Model reduction techniques for fast blood flow simulation in parametrized geometries

In this paper, we propose a new model reduction technique aimed at real‐time blood flow simulations on a given family of geometrical shapes of arterial vessels. Our approach is based on the combination of a low‐dimensional shape parametrization of the computational domain and the reduced basis method to solve the associated parametrized flow equations. We propose a preliminary analysis carried on a set of arterial vessel geometries, described by means of a radial basis functions parametrization. In order to account for patient‐specific arterial configurations, we reconstruct the latter by solving a suitable parameter identification problem. Real‐time simulation of blood flows are thus performed on each reconstructed parametrized geometry, by means of the reduced basis method. We focus on a family of parametrized carotid artery bifurcations, by modelling blood flows using Navier–Stokes equations and measuring distributed outputs such as viscous energy dissipation or vorticity. The latter are indexes that might be correlated with the assessment of pathological risks. The approach advocated here can be applied to a broad variety of (different) flow problems related with geometry/shape variation, for instance related with shape sensitivity analysis, parametric exploration and shape design. Copyright © 2011 John Wiley & Sons, Ltd.

[1]  Jacques Rappaz,et al.  Finite Dimensional Approximation of Non-Linear Problems .1. Branches of Nonsingular Solutions , 1980 .

[2]  J. P. Benque,et al.  A finite element method for Navier-Stokes equations , 1980 .

[3]  D. Giddens,et al.  Steady flow in a model of the human carotid bifurcation. Part I--flow visualization. , 1982, Journal of biomechanics.

[4]  D. Ku,et al.  Pulsatile Flow and Atherosclerosis in the Human Carotid Bifurcation: Positive Correlation between Plaque Location and Low and Oscillating Shear Stress , 1985, Arteriosclerosis.

[5]  Vivette Girault,et al.  Finite Element Methods for Navier-Stokes Equations - Theory and Algorithms , 1986, Springer Series in Computational Mathematics.

[6]  Thomas W. Sederberg,et al.  Free-form deformation of solid geometric models , 1986, SIGGRAPH.

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

[8]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Michael M. Resch,et al.  Numerical flow studies in human carotid artery bifurcations: basic discussion of the geometric factor in atherogenesis. , 1990, Journal of biomedical engineering.

[10]  Nadine Aubry,et al.  On The Hidden Beauty of the Proper Orthogonal Decomposition , 1991 .

[11]  J. Zolésio,et al.  Introduction to shape optimization : shape sensitivity analysis , 1992 .

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

[13]  C Kleinstreuer,et al.  Effect of carotid artery geometry on the magnitude and distribution of wall shear stress gradients. , 1996, Journal of vascular surgery.

[14]  J. Rappaz,et al.  Numerical analysis for nonlinear and bifurcation problems , 1997 .

[15]  B. Rutt,et al.  Hemodynamics of human carotid artery bifurcations: computational studies with models reconstructed from magnetic resonance imaging of normal subjects. , 1998, Journal of vascular surgery.

[16]  Alfio Quarteroni,et al.  Computational vascular fluid dynamics: problems, models and methods , 2000 .

[17]  Johan Montagnat,et al.  A review of deformable surfaces: topology, geometry and deformation , 2001, Image Vis. Comput..

[18]  J. Li,et al.  Flow field and oscillatory shear stress in a tuning-fork-shaped model of the average human carotid bifurcation. , 2001, Journal of biomechanics.

[19]  Andrew D. Back,et al.  Radial Basis Functions , 2001 .

[20]  Leif Kobbelt,et al.  Freeform shape representations for efficient geometry processing , 2003, 2003 Shape Modeling International..

[21]  Alexander I. J. Forrester,et al.  Shape optimization of the carotid artery bifurcation , 2004 .

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

[23]  Gianluigi Rozza,et al.  On optimization, control and shape design of an arterial bypass , 2005 .

[24]  Nguyen Ngoc Cuong,et al.  Certified Real-Time Solution of Parametrized Partial Differential Equations , 2005 .

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

[26]  Gianluigi Rozza,et al.  Shape Design in Aorto-Coronaric Bypass Anastomoses Using Perturbation Theory , 2006, SIAM J. Numer. Anal..

[27]  A. Patera,et al.  Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations , 2007 .

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

[29]  J. Hesthaven,et al.  Reduced Basis Approximation and A Posteriori Error Estimation for Parametrized Partial Differential Equations , 2007 .

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

[31]  Neil W Bressloff,et al.  Parametric geometry exploration of the human carotid artery bifurcation. , 2007, Journal of biomechanics.

[32]  Neil W Bressloff,et al.  Mining data from hemodynamic simulations via Bayesian emulation , 2007, Biomedical engineering online.

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

[34]  Christian B Allen,et al.  CFD‐based optimization of aerofoils using radial basis functions for domain element parameterization and mesh deformation , 2008 .

[35]  Simone Deparis,et al.  Reduced Basis Error Bound Computation of Parameter-Dependent Navier-Stokes Equations by the Natural Norm Approach , 2008, SIAM J. Numer. Anal..

[36]  L. Antiga,et al.  Geometry of the Carotid Bifurcation Predicts Its Exposure to Disturbed Flow , 2008, Stroke.

[37]  P. Fischer,et al.  Blood Flow in End-to-Side Anastomoses ∗ , 2008 .

[38]  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..

[39]  Alfio Quarteroni,et al.  Cardiovascular mathematics : modeling and simulation of the circulatory system , 2009 .

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

[41]  Miguel A. Fernández,et al.  Atlas-Based Reduced Models of Blood Flows for Fast Patient-Specific Simulations , 2010, STACOM/CESC.

[42]  G. Rozza,et al.  Model Order Reduction by geometrical parametrization for shape optimization in computational fluid dynamics , 2010 .

[43]  G. Rozza,et al.  Parametric free-form shape design with PDE models and reduced basis method , 2010 .

[44]  Aichi Chien,et al.  Computational hemodynamics framework for the analysis of cerebral aneurysms , 2011, International journal for numerical methods in biomedical engineering.

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

[46]  Gianluigi Rozza,et al.  Modeling of physiological flows , 2012 .

[47]  Gianluigi Rozza,et al.  Numerical Simulation of Sailing Boats: Dynamics, FSI, and Shape Optimization , 2012 .

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

[49]  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.

[50]  Anuj Srivastava,et al.  Statistical Shape Analysis , 2014, Computer Vision, A Reference Guide.