An a priori analysis of a DNS database of turbulent lean premixed methane flames for LES with finite-rate chemistry

Aspden, A. J.; Zettervall, Niklas; Fureby, Christer

doi:10.1016/j.proci.2018.05.017

Cited by 12 publications

(3 citation statements)

References 32 publications

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“…18 (blue) conditional upon bins of c for Δ∕d n = 0.03 and 0.15 in cases A, C, F of numerical errors because a posteriori assessment of models using LES is intrinsically code dependent as a result of the interaction of numerical and modelling errors (Vervisch et al 2008). It is worth noting that the same approach has been adopted in several previous studies (Ranjan et al 2016;Nilsson et al 2019;Reddy and Abraham 2012;Veynante et al 1997;Charlette et al 2002a;Selle and Bellan 2007;Lignell et al 2009;Chatakonda et al 2013;Lapointe and Blanquart 2017;Aspden et al 2019;Devaud et al 2019;Bushe et al 2020) by other authors and there are several examples where the models proposed based on a priori DNS analysis (e.g. Gao et al 2014a;Charlette et al 2002a) have been demonstrated to perform well based on a posteriori assessment (e.g.…”

Section: Resultsmentioning

confidence: 99%

Analysis of the Closures of Sub-grid Scale Variance of Reaction Progress Variable for Turbulent Bunsen Burner Flames at Different Pressure Levels

Keil

Chakraborty

Klein

2020

Flow Turbulence Combust

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The statistical behaviour and modelling of the sub-grid variance of reaction progress variable have been analysed based on a priori analysis of direct numerical simulation (DNS) data of turbulent premixed Bunsen burner flames at different pressure levels. An algebraic expression for sub-grid variance, which can be derived based on a presumed bi-modal subgrid distribution of reaction progress variable with impulses at unburned reactants and fully burned products, has been found to be inadequate for the purpose of prediction of sub-grid variance even for the flames in the wrinkled flamelets/corrugated flamelets regime. Moreover, an algebraic model, which is often used for modelling sub-grid variance of passive scalars, has been found to significantly overpredict the sub-grid variance of reaction progress variable for all the cases considered here. The modelling of the unclosed terms of the sub-grid variance transport equation has been analysed in detail. Suitable model expressions have been identified for the sub-grid flux of variance, reaction rate contribution and scalar dissipation rate based on a priori analysis of DNS data. It has been found that the alternation of pressure does not have any significant impact on the closures of sub-grid flux of variance but a model parameter for the Favre-filtered scalar dissipation rate needs to be modified to account for the variation of pressure.

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Section: Resultsmentioning

confidence: 99%

Analysis of the Closures of Sub-grid Scale Variance of Reaction Progress Variable for Turbulent Bunsen Burner Flames at Different Pressure Levels

Keil

Chakraborty

Klein

2020

Flow Turbulence Combust

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“…These findings were also confirmed using numerical simulations [6]. Since fuel oxidation involves a range of time-scales, its interaction with the multi-scale turbulent flow can alter the impact of specific reaction pathways [7]. Hence, the ability to determine this coupling between turbulence and ignition is essential for ensuring robust relight.…”

Section: Introductionmentioning

confidence: 73%

Data-driven analysis of relight variability of jet fuels induced by turbulence

Hassanaly

Tang

Barwey

et al. 2021

Combustion and Flame

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For safety purposes, reliable reignition of aircraft engines in the event of flame blow-out is a critical requirement. Typically, an external ignition source in the form of a spark is used to achieve a stable flame in the combustor. However, such forced turbulent ignition may not always successfully relight the combustor, mainly because the state of the combustor cannot be precisely determined. Uncertainty in the turbulent flow inside the combustor, inflow conditions, and spark discharge characteristics can lead to variability in sparking outcomes even for nominally identical operating conditions. Prior studies have shown that of all the uncertain parameters, turbulence is often dominant and can drastically alter ignition behavior. For instance, even when different fuels have similar ignition delay times, their ignition behavior in practical systems can be completely different. In practical operating conditions, it is challenging to understand why ignition fails and how much variation in outcomes can be expected. The focus of this work is to understand relight variability induced by turbulence for two different aircraft fuels, namely Jet-A and a variant named C1. A detailed, previously developed simulation approach is used to generate a large number of successful and failed ignition events. Using this data, the cause of misfire is evaluated based on a discriminant analysis that delineates the difference between turbulent initial conditions that lead to ignition or failure. From the discriminant analysis, a compressed sensing algorithm is then applied to help pinpoint the locations of relevant turbulent features. Findings from the discriminant analysis are confirmed with the time history of near kernel properties. Next, a clustering strategy is used to identify ignition and misfire modes. With this approach, it was determined that the cause of ignition failure is different for the two fuels. While it was found that Jet-A is influenced by fuel entrainment, C1 was found to be more sensitive to small scale turbulence features. A larger variability is found in the ignition modes of C1, which can be subject to extreme events induced by kernel breakdown.

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“…21 Combustion models such as the Flamelet/Progress Variable (FPV) 23 and dynamic Scale Similarity (SS) models 24 were a priori tested for nonpremixed turbulent flames. For turbulent premixed flames with finite-rate chemistry, Aspden et al 25 investigated the relative effects of turbulence and LES filtering and proposed a scaled reaction rates modeling approach based on filtered laminar flame profiles. For a non-conventional combustion regime, Chen et al 26 a priori assessed the capabilities of a machine learning (ML) tool, namely, the Deep Neural Network (DNN), to predict joint Filtered Density Functions (FDFs) of mixture fraction and reaction progress variable, and Iavarone et al 17 performed an a priori testing on the PaSR model performances, finding satisfactory results when specific characteristic time scales formulations are used.…”

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confidence: 99%

The partially stirred reactor model for combustion closure in large eddy simulations: Physical principles, sub-models for the cell reacting fraction, and open challenges

et al. 2022

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For their ability to account for finite-rate chemistry, reactor-based models are well suited Turbulence–Chemistry Interactions (TCI) Sub-Grid Scale (SGS) closures for Large Eddy Simulations (LES). The SGS closure in the Partially Stirred Reactor (PaSR) model relies on the determination of the reacting fraction of each computational cell, whose definition is based on estimates of the characteristic mixing and chemical time scales. Direct Numerical Simulations (DNS) of turbulent combustion can supply key information on TCI for the development, validation, and comparison of combustion models. In particular, a priori testing allows the direct validation of model assumptions. In the present work, an a priori assessment of the PaSR model is conducted. Its ability to reconstruct thermo-chemical quantities of interest is investigated along with model assumptions. Sub-grid quantities are extracted from the DNS to investigate the role of the cell reacting fraction. Various definitions are then proposed to estimate the characteristic chemical timescale in the PaSR model. Modeled chemical source terms and heat release rates are compared against the filtered quantities from DNS data of a two-dimensional, spatially developing, turbulent nonpremixed jet flame with detailed kinetics. The results demonstrate the importance of accounting for the fine structures quantities in the context of reactor-based models. A new formulation of the chemical timescale is proposed and provides improved overall predictions. Several issues are raised in the discussion, representing realistic prospects for further developments of the PaSR model as a SGS combustion closure for LES.

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An a priori analysis of a DNS database of turbulent lean premixed methane flames for LES with finite-rate chemistry

Cited by 12 publications

References 32 publications

Analysis of the Closures of Sub-grid Scale Variance of Reaction Progress Variable for Turbulent Bunsen Burner Flames at Different Pressure Levels

Analysis of the Closures of Sub-grid Scale Variance of Reaction Progress Variable for Turbulent Bunsen Burner Flames at Different Pressure Levels

Data-driven analysis of relight variability of jet fuels induced by turbulence

The partially stirred reactor model for combustion closure in large eddy simulations: Physical principles, sub-models for the cell reacting fraction, and open challenges

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