Systematic Analysis Strategies for the Development of Combustion Models from DNS: A Review

Trisjono, Philipp; Pitsch, Heinz

doi:10.1007/s10494-015-9645-x

Cited by 44 publications

(23 citation statements)

References 94 publications

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“…Such DNS data can be utilized to develop/assess combustion models in LES of reactive flows. Among the two main ways to utilize DNS data, namely a priori and a posteriori testing [38], an a priori analysis was adopted in this study by comparing modeled production/consumption and heat release rates with directly filtered production/consumption and heat release rates from DNS data. The modeled rates use DNS filtered quantities.…”

Section: A Priori Assessment Of Models Based On Dns Datamentioning

confidence: 99%

Prediction of Combustion and Heat Release Rates in Non-Premixed Syngas Jet Flames Using Finite-Rate Scale Similarity Based Combustion Models

et al. 2018

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Generating energy from combustion is prone to pollutant formation. In energy systems working under non-premixed combustion mode, rapid mixing is required to increase the heat release rates. However, local extinction and re-ignition may occur, resulting from strong turbulence–chemistry interaction, especially when rates of mixing exceed combustion rates, causing harmful emissions and flame instability. Since the physical mechanisms for such processes are not well understood, there are not yet combustion models in large eddy simulation (LES) context capable of accurately predicting them. In the present study, finite-rate scale similarity (SS) combustion models were applied to evaluate both heat release and combustion rates. The performance of three SS models was a priori assessed based on the direct numerical simulation of a temporally evolving syngas jet flame experiencing high level of local extinction and re-ignition. The results show that SS models following the Bardina’s “grid filtering” approach (A and B) have lower errors than the model based on the Germano’s “test filtering” approach (C), in terms of mean, root mean square (RMS), and local errors. In mean, both Bardina’s based models capture well the filtered combustion and heat release rates. Locally, Model A captures better major species, while Model B retrieves radicals more accurately.

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Section: A Priori Assessment Of Models Based On Dns Datamentioning

confidence: 99%

Prediction of Combustion and Heat Release Rates in Non-Premixed Syngas Jet Flames Using Finite-Rate Scale Similarity Based Combustion Models

et al. 2018

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“…This parameter is crucial since an excessive computational complexity could compromise the application of the algorithm for large datasets. In the field of combustion, in fact, it is more and more common to develop reduced models, either global or local, from massive datasets obtained from DNS simulations of reactive jets [44][45][46][47][48], accounting for millions of observations. Moreover, also in case canonical reactors (0D or 1D) are used to generate training data for model order reduction, large datasets are anyway required to properly train the machine learning models [49][50][51].…”

Section: Adaptive Simulation With Lpca Partitioningmentioning

confidence: 99%

Impact of the Partitioning Method on Multidimensional Adaptive-Chemistry Simulations

et al. 2020

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The large number of species included in the detailed kinetic mechanisms represents a serious challenge for numerical simulations of reactive flows, as it can lead to large CPU times, even for relatively simple systems. One possible solution to mitigate the computational cost of detailed numerical simulations, without sacrificing their accuracy, is to adopt a Sample-Partitioning Adaptive Reduced Chemistry (SPARC) approach. The first step of the aforementioned approach is the thermochemical space partitioning for the generation of locally reduced mechanisms, but this task is often challenging because of the high-dimensionality, as well as the high non-linearity associated to reacting systems. Moreover, the importance of this step in the overall approach is not negligible, as it has effects on the mechanisms’ level of chemical reduction and, consequently, on the accuracy and the computational speed-up of the adaptive simulation. In this work, two different clustering algorithms for the partitioning of the thermochemical space were evaluated by means of an adaptive CFD simulation of a 2D unsteady laminar flame of a nitrogen-diluted methane stream in air. The first one is a hybrid approach based on the coupling between the Self-Organizing Maps with K-Means (SKM), and the second one is the Local Principal Component Analysis (LPCA). Comparable results in terms of mechanism reduction (i.e., the mean number of species in the reduced mechanisms) and simulation accuracy were obtained for both the tested methods, but LPCA showed superior performances in terms of reduced mechanisms uniformity and speed-up of the adaptive simulation. Moreover, the local algorithm showed a lower sensitivity to the training dataset size in terms of the required CPU-time for convergence, thus also being optimal, with respect to SKM, for massive dataset clustering tasks.

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“…DNS databases of reactive flows with relatively detailed chemistry, which are now available thanks to massively large parallel computational resources, can be utilized to assess combustion models for LES of reactive flows. The two main ways of DNS data utilization are through a priori and a posteriori tests [24]. In this work, an a priori analysis is adopted by comparing "modeled" targets (i.e., filtered combustion and heat release rates) with the "exact" filtered ones from DNS databases.…”

Section: A Priori Analysis and The Dns Databasesmentioning

confidence: 99%

New Dynamic Scale Similarity Based Finite-Rate Combustion Models for LES and a priori DNS Assessment in Non-premixed Jet Flames with High Level of Local Extinction

Shamooni

Cuoci

Faravelli

et al. 2019

Flow Turbulence Combust

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In this work, the performances of two recently developed finite-rate dynamic scale similarity (SS) sub-grid scale (SGS) combustion models (named DB and DC) for non-premixed turbulent combustion are a priori assessed based on three Direct Numerical Simulation (DNS) databases. These numerical experiments feature temporally evolving syngas jet flames with different Reynolds (Re) numbers (2510, 4487 and 9079), experiencing a high level of local extinction. For comparison purposes, the predicting capability of these models is compared with three classical non-dynamic SS models, namely the scale similarity resolved reaction rate model (SSRRRM or A), the scale similarity filtered reaction rate model (SSFRRM or B), and a SS model derived by the "test filtering" approach (C), as well as an existing dynamic version of SSRRRM (DA). Improvements in the prediction of heat release rates using a new dynamic model DC are observed in high Re flame case. By decreasing Re, dynamic procedures produce results roughly similar to their non-dynamic counterparts. In the lowest Re, the dynamic methods lead to higher errors. Keywords Dynamic scale similarity combustion model • Finite-rate SGS combustion model • A priori DNS analysis • LES • Non-premixed jet flame • Extinction re-ignition Electronic supplementary material The online version of this article (

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Systematic Analysis Strategies for the Development of Combustion Models from DNS: A Review

Cited by 44 publications

References 94 publications

Prediction of Combustion and Heat Release Rates in Non-Premixed Syngas Jet Flames Using Finite-Rate Scale Similarity Based Combustion Models

Prediction of Combustion and Heat Release Rates in Non-Premixed Syngas Jet Flames Using Finite-Rate Scale Similarity Based Combustion Models

Impact of the Partitioning Method on Multidimensional Adaptive-Chemistry Simulations

New Dynamic Scale Similarity Based Finite-Rate Combustion Models for LES and a priori DNS Assessment in Non-premixed Jet Flames with High Level of Local Extinction

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