Data-driven wall modeling for turbulent separated flows

Dupuy, Dorian; Odier, Nicolas; Lapeyre, Corentin

doi:10.1016/j.jcp.2023.112173

Cited by 7 publications

(2 citation statements)

References 94 publications

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“…Only the 'universal' and better-understood small scales are subject to closure modeling. There is significant literature on ML-LES approaches that aim to develop subgrid stress models from high-fidelity data [78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94]. This body of work can be broadly classified into parametric and non-parametric approaches.…”

Section: Machine-learning and Lesmentioning

confidence: 99%

Turbulence closure modeling with machine learning: a foundational physics perspective

Girimaji

2024

New J. Phys.

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Turbulence closure modeling using machine learning is at an early crossroads. The extraordinary success of machine learning (ML) in a variety of challenging fields had given rise to an expectation of similar transformative advances in the area of turbulence closure modeling. However, by most accounts, the current rate of progress toward accurate and predictive ML-RANS (Reynolds Averaged Navier-Stokes) closure models has been very slow. Upon retrospection, the absence of rapid transformative progress can be attributed to two factors: theunderestimation of the intricacies of turbulence modeling and the overestimation of ML’s ability to capture all features without employing targeted strategies. To pave the way for moremeaningful ML closures tailored to address the nuances of turbulence, this article seeks toreview the foundational flow physics to assess the challenges in the context of data-drivenapproaches. Revisiting analogies with statistical mechanics and stochastic systems, the keyphysical complexities and mathematical limitations are explicated. It is noted that the currentML approaches do not systematically address the inherent limitations of a statistical approachor the inadequacies of the mathematical forms of closure expressions. The study underscoresthe drawbacks of supervised learning-based closures and stresses the importance of a morediscerning ML modeling framework. As ML methods evolve (which is happening at a rapidpace) and our understanding of the turbulence phenomenon improves, the inferences expressedhere should be suitably modified.

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Section: Machine-learning and Lesmentioning

confidence: 99%

Turbulence closure modeling with machine learning: a foundational physics perspective

Girimaji

2024

New J. Phys.

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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; Lozano-Durán and 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

Self Cite

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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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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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Data-driven wall modeling for turbulent separated flows

Cited by 7 publications

References 94 publications

Turbulence closure modeling with machine learning: a foundational physics perspective

Turbulence closure modeling with machine learning: a foundational physics perspective

Using graph neural networks for wall modeling in compressible anisothermal flows

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

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