Reduced basis model order reduction for Navier–Stokes equations in domains with walls of varying curvature

ABSTRACT We consider the Navier–Stokes equations in a channel with a narrowing and walls of varying curvature. By applying the empirical interpolation method to generate an affine parameter dependency, the offline-online procedure can be used to compute reduced order solutions for parameter variations. The reduced-order space is computed from the steady-state snapshot solutions by a standard POD procedure. The model is discretised with high-order spectral element ansatz functions, resulting in 4752 degrees of freedom. The proposed reduced-order model produces accurate approximations of steady-state solutions for a wide range of geometries and kinematic viscosity values. The application that motivated the present study is the onset of asymmetries (i.e. symmetry breaking bifurcation) in blood flow through a regurgitant mitral valve, depending on the Reynolds number and the valve shape. Through our computational study, we found that the critical Reynolds number for the symmetry breaking increases as the wall curvature increases.

[1]  Danny C. Sorensen,et al.  Nonlinear Model Reduction via Discrete Empirical Interpolation , 2010, SIAM J. Sci. Comput..

[2]  Gianluigi Rozza,et al.  On the Application of Reduced Basis Methods to Bifurcation Problems in Incompressible Fluid Dynamics , 2017, J. Sci. Comput..

[3]  Pascal Verdonck,et al.  In Vitro Flow Modelling for Mitral Valve Leakage Quantification , 2009 .

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

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

[6]  J. Hesthaven,et al.  Certified Reduced Basis Methods for Parametrized Partial Differential Equations , 2015 .

[7]  Annalisa Quaini,et al.  A Spectral Element Reduced Basis Method for Navier–Stokes Equations with Geometric Variations , 2018, Lecture Notes in Computational Science and Engineering.

[8]  A. Patera A spectral element method for fluid dynamics: Laminar flow in a channel expansion , 1984 .

[9]  B. R. Noack Turbulence, Coherent Structures, Dynamical Systems and Symmetry , 2013 .

[10]  Annalisa Quaini,et al.  A localized reduced-order modeling approach for PDEs with bifurcating solutions , 2018, Computer Methods in Applied Mechanics and Engineering.

[11]  Ivo Wolf,et al.  Regurgitant jet evaluation using three-dimensional echocardiography and magnetic resonance. , 2004, The Annals of thoracic surgery.

[12]  G. Karniadakis,et al.  Spectral/hp Element Methods for Computational Fluid Dynamics , 2005 .

[13]  Anthony T. Patera,et al.  The Generalized Empirical Interpolation Method: Stability theory on Hilbert spaces with an application to the Stokes equation , 2015 .

[14]  Gianluigi Rozza,et al.  Reduced basis methods for Stokes equations in domains with non-affine parameter dependence , 2009 .

[15]  A. Quarteroni,et al.  Numerical Approximation of Partial Differential Equations , 2008 .

[16]  David Amsallem,et al.  Efficient model reduction of parametrized systems by matrix discrete empirical interpolation , 2015, J. Comput. Phys..

[17]  H. H. Fernholz,et al.  Report on the first European Mechanics Colloquium, on the Coanda effect , 1965, Journal of Fluid Mechanics.

[18]  Annalisa Quaini,et al.  Symmetry breaking and preliminary results about a Hopf bifurcation for incompressible viscous flow in an expansion channel , 2016 .

[19]  M. Fortin,et al.  Mixed Finite Element Methods and Applications , 2013 .

[20]  Martin Burger,et al.  Numerical Methods for Incompressible Flow , 2004 .

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

[22]  Gianluigi Rozza,et al.  A Spectral Element Reduced Basis Method in Parametric CFD , 2017, Lecture Notes in Computational Science and Engineering.

[23]  Anthony T. Patera,et al.  A stabilized POD model for turbulent flows over a range of Reynolds numbers: Optimal parameter sampling and constrained projection , 2018, J. Comput. Phys..

[24]  Annalisa Quaini,et al.  3D Experimental and Computational Analysis of Eccentric Mitral Regurgitant Jets in a Mock Imaging Heart Chamber , 2017, Cardiovascular engineering and technology.

[25]  Yvon Maday,et al.  RB (Reduced basis) for RB (Rayleigh–Bénard) , 2013 .

[26]  Carmen Ginghină The Coandă effect in cardiology , 2007, Journal of cardiovascular medicine.

[27]  Anthony T. Patera,et al.  A space–time variational approach to hydrodynamic stability theory , 2013, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[28]  Claudio Canuto,et al.  Spectral Methods: Evolution to Complex Geometries and Applications to Fluid Dynamics (Scientific Computation) , 2007 .

[29]  Annalisa Quaini,et al.  Computational reduction strategies for the detection of steady bifurcations in incompressible fluid-dynamics: Applications to Coanda effect in cardiology , 2017, J. Comput. Phys..

[30]  T. A. Zang,et al.  Spectral Methods: Fundamentals in Single Domains , 2010 .