A Novel Explicit Filter for the Approximate Deconvolution in Large-Eddy Simulation on General Unstructured Grids: A posteriori tests on highly stretched grids
Mohammad Bagher Molaei, Ehsan Amani, Morteza Ghorbani

TL;DR
This paper introduces a new explicit filter for large-eddy simulation on unstructured grids, improving turbulence predictions especially on highly stretched grids by optimizing spectral properties and reducing errors.
Contribution
A novel explicit filter combining face-averaging and recursive filtering, optimized via multi-objective methods, enhances LES accuracy on unstructured, stretched grids.
Findings
Improved turbulent flow predictions on highly stretched grids.
Enhanced spectral attenuation near Nyquist wavenumber.
Successful application to 3D Taylor-Green vortex benchmark.
Abstract
Explicit filters play a pivotal role in the scale separation and numerical stability of advanced Large Eddy Simulation (LES) closures, such as dynamic eddy-viscosity or Approximate Deconvolution (AD) methods. In the present study, it is demonstrated that the performance of commonly used explicit filters applicable to general unstructured grids highly depends on the grid configuration, specifically the cell aspect ratio, which can result in poor filter spectral properties, ultimately leading to large errors and even solution divergence. This study introduces a novel, efficient explicit filter for general unstructured grids, addressing this shortcoming through a combination of a face-averaging technique and recursive filtering. The filter parameters are then determined through a constrained multi-objective optimization, ensuring desirable spectral properties, including high-wavenumber…
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Taxonomy
TopicsComputational Fluid Dynamics and Aerodynamics · Fluid Dynamics and Turbulent Flows · Aerodynamics and Acoustics in Jet Flows
