A mesh adaptation strategy for complex wall-modeled turbomachinery LES

Odier, Nicolas; Thacker, Adrien; Harnieh, Mael; Staffelbach, Gabriel; Gicquel, Laurent; Duchaine, Florent; Rosa, Nicolás García; Müller, Jens-Dominik

doi:10.1016/j.compfluid.2020.104766

Cited by 6 publications

(2 citation statements)

References 67 publications

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“…This range of implies that the first point off the wall of the WMLES mesh is typically within the logarithmic layer or the upper part of the buffer layer, in the case of a fully developed turbulent boundary layer. It is suitable for instance in turbomachinery-flow simulations (Leonard et al, 2016; Dombard et al, 2020; Odier et al, 2021). Indeed, while real turbomachines are typically not instrumented for boundary-layer measurements, experimental measurements, and high-fidelity LESs show that the friction Reynolds number based on boundary-layer thickness is in the range of 600–1000 in academic linear blade cascades with realistic operating conditions in terms of Reynolds and Mach numbers (Arts et al, 1990; Ma et al, 2011; Gao et al, 2015; Zambonini et al, 2017; Dupuy et al, 2020).…”

Section: Databasementioning

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: Databasementioning

confidence: 99%

Using graph neural networks for wall modeling in compressible anisothermal flows

Dupuy,

Odier,

Lapeyre

2024

DCE

Self Cite

View full text Add to dashboard Cite

show abstract

“…This range determines the range of mesh resolution that the machine-learning model can operate on, and thus indirectly the maximal Reynolds number that can be addressed a posteriori at moderate computational cost. The presently used range of ∆ + y =25 -75 is suitable for instance in turbomachinery-flow simulations [24,54,70]. The two tangential axes of the WMLES grid are randomly generated, and thus not necessarily aligned with those of the corresponding highly-resolved simulation.…”

Section: Data Preparationmentioning

confidence: 99%