A physics-inspired alternative to spatial filtering for large-eddy simulations of turbulent flows

Abstract

Large-eddy simulations (LES) are widely-used for computing high Reynolds number turbulent flows. Spatial filtering theory for LES is not without its shortcomings, including how to define filtering for wall-bounded flows, commutation errors for non-uniform filters, and extensibility to flows with additional complexity, such as multiphase flows. In this paper, the theory for LES is reimagined using a coarsening procedure that imitates nature. This physics-inspired approach is equivalent to Gaussian filtering for single-phase wall-free flows but opens up new insights for modeling even in that simple case. Boundaries and nonuniform resolution can be treated seemlessly in this framework without commutation errors, and the divergence-free condition is retained for incompressible flows. An alternative to the Germano identity is introduced and used to define a dynamic procedure without the need for a test filter. Potential extensions to more complex physics are briefly discussed.

References in corpus (17)

• Deep Neural Networks for Data-Driven Turbulence Models
• Unsupervised deep learning for super-resolution reconstruction of turbulence
• Perspectives on Machine Learning-augmented Reynolds-averaged and Large Eddy Simulation Models of Turbulence
• Searching for turbulence models by artificial neural network
• A neural network approach for the blind deconvolution of turbulent flows
• Accurate calculation of Stokes drag for point-particle tracking in two-way coupled flows
• Energy transfer from large to small scales in turbulence by multi-scale nonlinear strain and vorticity interactions
• Deconvolutional artificial neural network models for large eddy simulation of turbulence
• On the role of vorticity stretching and strain self-amplification in the turbulence energy cascade
• Onsager's conjecture and anomalous dissipation on domains with boundary
• Optimal estimation for Large-Eddy Simulation of turbulence and application to the analysis of subgrid models
• Interpreting neural network models of residual scalar flux
• Invariant Data-Driven Subgrid Stress Modeling in the Strain-Rate Eigenframe for Large Eddy Simulation
• Resolution-induced anisotropy in LES
• Effects of resolution inhomogeneity in large-eddy simulation
• Deep learning for subgrid-scale turbulence modeling in large-eddy simulations of the atmospheric boundary layer
• Interface Retaining Coarsening of Multiphase Flows

Cited by in corpus (6)

• Long-term predictions of turbulence by implicit U-Net enhanced Fourier neural operator
• Adjoint-based variational optimal mixed models for large-eddy simulation of turbulence
• Normality-based analysis of multiscale velocity gradients and energy transfer in direct and large-eddy simulations of isotropic turbulence
• Artificial Bottleneck Effect in Large Eddy Simulations
• Uncertainty quantification and stability of neural operators for prediction of three-dimensional turbulence
• Interface Retaining Coarsening of Multiphase Flows