Optimal large eddy simulation of turbulent channel flow based on direct numerical simulation statistical data

2002 ◽  
Vol 14 (10) ◽  
pp. 3675-3691 ◽  
Author(s):  
S. Völker ◽  
R. D. Moser ◽  
P. Venugopal
Keyword(s):  
Numerical Simulation ◽  
Large Eddy Simulation ◽  
Direct Numerical Simulation ◽  
Channel Flow ◽  
Statistical Data ◽  
Turbulent Channel Flow ◽  
Eddy Simulation ◽  
Large Eddy

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

2020 ◽  
Author(s):  
Robin Stoffer ◽  
Caspar M. van Leeuwen ◽  
Damian Podareanu ◽  
Valeriu Codreanu ◽  
Menno A. Veerman ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Large Eddy Simulation ◽  
Direct Numerical Simulation ◽  
Channel Flow ◽  
Turbulent Channel Flow ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Simplifying Assumptions

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 code MicroHH (v2.0), which can be run in direct numerical simulation (DNS) and LES mode. We used a turbulent channel flow (with a friction Reynolds number Reτ = 590) as a test case. The developed SGS model has been designed to require fewer simplifying assumptions, and to compensate for the instantaneous 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 (with the correlation coefficient ρ mostly varying between 0.6 and 1.0), showing the potential ANNs may have to construct highly accurate SGS models. However, we observed an artificial build-up of turbulence kinetic energy at high wave modes when we directly incorporated our ANN SGS model into a LES simulation of the selected channel flow, eventually resulting in numeric instability. We hypothesized that error accumulation and aliasing errrors, were both important contributors to the observed instability. Several obstacles therefore remain before the a priori promise of our ANN LES SGS model, can be successfully leveraged in practical applications.

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