error estimator correctly identifies that regime; when two or more regimes are possible, it gives a systematic way of choosing one regime. For regimes where more than one model is appropriate, the error estimator’s predicted hierarchy is correct

The subgrid-scale scalar variance under supercritical pressure conditions

To model the subgrid-scale (SGS) scalar variance under supercritical-pressure conditions, an equation is first derived for it. This equation is considerably more complex than its equivalent for atmospheric-pressure conditions. Using a previously created direct numerical simulation (DNS) database of transitional states obtained for binary-species systems in the context of temporal mixing layers, the activity of terms in this equation is evaluated, and it is found that some of these new terms have magnitude comparable to that of governing terms in the classical equation. Most prominent among these new terms are those expressing the variation of diffusivity with thermodynamic variables and Soret terms having dissipative effects. Since models are not available for these new terms that would enable solving the SGS scalar variance equation, the adopted strategy is to directly model the SGS scalar variance. Two models are investigated for this quantity, both developed in the context of compressible flows. The first one is based on an approximate deconvolution approach and the second one is a gradient-like model which relies on a dynamic procedure using the Leonard term expansion. Both models are successful in reproducing the SGS scalar variance extracted from the filtered DNS database, and moreover, when used in the framework of a probability density function (PDF) approach in conjunction with the β-PDF, they excellently reproduce a filtered quantity which is a function of the scalar. For the dynamic model, the proportionality coefficient spans a small range of values through the layer cross-stream coordinate, boding well for the stability of large eddy simulations using this model

Machine Learning and Its Application to Reacting Flows

This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation

A Higher-Order Flamelet Model for Turbulent Combustion Simulations.

Current projection of energy consumption trends has shown that combustion of fossil fuel will continue to play an important role in industrial thermal processes, power generation, and transportation for a substantial period. In order for these sectors to sustain under the finite fossil fuel reserves, improvements in existing devices and development of novel concepts that emphasize on energy efficiency are necessary. Numerical simulations can be used to address this need, in particular by complementing experiments with extensive and quick parametric studies. However, this is only viable if numerical predictions of the combustion processes are accurate, which requires adequate modeling of the multi-physics phenomena in turbulent reacting flows. In this work, the flamelet-type combustion model, one of the most widely used approaches for turbulent reacting flow simulations, is thoroughly analyzed in terms of the validity of its underlying assumptions and limitations in its description of different combustion regimes. Diagnostic tools that account for the flamelet formulation are developed and applied to two different direct numerical simulation (DNS) results. These analyses show that the omission of the higher-order and unsteady flamelet effects by most conventional flamelet models is not valid in realistic configurations that are characterized by complex vortical structures, flame extinction and reignition, and turbulence-chemistry interactions. Following these findings, a higher-order flamelet model that describes the conventionally omitted flamelet effects is developed for large-eddy simulations (LES) applications. This model is based on the physical interpretation of flamelets as quasi one-dimensional structures in the turbulent flow, and the consideration of the effects that the spatial-filtering in LES methodology has on these structures. The model is applied in LES of a turbulent counterflow diffusion flame configuration, demonstrating improved agreement with the reference DNS solutions of the same case than the steady flamelet/progress variable (FPV) and laminar approximation models.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133466/1/chanyli_1.pd

Prediction of local extinctions in piloted jet flames with inhomogeneous inlets using unstrained flamelets

Multi-regime turbulent combustion modelling remains challenging and is explored with occurrence of local extinction in this study. A partially premixed model based on unstrained premixed flamelets is used in this work to investigate a piloted jet flame configuration with inhomogeneous inlets. Three different cases are simulated, which differ in the bulk mean velocity that amounts respectively to about 50\%, 70\% and 90\% of the blow off velocity measured experimentally. As the jet velocity approaches the blow off limit, local extinctions start to occur along the flame surface and thus these flames are challenging from a modelling prospective. Two different numerical approaches, involving scaled and unscaled progress variable respectively, are compared to elucidate their abilities and limitations to predict local extinctions and to deal with the three-stream problem at the pilot/coflow interface. The key modelling details for such predictions are indicated and discussed. LES results are systematically compared to two sets of experimental measurements available in the literature for the three flames. The differences observed in the two experimental datasets are also discussed with the help of LES results. Although both approaches show promising agreement for the flame statistics, the scaled progress variable approach better predicts the local extinctions. The unscaled approach shows to naturally handle the three-stream problem without additional treatment for the pilot/coflow interface, which is required for the scaled approach. Furthermore, computed scalar dissipation rate of mixture fraction is compared with the measurements showing good agreement for the conditions investigated. This further suggests that local extinctions can be predicted using unstrained flamelets if the correct scalar mixing and its dissipation are captured.EPSR