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

Domingo, Pascale; Nikolaou, Zacharias M.; Seltz, Andréa

doi:10.1007/978-3-030-44718-2_11

Cited by 2 publications

(1 citation statement)

References 42 publications

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“…Since then, many non-reacting LES studies have also employed reconstruction-modelling approaches (Schlatter, Stolz & Kleiser 2004;Loginov, Adams & Zheltovodov 2006;Boguslawski et al 2021). In reacting flows, reconstruction-based models have been successfully applied probably first by Mathew (2002), and in a number of later studies (Domingo & Vervisch 2015;Wang & Ihme 2019;Domingo et al 2020;Datta, Mathew & Hemchandra 2022). In the majority of these studies, approximate reconstruction methods were employed while in more recent a priori studies iterative and constrained reconstruction algorithms were developed.…”

Section: Introductionmentioning

confidence: 99%

Revisiting the modelling framework for the unresolved scalar variance

Nikolaou,

Domingo,

Vervisch

2024

J. Fluid Mech.

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The unresolved scalar variance in large-eddy simulations of turbulent flows is a fundamental physical and modelling parameter. Despite its importance, relatively few algebraic models have been developed for this important variable with the most prominent models to date being the classic scale-similarity and gradient models. In this work a new generalized modelling framework based on reconstruction has been developed, which in contrast to classic modelling approaches allows the construction of base static variance models of arbitrary accuracy. It is demonstrated that higher-order reconstructions naturally lead to base static variance models of increased accuracy, and that the classic scale-similarity and gradient models are subsets of more general and higher-order models. The classic scale-similarity assumption for developing dynamic models is also revisited, and it is demonstrated that this can essentially be reinterpreted as a two-level reconstruction approach. Based on this result, a new general methodology is proposed that allows the construction of dynamic models for any given base static model, and a corresponding general reconstruction operator, algebraic or iterative. Consequently, improved static and dynamic models for the scalar variance are developed. The newly developed models are then thoroughly tested a priori using two high-fidelity direct numerical simulation databases corresponding to two substantially different flame and flow configurations, and are shown to outperform classic algebraic models for the variance.

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Section: Introductionmentioning

confidence: 99%

Revisiting the modelling framework for the unresolved scalar variance

Nikolaou,

Domingo,

Vervisch

2024

J. Fluid Mech.

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Machine-Learning for Stress Tensor Modelling in Large Eddy Simulation

Nikolaou

Minamoto

Chrysostomou

2023

Lecture Notes in Energy

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The accurate modelling of the unresolved stress tensor is particularly important for Large Eddy Simulations (LES) of turbulent flows. This term affects the transfer of energy from the largest to the smallest scales and vice versa, thus controlling the evolution of the flow field-in reacting flows, the flow field transports scalar fields such as mass fractions and temperature both of which control the species production and destruction rates. A large number of models have been developed in past years for the stress tensor in incompressible and non-reacting flows. A common characteristic of the majority of the classical models is that simplifying assumptions are typically involved in their derivation which limits their predictive ability. At the same time, various tunable parameters appear in the relevant closures whose value depends on the flow geometry/configuration/spatial location, and which require careful regularisation. Data-driven methods for the stress tensor is an emerging alternative modelling approach which may help to circumvent the above issues, and in recent studies several such models were developed and evaluated. This chapter discusses the modelling problem, presents some of the most popular algebraic models, and reviews some recent advances on data-driven methods.

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

Cited by 2 publications

References 42 publications

Revisiting the modelling framework for the unresolved scalar variance

Revisiting the modelling framework for the unresolved scalar variance

Machine-Learning for Stress Tensor Modelling in Large Eddy Simulation

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