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“…Lately, increasing attention has been devoted to eigenvalue calculation (as bifurcation detector tool) at the reduced order level [47,34]. We refer to [30] for a theoretical analysis of bifurcation detection techniques in Navier-Stokes equations and to [21] for a bifurcation detection method in a similar geometry.…”
We focus on reducing the computational costs associated with the hydrodynamic stability of solutions of the incompressible Navier-Stokes equations for a Newtonian and viscous fluid in contraction-expansion channels. In particular, we are interested in studying steady bifurcations, occurring when non-unique stable solutions appear as physical and/or geometric control parameters are varied. The formulation of the stability problem requires solving an eigenvalue problem for a partial differential operator. An alternative to this approach is the direct simulation of the flow to characterize the asymptotic behavior of the solution. Both approaches can be extremely expensive in terms of computational time. We propose to apply Reduced Order Modeling (ROM) techniques to reduce the demanding computational costs associated with the detection of a type of steady bifurcations in fluid dynamics. 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 regurgitant mitral valve orifice shape.
“…Lately, increasing attention has been devoted to eigenvalue calculation (as bifurcation detector tool) at the reduced order level [47,34]. We refer to [30] for a theoretical analysis of bifurcation detection techniques in Navier-Stokes equations and to [21] for a bifurcation detection method in a similar geometry.…”
We focus on reducing the computational costs associated with the hydrodynamic stability of solutions of the incompressible Navier-Stokes equations for a Newtonian and viscous fluid in contraction-expansion channels. In particular, we are interested in studying steady bifurcations, occurring when non-unique stable solutions appear as physical and/or geometric control parameters are varied. The formulation of the stability problem requires solving an eigenvalue problem for a partial differential operator. An alternative to this approach is the direct simulation of the flow to characterize the asymptotic behavior of the solution. Both approaches can be extremely expensive in terms of computational time. We propose to apply Reduced Order Modeling (ROM) techniques to reduce the demanding computational costs associated with the detection of a type of steady bifurcations in fluid dynamics. 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 regurgitant mitral valve orifice shape.
“…has at least one nonzero solution v. From a reduced order modelling perspective, following [9] we search for a change of sign of the eigenvalues of the matrix T (u * ), defined as…”
In this paper we apply a reduced basis framework for the computation of flow bifurcation (and stability) problems in fluid dynamics. The proposed method aims at reducing the complexity and the computational time required for the construction of bifurcation and stability diagrams. The method is quite general since it can in principle be specialized to a wide class of nonlinear problems, but in this work we focus on an application in incompressible fluid dynamics at low Reynolds numbers. The validation of the reduced order model with the full order computation for a benchmark cavity flow problem is promising.
“…An investigation of symmetry breaking in an expansion channel can be found in [38]. In [9][10][11], reliable error estimation is used to determine the critical parameter points where bifurcations occur in the Navier-Stokes setting. A recent work on ROMs for bifurcating solutions in structural mechanics is [34].…”
Reduced-order modeling (ROM) commonly refers to the construction, based on a few solutions (referred to as snapshots) of an expensive discretized partial differential equation (PDE), and the subsequent application of low-dimensional discretizations of partial differential equations (PDEs) that can be used to more efficiently treat problems in control and optimization, uncertainty quantification, and other settings that require multiple approximate PDE solutions. In this work, a ROM is developed and tested for the treatment of nonlinear PDEs whose solutions bifurcate as input parameter values change. In such cases, the parameter domain can be subdivided into subregions, each of which corresponds to a different branch of solutions. Popular ROM approaches such as proper orthogonal decomposition (POD), results in a global low-dimensional basis that does no respect not take advantage of the often large differences in the PDE solutions corresponding to different subregions. Instead, in the new method, the k-means algorithm is used to cluster snapshots so that within cluster snapshots are similar to each other and are dissimilar to those in other clusters. This is followed by the construction of local POD bases, one for each cluster. The method also can detect which cluster a new parameter point belongs to, after which the local basis corresponding to that cluster is used to determine a ROM approximation. Numerical experiments show the effectiveness of the method both for problems for which bifurcation cause continuous and discontinuous changes in the solution of the PDE. 1 For finite-dimensional spaces, we use the nomenclature "points" and "vectors" interchangeably.2 In §3, we use the Navier-Stokes equations as a concrete setting to illustrate our methodology. 1 arXiv:1807.08851v1 [math.NA] 23 Jul 20183 In general, the forms N and F themselves are also discretized, e.g., because quadrature rules are used to approximate integrals appearing in their definition. However, here, we ignore such approximations, again to keep the exposition simple.4 Equation (1.2) represents a Galerkin type setting in which the trial function u N and test function v belong to the same space V N . We could easily generalize our discussion to the Petrov-Galerkin case for which these function would belong to different approximating spaces.
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