scholarly journals Finite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier–Stokes equations

2018 ◽  
Vol 173 ◽  
pp. 273-284 ◽  
Author(s):  
Giovanni Stabile ◽  
Gianluigi Rozza
Keyword(s):  
Finite Volume ◽  
Stokes Equations ◽  
Navier Stokes ◽  
Navier Stokes Equations ◽  
Reduced Order ◽  
Reduced Order Methods

scholarly journals The effort of increasing Reynolds number in POD-Galerkin Reduced Order Methods: from laminar to turbulent flows

2018 ◽  
Author(s):  
Shafqat Ali ◽  
Saddam Hijazi ◽  
Sokratia Georgaka ◽  
Francesco Ballarin ◽  
Giovanni Stabile ◽  
...  
Keyword(s):  
Reynolds Number ◽  
Finite Element ◽  
Turbulent Flows ◽  
Stokes Equations ◽  
Reynolds Numbers ◽  
Navier Stokes ◽  
Navier Stokes Equations ◽  
Reduced Order ◽  
Volume Method ◽  
Reduced Order Methods

We present different strategies to be able to increase Reynolds number in Reduced Order Methods (ROMs), from laminar to turbulent flows, in the context of the incompressible parametrised Navier-Stokes equations. The proposed methodologies are based on different full order discretisation techniques: the finite element method and the finite volume method. For what concerns finite element full order discretisations which in this work aim to be used from low to moderate Reynolds numbers the

scholarly journals Local improvements to reduced-order models using sensitivity analysis of the proper orthogonal decomposition

2009 ◽  
Vol 629 ◽  
pp. 41-72 ◽  
Author(s):  
ALEXANDER HAY ◽  
JEFFREY T. BORGGAARD ◽  
DOMINIQUE PELLETIER
Keyword(s):  
Sensitivity Analysis ◽  
Proper Orthogonal Decomposition ◽  
Stokes Equations ◽  
Orthogonal Decomposition ◽  
Navier Stokes ◽  
Navier Stokes Equations ◽  
Reduced Order ◽  
Selection Step ◽  
Low Dimensional ◽  
Proper Orthogonal

The proper orthogonal decomposition (POD) is the prevailing method for basis generation in the model reduction of fluids. A serious limitation of this method, however, is that it is empirical. In other words, this basis accurately represents the flow data used to generate it, but may not be accurate when applied ‘off-design’. Thus, the reduced-order model may lose accuracy for flow parameters (e.g. Reynolds number, initial or boundary conditions and forcing parameters) different from those used to generate the POD basis and generally does. This paper investigates the use of sensitivity analysis in the basis selection step to partially address this limitation. We examine two strategies that use the sensitivity of the POD modes with respect to the problem parameters. Numerical experiments performed on the flow past a square cylinder over a range of Reynolds numbers demonstrate the effectiveness of these strategies. The newly derived bases allow for a more accurate representation of the flows when exploring the parameter space. Expanding the POD basis built at one state with its sensitivity leads to low-dimensional dynamical systems having attractors that approximate fairly well the attractor of the full-order Navier–Stokes equations for large parameter changes.

Sign in / Sign up

Export Citation Format

Share Document