6 papers
Approaching the optimal closure: equivariance, inductive bias, and Reynolds-number generalization in data-driven LES
Syver Døving Agdestein, Benjamin Sanderse
Data-driven closures for large-eddy simulation (LES) are commonly built to respect the symmetries of the Navier--Stokes equations, on the premise that this improves accuracy and ge…
Time integration as filtering: a space-time discretization-aware LES formulation
Syver Døving Agdestein
Discretization-aware LES yields an exact expression for the discrete target flux in finite-volume LES by recognizing that a coarse finite difference is a top-hat-filtered exact der…
A differentiable software suite for accelerated simulation of turbulent flows
Syver Døving Agdestein, Benjamin Sanderse
We present IncompressibleNavierStokes.jl, an open-source Julia package for solving the incompressible Navier--Stokes equations on staggered Cartesian grids. The package features ma…
Exact expressions for the unresolved stress in a finite-volume based large-eddy simulation
Syver Døving Agdestein, Roel Verstappen, Benjamin Sanderse
In this article we propose new discretization-informed expressions for the residual stress tensor (RST) in a finite-volume based large-eddy simulation (LES-FVM). In addition to the…
A new data-driven energy-stable Evolve-Filter-Relax model for turbulent flow simulation
Anna Ivagnes, Toby van Gastelen, Syver Døving Agdestein +3
We present a novel approach to define the filter and relax steps in the evolve-filter-relax (EFR) framework for simulating turbulent flows. The EFR main advantages are its ease of…
Discretize first, filter next: learning divergence-consistent closure models for large-eddy simulation
Syver Døving Agdestein, Benjamin Sanderse
We propose a new neural network based large eddy simulation framework for the incompressible Navier-Stokes equations based on the paradigm "discretize first, filter and close next"…