A POD-Galerkin reduced order model for a LES filtering approach

Abstract

We propose a Proper Orthogonal Decomposition (POD)-Galerkin based Reduced Order Model (ROM) for a Leray model. For the implementation of the model, we combine a two-step algorithm called Evolve-Filter (EF) with a computationally efficient finite volume method. The main novelty of the proposed approach relies in applying spatial filtering both for the collection of the snapshots and in the reduced order model, as well as in considering the pressure field at reduced level. In both steps of the EF algorithm, velocity and pressure fields are approximated by using different POD basis and coefficients. For the reconstruction of the pressures fields, we use a pressure Poisson equation approach. We test our ROM on two benchmark problems: 2D and 3D unsteady flow past a cylinder at Reynolds number 0 <= Re <= 100. The accuracy of the reduced order model is assessed against results obtained with the full order model. For the 2D case, a parametric study with respect to the filtering radius is also presented.

arXiv comment29 pages, 16 figures, 9 tables

References in corpus (11)

• The GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows
• Proper Orthogonal Decomposition Closure Models For Turbulent Flows: A Numerical Comparison
• Finite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier-Stokes equations
• Data-Driven POD-Galerkin Reduced Order Model for Turbulent Flows
• Pod-Galerkin Reduced Order Methods for CFD Using Finite Volume Discretisation: Vortex Shedding Around a Circular Cylinder
• A reduced order variational multiscale approach for turbulent flows
• Computational reduction strategies for the detection of steady bifurcations in incompressible fluid-dynamics: applications to Coanda effect in cardiology
• On the comparison of LES data-driven reduced order approaches for hydroacoustic analysis
• A split-step finite-element method for incompressible Navier-Stokes equations with high-order accuracy up-to the boundary
• Regularized Reduced Order Models for a Stochastic Burgers Equation
• Parametric solutions of turbulent incompressible flows in OpenFOAM via the proper generalised decomposition

Cited by in corpus (13)

• On closures for reduced order models $-$ A spectrum of first-principle to machine-learned avenues
• The Neural Network shifted-Proper Orthogonal Decomposition: a Machine Learning Approach for Non-linear Reduction of Hyperbolic Equations
• Hull shape design optimization with parameter space and model reductions, and self-learning mesh morphing
• Non-intrusive PODI-ROM for patient-specific aortic blood flow in presence of a LVAD device
• A non-intrusive data-driven ROM framework for hemodynamics problems
• Full and Reduced Order Model Consistency of the Nonlinearity Discretization in Incompressible Flows
• GoRINNs: Godunov-Riemann Informed Neural Networks for Learning Hyperbolic Conservation Laws
• Approximate Deconvolution Leray Reduced Order Model for Convection-Dominated Flows
• Fluid-structure interaction simulations with a LES filtering approach in solids4Foam
• A meshless method to compute the proper orthogonal decomposition and its variants from scattered data
• Model-based time super-sampling of turbulent flow field sequences
• A Hybrid Reduced Order Model for nonlinear LES filtering
• Consistency of the Full and Reduced Order Models for Evolve-Filter-Relax Regularization of Convection-Dominated, Marginally-Resolved Flows