Toward neural-network-based large eddy simulation: application to turbulent channel flow

Park, Jong‐Hwan; Choi, Haecheon

doi:10.1017/jfm.2020.931

Cited by 77 publications

(84 citation statements)

References 68 publications

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“…The governing LES equations are usually derived by applying a generic, unspecified filtering operation G to Eq. (1), which introduces a subgrid term τ ij ≡ u i u j − u i u j that has to be modelled (Pope, 2001;Sagaut, 2006). Traditional subgrid models like that of Smagorinsky (Smagorinsky, 1963;Lilly, 1967) attempt to model τ ij associated with G. However, by only considering the generic operation G, they cannot directly compensate for the discretization errors arising on a specific finite numerical grid.…”

Section: Theoretical Framework Of Our Ann Finite-volume Les Sgs Modelmentioning

confidence: 99%

“…The friction Reynolds number is a variant of the standard Reynolds number based on the friction velocity, which is typically lower in magnitude than the standard Reynolds number and is often used in the context of wall-bounded turbulent flows (e.g. Pope, 2001). The friction velocity, in turn, is a velocity scale that measures the amount of mechanically generated turbulence and consequently is a logical scale to consider in neutral channel flow.…”

Section: Dns Test Casementioning

confidence: 99%

“…τ wu , in turn, is also of particular interest in channel flow: it is the vertical gradient of τ wu that has to balance the imposed horizontal pressure gradient (e.g. Pope, 2001), making τ wu critical for the quality of the achieved steady-state solution. The log layer is mainly interesting because of its universal character.…”

Section: A Priori (Offline) Testmentioning

confidence: 99%

“…In the log layer, the horizontally averaged profiles of the mean velocity and Reynolds stress tensor components become partly independent of the Reynolds number when properly scaled with wall units (e.g. Pope, 2001).…”

Section: A Priori (Offline) Testmentioning

confidence: 99%

“…As an SGS model based on physical principles alone does not exist, the SGS models used today typically rely on simplifying assumptions in combination with ad hoc empirical corrections (e.g. Pope, 2001;Sagaut, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Development of a large-eddy simulation subgrid model based on artificial neural networks: a case study of turbulent channel flow

et al. 2021

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Abstract. Atmospheric boundary layers and other wall-bounded flows are often simulated with the large-eddy simulation (LES) technique, which relies on subgrid-scale (SGS) models to parameterize the smallest scales. These SGS models often make strong simplifying assumptions. Also, they tend to interact with the discretization errors introduced by the popular LES approach where a staggered finite-volume grid acts as an implicit filter. We therefore developed an alternative LES SGS model based on artificial neural networks (ANNs) for the computational fluid dynamics MicroHH code (v2.0). We used a turbulent channel flow (with friction Reynolds number Reτ=590) as a test case. The developed SGS model has been designed to compensate for both the unresolved physics and instantaneous spatial discretization errors introduced by the staggered finite-volume grid. We trained the ANNs based on instantaneous flow fields from a direct numerical simulation (DNS) of the selected channel flow. In general, we found excellent agreement between the ANN-predicted SGS fluxes and the SGS fluxes derived from DNS for flow fields not used during training. In addition, we demonstrate that our ANN SGS model generalizes well towards other coarse horizontal resolutions, especially when these resolutions are located within the range of the training data. This shows that ANNs have potential to construct highly accurate SGS models that compensate for spatial discretization errors. We do highlight and discuss one important challenge still remaining before this potential can be successfully leveraged in actual LES simulations: we observed an artificial buildup of turbulence kinetic energy when we directly incorporated our ANN SGS model into a LES simulation of the selected channel flow, eventually resulting in numeric instability. We hypothesize that error accumulation and aliasing errors are both important contributors to the observed instability. We finally make several suggestions for future research that may alleviate the observed instability.

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Section: Theoretical Framework Of Our Ann Finite-volume Les Sgs Modelmentioning

confidence: 99%

Section: Dns Test Casementioning

confidence: 99%

Section: A Priori (Offline) Testmentioning

confidence: 99%

Section: A Priori (Offline) Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of a large-eddy simulation subgrid model based on artificial neural networks: a case study of turbulent channel flow

et al. 2021

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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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Super-resolution Reconstruction of Transitional Boundary Layers Using a Deep Neural Network

Jeon

You

2023

Int. J. Aeronaut. Space Sci.

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Toward neural-network-based large eddy simulation: application to turbulent channel flow

Abstract: Abstract

Cited by 77 publications

References 68 publications

Development of a large-eddy simulation subgrid model based on artificial neural networks: a case study of turbulent channel flow

Development of a large-eddy simulation subgrid model based on artificial neural networks: a case study of turbulent channel flow

Machine-Learning for Stress Tensor Modelling in Large Eddy Simulation

Super-resolution Reconstruction of Transitional Boundary Layers Using a Deep Neural Network

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