Scalar flux modeling in turbulent flames using iterative deconvolution

Nikolaou, Zacharias M.; Cant, RS; Vervisch, Luc

doi:10.1103/physrevfluids.3.043201

Cited by 21 publications

(25 citation statements)

References 49 publications

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“…As such, it becomes clear that further extensions of manifold-based combustion models will face limitations. In light of rapidly increasing computational resources, efficient time-integration schemes with linear complexity [26], efficient chemical reduction techniques [27][28][29][30], and the development of realizable reconstruction techniques for subgrid contributions [8,9,31], it is expected that the utilize of topology-based combustion models will decline in the foreseeable future. Combustion models are commonly employed in a monolithic form, meaning that only a single model is utilized to describe the entire combustion process encountered in a combustor configuration.…”

Section: How Should a Combustion Model Be Selected?mentioning

confidence: 99%

Requirements Towards Predictive Simulations of Turbulent Combustion

Ihme

2019

AIAA Scitech 2019 Forum

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Significant progress has been made on the development of modeling approaches for simulating turbulent reacting flows. We are currently in a position where keyphysical aspects of fairly complex combustion processes are reasonably well understood at a qualitative and-in many cases-also at a quantitative level. Examples are the prediction of temperature and major species, statistically stationary flames, gaseous combustion, turbulence/flame coupling, and turbulent scalar transport. However, major challenges arise in capturing transient processes, minor species, multi-phase flows, and multidimensional flame/flow interactions. With this, the question arises what steps need to be taken to elevate the current state of modeling capabilities to address these deficiencies? This paper seeks to address this multifaceted question. For this, we begin by briefly reviewing the current state of combustion model approaches, our quest for improving existing models, and ideas on model evaluations. We then proceed by examining concepts on quantitative model evaluations, requirements on predictability, quantities of interest, and cost/accuracy trade-offs. We close by introducing recent concepts on model evaluations that directly incorporate time-resolved measurements.

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Section: How Should a Combustion Model Be Selected?mentioning

confidence: 99%

Requirements Towards Predictive Simulations of Turbulent Combustion

Ihme

2019

AIAA Scitech 2019 Forum

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“…In practice, the deconvolution algorithm is implemented with an error controller in order to ensure that the deconvolved fields lie within correct physical limits. Further details of the exact implementation of the method are given in [32,33]. The estimate of the variance as given by Eq.…”

Section: Filtering and Deconvolution Operationsmentioning

confidence: 99%

“…In contrast to most a priori assessments which are conducted on the fine DNS mesh, the assessment in this study is conducted on a much coarser LES mesh in order to simulate an actual LES. Even though a priori assessments in general using DNS data do not guarantee functionality of the model in actual LES, the results in [32,33] indicate that the simulated LES a priori assessment procedure is more suitable for evaluating LES sub-grid models. This is particularly true for sub-grid scale models which include gradients of the resolved fields [32].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of deconvolution-based flamelet methods for progress variable rate modeling

Nikolaou¹

2018

AAOAJ

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A novel approach for modeling the progress variable reaction rate in Large Eddy Simulations of turbulent and reacting flows is proposed. This is done in the context of two popular flamelet models which require the progress variable variance as input. The approach is based on using a recently proposed deconvolution method for modeling the variance. The deconvolution-modeled variance, is used as an input in the flamelet models for modeling the filtered progress variable rate. The assessment of the proposed approach is conducted a priori using direct numerical simulation data of turbulent premixed flames. For the conditions tested in this study, deconvolution does not introduce a significant bias in the flamelet models' predictions, while a quantitatively good prediction of the progress variable rate is obtained for both flamelet models considered. method has both its merits and drawbacks, and an in-depth discussion on the subject can be found in the reviews [3]- [5].Flamelet methods in particular are popular since they are relatively straightforward to implement, and have been applied in a number of LES studies both for premixed and for non-premixed flames, with overall good results [6]- [16]. These studies include flows in complex geometries and for different flow configurations (freely-propagating, recirculating, etc.). The filtered progress variablec which is a main LES solution variable, and it's variance σ 2 = c 2 −cc as obtained from a suitable model, are used as parameters in prebuilt tables for obtaining important information and for providing closures for un-closed terms in the governing equations. This includes species mean (filtered) mass fractions, mean (filtered) reaction rates etc. Such methods offer a simple, yet effective way of providing detailed chemistry information (a posteriori), while keeping the computational requirements to a minimum.A key unclosed term in the transport equation for the progress variable,c, is the filtered progress variable reaction rate,w c , where the overbar denotes a spatial filtering operation as defined in the context of LES. This is a dominant term, particularly in the reaction zone of the flame [17], and an accurate model is required for this term. Two popular flamelet methods for modelingw c , and which involve the progress variable variance, are the presumed-pdf approach and the Filtered Laminar Flame (FLF) approach [7,13]. In the presumed-pdf approach, the progress variablec and it's variance σ 2 , are used to parameterise the progress variable pdfp(ζ;c, σ 2 ), where ζ is the sample-space variable forc. This pdf is usually taken to be a β-function. A usually steady, 1D, laminar flame solution is used for integrating with the pdf thus obtaining a closure for the rate. In the FLF approach, a 1D laminar flame solution is pre-filtered, and a table is constructed which contains values of the filtered reaction rate (or other variables of interest). This is done for discrete filter-width values, and the table contains the filtered values of the variables at all spatial p...

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“…The above error estimate includes contributions both from under-predicting and over-predicting a target variable y using a model variable x, and is therefore a stringent error measure. In addition, in order to avoid any On this basis, the iterative deconvolution was further applied to compute unresolved SGS fluxes, as the convective one [31]…”

mentioning

confidence: 99%

From Discrete and Iterative Deconvolution Operators to Machine Learning for Premixed Turbulent Combustion Modeling

Domingo

Nikolaou

Seltz

2020

Data Analysis for Direct Numerical Simulations of Turbulent Combustion

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Following the rapid and continuous progress of computing power allowing for increasing the mesh resolution in large eddy simulation (LES), new modeling strategies appear which are based on a direct treatment of the now well-resolved, but still not fully-resolved scalar signals. Along this line, deconvolution or inverse filtering, either based on discrete or iterative operators, is first discussed. Recent results obtained from a direct numerical simulation (DNS) database and LES of a premixed turbulent jet flame are presented. The analysis confirms the potential of deconvolution to approximate the unclosed non-linear terms and the SGS fluxes. Then, the introduction of machine learning in turbulent combustion modeling is illustrated in the context of convolutional neural networks.

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Scalar flux modeling in turbulent flames using iterative deconvolution

Cited by 21 publications

References 49 publications

Requirements Towards Predictive Simulations of Turbulent Combustion

Requirements Towards Predictive Simulations of Turbulent Combustion

Assessment of deconvolution-based flamelet methods for progress variable rate modeling

From Discrete and Iterative Deconvolution Operators to Machine Learning for Premixed Turbulent Combustion Modeling

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