Abstract:Coupled partial differential equations (PDEs) defined on domains with different dimensionality are usually called mixed-dimensional PDEs. We address mixed-dimensional PDEs on three-dimensional (3D) and one-dimensional (1D) domains, which gives rise to a 3D-1D coupled problem. Such a problem poses several challenges from the standpoint of existence of solutions and numerical approximation. For the coupling conditions across dimensions, we consider the combination of essential and natural conditions, which are b… Show more
“…The estimate (17) holds true if a method with optimal computational complexity is used to solve the auxiliary linear systems that appear in (15) or (16). In the multidimensional case, this means that such a preconditioned iterative solver is applied.…”
Section: The Bura Methodsmentioning
confidence: 99%
“…As it can be seen in [17], the standard finite element methods provide accurate error estimates in the norms of the introduced fractional order Sobolev spaces. The saddlepoint matrix corresponding to the obtained system of linear algebraic equations is sparse.…”
Section: Example 2: Couplingmentioning
confidence: 99%
“…Let Ω ⊂ R 3 be a bounded domain, while Γ represents a 1D manifold (structure) inside Ω, see Figure 2. The following trace coupled problem is considered [17]: Here u, v, p are unknowns, the 1D manifold Γ is parameterized in terms of s, the Laplacian on Γ is understood in terms of the Laplace-Beltrami operator, T : Ω → Γ is a suitable trace operator, and pδ Γ is a Dirac measure such that Ω p(x)δ Γ w(x)dx = Γ p(s)δ Γ w(s)ds for a continuous function w.…”
Multiphysics or multiscale problems naturally involve coupling at interfaces which are manifolds of lower dimensions. The block-diagonal preconditioning of the related saddle-point systems is among the most efficient approaches for numerically solving large-scale problems in this class. At the operator level, the interface blocks of the preconditioners are fractional Laplacians. At the discrete level, we propose to replace the inverse of the fractional Laplacian with its best uniform rational approximation (BURA). The goal of the paper is to develop a unified framework for analysis of the new class of preconditioned iterative methods. As a final result, we prove that the proposed preconditioners have optimal computational complexity O(N), where N is the number of unknowns (degrees of freedom) of the coupled discrete problem. The main theoretical contribution is the condition number estimates of the BURA-based preconditioners. It is important to note that the obtained estimates are completely analogous for both positive and negative fractional powers. At the end, the analysis of the behavior of the relative condition numbers is aimed at characterizing the practical requirements for minimal BURA orders for the considered Darcy–Stokes and 3D–1D examples of coupled problems.
“…The estimate (17) holds true if a method with optimal computational complexity is used to solve the auxiliary linear systems that appear in (15) or (16). In the multidimensional case, this means that such a preconditioned iterative solver is applied.…”
Section: The Bura Methodsmentioning
confidence: 99%
“…As it can be seen in [17], the standard finite element methods provide accurate error estimates in the norms of the introduced fractional order Sobolev spaces. The saddlepoint matrix corresponding to the obtained system of linear algebraic equations is sparse.…”
Section: Example 2: Couplingmentioning
confidence: 99%
“…Let Ω ⊂ R 3 be a bounded domain, while Γ represents a 1D manifold (structure) inside Ω, see Figure 2. The following trace coupled problem is considered [17]: Here u, v, p are unknowns, the 1D manifold Γ is parameterized in terms of s, the Laplacian on Γ is understood in terms of the Laplace-Beltrami operator, T : Ω → Γ is a suitable trace operator, and pδ Γ is a Dirac measure such that Ω p(x)δ Γ w(x)dx = Γ p(s)δ Γ w(s)ds for a continuous function w.…”
Multiphysics or multiscale problems naturally involve coupling at interfaces which are manifolds of lower dimensions. The block-diagonal preconditioning of the related saddle-point systems is among the most efficient approaches for numerically solving large-scale problems in this class. At the operator level, the interface blocks of the preconditioners are fractional Laplacians. At the discrete level, we propose to replace the inverse of the fractional Laplacian with its best uniform rational approximation (BURA). The goal of the paper is to develop a unified framework for analysis of the new class of preconditioned iterative methods. As a final result, we prove that the proposed preconditioners have optimal computational complexity O(N), where N is the number of unknowns (degrees of freedom) of the coupled discrete problem. The main theoretical contribution is the condition number estimates of the BURA-based preconditioners. It is important to note that the obtained estimates are completely analogous for both positive and negative fractional powers. At the end, the analysis of the behavior of the relative condition numbers is aimed at characterizing the practical requirements for minimal BURA orders for the considered Darcy–Stokes and 3D–1D examples of coupled problems.
“…For the case of mixed-dimensional problems, the form of such stable Lagrange multipliers is not yet well-studied. Alternatively, analogous to Nitsche's method for classical equal-dimensional embedded finite element problems [32,31], stabilized Lagrange multiplier methods can also be applied to mixed-dimensional embedded finite element problems [33,34].…”
mentioning
confidence: 99%
“…In contrast to well-known trace theorems such as [35], which postulate existence of such a trace operator on smooth boundaries of codimension one, existence conditions on the restriction operator Π in the context of a greater dimensionality gap are not yet well-studied [36]. As one of the first publications addressing the lack of trace-type theorems for mixed-dimensional problems with codimension two, Kuchta et al [33] show sufficient regularity of such a restriction operator in the context of a mixed-dimensional model problem via averaging over a three-dimensional domain around the embedded manifold.…”
This work addresses research questions arising from the application of geometrically exact beam theory in the context of fluid-structure interaction (FSI). Geometrically exact beam theory has proven to be a computationally efficient way to model the behavior of slender structures while leading to rather well-posed problem descriptions. In particular, we propose a mixed-dimensional embedded finite element approach for the coupling of one-dimensional geometrically exact beam equations to a three-dimensional background fluid mesh, referred to as fluid-beam interaction (FBI) in analogy to the well-established notion of FSI. Here, the fluid is described by the incompressible isothermal Navier-Stokes equations for Newtonian fluids. In particular, we present algorithmic aspects regarding the solution of the resulting one-way coupling schemes and, through selected numerical examples, analyze their suitability not only as stand-alone methods but also for an extension to a full two-way coupling scheme.
Oxygen transfer from blood vessels to cortical brain tissue is representative of a class of problems with mixed‐domain character. Large‐scale efficient computation of tissue oxygen concentration is dependent on the manner in which the tubular network of blood vessels is coupled to the tissue mesh. Models which explicitly resolve the interface between the tissue and vasculature with a contiguous mesh are prohibitively expensive for very dense cerebral microvasculature. We propose a mixed‐domain mesh‐free technique whereby a vascular anatomical network (VAN) represented as a thin directed graph serves for convection of blood oxygen, and the surrounding extravascular tissue is represented as a Cartesian grid of 3D voxels throughout which oxygen is transported by diffusion. We split the network and tissue meshes by the Schur complement method of domain decomposition to obtain a reduced set of system equations for the tissue oxygen concentration at steady state. The use of a Cartesian grid allows the corresponding matrix equation to be solved approximately with a fast Fourier transform‐based Poisson solver, which serves as an effective preconditioner for Krylov subspace iteration. The performance of this method enables the steady‐state simulation of cortical oxygen perfusion for anatomically accurate vascular networks down to single micron resolution without the need for supercomputers.
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