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Applicable Analysis
An International Journal
Volume 100, 2021 - Issue 13
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Articles

Discontinuous Galerkin isogeometric analysis for segmentations generating overlapping regions

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Pages 2749-2776 | Received 20 Dec 2018, Accepted 30 Oct 2019, Published online: 06 Dec 2019

Abstract

In the Isogeometric Analysis (IGA) framework, the computational domain has very often a multipatch representation. The multipatch domain can be obtained by a volume segmentation of a boundary represented domain, e.g. provided by a Computer Aided Design model. Typically, small gaps and overlapping regions can appear at the patch interfaces of such multipatch representations. In the current work, we consider multipatch representations having only small overlapping regions between the patches. We develop a Discontinuous Galerkin (DG)-IGA method that can be immediately applied to these representations. Our method appropriately connects the fluxes of the one face of the overlapping region with the flux of the opposite face. We provide a theoretical justification of our approach by splitting the whole error into two components: the first is related to the incorrect representation of the patches (consistency error) and the second to the approximation properties of the IGA space. We show bounds for both components of the error. We verify the theoretical error estimates in a series of numerical examples.

2010 Mathematics Subject Classifications:

1. Introduction

Isogeometric Analysis (IGA) has been introduced in [Citation1] as a new methodology for solving numerically Partial Differential Equations (PDE). The key idea of the IGA concept is to use the superior finite dimensional spaces, which are used in Computer Aided Design (CAD), e.g. B-splines, NURBS, for both the exact representation of the computational domain Ω and discretizing the PDE problem. Since this work, many applications of the IGA methodology in several fields have been discussed in several papers, see, e.g. the monograph [Citation2] and the references therein, as well as the survey paper [Citation3]. From a computational point of view, we can say that the numerical algorithm for constructing the B-spline (or NURBS) basis functions is quite simple. This helps to produce high-order approximate solutions. From the theoretical point of view, the fundamental approximation properties of the B-spline spaces on a reference domain are discussed in [Citation4]. The approximation properties of the mapped B-spline (or NURBS) spaces are discussed in several papers, see e.g. [Citation3,Citation5–7].

Let us consider a complex domain Ω where its boundary is prescribed by CAD models. The CAD models can not be directly used in IGA in order to discretize the PDE problems. We need to create volumetric patch parametrizations from the CAD models. The boundary represented domain is first segmented into a collection of suitable blocks and consequently, a parametrization procedure is applied to each block. This produces the volumetric multipatch representation i=1NΩi¯ of Ω¯ suitable for IGA. Several segmentation algorithms and associated parametrization procedures have been discussed in the literature, see, e.g. [Citation8–12]. Furthermore, we refer to [Citation13–16] for different approaches for constructing IGA planar parametrizations without utilizing segmentation algorithms, and to [Citation17Citation18] for constructing parametrizations using Bézier triangular meshes. We mention the segmentation approach presented in [Citation19,Citation20], from which, we have been motivated to present the current work. The main idea is to split the given boundary represented domain, using a spline curve (or face in 3d case) with the following properties: (i) must have the end points on the boundary and the tangents to be specified, (ii) the curve is reasonably regular and does not intersect the boundary of the domain, (iii) the curve cuts the domain into new subdomains with good shapes. Consequently, tensor product B-spline spaces are fitted in the collection of the subdomains for defining the tensor product B-spline surfaces or volumes [Citation14]. Note that the previous consideration also concerns CAD models that are connected along a non-matching interface. It is important to obtain a curve that splits Ω into new simple domains with good shapes being suitable for IGA. During the computation of the multipatch representation, errors can occur when defining the corresponding control points, see [Citation10,Citation14,Citation19]. A consequence of this is non-conforming parametrizations of the patches in the sense that the images of the patch interfaces under the parametrizations are not identical. This, in turn, leads to the existence of gap and/or overlapping regions between the adjoining patches, see a schematic illustration in Figure (b).

Figure 1. (a) A conforming multipatch representation of Ω, (b) the inaccurate control points and the non-conforming multipatch representation of Ω.

Figure 1. (a) A conforming multipatch representation of Ω, (b) the inaccurate control points and the non-conforming multipatch representation of Ω.

This paper considers the case where there are only overlapping regions between the patches. If we apply an IGA methodology to this multipatch representation, a direct consequence is that the whole discretization error will include two (main) parts: the first naturally comes from the approximation properties of the B-spline spaces (for the purposes of this work we use B-spline spaces) and, the second comes from the geometric error. The later is due to the incorrect parametrization of the patch interfaces. Furthermore, the geometric error can be characterized as a consistency error, which consists of two error components. The first error component is related to the approximation of the jumps of the flux of the solution on the non-matching interfaces. The second component is related to the existence of more than one numerical solution in the overlapping regions.

The contribution of this paper is to develop a DG-IGA method which can be applied on volumetric patch representations with non-matching interface parametrizations. We present our methodology for discretizing the following elliptic Dirichlet boundary value problem (1) div(ρu)=f in Ωandu:=uD=0 on Ω,(1) where the diffusion coefficient ρ(x) can be discontinuous across a smooth internal interface. We derive bounds for the two main parts of the whole error. In our analysis, we derive separate bounds for the two components of the geometric error. To the best of our knowledge, we believe that this is a new area of analysis to be investigated. Our current work is the first step in the analysis, where we are developing our methodology for the numerical solution of the simple stationary diffusion problem (Equation1). Our intention for future works is to extend the current methodology to more complicated time dependent problems, where the interface can move with time, cf. [Citation21].

Due to the non-matching interior patch interfaces, a direct application of the classical DG numerical fluxes proposed in the literature, see e.g. [Citation6,Citation22], is not possible, as these fluxes are only applicable for matching interface parametrizations. In our recent papers, [Citation23,Citation24], we developed DG-IGA schemes for multipatch unions that include only gap regions. In particular, we considered the PDE model given in (Equation1) and we denoted by dg the maximum distance between the diametrically opposite points located on the gap boundary. We applied Taylor expansions using the diametrically opposite points of the gap, in order to give estimates for the jumps of the solution with respect to dg. Finally, we used the same Taylor expansions in the DG-IGA scheme for constructing suitable DG numerical fluxes across the gap boundary that help on the weakly coupling of the local patch-wise discrete problems. We developed a discretization error analysis and showed a priori estimates in the DG-norm, expressed in terms of the mesh size and the gap width, i.e. O(hr)+O(dg), where r depends on the B-spline degree p and the regularity of the solution. In [Citation23,Citation24], we have shown that, if dg=O(hp+1/2), the proposed DG-IGA scheme has optimal approximation properties.

In this paper, we extend the previous work to multipatch unions with overlapping regions. In the analysis presented in [Citation23,Citation24], the whole geometric error does not include the component coming from the coexistence of different IGA solutions in the overlapping regions. Here, the new approach is to introduce local (patch-wise) auxiliary variational problems, which are compatible with the overlapping nature of the multipatch representation of Ω. We denote the solutions of the new variational problems by u. These problems are not consistent, in the sense that the original solution u of (Equation1) does not satisfy them. Following the IGA concept, the B-spline spaces used for the parametrization of the patches are also used for discretizing the local auxiliary problems. We denote by uh the produced IGA solutions. Under some regularity assumptions on u, we can expect (see Section 3) that the IGA solution uh has optimal approximation properties associated with u. However, we can not directly infer that uh can approximate in an optimal way the solution u of the original problem. In our analysis, we provide an estimate for the consistency error uu and consequently using the triangle inequality uuhDGuuhDG+uuDG, we can derive an estimate for the error between the exact solution u and the IGA solution uh. The mesh-dependent norm DG is defined in Section 2. We give error estimates for both terms uuhDG and uuDG expressed in terms of the mesh size h and the quantity do, which is introduced in our analysis in order to quantify the width of the overlapping regions. In particular, we show that under appropriate assumptions on the data and for the case where do is of order hλ,λp+12, the proposed DG-IGA scheme has optimal convergence properties. This convergence result is similar to the result in [Citation23,Citation24].

In another work, which is under preparation, we apply the same approach to solve problems on multipatch partitions, which can include gap and overlapping regions. We present numerical solutions in multipatch unions with more complicated gaps and overlapping regions. We also provide details related to the implementation of the proposed DG-IGA scheme. In the same work, we also discuss issues related to the construction of domain-decomposition methods on these multipatch representations and provide several numerical tests for evaluating their performance. The first results in this direction can be found in [Citation25].

We note that IGA multipatch representations with non-matching interfaces meshes, overlapping regions and trimmed patches have been considered in many publications. For the communication of the discrete patch-wise problems, several Nitsche's type coupling methods involving normal flux terms have been applied across the interfaces, see e.g. [Citation22,Citation26–28] and references therein. We mention also that in [Citation29], DG-IGA methods have been presented to discretize Laplace problems on multipatch unions with large overlapping regions. The proposed strategy follows the additive Schwartz methodology. To the knowledge of the authors, there are no works that analytically discuss estimates for the error, which is caused by the incorrect representation of the shape of the patches. The purpose of this work is to present such an error analysis.

The structure of the paper is as follows: Section 2 presents the PDE model, briefly reviews the B-spline spaces and describes the case of having non-matching parametrized interfaces with overlapping regions. Section 3, presents in detail the perturbation problems, the bounds for the consistency error, the proposed DG-IGA scheme and the error analysis. Section 4, includes several numerical examples that confirm the theoretical estimates. The paper closes with the Conclusions.

2. The model problem

2.1. Preliminaries

Let Ω be a bounded Lipschitz domain in Rd,d=2,3, and let α=(α1,,αd) be a multi-index of non-negative integers α1,,αd with degree |α|=j=1dαj. For any α, we define the differential operator Dα=D1α1Ddαd, with Dj=/xj, j=1,,d, and D(0,,0)φ=φ. For a non-negative integer m, let Cm(Ω) denote the space of all functions φ:ΩR, whose partial derivatives Dαφ of all orders |α|m are continuous in Ω. Let ℓ be a non-negative integer. As usual, L2(Ω) denotes the Sobolev space for which Ω|φ(x)|2dx<, endowed with the norm φL2(Ω)=(Ω|φ(x)|2dx)1/2, and L(Ω) denotes the functions that are essentially bounded. Also H(Ω)={φL2(Ω):DαφL2(Ω), for all |α|}, denote the standard Sobolev spaces endowed with the following norms φH(Ω)=0|α|DαφL2(Ω)21/2. We identify L2 and H0 and also define the subspace H01(Ω) and HΓ1(Ω) of H1(Ω) H01(Ω)={φH1(Ω):φ=0onΩ},HΓ1(Ω)={φH1(Ω):φ=0 on ΓΩ, |Γ|>0.}. We recall Hölder's and Young's inequalities (2) Ωφ1φ2dxφ1L2(Ω)φ2L2(Ω)andΩφ1φ2dxϵ2φ1L2(Ω)2+12ϵφ2L2(Ω)2,(2) that hold for all φ1L2(Ω) and φ2L2(Ω) and for any fixed ϵ(0,). In addition, we recall trace and Poincare's inequalities, [Citation30], (3) φL2(Ω)2CtrφL2(Ω)φH1(Ω),φL2(Ω)measRd(Ω)φL2(Ω),for φHΓ1(Ω).(3)

2.2. The elliptic diffusion problem

The weak formulation of the boundary value problem (Equation1) reads as follows: for given source function fL2(Ω) find a function uH01(Ω) such that the variational identity (4) a(u,φ)=lf(φ), φH01(Ω),(4) is satisfied, where the bilinear form a(,) and the linear form lf() are defined by (5) a(u,φ)=Ωρuφdxandlf(φ)=Ωfφdx,(5) respectively. The given diffusion coefficient ρL(Ω) is assumed to be uniformly positive and piece-wise (patch-wise, see below) constant. These assumptions ensure the existence and uniqueness of the solution due to Lax-Milgram's lemma. For simplicity, we only consider pure Dirichlet boundary conditions on Ω. However, the analysis presented in our paper can easily be generalized to other constellations of boundary conditions that ensure existence and uniqueness such as Robin or mixed boundary conditions.

In what follows, positive constants c and C appearing in inequalities are generic constants that do not depend on the mesh-size h. In many cases, we will indicate on what may the constants depend on.

2.3. B-spline spaces

In this section, we briefly present the B-spline spaces and the form of the B-spline parametrizations for the physical subdomains. For a better presentation of the B-spline spaces, we start our discussion for the one-dimensional case. Then we proceed to higher dimensions. We refer to [Citation2,Citation4,Citation31] for a more detailed presentation.

Consider, Z={0=z1<z2<<zM=1} to be a partition of I¯=[0,1] with I¯j=[zj,zj+1],j=1,,M1 to be the intervals of the partition. Let the integers p and n1 denote the p spline degree and the number of the B-spline basis functions.

Based on Z, we introduce the open knot vector Ξ={0=ξ1,ξ2,,ξn1+p+1=1}, and the associated vector M={m1,,mM} of knot multiplicities with m1=mM=p+1, i.e. (6) Ξ={0=ξ1,,ξm1=z1,ξm1+1==ξm1+m2=z2,,ξn1+p+1mM,,ξn1+p+1=1=zM}.(6) The B-spline basis functions are defined by the Cox-de Boor formula, see, e.g. [Citation2,Citation31], (7) Bi,p=xξiξi+pξiBi,p1(x)+ξi+p+1xξi+p+1ξi+1Bi+1,p1(x),withBi,0(x)=1,if ξixξi+1,0,otherwise(7) We assume that mjp for all internal knots, which in turn gives that, at zj the B-spline basis functions have κj=pmj continuous derivatives.

Let us now consider the unit cube Ωˆ=(0,1)dRd, which we will refer to as the parametric domain. Let the integers p and nk denote the given B-spline degree and the number of basis functions of the B-spline space that will be constructed in xk-direction with k=1,,d. We introduce the d-dimensional vector of knots Ξ=(Ξ1,,Ξk,,Ξd), with the particular components given by Ξk={0=ξ1k,ξ2k,,ξnk+p+1k=1}, k=1,,d,.

Given the knot vector Ξk in every direction k=1,,d and using (Equation7), we construct the associated univariate B-spline basis functions, {Bˆ1,k(xˆk),,Bˆnk,k(xˆk)} of the space BˆΞk,p,

see, e.g. [Citation31] for more details. Accordingly, the tensor product B-spline space is defined (8) BˆΞ,p=k=1dBˆΞk,p=span{Bˆj(xˆ)}j=1n=n1nknd,(8) where each Bˆj(xˆ) has the form (9) Bˆj(xˆ)=Bˆj1(xˆ1)Bˆjk(xˆk)Bˆjd(xˆd),withBˆjk(xˆk)BˆΞk,p.(9) In the IGA framework, the computational domain Ω is described as the image of Ωˆ under a B-spline parametrization mapping of the form (10) Φ:ΩˆΩ,x=Φ(xˆ)=j=1nCjBˆj(xˆ)Ω,(10) where Cj,j=1,,n are the control points and xˆ=Φ1(x), see Figure (a). Following the IGA methodology, [Citation1,Citation2], the B-spline spaces for discretizing the PDE problem are defined by using the mapping given in (Equation10), i. e. we define the B-spline space in Ω by (11) BΞ,p:=span{Bj|Ω:Bj(x)=BˆjΦ1(x), for BˆjBˆΞ,p}.(11)

2.3.1. B-spline spaces on multipatch representations

Let us suppose that the domain Ω can be described as a union of N-subdomains (12) Ω¯=i=1NΩ¯i,withΩiΩj=,for ij,(12) with interior interfaces Fij=ΩiΩj, for 1ijN. We further suppose that every subdomain Ωi has its own parametrization Φi, which is defined by the corresponding B-spline space BˆΞi,p and the corresponding control points Cj(i), see (Equation10). Here Ξi denotes the knot vector related to Ωi. An illustration for N = 2 is given in Figure (a). The subdomains Ωi are referred to as patches. In an analogous way as in (Equation11), we define the physical patch-wise B-spline spaces BΞi,p for i=1,,N. We define the global discontinuous B-spline space VB with components on every BΞi,p (13) VB:={φhL2(Ω):φh|ΩiBΞi,p}.(13)

Assumption 2.1

Assume that every Φi,i=1,,N is sufficiently smooth and there exist constants 0<c<C such that c|detJΦi|C, where JΦi is the Jacobian matrix of Φi.

The components of Ξi form a mesh Thi,Ωˆ(i)={Eˆm}m=1Mi in Ωˆ, where Eˆm are the micro-elements and hi is the mesh size, which is defined as follows. Given an element EˆmThi,Ωˆ(i), we set hEˆm=diameter(Eˆm) and the mesh size hi is defined to be hi=max{hEˆm}. We set h=maxi=1,,N{hi}. For every Ωi, we construct a mesh Thi,Ωi(i)={Em}m=1Mi, whose vertices are the images of the vertices of the corresponding parametric mesh Thi,Ωˆ(i) under Φi.

Assumption 2.2

The meshes Thi,Ωˆ(i) are quasi-uniform, i.e. there exist a constant θ1 such that θ1hEˆm/hEˆm+1θ. Also, we assume that hihj for 1ijN.

2.4. Multipatch representation of the computational domain

In many practical applications, the parametrization of a boundary represented domain Ω by a single B-spline patch may not be possible. In order to discretize a PDE problem following the IGA framework in this situation, we represent the domain Ω as a multipatch. Following the methodology presented in [Citation9,Citation19], the initial domain Ω is firstly segmented into a collection of simple subdomains, e.g. topological hexahedra. Consequently, a suitable parametrization mapping is constructed for each subdomain for obtaining the multipatch representation of Ω. The final parametrization mappings of the adjoining patches must provide identical images for the common interfaces. In particular, for a DG-IGA discretization of the model (Equation1), it would be preferable to produce a multipatch partition of Ω compatible with the variations of the coefficient ρ, i.e. the patches to be coincided with the parts of Ω where the coefficient ρ is constant. For example, let us consider Figure (a). In this case, the domain Ω is described as a union of two non overlapping patches, see (Equation12), i.e. (14) Ω¯=Ω1¯Ω2¯,Ω1¯Ω2¯=,withF12=Ω1Ω2,(14) where the interface F12 coincides with the physical interface. We use the notation TH(Ω):={Ω1,Ω2} for the union (Equation14). For each Ωi,i=1,2, there exists a matching parametrization mapping such that Φi:ΩˆΩi with Ωi=Φi(Ωˆ). The control points, which are related to the patch interface F12, are appropriately matched in order for the parametrizations Φ1 and Φ2 of the neighboring patches to give the same image for the parametrized interface F12. Based on TH(Ω), we can independently discretize the problem on the different patches Ωi,i=1,2, using interface conditions across F12 for coupling the local problems. Typically, the interface conditions across F12 concern continuity requirements of the solution u of (Equation1), i.e. (15) u:=u1u2=0 on F12,andρunF12:=(ρ1u1ρ2u2)nF12=0 on Fi12,(15) where nF12 is the unit normal vector on F12 with direction towards Ω2, and ρi,ui,i=1,2 denote the restrictions of ρ and u to Ωi correspondingly. The conditions (Equation15) can be ensured by considering appropriate regularity assumptions on the solution u. We note that these types of multipatch representations have been considered in [Citation6] and DG-IGA methods have been proposed for discretizing the problem (Equation1).

Anyway, for simplicity, we develop our analysis based on Figure . We introduce the appropriate spaces. Let 2 be an integer, we define the broken Sobolev space (16) H(TH(Ω))={uL2(Ω):ui=u|ΩiH(Ωi), for i=1,2}.(16)

Assumption 2.3

We assume that the solution u of (Equation4) belongs to V=H01(Ω)H2(Ω)H(TH(Ω)) with 2.

2.5. Problem statement

2.5.1. Non-matching parametrized interfaces

Typically, the segmentation procedure will generate multipatch representations that have possibly non-matching interface parametrizations, [Citation10]. The result is the existence of gap and overlapping regions in the multipatch representation of the domain Ω. In [Citation23,Citation24], we developed DG-IGA schemes for multipatch unions that only include gap regions. In this work, we focus on multipatch representations with small overlapping regions, see Figures (b) and (a,b). Due to the non-matching parametrization of the interior patch interfaces, a direct application of the interface conditions (Equation15) for deriving DG-IGA methods, is not possible. The purpose of this paper is to investigate the construction of auxiliary interface conditions on the boundary of the overlapping regions; which can be used for constructing DG-IGA schemes. We present a discretization error analysis separating the whole discretization error into two parts: the first naturally comes from the approximation properties of the B-spline spaces and the second, is the geometric error coming from the incorrect parametrization of the patches. The geometric error is considered as a consistency error and it is further separated into two components. The first error component is related to the approximation of the auxiliary flux terms across the non-matching interfaces and the second component is related to the existence of more than one numerical solution in the overlapping regions.

Figure 2. (a) Illustration of a patch representation with the overlapping region Ωo21 in 2d and the diametrically opposite points on Ωo21, (b) overlapping patches in 3d, (c) the images of the faces of Ωˆ under the mappings Φi,i=1,2 in 2d, (d) the images of the faces of Ωˆ in 3d.

Figure 2. (a) Illustration of a patch representation with the overlapping region Ωo21 in 2d and the diametrically opposite points on ∂Ωo21, (b) overlapping patches in 3d, (c) the images of the faces of ∂Ωˆ under the mappings Φi∗,i=1,2 in 2d, (d) the images of the faces of ∂Ωˆ in 3d.

Remark 2.1

Alternatively, one can perform additional post-processing steps after the segmentation procedure to obtain matching interfaces. However, this procedure may increase the number of patches and the number of control points. Moreover, the newly obtained patch interfaces may not coincide with the original interface of the PDE problem, and thus the geometrical consistency error will still exist.

2.5.2. The overlapping regions

As we mentioned above, for the sake of simplicity, we restrict our investigation to the case where the multipatch representation of Ω has two overlapping patches, see Figure . Let us suppose that (17) Ω¯=Ω1¯Ω2¯,(17) where each patch has its own parametrization Φ1:ΩˆΩ1 and Φ2:ΩˆΩ2, as it is shown in Figure (c,d).

We denote the overlapping region by Ωo21, i.e. Ωo21=Ω1Ω2Ω. We denote the interior boundary faces of the overlapping region by Fo12=Ω1Ω2 and Fo21=Ω2Ω1, which implies that Ωo21=Fo12Fo21. Finally, let nFoij denote the unit exterior normal vector to Foij, for 1ij2. For functions ui defined in Ωi,i=1,2 we identify their pair (u1,u2) by u, which is equal to ui on Ωi. We develop our analysis for the case where the overlapping region is, at least locally, a convex region. We introduce an assumption related to the form of the faces Fo21 and Fo12. This assumption will help us to simplify the analysis, to explain our ideas in a better way, and to keep the notation to a minimum, e.g. the form of Jacobians, the form of face integrals, etc.

Assumption 2.4

  1. Ω1¯:=Ω1¯. The face Fo12 is an elementary face in the plane, i.e. is described by the points {(x,y,z):0xxMo, 0yyMo, z=0}, and it coincides with the physical interface, i.e. Fo12=F12, see (Equation14).

  2. The face Fo21 can be described as the set of points (x,y,z) satisfying (18) 0xxMo,0yyMo,z=ζ0(x,y),(18) where xMo and yMo are real numbers, and ζ0(x,y) is a given smooth function, see Figure .

We note that we will discretize the PDE problem using the B-spline spaces defined in Ω1 and Ω2, see (Equation11). We will couple the resulting discrete problems in Ω1 and in Ω2 following discontinuous Galerkin techniques, this means by introducing appropriate numerical fluxes on Fo12 and on Fo21. In order to construct these fluxes, we assign the points located on Fo12 to the diametrically opposite points located on Fo21. Based on Assumption 2.4, we can construct a parametrization for the face Fo21, i.e. a mapping Φo12:Fo12Fo21, of the form (19) xo1Fo12Φo12(xo1):=xo2Fo21,withΦo12(xo1)=xo1ζo(xo1)nFo12,(19) where nFo12 is the unit normal vector on Fo12 and ζo has the same form as in (Equation18), and it is a B-spline function with the same degree as the mapping Φ2, since the face Fo21 is the image of a face of Ωˆ under the mapping Φ2. For the schematic illustration in Figure (c,d), we have Fo21=Φ2(Fˆ3). Utilizing the mapping Φo12 given in (Equation19), we consider each point xo2Fo21 as an image of a point xo1Fo12 under the Φo12, see Figure (a,c). Finally, we introduce a parameter do, which quantifies the width of the overlapping region Ωo21, i.e. (20) do=maxxo1Fo12|xo1Φo12(xo1)|.(20) In the present work, we are interested in overlapping regions with small size, and in particular for regions where their width do decreases polynomially in h, i.e. (21) dohλ,with someλ1.(21) Based on this, we assume that nFo12nFo21, and define the mapping Φo21:Fo21Fo12 as (22) Φo21(xo2)=xo1,withΦo12(xo1)=xo2,(22) where Φo21 is the inverse of Φo12.

Remark 2.2

By introducing Assumption 2.4, the face Fo21 can be considered to be the graph of ζ0. This helps us to determine a parametrization for Fo21 using the function ζ0, and through this, we can relate to every point xo2Fo21 a point xo1Fo12. This has been achieved by the mapping Φo12 in (Equation19). The mapping Φo12 here has a simple form and simplifies the analysis. Other mappings (even for more complex overlapping regions), for relating the points xo2Fo21 to the points xo1Fo12 can be constructed. For example, one can follow the ideas of minimum distance problem presented in [Citation32] and can relate the points xo2Fo21 to the points xo1Fo12 by introducing the condition (xo1xo2)//nFo12. In any case, the parametrization mappings must satisfy the maximum width condition given in (Equation21).

Remark 2.3

As we previously said, the face Fo12 is the image of a face of Ωˆ under the mapping Φ1, for example in Figure (c) we have Fo12=Φ1(Fˆ1). On the other hand, the face Fo21 is an interior curve for Ω1, see Figure (a,c). Thus, one could try to see Fo21 as an image of a curve Fˆo21Ωˆ under the mapping Φ1, i.e. Fo21=Φ1(Fˆo21). In that way, it would be advantageous to have a parametric description of Ωo21 using the mapping Φ1, which in turn would help to link the diametrically opposite points xo1 and xo2, see (Equation19). This approach requires the computation of the inverse (Φ1)1, which in general is very costly and demands the use of a Newton approach for solving many nonlinear systems. We are thus led to see the faces of Ωo21 as images of both mappings Φ1 and Φ2. We note also that the mappings Φo12 and Φo21 are introduced and used only for deriving the discretization error analysis. They are not used in the computation of the entries of the system matrix of the discrete DG-IGA scheme, see also discussion in Subsection 4.1.

Remark 2.4

In Section 4, we give details of implementing the proposed method to more complicated overlapping regions. We present examples where Fo12 is not an elementary face in the plane and does not coincide with the physical phase F12. Also, we note that for multipatch representations with large overlapping regions, one needs to work in a different direction and to use ideas coming from Schwarz domain-decomposition methods, see [Citation33].

Remark 2.5

The methodology can also be applied to the case where the interior faces of Ωo21 do not touch the boundary Ω.

3. The patch-wise problems and the fluxes

We compute a numerical solution in each Ωi,i=1,2 using the corresponding diffusion coefficient ρi and the corresponding B-spline spaces defined in Ωi, lets us say BΞi,p. Therefore on Ωo21 we will have the coexistence of two different numerical solutions and this makes the computation of the bounds for the error uuhDG more complicated. The norm DG is defined in (Equation24). The idea in our approach is to introduce local (patch-wise) problems ai(ui,φi)=li,f(φi) in every Ωi, with appropriate bilinear forms ai(,). Using the triangle inequality, we split the error as uuhDGuuDG+uuhDG. Then we estimate every term separately.

3.1. The patch-wise variational problems

Denote TH(Ω):={Ω1,Ω2}, let 1 be an integer and let the B-spline spaces BΞi,p defined in Ωi,i=1,2. Accordingly to the spaces (Equation16) and (Equation13), we introduce the spaces (23) H(TH(Ω)):={u=(u1,u2):uiH(Ωi),ui|ΩiΩ=0, for i=1,2},H0(TH(Ω)):={u=(u1,u2):uiH0(Ωi), for i=1,2},VB:={φh=(φ1,h,φ2,h):φi,hBΞi,p, for i=1,2}.(23) In order to proceed, we first define the DG-norm DG associated with TH(Ω). For all vVh:=H(TH(Ω))+VB, (24) vDG2=i=12ρiviL2(Ωi)2+ρihviL2(ΩiΩ)2+FoijΩi{ρ}hviL2(Foij)2,for 1ij2,(24) where Foij are the interior faces related to overlapping regions, see Figure (a), and {ρ}=12(ρi+ρj).

We recall Assumption 2.4 and the Dirichlet boundary conditions given in (Equation1). On each Ωi,i=1,2, we consider the auxiliary problems: (25a) div(ρ1u1)=f,in Ω1,u1=uDon Ω1Ω,u1=uon Fo12,(25a) (25b) div(ρ2u2)=f,in Ω2,u2=uDon Ω2Ω,u2=uon Fo21,(25b) and furthermore, we consider the corresponding variational problems, (26a) find u1HΩ1Ω1(Ω1) such thatu1=u on Fo12,anda1(u1,φ1)=l1,f(φ1), for φ1H01(Ω1),(26a) where (26b) a1(u1,φ1)=Ω1ρ1u1φ1dx,andl1,f(φ1)=Ω1fφ1dx,(26b) (26c) find u2HΩ2Ω1(Ω2) such thatu2=u on Fo21,anda2(u2,φ2)=l2,f(φ2), for φ2H01(Ω2),(26c) where (26d) a2(u2,φ2)=Ω2ρ2u2φ2dx,andl2,f(φ2)=Ω2fφ2dx,(26d)

Remark 3.1

By Assumption 2.4 and the definition of problem (Equation25a), we can imply that the solution u of (Equation4) satisfies the problem (Equation26a). The definition of (Equation25b) and the fact that ρ2ρ1 on Ωo21 imply that u of (Equation4) does not satisfy the problem (Equation26c).

According to Assumption 2.3, we make the following assumption.

Assumption 3.1

The solutions ui,i=1,2 in (Equation26) belong to H(TH(Ω)) with 2.

In Appendix, see Subsection A.1, we give an estimate for the distance of the solutions u and u.

3.2. The non-consistent terms

We multiply (Equation25b) by φ2,hBΞ2,p, integrate over Ω2 and apply integration by parts, then after a few calculations we find that (27) Ω2ρ2u2φ2,hdxΩ2ρ2u2nΩ2φ2,hdσ=Ωo21ρ1u2φ2,hdx+Ω2ρ2u2φ2,hdxΩ2Ωρ2u2nΩ2φ2,hdσFo21ρ1u2nFo21φ2,hdσFo12ρ1u2nFo12φ2,hdσFo12ρ2u2(nFo12)φ2,hdσ+Ωo21(ρ2ρ1)u2φ2,hdxFo12(ρ2ρ1)u2nFo12φ2,hdσFo21(ρ2ρ1)u2nFo21φ2,hdσ=l2,f(φ2,h).(27) Working in a similar way, we multiply (Equation25a) by φ1,hBΞ1,p, and we have (28) Ω1ρ1u1φhdxFo12ρ1u1nFo12φhdσΩ1Ωρ1u1nΩ1φ1,hdσ=l1,f(φ1,h).(28) We define the forms (29a) a2,h(u2,φ2,h):=Ω2ρ2u2φ2,hdxFo21ρ2u2nFo21φ2,hdσΩ2Ωρ2u2nΩ2φ2,hdσ,(29a) (29b) a1,h(u1,φ1,h):=Ω1ρ1u1φ1,hdxFo12ρ1u1nFo12φ1,hdσΩ1Ωρ1u1nΩ1φ1,hdσ(29b) as well as (30a) ao,2(u2,φ2,h)=Ωo21ρ1u2φ2,hdx+Ω2ρ2u2φ2,hdxΩ2Ωρ2u2nΩ2φ2,hdσFo21ρ1u2nFo21φ2,hdσFo12ρ1u2nFo12φ2,hdσFo12ρ2u2(nFo12)φ2,hdσ(30a) (30b) ares(u2,φ2,h)=Ωo21(ρ2ρ1)u2φ2,hdxFo12(ρ2ρ1)u2nFo12φ2,hdσFo21(ρ2ρ1)u2nFo21φ2,hdσ.(30b) By (Equation27), (Equation29) and (Equation30), we get that (31) a2,h(u2,φh)=ao,2(u2,φ2,h)+ares(u2,φ2,h)=l2,f(φ2,h),(31) Also for the solution u of (Equation4) we have that (32) Ωo21ρ1uφ2,hdx+Ω2ρ2uφ2,hdxFo21ρ1unFo21φ2,hdσΩ2Ωρ2unΩ2φ1,hdσ=l2,f(φ2,h).(32) From the conditions (Equation15), the forms defined in (Equation29), (Equation30) and the relations (Equation31) and (Equation32), we derive that (33a) ao,2(u2,φ2,h)+ares(u2,φ2,h)=ao,2(u,φ2,h)=l2,f(φ2,h)(33a) and (33b) a2,h(u,φ2,h)ares(u,φ2,h)=l2,f(φ2,h).(33b) By a simple application of divergence theorem, we get (34) ares(u,φ2,h)=Ωo21div((ρ2ρ1)u)φ2,hdx=Ωo21(ρ2ρ1)ρ1fφ2,hdx.(34) Finally, by (Equation33b) and (Equation34), we deduce that (35) a2,h(u,φ2,h)+Ωo21(ρ1ρ2)ρ1fφ2,hdx=l2,f(φ2,h).(35)

Proposition 3.1

Let φ2,hBΞ2,p. There is a c>0 dependent on ρ but independent of u and Ωo21 such that (36) φ2,hL2(Ωo21)2cdohΩ2|φ2,h|2dx+{ρ}hFo21φ2,h2dσ.(36)

Proof.

Let v=(0,yφ2,h2). The divergence theorem for v on Ωo21 yields, (37) Ωo21φ2,h2dx+Ωo212yφ2,hyφ2,hdx=Fo21yφ2,h2dσ.(37) Using that ydo and applying (Equation2) in (Equation37) we obtain (38) φ2,hL2(Ωo21)2ϵ2Ωo21φ2,h2dx+4ϵ2Ωo21do2|φ2,h|2dx+doh1hFo21φ2,h2dσ(38) Gathering similar terms and choosing ε such that 1ϵ2>0, we get (39) c1,ϵφ2,hL2(Ωo21)2c2,ϵcρdohΩ2ρ2|φ2,h|2dx+{ρ}hFo21φ2,h2dσ,(39) where cρ:=max{1/ρ2,1/{ρ}} and we used that do2doh, see (Equation21). Rearranging appropriately the constants in (Equation39) yields (Equation36).

Corollary 3.1

Let fL(Ω), φ2,hBΞ2,p and let u2 and u be the solutions of (Equation26d) and (Equation4) respectively. There are constants c1,cρ>0 dependent on Fo21 but independent of h such that (40a) Ωo21fφ2,hdxc1dofL(Ωo21)φ2,hDG,(40a) (40b) |ares(u,φ2,h)|cρdofL(Ωo21)φ2,hDG.(40b)

Proof.

The Cauchy–Schwartz inequality implies that (41) Ωo21fφ2,hdxfL2(Ωo21)φ2,hL2(Ωo21)cFo21do1/2fL(Ωo21)φ2,hL2(Ωo21).(41) Using (Equation36) in (Equation41), the required assertion follows easily.

Inequality (Equation40b) follows immediately from (Equation34) and (Equation40a).

3.3. The discrete problem

In this section, we use the bilinear forms given in (Equation29) to define the patch-wise discrete problems. Based on Remark 3.1, and using the interface conditions on Fo21 and Fo12, which are introduced in (Equation25a) and (Equation25b), we imply the following interface condition (42) u1u2=0,on Fo21.(42) Next, we appropriately modify the flux terms Fo21ρ2u2nFo21φ2,hdσ and Fo12ρ1u1nFo12φ1,hdσ in (Equation29) using Taylor expansions.

3.3.1. Taylor expansions

Let x,yΩ¯2 and let fCm2(Ω¯2). We recall Taylor's formula with integral remainder (43a) f(y)=f(x)+f(x)(yx)+R2f(y+s(xy)),(43a) (43b) f(x)=f(y)f(y)(yx)+R2f(x+s(yx)),(43b) where R2f(y+s(xy)) and R2f(x+s(yx)) are the second order remainder terms defined by (44a) R2f(y+s(xy))=|α|=2(yx)α2α!01sDαf(y+s(xy))ds,(44a) (44b) R2f(x+s(yx))=|α|=2(xy)α2α!01sDαf(x+s(yx))ds.(44b)

3.3.2. Modifications of the fluxes on Ωo21

To illustrate the use of (Equation43) in our analysis, we consider the simple case of Figure (a). Let the points xo1Fo12 and xo2Fo21 be such that xo2=Φo12(xo1) as in Figure (a). We apply (Equation43) using the points xo1 and xo2 and we have (45a) f(xo1)f(xo2)=f(xo2)(xo1xo2)+R2f(xo1+s(xo2xo1)),(45a) and setting xo2=Φo12(xo1) we have (45b) f(xo1)f(Φo12(xo1))=f(xo2))(xo1xo2)+R2f(xo1+s(xo2xo1)).(45b) In the same way we can get (45c) f(xo2)f(Φo21(xo2))=f(xo1))(xo2xo1)+R2f(xo2+s(xo1xo2)).(45c) Now denoting roij=xoixoj we have that nFoij=roij/|roij|. For keeping notation simple, we denote the Taylor's residuals as R2uxo1:=R2u(xo1+s(xo2xo1)) and R2uxo2:=R2u(xo2+s(xo1xo2)). Note that by the interface conditions (Equation42), we have that ({ρ}/h)Fo21(u2(xo2)u1(xo2))φ2,hdσ=0, and using also (Equation45) we modify the fluxes in (Equation29) as follows (46a) Fo21ρ2u2(xo2)nFo21φ2,hdσ{ρ}hFo21(u2(xo2)u1(xo2))φ2,hdσ=Fo21ρ2u2(xo2)nFo21φ2,hdσ{ρ}hFo21(u2(xo2)u1(Φo21(xo2))φ2,hdσ+Fo21{ρ}h(|ro21|u2(xo1)nFo21+R2uxo2)φ2,hdσ,(46a) where {ρ}=12(ρ1+ρ2). Similarly, we have (46b) Fo12ρ1u1(xo1)nFo12φ1,hdσ=Fo12ρ1u1(xo1)nFo12φ1,hdσ{ρ}hFo12(u1(xo1)u1(xo1))φ1,hdσ=Fo12ρ1u1(xo1)nFo12φ1,hdσ{ρ}hFo12(u1(xo1)u1(Φo12(xo1))))φ1,hdσ+{ρ}hFo12u1(xo2)(xo1xo2)+R2u1(xo1+s(xo2xo1)),(by (45a), (45b))=Fo12ρ1u1(xo1)nFo12φ1,hdσ{ρ}hFo12(u1(xo1)u2(Φo12(xo1)))φ1,hdσ,+Fo12{ρ}h(|ro12|u1(xo2)nFo12+R2uxo1)φ1,hdσ,(u2(Φo12(xo1))=u1(Φo12(xo1)), see (42)).(46b)

3.3.3. The global modified form

We consider the global bilinear form a(,):Vh×VBR, which is formed by the contributions of ai,h(,),i=1,2 given in (Equation29) and the flux forms given in (Equation46), that is (47) a(u,φh)=a2,h(u2,φ2,h)+a1,h(u1,φ1,h)=Ω1ρ1u1φ1,hdx+Ω2ρ2u2φ2,hdxΩ1Ωρ1u1nΩ1φ1,hdσΩ2Ωρ2u2nΩ2φ2,hdσ+ρ1hΩ1Ω(u1uD)φ1,hdσ+ρ2hΩ2Ω(u2uD)φ2,hdσFo12ρ1u1(xo1)nFo12+{ρ}h(u1(xo1)u2(Φo12(xo1))φ1,hdσFo21ρ2u2(xo2)nFo21+{ρ}h(u2(xo2)u1(Φo21(xo2))φ2,hdσ+Fo21{ρ}h(|ro21|u2(xo1)nFo21+R2uxo2)φ2,hdσFo12{ρ}h(|ro12|u1(xo2)nFo12+R2uxo1)φ1,hdσ.(47)

Remark 3.2

Note that the exact solution u has similar regularity properties to the solution u, see Assumption 2.3, and thus we can derive for u an analogous formulation as this in (Equation47).

3.3.4. The DG-IGA scheme

In view of (Equation47), we define the forms AΩi(,):Vh×VBR, RΩo21(,):Vh×VBR, and the linear functional lf,Ωi:VBR by (48a) AΩi(u,φh)=i=12(Ωiρiuiφi,h,dxΩiΩρiuinΩiφi,hdσFoijΩiFoijρiuinFoijφi,hη{ρ}h(uiuj)φi,hdσ),for 1ij2,(48a) (48b) RΩo21(u,φh)=Fo21{ρ}h(|ro21|u2(xo1)nFo21+R2uxo2)φ2,hdσ+Fo12{ρ}h(|ro12|u1(xo2)nFo12+R2uxo1)φ1,hdσ,lf,Ωi(φh)=i=12Ωifφi,hdx,(48b) where η>0 is a parameter that is going to be determined later. Based on the forms defined in (Equation48), we introduce the discrete bilinear form Ah(,):VB×VBR and the linear form Fh:VBR as follows (49) Ah(uh,φh)=AΩi(uh,φh)+i=12ηρihΩiΩui,hφi,hdσ,(49) (50) Fh(φh)=lf,Ωi(φh)+i=12ηρihΩiΩuDφi,hdσ.(50) Finally, the DG-IGA scheme reads as follows: find uhVB such that (51) Ah(uh,φh)=Fh(φh),for all φhVB.(51)

Remark 3.3

From the relations (Equation31), (Equation33), the Remark 3.2 and the forms given in (Equation49) and in (Equation50), we can derive that (52) a2,h(u2,φ2,h)+a1,h(u1,φ1,h)=ao,2(u2,φ2,h)+ares(u2,φ2,h)+a1,h(u1,φ1,h)=Ah(u,φh)+RΩo21(u,φh)=ao,2(u,φ2,h)+a1,h(u,φ1,h)=a2,h(u,φ2,h)ares(u,φ2,h)+a1,h(u,φ1,h)=Ah(u,φh)+RΩo21(u,φh)ares(u,φ2,h)=Fh(φh),for φh:=(φ1,h,φ2,h)VB.(52)

Below, we quote few results that are useful for our error analysis. For the proofs we refer to [Citation23–25].

Lemma 3.1

Under the assumption (Equation21), there exist positive constants C1 and C2 independent of h such that the estimates (53) |RΩo21(u,φh)|C1Ko(u)φhDGhλ0.5,|RΩo21(u,φh)|C2Ko(u)φhDGhλ0.5,(53) hold for the solutions u and u, and φhVB, where Ko(v)=vL2(Ωo21)+|α|=2|Dαv|L2(Ωo21).

Lemma 3.2

The bilinear form Ah(,) in (Equation49) is bounded and elliptic on VB, i.e. there are positive constants CM and Cm such that the estimates (54) Ah(vh,φh)CMvhDGφhDGandAh(vh,vh)CmvhDG2,(54) hold for all vh,φhVB provided that η is sufficiently large, see [Citation25].

Lemma 3.3

Let the assumption (Equation21) and let β=λ12. Then there is a constant C>0 depending on the parametrization mappings but independent of h such that the inequality (55) Ah(v,φh)CvDG2+i=12hρi1/2vL2(Ωi)21/2φhDG,(55) holds for all (v,φh)Vh×VB and (v,φh)(V+VB)×VB.

Proof.

Recall the definition of the pair function spaces in (Equation23). In view of the form of Ah(,) and applying (Equation2), we have (56) i=12Ωiρiviφi,hdx)i=12ρi1/2viL2(Ωi)21/2i=12ρi1/2φi,hL2(Ωi)21/2.(56) Now, let us first show an estimate for the normal fluxes on Foij. Since vVh the normal traces on the interfaces are well defined. Using again (Equation2), we obtain (57) FoijρivinFoijφi,hdσCiFoijh1/2|ρi1/2vi|{ρ}h1/2|φi,h|dσCih1/2ρi1/2viL2(Foij)η{ρ}hφi,hL2(Foij)21/2Cih1/2ρi1/2viL2(Foij)φhDG,(57) for 1ij2. Also, we have η{ρ}hFo12(v1v2(Φo12))φ1,hdσ2η{ρ}hFo12v12+v22(Φo12)|JΦo12||JΦo12|dσ1/2×η{ρ}hφ1,hL2(Fo12)21/2CJΦo12η{ρ}hv1L2(Fo12)2+η{ρ}hv2L2(Fo21)2)1/2×η{ρ}hφ1,hL2(Fo12)21/2CJΦo12vDGφhDG, where |JΦo12| is the measure of the Jacobian of Φo12. In the same way, we show η{ρ}hFo21(v2v1(Φo21))φ2,hdσCJΦo21vDGφhDG. Gathering together the above bounds, we show (Equation55). For the case where (v,φh)(V+VB)×VB we work similarly.

3.4. Discretization error analysis

Next, we discuss interpolation estimates that we will use to bound the discretization error. We recall the definition of the pair function spaces in (Equation23). Let vH(TH(Ω)) with 2. Under Assumptions 2.1, and using the results of [Citation3,Citation5], we can construct a quasi-interpolant Πhv:=(Π1,hv1,(Π2,hv2)VB such that the estimates (58) i=1,2|vΠhv|H1(Ωi)hsi=1,2C1,ivH(Ωi),i=1,2|vΠhv|L2(Ωi)hs1/2i=1,2C2,ivH(Ωi),(58) hold, where s=min(1,p) and the C1,i, C2,i depend on p,Φi,θ but not on h.

Lemma 3.4

Let vH(TH(Ω)) with 2 and let Πhv be as in (Equation58). Then there exist constants Ci>0, i=1,2, depending on p,Φi,i=1,2 and the quasi-uniformity of the meshes but not on h such that (59) vΠhvDG2+i=12hρi1/2(vΠhv)L2(Ωi)21/2i=12CihsvH(Ωi),(59) where s=min(1,p).

Proof.

The estimate (Equation59) can be shown using trace inequality and the estimates (Equation58), see details in Lemma 10 in [Citation6]. See also [Citation23,Citation24].

Theorem 3.1

Let β=λ12 and do=hλ with λ1. Let uH(TH(Ω)) with 2 be the solution of the problems in (Equation26), and let uhVB be the corresponding DG-IGA solution of (Equation51). Then the error estimate (60) uuhDGhri=12uH(Ωi),(60) holds, where r=min(s,β) with s=min(1,p).

Proof.

Let zhVB. We set uhzh=φh. The properties (Equation54), (Equation55) of Ah(,) and (Equation52) imply (61) cmuhzhDG2Ah(uhzh,φh)=Ah(u,φh)+RΩo21(u,φh)Ah(zh,φh)=Ah(uzh,φh)+RΩo21(u,φh)CuzhDG2+i=1Nhρi1/2(uzh)L2(Ωi)21/2φhDG+C2Ko(u)φhDGhλ0.5,(61) where the bound (Equation53) has been used previously. Setting in (Equation61) zh=Πhu, and then using the triangle inequality cmuhuDGcmuhΠhuDG+cmuΠhuDG together with the estimate in (Equation59), we derive (Equation60).

3.4.1. Main error estimate

The estimate given in (Equation60) concerns the distance between the DG-IGA solution uhVB and the solution uH(TH(Ω)) of the problems in (Equation26). Below we give an estimate between the solution u of (Equation4) and the DG-IGA solution uh. In the proof of this result we need the following interpolation estimate for vV (62) vΠhvDG2+i=12hρi1/2(vΠi,hv)L2(Ωi)21/2i=12CihsvH(Ωi),(62) where the quasi-interpolant Πhv=(Π1,hv,Π2,hv) is defined in (Equation58) and s=min(1,p).

The proof of (Equation62) is provided in the Appendix.

Theorem 3.2

main error estimate

Let u be the solution of (Equation4) and let Assumption 2.3 with 2. We suppose further that do=hλ,λ1 is the width of Ωo21. The following error estimate holds (63) uuhDGC~hsi=12(uH(Ωi)+uiH(Ωi))+dofL2(Ω)+hβ(Ko(u)+Ko(u)),(63) where β=λ12, s=min(1,p), the constant C~ depends on the constants in (Equation59), (Equation55) and (Equation54), and Ko has the form given in Lemma 3.1.

Proof.

Let zhVB and let φh=uhzh. By the definition of the discrete DG-IGA scheme in (Equation51), the properties of Ah(,) and the Remark 3.3 we have (64) cmuhzhDG2Ah(uhzh,φh)Ah(u,φh)RΩo21(u,φh)+Fh(φh)Ah(Πhu,φh)+Ah(Πhu,φh)=Ah(uhΠhu,φh)+Ah(uΠhu,φh)+Ah(zh,φh)+Ah(u,φh)ares(u,φ2,h)+RΩo21(u,φh)RΩo21(u,φh)=Ah(uhΠhu,φh)+Ah(uΠhu,φh)+Ah(uzh,φh)ares(u,φ2,h)+RΩo21(u,φh)RΩo21(u,φh)CMuhΠhuDGφhDGby (53),(54),(55),(40)+CuΠhuDG2+i=1Nhρi1/2(uΠhu)L2(Ωi)21/2φhDG+CuzhDG2+i=1Nhρi1/2(uzh)L2(Ωi)21/2φhDG+c2dofL2(Ω)φhDG+C2(Ko(u)+Ko(u))φhDGhβ(64) Setting zh=Πhu into (Equation64), using (Equation61), (Equation59), and (Equation62) and gathering together the similar terms we deduce that (65) cmuhΠhuDGi=12CihsuH(Ωi)+i=12CihsuiH(Ωi)+c2dofL2(Ω)+C2(Ko(u)+Ko(u))hβ(65) Applying the triangle inequality (66) uuhDGuΠhuDG+ΠhuuhDG,(66) the desired estimate follows.

Remark 3.4

In the description of the problem and in the derivation of the DG-IGA scheme, we focused on using B-spline spaces. The same derivation can be applied for the case of NURBS spaces.

4. Implementation and numerical tests

4.1. Implementation remarks

In this paragraph we focus on the implementation of the proposed scheme for both two and three dimensional problems. For simplicity of the presentation, we first discuss the case of having two patches. Afterwards, we explain how the same ideas can be generalized to the multipatch case.

Initially, we consider interfaces with matching meshes, i.e. the number of edge elements on Fo21 is the same as the number on Fo12, as shown in Figure .

Figure 3. (a) Configuration of the faces and the edges on Ωo12 and their corresponding edges on Ωˆ which are used to compute the interface integrals, (b) an example of an overlapping region with more than two faces. The relative edges on the opposite faces must again match.

Figure 3. (a) Configuration of the faces and the edges on ∂Ωo12 and their corresponding edges on ∂Ωˆ which are used to compute the interface integrals, (b) an example of an overlapping region with more than two faces. The relative edges on the opposite faces must again match.

For the computation of the numerical flux terms of the DG-IGA scheme given in (Equation48a), a Gauss quadrature rule is applied on every edge. The first term of the numerical flux can be directly computed by using the Gauss rule and the related Jacobian term. For the computation of the jump terms, we must know the diametrically opposite edge and the associated quadrature point that are located on the other interface. We could proceed to this direction by constructing and using the mappings Φo21 and Φo12 given in (Equation19) and (Equation22) respectively. For the practical implementation, it would be preferable to proceed without the construction of these mappings.

We first assign the edges belonging to Fo21 to the edges belonging to Fo12, for the example given in Figure (a), the edge e21 of Fo21 is assigned to e22 of Fo12. In Figure (a) the Gauss point is denoted by xq and yq correspondingly. The edge e21 is the image of the edge eˆ21 under the parametrization Φ2, and also the edge e22 is the image of the edge eˆ22 under the parametrization Φ1. Hence, the Gauss rule is transformed back to boundary edges of the parametric domain, and for every Gauss point yˆq there is always a corresponding Gauss point on the other associated edge to perform the numerical integration. For the configuration given in Figure (a), the other associated edge is located on face Fˆ1 and the corresponding Gauss point is denoted by xˆq. Thus, having defined the quadrature points on the boundary edges of Ωˆ, we can compute the interface terms of the numerical flux of the DG-IGA scheme.

Note that the above approach is quite simple and it follows the same ideas that we use for computing the numerical fluxes in the case of matching parametrized interfaces. It can be also applied for the case of having gap regions between the patches. The advantage of implementing this approach is that we can develop a flexible DG-IGA code which can treat patch unions with matching and non-matching interfaces in a similar way. Note also that the previous approach can be easily combined with the adaptive numerical quadrature methods presented in [Citation34], in order to discretize the problem using non-matching structured meshes on the overlapping faces.

Overlapping regions with boundary consisting of more than two faces are shown in Figure (b). We consider again the case where the maximum number of the overlapping patches is two. For the example shown in Figure (b) the domain has four patches and the boundary of the overlapping region is compromised of the four faces Foi,i=1,,4. Anyway, the evaluation of the interface numerical fluxes, in this case, needs more work. We first find the faces that form the boundary of the overlapping regions. Then between these faces, we determine those that are diametrically opposite, and we continue following the procedure described in the previous paragraph. This type of overlapping regions are discussed in the numerical Example 4.3.

It is clear that through a segmentation and parametrization procedure, overlapping regions with more complicated shapes than the shapes in the examples shown here can exist, e.g. more than two overlapping patches, T-joint faces on the boundary, see, e.g. [Citation10]. In an ongoing work, we are extending the present methodology to treat these cases. We also are constructing domain-decomposition methods, [Citation35], on these type of multipatch representations and we are discussing the influence of the size of the overlapping region on the performance of the proposed methods. We treat these cases by extending the ideas presented here. Again, we first find the interior faces that form the boundary of the overlapping regions and then we construct the numerical fluxes between the opposite faces in the same way as we presented in the previous sections. The first results of this work are included in [Citation25]. We point out that when there are more than two overlapping patches, it is possible to have more than two (overlapped) numerical solutions on the overlapping regions. Consequently, in the error analysis, we will have more than one non-consistency terms to estimate, see Subsection 3.2. Also, we add that for multipatch unions with large overlapping regions, we can apply ideas coming from Schwarz domain-decomposition methods in order to treat the whole problem, see [Citation29,Citation33].

Finally, we mention that during the investigation of the proposed methodology in Section 3, we considered simple interior penalty fluxes on Ωo21. For the performance of the numerical examples below, we have implemented the corresponding symmetric numerical fluxes, i.e. Fo1212(ρ1u1,h+ρ2u2,h(Φo12))nFo12φ1,h+η{ρ}h(u1,hu2,h(Φo12))φ1,hds. See [Citation6,Citation23]. During the derivation of the discretization error analysis in [Citation25], estimates and bounds for the values of the parameter η are introduced. Anyway for the numerical examples here we set η=2((p+1)(p+d)/d)1/2 where d is the dimension of Ω.

4.2. Numerical examples

In this section, we perform several numerical tests with different shapes of overlapping regions as well as combinations with non-homogeneous diffusion coefficients for two- and three-dimensional problems. We investigate the order of accuracy of the DG-IGA scheme proposed in (Equation49). All examples have been performed using a second degree (p = 2) B-spline spaces. We present the asymptotic behavior of the error convergence rates for widths do=hλ with λ{1,2,2.5,3}. Every example has been solved applying several mesh refinement steps with ,hi,hi+1,, satisfying Assumption 2.2. The numerical convergence rates r have been computed by the ratio r=ln(ei/ei+1)/ln(hi/hi+1),i=1,2,, where the error ei:=uuhDG is always computed on the meshes i=12Thi,Ωi(i). We mention that, in the test cases, we use highly smooth solutions in each patch, i.e. p+1, and therefore the order s in (Equation60) and (Equation63) becomes s = p. The predicted values of power β, the order s and the expected convergence rate r, for several values of λ, are displayed in Table . In any test case, the overlap regions are artificially created by moving the control points, which are related to the interfaces Fij, in the direction of nFij or of nFij.

Table 1. The values of the expected rates r as they result from estimate (Equation63).

All tests have been performed in G+SMO [Citation36], which is a generic object-oriented C++ library for IGA computations, [Citation37,Citation38]. In Section 3, we developed and provided a rigorous analysis for the DG-IGA method (Equation51) which includes a non-symmetric numerical flux. In the materialization of the method, we utilized the associated symmetrized version of the numerical flux, [Citation39]. For solving the resulting linear system, we use the DG-IETI-DP method presented in [Citation35], see also [Citation40] for an analysis of the method and [Citation41] for results on parallel scalability.

Although in the analysis, we consider meshes with similar quasi-uniform patch-wise properties, it is known that the introduction of DG techniques on the subdomain interfaces makes the use of non-matching and non-uniform meshes easier, see [Citation6]. Keeping a constant linear relation between the sizes of the different patch meshes, the approximation properties of the method are not affected, [Citation6]. In the examples below, we exploit this advantage of the DG methods and first solve two-dimensional problems considering non-matching meshes. The convergence rates are expected to be the same as those displayed in Table .

4.3. Two-dimensional numerical examples

The control points with the corresponding knot vectors of the domains given in Example 4.1–4.3 are available under the names yeti_mp2, 12pSquare and bumper as .xml files in G+SMO.Footnote1

Example 4.1

Uniform diffusion coefficient ρi=1,i=1,,N

The first numerical example is a simple test case demonstrating the applicability of the proposed technique for constructing the DG-IGA scheme on segmentations including overlaps with the general shape. The domain Ω with the N = 21 subdomains Ωi and the initial mesh are shown in Figure (a). We note that we consider non-matching meshes across the interior interfaces. The Dirichlet boundary condition and the right-hand side f are determined by the exact solution u(x,y)=sin(π(x+0.4)/6)sin(π(y+0.3)/3)+x+y. In this example, we consider the homogeneous diffusion case, i.e. ρi=1 for all Ωi,i=1,,N.

Figure 4. Example 4.1: (a) The patches Ωi with the initial non-matching meshes and the contours of the exact solution. (b) The contours of the uh solution for do=h. (c) The convergence rates for the different values of λ.

Figure 4. Example 4.1: (a) The patches Ωi∗ with the initial non-matching meshes and the contours of the exact solution. (b) The contours of the uh∗ solution for do=h. (c) The convergence rates for the different values of λ.

We performed four groups of computations, where for every group the maximum size of do was defined to be O(hλ), with λ{1,2,2.5,3}. In Figure (b) we present the discrete solution for d0=h. Since we are using second-order (p = 2) B-spline space, based on Table , we expect optimal convergence rates for λ=2.5 and λ=3. The numerical convergence rates for several levels of mesh refinement are plotted in Figure (c). They are in very good agreement with the theoretically predicted estimates given in Theorem 3.2, see also Table . We observe that we have optimal rates r for the cases where λ2.5 and suboptimal for the rest values of λ.

Example 4.2

Different diffusion coefficients ρ1ρ2

In the second example, we consider a rectangular domain Ω, that is described as a union of N = 12 patches, see Figure (a). Here, we study the case of having smooth solutions in each Ωi but discontinuous coefficient, i.e. we set ρi=3π/2 for the patches belonging to half plane x0 and we set ρi=2 for the rest patches according to the pattern in Figure (a). By this example, we numerically validate the predicted convergence rates on TH with overlaps, for the case of having smooth solutions and discontinuous coefficient ρ. The exact solution is given by the formula (67) u(x,y)=sin(π(2x+y))ifx<0sinπ3π2x+yotherwise.(67) The boundary conditions and the source function f are determined by (Equation67). Note that, we have u|Fij=0 as well as ρu|FijnFij=0 for all the interior physical interfaces Fij.

Figure 5. Example 4.2: (a) The overlapping patches Ωi and the pattern of diffusion coefficients ρi, (b) The contours of uh on every Ωi computed with d0=0.06, (c) The convergence rates for the four choices of λ.

Figure 5. Example 4.2: (a) The overlapping patches Ωi∗ and the pattern of diffusion coefficients ρi, (b) The contours of uh∗ on every Ωi computed with d0=0.06, (c) The convergence rates for the four choices of λ.

The problem has been solved on a sequence of meshes with h0,,hi,hi+1,, following a sequential refinement process, i.e. hi+1=hi/2, where we set do=hiλ, with λ{1,2,2.5,3}. For the numerical tests, we use B-splines of the degree p = 2. Hence, we expect optimal rates for λ2.5. In Figure (b) the approximate solution uh is presented on a relative coarse mesh with do=0.06. The results of the computed rates are presented in Figure (c). For all test cases, we can observe that our theoretical results presented in Table  are confirmed.

Example 4.3

Overlapping regions with more than two faces

The proposed method is now applied to a more complicated overlapping boundary with multiple faces. The geometric description of the problem in shown in Figure (a), the domain is decomposed into four patches and the overlapping region is defined by four interfaces. The exact solution is given by (68) u(x,y)=sin(π(x+0.4))sin(2π(y+0.3))+x+y.(68) The diffusion coefficient is globally constant, i.e. ρ=1, the right-hand side f and the Dirichlet boundary conditions are manufactured by the solution (Equation68). We solved the problem using B-splines of degree p = 2. In Figure (b), we present the contours of the DG-IGA solution uh computed on the second mesh in a sequence. The corresponding error convergence results for the four values of λ, i.e. λ{1,2,2.5,3}, are given in Figure (c). We can observe the suboptimal behavior of the rate for λ=1 and λ=2 as we move to the last mesh levels. On the other hand, we have optimal rates for the rest values of λ. The numerical rates for all λ cases are in agreement with the theoretical results.

Figure 6. Example 4.3: (a) The overlapping patches Ωi and the multiple curve boundary of the overlapping region, (b) The contours of uh on every Ωi computed on the second mesh level, (c) The convergence rates for the four choices of λ.

Figure 6. Example 4.3: (a) The overlapping patches Ωi∗ and the multiple curve boundary of the overlapping region, (b) The contours of uh∗ on every Ωi computed on the second mesh level, (c) The convergence rates for the four choices of λ.

4.4. Three-dimensional numerical examples

As a final example, we consider a three-dimensional test. The domain Ω has been constructed by a straight prolongation to the z-direction of a two-dimensional (curved) domain, see Figure (a). The two physical domains Ω1 and Ω2 have the physical interface F12 consisting of all points (x,y,z) such that 1x0,x+y=0 and 0z1, see Figure (a). The knot vector in the z-direction is simply Ξi3={0,0,0,0.5,1,1,1} with i = 1, 2. We solve the problem using matching meshes, as depicted in Figure (a). The B-spline parametrizations of these domains are constructed by adding a third component to the control points with the following values {0,0.5,1}. The completed knot vectors Ξi=1,2k=1,2,3 together with the associated control nets can be found in G+SMO library in the file bumper.xml. The overlap region is artificially constructed by moving only the interior control points located at the interface into the normal direction nF12 of the related interface F12. Due to the fact that the overlap has to be inside the domain, we have to provide cuts through the domain in order to visualize them, cf. Figure (b). The Dirichlet boundary conditions uD and the right-hand side f, see (Equation1), are chosen such that the exact solution is (69) u(x,y,z)=sinπ2(x+y)if (x,y)Ω1,esin(x+y)if (x,y)Ω2.(69) with diffusion coefficient ρ={1,π/2}. Note that the interface conditions (Equation15) are satisfied. The two physical subdomains, the initial matching meshes and the exact solution are illustrated in Figure (a). We construct an overlap region with do=0.5 and solve the problem using p = 2 B-spline functions. In Figure (b), we show the domain meshes Thi,Ωi(i),i=1,2, the overlapped meshes in Ωo12 and we plot the contours of the produced solution uh for the interior plane z = 0.5. We can see that, both faces of Ωo12 are not parallel to the Cartesian axes. Moreover, we point out that the problem has been solved using non-matching meshes on the overlapping interfaces. We have computed the convergence rates for four different values λ{1,2,2.5,3} related to the overlapping region width do=hλ. The results of the computed rates are plotted in Figure (c). We observe from the plots that the rates r are in agreement with the rates predicted by the theory, see estimate (Equation63) and Table .

Figure 7. Example 4, ΩR3: (a) The physical patches with an initial coarse mesh and the contours of the exact solution, (b) The contours of uh computed on Ω1Ω2 with do=1.5, (c) Convergence rates r for the four values of λ.

Figure 7. Example 4, Ω⊂R3: (a) The physical patches with an initial coarse mesh and the contours of the exact solution, (b) The contours of uh∗ computed on Ω1∗∪Ω2∗ with do=1.5, (c) Convergence rates r for the four values of λ.

5. Conclusions

In this article, we have proposed and analyzed a DG-IGA scheme for discretizing linear, second-order, diffusion problems on IGA multipatch representations with small overlapping regions. This type of multipatch representation leads to the use of different diffusion coefficients on the overlapping patches. Auxiliary problems were introduced in every patch and DG-IGA methodology applied for discretizing these problems. The normal fluxes on the overlapped interior faces were appropriately modified using Taylor expansions, and these fluxes were further used to construct numerical fluxes in order to couple the associated discrete DG-IGA problems. The method was successfully applied to the discretization of the diffusion problem in cases with complex overlaps. A priori error estimates in the DG-norm were shown in terms of the mesh-size h and the maximum width do of the overlapping regions. The estimates were confirmed by solving several two- and three- dimensional test problems with known exact solutions. The theoretical estimates were also confirmed by performing numerical tests using non-matching grids on the overlapping faces.

Acknowledgments

The authors wish to thank Prof. Ulrich Langer, Prof. Bert Jüttler and Prof. Dirk Pauly for many interesting discussions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Austrian Science Fund (FWF) under the grant NFN S117-03 and grant W1214-N15, project DK4. The second author was further supported by the Project: COMET K2 XTribology, No. 849109; Project grantee: Excellence Center of Tribology.

Notes

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Appendix

A.1. A bound for the extra non-consistent term

Comparing the relations given in (Equation27) and (Equation33) we can see that there is an extra term (ρ2ρ1)u2 in Ωo21, which is a non consistent term. We derive below a bound for this term.

Let φH01(Ω2). By a simple computations on the forms in (Equation26), we have that (A1) a2(u2,φh)=Ωo21ρ1u2φdx+Ω2ρ2u2φdxΩ2Ωρ2u2nΩ2φdσFo21ρ2u2nFo21φdσ=Ωo21(ρ1ρ2)u2φdx+l2,f(φ).(A1) On the other hand, under the Assumption 2.3, we immediately have that (A2) ao,2(u,φ2)=Ωo21ρ1uφdx+Ω2ρ2uφdxFo21ρ1unFo21φdσΩ2Ωρ2unΩ2φdσ=l2,f(φ).(A2) Subtracting (EquationA2) from (EquationA1) and using φ|Ω2=0 we obtain (A3) Ωo21ρ1(u2u)φdx+Ω2ρ2(u2u)φdx=Ωo21(ρ1ρ2)u2φdx.(A3) Applying integration by parts on the right-hand side in (EquationA3) and then setting φ=u2u, we derive that (A4) Ω2ρ|(u2u)|2dx=cρΩo21ρ2Δu2(u2u)dx+Fo12ρ2u2nFo12(u2u)dσcρΩo21f(u2u)dx+Fo12ρ2u2nFo12(u2u)dσ(2)cρfL2(Ωo21)u2uL2(Ωo21)+ρ2u2L2(Fo12)u2uL2(Fo12)(3)cρfL2(Ωo21)u2uL2(Ωo21)+ρ2u2L2(Fo12)u2uL2(Ωo21)1/2u2uH1(Ωo21)1/2(3)c1(fL2(Ωo21)do(u2u)L2(Ωo21)+ρ2u2L2(Fo12)do1/2(u2u)L2(Ωo21)1/2(do+1)(u2u)L2(Ωo21)1/2c2(fL2(Ωo21)+ρ2u2L2(Fo12))do1/2(u2u)L2(Ωo21),(A4) where we have used that 0<do<1. By (EquationA4), we can easily obtain that (A5) ρ(u2u)L2(Ω2)c2do1/2(fL2(Ωo21)+ρ2u2L2(Fo12)),(A5) and this gives an estimate of the difference between the physical solution u and the perturbed solution u.

A.2. Proof of the interpolation estimate (62)

Note that by Assumption 2.4 and the definition of (Equation25a) we can conclude that Ω1=Ω1 and u|Ω1=uΩ1. Hence we can construct an interpolant Π1,hu such that (A6) ((u1Π1,hu1)L2(Ω1)2+h(u1Π1,hu1)L2(Fo12)2+1h(u1Π1,hu1)L2(Fo21)2)1/2C1hmin(1,p)uH(Ω1).(A6) Next we show an interpolation estimate for u on Ω2. Let us denote D1=Ωo21 and D2=Ω2. Let the extension operator Ei:H(Di)H(Ωi),i=1,2, such that for each vH(Di) it holds (i) (Eiv)|Di=v and (ii) EivH(Ωi)CEivH(Di), where the constant CEi depending only on Di and Ωi, see [Citation30]. We recall the B-spline interpolation operatorΠh given in (Equation58) and define (A7) Πhv~:=(Π1,hv~1,Π2,hv~2),whereΠi,hv~i:=Πi,hEiv.(A7) Recalling ui=u|Ωi and using the properties of the extension operator and (Equation58) we have (A8a) (u1Π1,hu~1)L2(Ωo21)(E1uΠ1,hu~1)L2(Ω1)CintpCE1hsu1H(Ω1),(A8a) and (A8b) (u2Π2,hu~2)L2(Ω2)(E2uΠ2,hu~2)L2(Ω2)CintpCE2hsu2Ω2,(A8b) where s=min(p,1).

Using the trace inequality, [Citation6], vL2(F21)2C(h1vL2(Ωo21)2+h|vL2(Ωo21)2) and proceeding as in (A8) we can show (A9a) (h(u1Π1,hu~1)L2(Fo21)2)1/2CintpCE1hsu1H(Ω1),(A9a) (A9b) 1h(u1Π1,hu~1)L2(Fo21)21/2CintpCE1hsu1H(Ω1).(A9b) Gathering the inequalities (EquationA6), (A8) and (EquationA9b) we can derive (Equation62).