Supremizer stabilization of POD-Galerkin approximation of parametrized Navier-Stokes equations

In this work we present a stable proper orthogonal decomposition (POD)-Galerkin approximation for parametrized steady Navier-Stokes equations. The stabilization is guaranteed by the use of supremizers solutions that enrich the reduced velocity space. Numerical results show that an equivalent inf-sup condition is fulfilled, yielding stability for both velocity and pressure. Our stability analysis is first carried out from a theoretical standpoint, then confirmed by numerical tests performed on a parametrized two-dimensional backward facing step flow.

[1]  A. Quarteroni Numerical Models for Differential Problems , 2009 .

[2]  G. Karniadakis,et al.  Stability and accuracy of periodic flow solutions obtained by a POD-penalty method , 2005 .

[3]  Benjamin S. Kirk,et al.  Library for Parallel Adaptive Mesh Refinement / Coarsening Simulations , 2006 .

[4]  Stefan Volkwein,et al.  Galerkin Proper Orthogonal Decomposition Methods for a General Equation in Fluid Dynamics , 2002, SIAM J. Numer. Anal..

[5]  Ali H. Nayfeh,et al.  On the stability and extension of reduced-order Galerkin models in incompressible flows , 2009 .

[6]  Klaus-Jürgen Bathe,et al.  The inf–sup condition and its evaluation for mixed finite element methods , 2001 .

[7]  Jens Nørkær Sørensen,et al.  Evaluation of Proper Orthogonal Decomposition-Based Decomposition Techniques Applied to Parameter-Dependent Nonturbulent Flows , 1999, SIAM J. Sci. Comput..

[8]  Dominique Chapelle,et al.  A Galerkin strategy with Proper Orthogonal Decomposition for parameter-dependent problems – Analysis, assessments and applications to parameter estimation , 2013 .

[9]  Jean-Antoine Désidéri,et al.  Stability Properties of POD–Galerkin Approximations for the Compressible Navier–Stokes Equations , 2000 .

[10]  F. Brezzi,et al.  A discourse on the stability conditions for mixed finite element formulations , 1990 .

[11]  Harbir Antil,et al.  Application of the Discrete Empirical Interpolation Method to Reduced Order Modeling of Nonlinear and Parametric Systems , 2014 .

[12]  A. Quarteroni,et al.  Numerical solution of parametrized Navier–Stokes equations by reduced basis methods , 2007 .

[13]  C. Farhat,et al.  On the Stability of Reduced-Order Linearized Computational Fluid Dynamics Models Based on POD and Galerkin Projection: Descriptor vs Non-Descriptor Forms , 2014 .

[14]  Arthur Veldman,et al.  Proper orthogonal decomposition and low-dimensional models for driven cavity flows , 1998 .

[15]  Lucas Chesnel,et al.  T-coercivity and continuous Galerkin methods: application to transmission problems with sign changing coefficients , 2013, Numerische Mathematik.

[16]  Nadine Aubry,et al.  The dynamics of coherent structures in the wall region of a turbulent boundary layer , 1988, Journal of Fluid Mechanics.

[17]  K. Bathe,et al.  The inf-sup test , 1993 .

[18]  Charbel Farhat,et al.  The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows , 2012, J. Comput. Phys..

[19]  Gianluigi Rozza,et al.  A reduced basis hybrid method for the coupling of parametrized domains represented by fluidic networks , 2012 .

[20]  Ramon Codina,et al.  Explicit reduced‐order models for the stabilized finite element approximation of the incompressible Navier–Stokes equations , 2013 .

[21]  G. Rozza,et al.  On the stability of the reduced basis method for Stokes equations in parametrized domains , 2007 .

[22]  A. Quarteroni,et al.  Shape optimization for viscous flows by reduced basis methods and free‐form deformation , 2012 .

[23]  D. Malkus Eigenproblems associated with the discrete LBB condition for incompressible finite elements , 1981 .

[24]  Janet S. Peterson,et al.  The Reduced Basis Method for Incompressible Viscous Flow Calculations , 1989 .

[25]  Patrick Ciarlet,et al.  T-coercivity: Application to the discretization of Helmholtz-like problems , 2012, Comput. Math. Appl..

[26]  Gianluigi Rozza,et al.  Reduced Order Methods for Modeling and Computational Reduction , 2013 .

[27]  A. Quarteroni,et al.  Model reduction techniques for fast blood flow simulation in parametrized geometries , 2012, International journal for numerical methods in biomedical engineering.

[28]  N. Nguyen,et al.  An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations , 2004 .

[29]  A. Quarteroni,et al.  A reduced computational and geometrical framework for inverse problems in hemodynamics , 2013, International journal for numerical methods in biomedical engineering.

[30]  Karen Veroy,et al.  Certified Reduced Basis Methods for Parametrized Saddle Point Problems , 2012, SIAM J. Sci. Comput..

[31]  Juan Du,et al.  Non-linear model reduction for the Navier-Stokes equations using residual DEIM method , 2014, J. Comput. Phys..

[32]  Max Gunzburger,et al.  POD and CVT-based reduced-order modeling of Navier-Stokes flows , 2006 .

[33]  J. Weller,et al.  Numerical methods for low‐order modeling of fluid flows based on POD , 2009 .

[34]  Charles-Henri Bruneau,et al.  Enablers for robust POD models , 2009, J. Comput. Phys..

[35]  A. Patera,et al.  Certified real‐time solution of the parametrized steady incompressible Navier–Stokes equations: rigorous reduced‐basis a posteriori error bounds , 2005 .

[36]  A. Hay,et al.  Local improvements to reduced-order models using sensitivity analysis of the proper orthogonal decomposition , 2009, Journal of Fluid Mechanics.

[37]  Barry Lee,et al.  Finite elements and fast iterative solvers: with applications in incompressible fluid dynamics , 2006, Math. Comput..

[38]  Traian Iliescu,et al.  Proper orthogonal decomposition closure models for turbulent flows: A numerical comparison , 2011, 1106.3585.

[39]  M. Gunzburger,et al.  Reduced-order modeling of time-dependent PDEs with multiple parameters in the boundary data , 2007 .

[40]  John W. Peterson,et al.  A high-performance parallel implementation of the certified reduced basis method , 2011 .

[41]  S. Ravindran A reduced-order approach for optimal control of fluids using proper orthogonal decomposition , 2000 .

[42]  Gilbert Strang,et al.  The application of quasi‐Newton methods in fluid mechanics , 1981 .

[43]  Gianluigi Rozza,et al.  Reduced basis approximation and a posteriori error estimation for Stokes flows in parametrized geometries: roles of the inf-sup stability constants , 2013, Numerische Mathematik.

[44]  Gianluigi Rozza,et al.  Model Order Reduction in Fluid Dynamics: Challenges and Perspectives , 2014 .

[45]  Andrea Manzoni,et al.  An efficient computational framework for reduced basis approximation and a posteriori error estimation of parametrized Navier–Stokes flows , 2014 .

[46]  Gianluigi Rozza,et al.  Reduced basis method for multi-parameter-dependent steady Navier-Stokes equations: Applications to natural convection in a cavity , 2009, J. Comput. Phys..

[47]  Traian Iliescu,et al.  A numerical investigation of velocity-pressure reduced order models for incompressible flows , 2014, J. Comput. Phys..

[48]  P. Holmes,et al.  The Proper Orthogonal Decomposition in the Analysis of Turbulent Flows , 1993 .

[49]  C. Farhat,et al.  Efficient non‐linear model reduction via a least‐squares Petrov–Galerkin projection and compressive tensor approximations , 2011 .