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

Radhakrishnan, Sarath; Gyamfi, Lawrence Adu; Miró, Arnau; Garcia, Bernat Font; Calafell, J.; Lehmkuhl, O.

doi:10.1007/978-3-030-90539-2_7

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…Finally, machine-learning wall models have recently emerged following the development of machine-learning technologies in image classification, speech recognition, natural language processing as well as turbulence simulation and modeling (LeCun et al, 2015;Duraisamy et al, 2019;Brunton et al, 2020). Data-driven wall-stress models were developed and assessed for various incompressible flow configurations, including fully developed wall turbulence and separated turbulent flows (Huang et al, 2019;Yang et al, 2019;Bae, 2020, 2022;Bhaskaran et al, 2021;Radhakrishnan et al, 2021;Zangeneh, 2021;Zhou et al, 2021;Bae and Koumoutsakos, 2022;Dupuy et al, 2023a). For complex configurations, Dupuy et al (2023b) introduced a machine-learning wall model that can directly operate on the unstructured grid of a LES, based on a graph neural network (GNN) architecture (Battaglia et al, 2018;Pfaff et al, 2020;Zhou et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Using graph neural networks for wall modeling in compressible anisothermal flows

Dupuy,

Odier,

Lapeyre

2024

DCE

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Compressible anisothermal flows, which are commonly found in industrial settings such as combustion chambers and heat exchangers, are characterized by significant variations in density, viscosity, and heat conductivity with temperature. These variations lead to a strong interaction between the temperature and velocity fields that impacts the near-wall profiles of both quantities. Wall-modeled large-eddy simulations (LESs) rely on a wall model to provide a boundary condition, for example, the shear stress and the heat flux that accurately represents this interaction despite the use of coarse cells near the wall, and thereby achieve a good balance between computational cost and accuracy. In this article, the use of graph neural networks for wall modeling in LES is assessed for compressible anisothermal flow. Graph neural networks are a type of machine learning model that can learn from data and operate directly on complex unstructured meshes. Previous work has shown the effectiveness of graph neural network wall modeling for isothermal incompressible flows. This article develops the graph neural network architecture and training to extend their applicability to compressible anisothermal flows. The model is trained and tested a priori using a database of both incompressible isothermal and compressible anisothermal flows. The model is finally tested a posteriori for the wall-modeled LES of a channel flow and a turbine blade, both of which were not seen during training.

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Section: Introductionmentioning

confidence: 99%

Using graph neural networks for wall modeling in compressible anisothermal flows

Dupuy,

Odier,

Lapeyre

2024

DCE

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“…Bae and Koumoutsakos (2022) assessed a multi-agent reinforcement learning approach to wall modeling in a turbulent channel flow. Radhakrishnan et al (2021) developed a wall model based on gradient boosted decision trees. Dupuy et al (2022) investigated the data-driven wall modeling of turbulent separated flows.…”

Section: Introductionmentioning

confidence: 99%

Modeling the wall shear stress in large-eddy simulation using graph neural networks

Dupuy

Odier

Lapeyre

et al. 2023

DCE

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As the Reynolds number increases, the large-eddy simulation (LES) of complex flows becomes increasingly intractable because near-wall turbulent structures become increasingly small. Wall modeling reduces the computational requirements of LES by enabling the use of coarser cells at the walls. This paper presents a machine-learning methodology to develop data-driven wall-shear-stress models that can directly operate, a posteriori, on the unstructured grid of the simulation. The model architecture is based on graph neural networks. The model is trained on a database which includes fully developed boundary layers, adverse pressure gradients, separated boundary layers, and laminar–turbulent transition. The relevance of the trained model is verified a posteriori for the simulation of a channel flow, a backward-facing step and a linear blade cascade.

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“…The everincreasing availability of high-fidelity simulation data [46][47][48] have motivated the use of machine-learning wall models (MLWMs). The past few years have seen the development of a number of ML WMs 5,20,[49][50][51][52][53][54][55][56][57] . Yang et al 5 was the first to apply ML in WM using supervised MLWM trained at Re τ = 1000 to predict the wall-shear stress.…”

Section: Introductionmentioning

confidence: 99%

Log-law recovery through reinforcement-learning wall model for large-eddy simulation

Vadrot¹,

Yang²,

Bae³

et al. 2023

Preprint

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This paper focuses on the use of reinforcement learning (RL) as a machine-learning (ML) modeling tool for near-wall turbulence. RL has demonstrated its effectiveness in solving high-dimensional problems, especially in domains such as games. Despite its potential, RL is still not widely used for turbulence modeling and is primarily used for flow control and optimization purposes. A new RL wall model (WM) called VYBA23 is developed in this work, which uses agents dispersed in the flow near the wall. The model is trained on a single Reynolds number (Re τ = 10 4 ) and does not rely on high-fidelity data, as the back-propagation process is based on a reward rather than output error. The states of the RLWM, which are the representation of the environment by the agents, are normalized to remove dependence on the Reynolds number. The model is tested and compared to another RLWM (BK22) and to an equilibrium wall model, in a half-channel flow at eleven different Reynolds numbers (Re τ ∈ [180; 10 10 ]). The effects of varying agents' parameters such as actions range, time-step, and spacing are also studied. The results are promising, showing little effect on the average flow field but some effect on wall-shear stress fluctuations and velocity fluctuations. This work offers positive prospects for developing RLWMs that can recover physical laws, and for extending this type of ML models to more complex flows in the future.

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

Cited by 5 publications

References 42 publications

Using graph neural networks for wall modeling in compressible anisothermal flows

Using graph neural networks for wall modeling in compressible anisothermal flows

Modeling the wall shear stress in large-eddy simulation using graph neural networks

Log-law recovery through reinforcement-learning wall model for large-eddy simulation

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