A time-average filtering technique to improve the efficiency of two-layer wall models for large eddy simulation in complex geometries

Calafell, J.; Trias, F. X.; Lehmkuhl, O.; Oliva, A.

doi:10.1016/j.compfluid.2019.03.026

Cited by 5 publications

(2 citation statements)

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“…Ny − 1) tanh(γ) (2) where N y is the number of elements in the wall normal direction, with i ranging from 1 to N y . γ is the factor which controls the stretching.…”

Section: Data-setsmentioning

confidence: 99%

“…This becomes prohibitively expensive at high Reynolds Number (Re). When it comes to wall-bounded flows, LES may become computationally unaffordable even at moderate Reynolds numbers, given the complex flow structure at the boundary layer.The estimated cost of calculation is of the order of Re 13 7 for Wall-Resolved LES (WRLES) [1] and it can be even more expensive when the temporal integration cost is considered [2]. Interestingly, over 50% of the computational resources are used to resolve only 10% of the flow [3] even at moderate Re.…”

Section: Introductionmentioning

confidence: 99%

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A Data-Driven Wall-Shear Stress Model for LES Using Gradient Boosted Decision Trees

Radhakrishnan

Gyamfi

Miró

et al. 2021

Lecture Notes in Computer Science

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With the recent advances in Machine Learning, strategies based on data could be used to augment wall modeling in Large Eddy Simulation(LES). In this work, a wall model based on gradient boosted decision trees is presented. The model is trained to learn the boundary layer of a turbulent channel flow so that it can be used to make predictions for significantly different flows where the equilibrium assumptions are valid.The methodology of building the model is presented in detail. The experiments conducted to choose the data for training the model, as well as to choose the model input features are described. The trained model is tested a posteriori on a Turbulent channel flow and the flow over a wall mounted hump. The results from the tests are compared with that of an algebraic equilibrium wall model and the performance is evaluated. The results show that the model has succeeded in learning the boundary layer and performs as good as an algebraic wall stress model.

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“…Ny − 1) tanh(γ) (2) where N y is the number of elements in the wall normal direction, with i ranging from 1 to N y . γ is the factor which controls the stretching.…”

Section: Data-setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Data-Driven Wall-Shear Stress Model for LES Using Gradient Boosted Decision Trees

Radhakrishnan

Gyamfi

Miró

et al. 2021

Lecture Notes in Computer Science

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show abstract

A Review of Domain Decomposition Methods for Simulation of Fluid Flows: Concepts, Algorithms, and Applications

Tang

Haynes

Houzeaux

2020

Arch Computat Methods Eng

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Artificial neural network-based wall-modeled large-eddy simulations of turbulent channel and separated boundary layer flows

Lee

2023

Aerospace Science and Technology

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A time-average filtering technique to improve the efficiency of two-layer wall models for large eddy simulation in complex geometries

Abstract: Portal del coneixement obert de la UPC http://upcommons.upc.edu/e-prints Aquesta és una còpia de la versió author's final draft d'un article publicat a la revista Computers and Fluids.

Cited by 5 publications

References 42 publications

A Data-Driven Wall-Shear Stress Model for LES Using Gradient Boosted Decision Trees

A Data-Driven Wall-Shear Stress Model for LES Using Gradient Boosted Decision Trees

A Review of Domain Decomposition Methods for Simulation of Fluid Flows: Concepts, Algorithms, and Applications

Artificial neural network-based wall-modeled large-eddy simulations of turbulent channel and separated boundary layer flows

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