MAXIMUM NORM A POSTERIORI ERROR ESTIMATE FOR A 3D SINGULARLY PERTURBED SEMILINEAR REACTION-DIFFUSION PROBLEM

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1 MAXIMUM NORM A POSTERIORI ERROR ESTIMATE FOR A 3D SINGULARLY PERTURBED SEMILINEAR REACTION-DIFFUSION PROBLEM NARESH M. CHADHA AND NATALIA KOPTEVA Abstract. A singularly perturbed semilinear reaction-diffusion problem in the unit cube, is discretized on arbitrary nonuniform tensor-product meshes. We establish a second-order maximum norm a posteriori error estimate that holds true uniformly in the small diffusion parameter. No mesh aspect ratio condition is imposed. This result is obtained by combining (i) sharp bounds on the Green s function of the continuous differential operator in the Sobolev W, and W, norms and (ii) a special representation of the residual in terms of an arbitrary current mesh and the current computed solution. Numerical results on a priori chosen meshes are presented that support our theoretical estimate. Key words. Semilinear reaction-diffusion, singular perturbation, a posteriori error estimate, maximum norm, no mesh aspect ratio condition, finite differences, layer-adapted mesh. AMS subject classifications. 65N06, 65N5, 65N50. Introduction We focus on the following singularly perturbed semilinear reaction-diffusion problem posed in the unit cube: (.) T u := ε u + b(x, u) = 0, x = (x, x, x 3 ) Ω = (0, ) 3, u(x) = 0, x Ω. Here ε is a small positive parameter, = /x + /x + /x 3 is the standard Laplace operator, the function b is sufficiently smooth and satisfies (.) 0 < β < b u (x, u) β for all (x, u) [0, ] 3 R. Under condition (.), problem (.) has a unique solution, which exhibits sharp boundary layers of width O(ε ln ε ) along the boundary Ω. The aim of the present paper is to extend the two-dimensional a posteriori error estimate of [9] to three dimensions. This result is obtained by combining a special representation of the residual and sharp bounds on the Green s function of the linearized continuous differential operator in the Sobolev W, and W, norms. Compared to [9], the main difficulties in the present paper lie in the analysis of the Green s function. First, we use the explicit fundamental solution for the constantcoefficient operator ε + γ, and it is different in two and three dimensions. Furthermore, some parts of the analysis [9] for the variable-coefficient case, e.g., [9, 3.], do not yield the desired estimates in three dimensions (one gets additional negative powers of ε in the right-hand sides of the asserted bounds). Other parts This publication has emanated from research conducted with the financial support of Science Foundation Ireland under the Research Frontiers Programme 008; Grant 08/RFP/MTH536. Department of Mathematics and Statistics, University of Limerick, Limerick, Ireland, naresh.chadha@ul.ie, natalia.kopteva@ul.ie.

2 of [9] were in some ways simplified in the current paper. We refer the reader to Remark 3. for a more detailed comparison. In a more general context, we note that sharp estimates for continuous Green s functions (or their generalized versions) frequently play a crucial role in a priori and a posteriori error analyses [, 4, 7]. Our error estimate will be in the maximum norm, which is sufficiently strong to capture layers and hence seems most appropriate for layer solutions. (The few known a posteriori error estimates for anisotropic meshes are in a weaker energy norm; see, e.g., [, ].) We also refer the reader to related papers on maximum norm a posteriori error estimation in one dimension [6, 7, 0, 4]. Another essential feature of our error estimate, is that we assume no mesh aspect ratio condition. It is crucial in the context of layer solutions, as for problems of type (.), a priori error analyses [6, 5, 8, 8] show that ε-uniform numerical approximations of layer solutions can be obtained using relatively small ε-independent numbers of mesh nodes. But this is attained by using a priori meshes that are anisotropic in layer regions, i.e. include extremely thin mesh cells that have extremely high aspect ratios (typically O(ε )). Note that a posteriori error estimates are typically obtained under the shape-regularity condition (equivalent to bounded-mesh-aspect-ratio condition) [, 3,, 7], so such estimates do not seem very suitable for constructing efficient layer-adapted meshes. We make no attempt to suggest or analyze any particular adaptive mesh generation algorithm. But we note that many successful algorithms are based on interpolation error estimates such as presented in [0, 9, 7], roughly speaking, the criterion on the generated mesh being a small interpolation error. Thus the generated, possibly, anisotropic mesh is supposed to be (quasi-)uniform under the metric induced by the positive definite Hessian matrix of the solution (or its scaled majorant); see, e.g., [6,, 5, 9]. It should be noted that such algorithms are not completely theoretically justified. E.g., the relation of the actual error of a numerical method to the interpolation error under no mesh aspect ratio condition is still to be established for many problems, in particular, in the maximum norm. Furthermore, linear interpolation error bounds involve the Hessian matrix of the unknown exact solution, which is replaced in the adaptive algorithm by its computed-solution analogue. To theoretically justify this replacement, one still needs to establish Hessian-matrix recovery formulas under no mesh aspect ratio condition, which are not available in the literature. An alternative theoretical justification, to which this paper aims to contribute, might be given by a posteriori error estimates that hold true under no mesh aspect ratio condition and directly relate the actual error to a certain discrete linearinterpolation-error-bound analogue, which involves the local mesh sizes and certain computed-solution approximations of the second-order derivatives. Indeed, roughly speaking, our a posteriori error estimate (.3), (.4) below is of this type, i.e. might be viewed as a discrete analogue of the linear interpolation error estimates. Our problem (.) will be discretized using the standard second-order seven-point difference scheme (see (.) below) on an arbitrary tensor-product mesh x ij l } in ) with 0 = x[0] s < x [] s < < x [N s] = for [0, ] 3, where x ij l = (x [i], x[j], x[l] 3 s =,, 3, while h i = x [i] x[i ], τ j = x [j] x[j ] and k l = x [l] 3 x[l ] 3 are the local mesh sizes. Note that such tensor-product meshes present an idealized situation, as practical a posteriori mesh construction algorithms use either irregular meshes or curvilinear s

3 3 tensor-product meshes. Therefore we consider our error estimate more interesting from a theoretical point of view since it shows that the bounded-mesh-aspect-ratio condition is not essential for a posteriori errors (as well as for interpolation errors). Also, if (possibly-curvilinear) tensor-product meshes are used in layer regions, where the mesh adaptation is most needed, one might conjecture that a local version of our a posteriori error estimate would apply there. Our main result is the following maximum norm a posteriori error estimate, in which the error is understood as the difference between the exact solution and the trilinear interpolant of the computed solution: τ (.3) U I u C 0 [ max i=,...,n j=0,...,n l=0,...,n 3 h i M,ij l } + max see Theorem.. Here, roughly speaking, i=0,...,n j=,...,n l=0,...,n 3 j M,ij l } + max i=0,...,n j=0,...,n l=,...,n 3 (.4) M s,ij l D su ij l ln ( + ε/κ ) +, s =,, 3, k l M 3,ij l } ] ; with κ = minminh i }, minτ j }, mink l }}. By U I we denote the trilinear interpolant of the computed solution U (the finite difference computed solution is orig- i j l inally defined at the mesh nodes only; hence to measure the error in the entire domain, one first has to interpolate the computed solution there). The quantities DsU ij l for s =,, 3 are the standard discrete approximations of u/xs defined in (.3). In (.4), a few terms are skipped, for which the one-dimensional analysis [7] and the numerical results of [9] and 6 suggest that they are less important; see Theorem. for the precise definitions of M s,ij l. The error constant C 0 in (.3) is independent of ε, the mesh and aspect ratios of its elements, but we do not specify its value. (Note that for singularly perturbed problems, the error constant may grow as ε becomes small; hence that it is ε-uniform is more important than its precise value.) The paper is organized as follows. In, we describe the numerical method, present our a posteriori error estimate in Theorem., and outline its proof. Next, in 3, we establish some sharp bounds on the Green s function of a linearized version of (.) in the Sobolev W, and W, norms. They imply certain stability properties of the differential operator T from (.), which are presented in 4. Then in 5, we obtain a special representation of the residual in terms of an arbitrary current mesh and the current computed solution, and therefore complete the proof of Theorem.. Finally, in 6, numerical results on a priori chosen meshes are given that support our theoretical estimate. Notation. Let p ; Ω, where p, denote the standard L p ( Ω) norm of scalar or vector functions defined in any domain Ω R 3. Furthermore, the standard notation W k,p ( Ω) is used for the Sobolev spaces with the norm k,p ; Ω defined, for any scalar function v(x) in a domain Ω, by v k,p ; Ω = v p ; Ω + k l= v l,p ; Ω, k =,, v,p ; Ω = 3 s= x s v p ; Ω, v,p ; Ω = 3 s,t= x s x t v p ; Ω ; see, e.g., [3]. We shall use the notation p and k,p for p ;Ω and k,p ;Ω when there is no ambiguity. Sometimes the domain of interest will be an open ball B(a ; ρ) = B(a, a, a 3 ; ρ) = x : 3 s= (x s a s ) < ρ } centered at a of radius ρ.

4 4 Throughout the paper we let C denote a generic positive constant that may take different values in different formulas, but is always independent of the mesh and ε. A subscripted C (e.g., C ) denotes a positive constant that is independent of the mesh and ε and takes a fixed value. For any two quantities w and w, the notation w = O(w ) means w Cw. Remark.. The assumption b u (x, u) β in (.) can be omitted as it follows, for some constant β, from 0 < β < b u (x, u) and u being a unique and bounded solution of (.), (to be more precise, u β b(, 0) ); see, e.g., [30, ]. Note that assumption (.) enables us to linearize (.) and then invoke the Green s function in our analysis.. Numerical method. Main result We consider problem (.) under the standard compatibility conditions at the corners of the domain Ω: (.) b(x, 0) = 0 for x = (x, x, x 3 ) : x, x, x 3 0, }, which guarantee that u C 3 ( Ω). Numerical method. The computed solution U is required to satisfy the standard seven-point finite difference discretization of (.) (.) ε [D U ij l + D U ij l + D 3U ij l ] + b(x ij l, U ij l ) = 0, where i =,... N, j =,..., N, l =,..., N 3, subject to U ij l = 0 on the boundary, i.e. if i = 0, N or j = 0, N or l = 0, N 3. Here, as usual, U ij l is associated with the mesh node x ij l = (x [i], x[j], x[l] 3 ), and we use the standard finite difference operators, defined for any discrete function V ij l by (.3) D V ij l = V ij l V i,j,l, D h V ij l = D V i+,j,l D V ij l, i (h i + h i+ )/ D V ij l = V ij l V i,j,l, D τ V ij l = D V i,j+,l D V ij l, j (τ j + τ j+ )/ D3 V ij l = V ij l V i,j,l, D k 3V ij l = D 3 V i,j,l+ D3 V ij l. l (k l + k l+ )/ By (.), there exists a unique solution of the discrete problem (.) on an arbitrary mesh x ij l }; see, e.g., [5]. At this stage, D U ij l is defined for i =,..., N, j = 0,..., N, l = 0,..., N 3. Similarly D U ij l and D 3U ij l are defined for j =,..., N, i, l, and l =,..., N 3, i, j, respectively. We now extend D U ij l to the mesh nodes i = 0, N as follows. The zero boundary conditions imply that D su 0,j,l = D su N,j,l = 0 for s =, 3. In view of this, the discrete equation (.) formally extended to i = 0 and i = N, becomes (.4a) D U ij l := ε b(x ij l, 0) for i = 0, N, j = 0,..., N, l = 0,..., N 3. Similarly, we extend D U ij l to j = 0, N and D 3U ij l to l = 0, N 3 by (.4b) (.4c) D U ij l := ε b(x ij l, 0) for j = 0, N, i = 0,..., N, l = 0,..., N 3. D 3U ij l := ε b(x ij l, 0) for l = 0, N 3, i = 0,..., N, j = 0,..., N. Note that, by (.), the above relations (.4) imply that D U ij l = D U ij l = D 3U ij l = 0 at the corners of our domain, which is consistent with the boundary condition in (.).

5 5 Remark.. Now that DsU ij l, where s =,, 3, are extended by (.4) to all i, j, l, our discrete equation (.) holds true for all i = 0,..., N, j = 0,..., N and l = 0,..., N 3. Trilinear interpolation notation. Let U I = U I (x) be the standard trilinear interpolant of the computed solution U ij l, i.e. U I is continuous in Ω, trilinear on each (x [i ], x [i] ) (x[j ], x [j] ) (x[l ] 3, x [l] 3 ), and equal to U ij l at the mesh nodes: (.5) U I (x ij l ) = U ij l for i = 0,..., N, j = 0,..., N, l = 0,..., N 3. Similarly, we define the trilinear interpolant v I (x) for any discrete function v ij l or any continuous function v(x). Furthermore, we shall use the standard one-dimensional linear interpolants v I s with respect to x s for s =,, 3, that are defined, for any function v, as follows. For each fixed x, x 3 in the domain of v, we have v I (x [i], x, x 3 ) = v(x [i], x, x 3 ), and v I (x) is linear on each (x [i ], x [i] ). Similarly, vi (x, x [j], x 3) = v(x, x [j], x 3), v I 3 (x, x, x [l] 3 ) = v(x, x, x [l] 3 ) and, furthermore, vi and v I 3 are linear on each (x [j ], x [j] ) and (x[l ] 3, x [l] 3 ), respectively. Note that the trilinear interpolation can be represented as a product of the three one-dimensional interpolation operators independently of the order of the interpolation steps. In particular, for the trilinear interpolant U I of U ij l we have (.6) U I (x) = [ U I ] II3 = [ U I ] II3 = [ U I3 ] II. The next theorem gives a maximum norm a posteriori error estimate, which is the main result of the present paper. Note that this is an extension to three dimensions of [9, Theorem.]. Theorem.. Let u(x) be a solution of problem (.), (.), (.), U ij l a solution of discrete problem (.) on an arbitrary mesh x ij l }, and U I (x) its trilinear interpolant (.5). Then [ U I } u C 0 max h i=,...,n i M,ij l + max j=0,...,n l=0,...,n 3 where i=0,...,n j=,...,n l=0,...,n 3 } τ j M,ij l + } ] max k i=0,...,n l M 3,ij l, j=0,...,n l=,...,n 3 M,ij l := min D U i,j,l, D U ij l } ln ( + ε/κ ) + ε D D U ij l + D U ij l +, M,ij l := min D U i,j,l, D U ij l } ln ( + ε/κ ) + ε D D U ij l + D U ij l +, M 3,ij l := min D 3U i,j,l, D 3U ij l } ln ( + ε/κ ) + ε D 3 D 3U ij l + D 3 U ij l +, with κ :=minminh i }, minτ j }, mink l }}, and the constant C 0 is independent of ε i j l and the mesh. Proof outline. To simplify the presentation, here and throughout 4-5, in which this proof is continued, we assume that N = N = N 3 = N. In view of (.), one gets T U I T u = ε [ /x + /x + /x 3] U I + b(x, U I ). Note that here U I /x s, for s =,, 3, are understood in the sense of distributions. Next, we introduce an auxiliary function q(x) := b(x, U I (x))

6 6 and its trilinear interpolant q I on the mesh (x ij l )}. Now one has T U I T u = ε [ /x + /x + /x 3] U I + q I + [ q q I]. As q ij l := q(x ij l ) = b(x ij l, U ij l ), in view of Remark., the discrete equation (.) yields q ij l = ε [D U ij l + D U ij l + D 3U ij l ] for i, j, l = 0,..., N. This observation leads to the decomposition q ij l = q,ij l + q,ij l + q 3,ij l, where (.7) q s,ij l := ε D s U ij l for s =,, 3, i,j, l = 0,..., N, and then to q I (x) = q I (x) + q I (x) + q I 3(x) = [ q I ]I I 3 + [ q I ]I I 3 + [ q I3 3 ]I I, x Ω. Here we also used a version of (.6) for functions q s with s =,, 3. We now get T U I T u = [ ε U I +q I ] I I x 3+ [ ε U I +q I ] I I x 3+ [ ε U I 3 +q I ] I I x 3 + [ 3 3 q q I ]. To obtain this equation, we also used the following relations: (.8) U I = [ U I ] I x x I 3, U I = [ U I ] I x x I 3, U I = [ U I ] I x 3 x 3 I. 3 The above three relations follow from (.6) as any operator / x s is commutative with I t for t s (but not with I s ); see also Remark.3. The proof will be completed in 5 by representing the residual T U I T u as (.9) T U I T u = x F (x) + x F (x) + x 3 F 3 (x) + [ q q I], where F, F and F 3 are some functions of the current mesh and computed solution. In view of (.9), the error U I u will be estimated in the maximum norm by linearizing the operator T and invoking its stability properties. Stability of T will be addressed in 4 using certain bounds for the Green s function of 3. Remark.3. We understand U I /xs, for s =,, 3, in the sense of distributions. To be more precise, in (.8) we use U I = N [ ] x i= i DU ij l δ(x x [i] ), [ ] II U I 3 N = i= [ ] i D II 3 U ij l δ(x x [i] ), x where i := (h i + h i+ )/ and δ( ) is the Dirac δ-distribution. 3. Green s function To investigate the error U I u, we shall linearize (.9) and then invoke certain estimates of the Green s function of the resulting linear equation. These estimates are the main result of this section. Thus, throughout the section, we focus on a linear case of (.), where we set b(x, u) := p(x)u f(x) and thus arrive at (3.) Lu := ε u + p(x)u = f(x) in Ω, u = 0 on Ω. Here p L (Ω), and, in agreement with (.), it also satisfies (3.) 0 < β p(x) β. Let G(x; ξ) be the Green s function of the linear self-adjoint operator L. For each x = (x, x, x 3 ) Ω, it satisfies (3.3) L ξ G = ε ξ G + p(ξ)g = δ(x ξ), ξ Ω, G(x; ξ) = 0, ξ Ω,

7 7 where ξ = (ξ, ξ, ξ 3 ) and ξ = /ξ + /ξ + /ξ, while δ( ) is the threedimensional Dirac δ-distribution. We now have an explicit formula for the unique solution u of problem (3.): (3.4) u(x) = G(x; ξ) f(ξ) dξ, where we used the notation dξ = dξ dξ dξ 3. Theorem 3.. The Green s function G(x; ξ) from (3.3) satisfies (3.5a) G(x; ) ;Ω + ε G(x; ), ;Ω C. Ω Furthermore, for any ball B( x ; ρ) of radius ρ centered at any x Ω we have (3.5b) G(x; ), ;B( x ;ρ) Ω C ε ρ; while for the ball B(x ; ρ) of radius ρ centered at x, we have (3.5c) G(x; ), ;Ω\B(x ;ρ) C ε ln( + ε/ρ). This entire section is devoted to the proof of this theorem, which is the main result of the section. The proof is in two steps. First, in 3. we shall estimate the auxiliary Green s function Ḡ in the constant-coefficient case in the positive octant space using the explicit fundamental solution. The remaining part of the proof ( ) will, roughly speaking, deal with G Ḡ. Remark 3.. Note that the statement of Theorem 3. is precisely as in the twodimensional case [9, Theorem 3.]. The proof, however, is different in a few ways. First, we note that the fundamental solutions for the constant-coefficient operator ε + γ, which are used in both analyses, are different in two and three dimensions. Furthermore, the argument of [9, 3.] for a variable-coefficient case does not yield the desired estimates in three dimensions (it leads to additional negative powers of ε in the right-hand sides of our asserted bounds). For this case, we therefore give a completely different proof and even weaken the assumption x i p C of [9, 3.] to ε x i p C in 3.. The final part of the proof for the most general case of p (see 3.3) has evolved from [9, 3.3], and is simpler in the sense that now we avoid using any cut-off functions and deal either with G Ḡ or sometimes directly with G. 3.. Constant-coefficient case. The first step in the proof of Theorem 3. consists in establishing it for a particular constant-coefficient case. Set p := γ for some γ = const > 0, and let Ω be the positive octant space R 3 + = x, x, x 3 > 0}. We denote the differential operator in this particular case by L, and the Green s function by Ḡ. Thus for each x we have (3.6) Lξ Ḡ(x; ξ) := ε ξ Ḡ + γ Ḡ = δ(x ξ), ξ, ξ, ξ 3 > 0. The fundamental solution for the operator ξ + ν in R 3 is e νr /(4πr); see, e.g., [8, 8.3]. This readily provides the fundamental solution for our differential operator L, which is (3.7) g(x; ξ) := e γr/ε 4πε, r := (ξ x ) r + (ξ x ) + (ξ 3 x 3 ).

8 8 Now the Green s function for L over the octant is easily obtained by the method of images and involves eight terms of the type ±g(±x, ±x, ±x 3 ; ξ); to be more precise we have (3.8) Ḡ(x; ξ) = (σ σ σ 3 ) g ( x [σ,σ,σ 3 ] ; ξ ), x [σ,σ,σ 3 ] := (σ x, σ x, σ 3 x 3 ). σ,σ,σ 3=, Lemma 3.3. (i) For Ḡ(x; ξ) of (3.8), estimates (3.5) of Theorem 3. hold true, in which G is replaced by Ḡ. (ii) Furthermore, we have (3.9) Ḡ(x; ) ;Ω C ε 3/, Ḡ(x; ) ;Ω\B(x ;ρ) C ε 3/ e γρ/ε. Proof. It suffices to prove estimates (3.5) and (3.9) with G and Ḡ, respectively, replaced by the term g(x; ξ) of the representation (3.8) of Ḡ, as the estimates for the other seven terms are similar. Let the stretching transformation from ξ = (ξ, ξ, ξ 3 ) to the new coordinates ˆξ = (ˆξ, ˆξ, ˆξ 3 ) := (ξ x)/ε map any domain Ω R 3 into ˆΩ. Furthermore, consider a scaled version ĝ(ˆξ) of g(x; ξ) from (3.7) defined by (3.0) ĝ(ˆξ) := ε 3 g(x; ξ) = e γˆr, where ˆr := ˆξ 4π ˆr + ˆξ + ˆξ 3, so that g dξ = ĝ dˆξ, where dˆξ = dˆξ dˆξ dˆξ = ε 3 dξ. Therefore for any domain Ω we have (3.) g(x; ) k, ;Ω = ε k ĝ k, ; ˆΩ, g(x; ) ;Ω = ε 3/ ĝ ;ˆΩ. Now we shall establish parts (i) and (ii) of our lemma. (i) A calculation using the standard differentiation formulas ĝ ˆξ i = ĝ ˆr ˆr ˆξ i, ĝ ˆξ i ˆξ = ĝ j ˆr ˆr ˆξ ˆr i ˆξ + ĝ j ˆr ˆr ˆξ i ˆξ, j where ĝ ˆr = γˆr + 4π ˆr e γˆr ĝ, ˆr = γ ˆr + γˆr + 4π ˆr 3 and also ˆr/ ˆξ i = ˆξ i /ˆr and ˆr/( ˆξ i ˆξ j ) /ˆr, yields e γˆr ˆr ĝ C(γˆr + )e ˆξ γˆr, ˆr ĝ ˆr + C i ˆξ i ˆξ e γˆr. j ˆr Combining this with the first relation in (3.), we obtain the required analogues of (3.5) for g as follows. First, note that g(x; ) ;Ω + ε g(x; ), ;Ω = ĝ, ;ˆΩ C Similarly, we obtain 0 (γˆr + )e γˆr dˆr C. ρ/ε g(x; ), ;B( x ;ρ) g(x; ), ;B(x ;ρ) Cε (γˆr + )e γˆr dˆr Cε (ρ/ε); here replacing the integral over B( x ; ρ) by the integral over B(x ; ρ) yields an upper bound, since (γˆr + )e γˆr is a positive decreasing function. Finally, we get g(x; ), ;Ω\B(x ;ρ) Cε ρ/ε 0 ˆr + e γˆr dˆr ε ln( + ε/ρ). ˆr

9 9 (ii) A straightforward calculation using (3.0) shows that ĝ ; ˆΩ C e γˆr dˆr C, 0 ĝ ; ˆΩ\ ˆB(x C e γˆr dˆr Ce γρ/ε. ;ρ) Combining these with the second relation in (3.), we immediately get the required analogues of (3.9) for g. Remark 3.4. An inspection of the proof of Lemma 3.3, in which we used the explicit representation (3.8),(3.7) of the Green s function in the constant-coefficient case, shows that the estimates of the Green s function in Theorem 3. are sharp. 3.. Smooth-coefficient case. In this subsection, we shall use the estimates for the constant-coefficient Green s function Ḡ to establish a variable-coefficient case of Theorem 3. under the additional assumption that the coefficient p is differentiable and (3.) ε x i p C for i =,, 3. Lemma 3.5. If the coefficient p satisfies (3.) and (3.), then the Green s function G(x; ξ) from (3.3) satisfies estimates (3.5). Proof. Set γ := β in the definition (3.6) of Ḡ. Note that in addition to (3.3), G satisfies L x G(x; ξ) = ε x G+p(x)G = δ(x ξ) subject to G(x; ξ) = 0 for x Ω. Similarly, Ḡ satisfies L x Ḡ(x; ξ) = ε x Ḡ + γ Ḡ = δ(x ξ) subject to Ḡ(x; ξ) = 0 if x = 0 or x = 0 or x 3 = 0. Hence we have L x (Ḡ G) = [p γ ]Ḡ = [p β]ḡ 0, while Ḡ G = Ḡ 0 for x Ω. Now, applying the maximum principle, one gets Ḡ G 0, or 0 G Ḡ, so G(x; ) ;Ω Ḡ(x; ) ;Ω C, where the bound for Ḡ is given by Lemma 3.3. Next, let ξ [0, ]3 and construct a function (3.3) v(x; ξ) := G(x; ξ) ω(x) Ḡ(x; ξ), where ω(x) is a smooth cut-off function that equals for x [0, 3 4 ]3 and vanishes on the boundaries x =, x = and x 3 =. Thus v = 0 for x Ω. A calculation using L x G = L x Ḡ yields L x v = (γ p)ḡ + L x[( ω)ḡ], or where φ = φ + φ with ρ/ε L x v(x; ξ) = [ ε x + p(x)] v(x; ξ) = φ(x; ξ), φ (x; ξ) := [ γ p(x) ] Ḡ(x; ξ), φ (x; ξ) := L x [ ( ω(x)) Ḡ(x; ξ) ]. Comparing the problem for v with problem (3.) and recalling (3.4), we arrive at (3.4) v(x, ξ) = G(x; η) φ(η; ξ) dη. Applying ξ i, i =,, 3, to this formula, one gets (3.5) v(x; ), ;Ω G(x; ),Ω max η Ω φ(η; ), ;Ω. Ω We have already proved that G(x; ),Ω C. Note also that φ (η; ), ;Ω C Ḡ(η; ), ;Ω. Furthermore, if Ω [0, ]3 then φ (η; ), ;Ω C (the latter estimate holds as φ (η; ξ) = 0 for η [0, 3 4 ]3 ; otherwise, ξ η 4 so any derivative of φ is bounded by some C). Combining these observations with (3.5) and then

10 0 noting that (3.3) implies G(x; ), ;Ω at G(x; ), ;Ω v(x; ), ;Ω + Ḡ(x; ), ;Ω, we arrive C ( max η Ω Ḡ(η; ), ;Ω + ), where Ω [0, ]3. Setting Ω = [0, ]3 and Ω = B( x; ρ) [0, ]3 yields (3.5a), (3.5b) with Ω replaced by its subdomain [0, ]3. Dealing with the remaining seven cubic subdomains of Ω in a similar manner, one finally gets (3.5a), (3.5b). To establish the remaining estimate (3.5c), it suffices, by (3.3), to obtain v(x; ), ;Ω Cε. Let v = v + v, where v k, for k =,, is defined by (3.5) with v and φ respectively replaced by v k and φ k. Imitating our above argument, we get v (x; ), ;Ω C max η Ω φ (η; ), ;Ω C. We have, however, to modify our approach to estimate v (x; ), ;Ω. First, examining (3.7) and (3.8), we note that ξ g(±x, σ x, σ 3 x 3 ; ξ) = x g(±x, σ x, σ 3 x 3 ; ξ), ξ Ḡ(x; ξ) = x G(x; ξ), where G involve the same eight terms as Ḡ in (3.8), only with possibly different signs. Furthermore, G satisfies (3.5) (imitate the proof ξ g(x; ξ) = x g(x; ξ), so so of Lemma 3.3). In view of this, one gets ξ v (x, ξ) = G(x; η)γ p(η)} ξ Ḡ(η; ξ)dη = η [G(x; η)γ p(η)}] G(η; ξ)dη. Next, applying Ω ξ ξ i v (x; ) ;Ω ξ i, i =,, 3, to this formula yields Ω G(x; )γ p( )}, ;Ω max η Ω G(η; ), ;Ω Cε, where we used (3.), (3.) and (3.5a) that we already have for both G and G. As all second-order derivatives of v can be estimated in a similar manner, we get v (x; ), ;Ω Cε and thus complete the proof of (3.5c). We have now proved Theorem 3. under condition (3.). This condition is suitable for the particular linear case L of T. When we linearize (.9) in the general semilinear case, the coefficient p depends on u and U. It may satisfy (3.), but it is more convenient to avoid this assumption in our error analysis. Thus in the next subsection, we prove the general case of Theorem 3. under assumption (3.) only. Remark 3.6. One beneficial feature of the argument used in 3. is that it does not require a pointwise barrier for G (readily provided by Ḡ in 3.3). Instead, it suffices to have a sharp bound on G(x; ) ;Ω. This feature is significant, when the above argument is extended in a future paper to more complicated singularly perturbed convection-diffusion equations General case. Proof of Theorem 3.. Fix x = (x, x, x 3 ) Ω; without loss of generality, we shall consider only the case of x [0, /] 3, as the other cases are similar. Set γ := β in the definition (3.6) of Ḡ. Since, by Lemma 3.3(i), estimates (3.5) hold true for Ḡ, to get the desired estimates (3.5) for G, it suffices to show that (3.6a) ε (Ḡ G)(x; ), ;B(x ;ε) Ω + ε (Ḡ G)(x; ), ;B(x ;ε) Ω C, (3.6b) ε (Ḡ G)(x; ), ;B( x ;ρ) Ω C ρ/ε for ρ ε, (3.6c) ε G(x; ), ;Ω\B(x ;ε) + ε G(x; ), ;Ω\B(x ;ε) C.

11 Indeed, (3.5a) follows from its analogue for Ḡ combined with (3.6a) and (3.6c). The next estimate (3.5b) follows from (3.5a) if ρ > ε, and from its analogue for Ḡ combined with (3.6b) otherwise. Finally, estimate (3.5c) follows from (3.6c) for ρ ε, and is obtained combining its analogue for Ḡ with (3.6a) and (3.6c) otherwise. Now, to complete the proof, we shall establish each of the estimates in (3.6). (a) Note that, by (3.3) and (3.6), we get (3.7) L ξ (Ḡ G) = [ ε ξ + p(ξ) ] (Ḡ G) = [p(ξ) β] Ḡ. Therefore, by (3.), we have L ξ (Ḡ G) 0. Combining this with Ḡ G 0 on Ω and then applying the maximum/comparison principle yields 0 G Ḡ. Next, using the variable ˆξ = (ξ x)/ε and the notation ŵ(ˆξ) := w(ξ) for any function w, rewrite (3.7) in terms of the variable ˆξ as [ + ˆp ]( ˆḠ Ĝ) = [ˆp β] ˆḠ, or ( ˆḠ Ĝ) = ˆp ˆḠ βĝ. Now, an application of [3, Lemma 8. (Chap. 3, p. 8)] yields (3.8) ˆḠ Ĝ, ; ˆB(x ;ε) ˆΩ C [ ( ˆḠ Ĝ) ; ˆB(x ;ε) ˆΩ + ˆḠ ] Ĝ ; ˆB(x ;ε) ˆΩ, where the constant C is independent of ε since dist( ˆB(x ; ε), ˆB(x ; ε)) =. Note that the condition of [3, Lemma 8.] that ˆḠ Ĝ = 0 for ˆξ ˆΩ ˆB(x ; ε), or, equivalently, Ḡ G = 0 for ξ Ω B(x ; ε), is immediately satisfied for ε < /4 since x [0, /] 3 (otherwise, if ε [/4, ], one gets a version of (3.8) for ˆB(x ; ε ) and ˆB(x ; ε ) with ε := ε/5 < /4; then an obvious modification of our further argument will again yield (3.5)). To estimate the right-hand side in (3.8), recall that 0 G Ḡ, thus 0 Ĝ ˆḠ, so ˆḠ Ĝ ˆḠ and ( ˆḠ Ĝ) = ˆp ˆḠ βĝ C ˆḠ. These observations lead to (3.9) ˆḠ Ĝ, ; ˆB(x ;ε) ˆΩ C ˆḠ ; ˆB(x ;ε) ˆΩ. Rewriting this in terms of the original variable ξ, we get ε (Ḡ G)(x; ), ;B(x ;ε) Ω + ε (Ḡ G)(x; ), ;B(x ;ε) Ω C Ḡ(x; ) ;B(x ;ε) Ω, where Ḡ(x; ) ;B(x ;ε) Ω C ε 3/, by the first estimate in (3.9). Combining this with Ḡ G k, ;B(x ;ε) Ω Cε 3/ Ḡ G k, ;B(x ;ε) Ω, for k =,, yields (3.6a). (b) Let x be an arbitrary point in Ω and x [0, /] 3 (as the other cases are similar). To show (3.6b), imitate the argument used to prove (3.6a) with B(x ; ε) and B(x ; ε) replaced by B( x ; ρ) and B( x ; ρ + ε), invoking Ḡ G k, ;B( x ;ρ) Cρ 3/ Ḡ G k, ;B( x ;ρ) and ρ/ε. (c) Let ρ j := j ε and divide the domain Ω\B(x ; ε) into the non-overlapping subdomains D j := ξ Ω : ρ j < r < ρ j+ } where j = 0,,.... Furthermore, D j D j := D j D j D j+, so that dist(d j \Ω, D j\ω) ε/. Let the stretching transformation from ξ to ˆξ = (ξ x)/ε map D j into ˆD j. Rewriting the equation from (3.3) for ξ x in terms of the stretched variable ˆξ as Ĝ + ˆp Ĝ = 0 yields (3.0) Ĝ, ; ˆD j C Ĝ ; ˆD ; j

12 see [3, Lemma 8. (Chap. 3, p. 8)]; here the constant C is independent of ε since dist( ˆD j \ ˆΩ, ˆD j \ ˆΩ) /. Note that the condition of [3, Lemma 8.] that Ĝ = 0 on ˆD j ˆΩ is satisfied due to the boundary condition in (3.3). Rewritten in terms of the original variable ξ, estimate (3.0) implies that (3.) ε G(x; ), ;Dj + ε G(x; ), ;Dj C G(x; ) ;D j C Ḡ(x; ) ;D j. where we also used G Ḡ. Noting that D j Ω\B(x ; ρ j ) and recalling the second estimate in (3.9), we get Ḡ(x; ) ;D j Cε 3/ e γρj /ε. Combining this with (3.) and G k, ;Dj Cρ 3/ j G k, ;Dj, for k =,, we arrive at j=0 ε G(x; ), ;Dj + ε G(x; ), ;Dj Cρ 3/ j ε 3/ e γρ j /ε. Now, the required estimate (3.6c) is obtained recalling that Ω\B(x ; ε) = j=0 D j and noting that we have ( γρj ) 3/ e γρ γ(ρ j /ε j ρ j ) C e γρj/(4ε) C e s ds, ε 4ε j=0 since s / e s Ce s and ρ j = (ρ j ρ j ), and for the decreasing function e s the right Riemann sum gives a lower estimate of the corresponding integral. 4. Stability properties of differential operators In this section, we are concerned with subtle stability properties of the semilinear differential operator T from (.). The main result of this section, Theorem 4., will be applied in 5 to equation (.9), which relates the exact solution and the computed solution. Suppose the right-hand side f is of the special form (4.a) f(x) = [F (x) + F (x) ] + f(x), where F = (F, F, F 3 ) and F = ( F, F, F 3 ) are vector functions, whose components together with f are in L (Ω), and F = F /x + F /x + F 3 /x 3. Furthermore, we assume that (4.b) F (x) F 3 (x) F (x) = A i (x, x 3 ) (x x [i /] ) for x (x [i ], x [i ] ) [0, ] [0, ], (4.c) = B j (x, x 3 ) (x x [j /] ) for x [0, ] (x [j ], x [j ] ) [0, ], (4.d) = Q l (x, x ) (x 3 x [l /] 3 ) for x [0, ] [0, ] (x [l ] 3, x [l ] 3 ), where i, j, l =,..., N, respectively, and the notation x s [i /] is used with s =,, 3. γ/8 := (x [i ] s + x [i ] s )/ Theorem 4.. Suppose the function b in the definition (.) of T satisfies (.), and f is defined by (4.). Then, for any functions v, w W, (Ω) such that T v(x) T w(x) = f(x), and v = w on Ω, we have v w Cε [ E + E + E 3 ] ln ( + ε/κ ) + Cε F + β f, where κ = minminh i }, minτ j }, mink l }} and i j l E := max h i max A i(x, x 3 ) }, E := max i=,...,n x,x 3 [0,] E 3 := max l=,...,n j=,...,n k l max x,x [0,] Q l(x, x ) }. τ j max B j(x, x 3 ) }, x,x 3 [0,]

13 The above theorem is a three-dimensional version of [9, Theorem 4.]. The proof is in lines with the one in [9], so we only sketch it below for completeness. Proof. Using the standard linearization technique, one gets T v T w = L [ v w ], where the linear operator L is defined by (3.), in which, by (.), the coefficient p(x) satisfies (3.). As we now have the linear equation L[u v] = f, we shall deal with various components of f separately and, in particular, invoke the Green s function G of the operator L in our analysis. First we note that if F = (F, F, F 3 ) := 0 in (4.a), then (4.) u v Cε F + β f. This is easily shown by imitating the proof of [9, Lemma 4.], more specifically, by combining (3.4) with estimate (3.5a). Next, we claim that if F := 0 and f := 0 in (4.), then (4.3) u v Cε [ E + E + E 3 ] ln ( + ε/κ ). Combining this with the observation (4.) yields the assertion of the theorem. Thus it remains to prove (4.3). We get (4.3) by extending the proof from [9, Lemma 4.3] to three dimensions as briefly described below. Note that it suffices to get (4.3) only in the case of f := F /x, i.e. F = F 3 := 0, as the cases of f := F s /x s, for s =, 3, are similar. Fix x and denote v(ξ) := G(x; ξ). Then, using (3.4), one gets N (u v)(x) = F (ξ) v ξ (ξ) dξ = Ω i= dξ dξ 3 A i (ξ, ξ 3 ) For the integral in ξ, a calculation shows that (4.4) [i] x x [i ] (ξ x [i /] h ) v ξ (ξ) dξ i x [i] x [i ] x [i] x [i ] 3 (ξ x [i /] ) v ξ (ξ) dξ. v ξ ξ (ξ) dξ. However, we have to be careful when integrating v ξ ξ = G ξ ξ as this function has such a singularity at ξ = x that it is not in L (Ω). Thus we form a rectangularbox neighbourhood Ω of ξ = x (of diameter not exceeding O(κ)). Outside this neighbourhood the integral F v ξ dξ is estimated using (4.4) and then (3.5c). Over this neighbourhood, the integral F v ξ dξ is estimated using (3.5b). This completes the proof of (4.3) in the case of f := F /x. 5. Analysis of the numerical method. Proof of Theorem. To complete the proof of our main result, Theorem., which we started in, we shall invoke the following lemma. Lemma 5.. [7, 9] We have ε x U I s + q I s s s = x s F s, s =,, 3, where the semi-discrete functions F = F (x, x [j], x[l] 3 ), F = F (x [i], x, x [l] 3 ) and F 3 = F 3 (x [i], x[j], x 3) are defined by (5.a) F := q,ij l (x x [i /] ) + D q,ij l (x [i] x ), x (x [i ], x [i] ), for i =,..., N and j, l = 0,..., N,

14 4 (5.b) F := q,ij l (x x [j /] ) + D q,ij l (x [j] x ), x (x [j ], x [j] ), for j =,..., N and i, l = 0,..., N, (5.c) F 3 := q 3,ij l (x 3 x [l /] 3 ) + D 3 q 3,ij l (x [l] 3 x 3), x 3 (x [l ] 3, x [l] 3 ), for l =,..., N and i, j = 0,..., N. Proof. Imitate the proofs of [7, Theorem 3.3] and [9, Lemma 5.]. Remark 5.. One can easily check that F s, for s =,, 3, of (5.) allow an alternative representation: F = [q ] i,j,l (x x [i /] ) + [D q,ij l] O(h i ), x (x [i ], x [i] ), F = [q ] i,j,l (x x [j /] ) + [D q,ij l] O(τ j ), x (x [j ], x [j] ), F 3 = [q 3 ] i,j,l (x 3 x [l /] 3 ) + [D 3 q 3,ij l] O(k l ), x 3 (x [l ] 3, x [l] 3 ). Here, e.g., the new representation of F follows from q,ij l = [q ] i,j,l +h i [D q,ij l]. Proof of Theorem. (continued from ). Extend F s, s =,, 3, of Lemma 5. onto the whole domain Ω by the trilinear interpolation F (x) := [ F (x, x [j], x[l] 3 )] I I 3, F (x) := [ F (x [i], x, x [l] 3 )] I I 3, F 3 (x) := [ F 3 (x [i], x[j], x 3) ] I I. Now, noting that any operator /x s is commutative with I t for t s, we obtain the representation (.9) for the residual T U I T u. So, by Theorem 4., one gets (5.) U I u Cε [ E + E + E 3 ] ln ( + ε/κ ) + Cε Ē + β q q I, where (5.3a) E = max h i max i=,...,n x,x 3 [0,] (q,ij l) I I 3 } = max i=,...,n j,l=0,...,n h i q,ij l }, and similarly (5.3b) E = max τ j q,ij l }, E 3 = max k l q 3,ij l }, j=,...,n i,l=0,...,n l=,...,n i,j=0,...,n while Ē = max i=,...,n j,l=0,...,n h i D q,ij l } + max τ j=,...,n i,l=0,...,n j D q,ij l } + max l=,...,n i,j=0,...,n k l D 3 q 3,ij l }. Furthermore, in view of Remark 5. (compare it with (5.)), we observe that the quantities q s,ij l, s =,, 3, in (5.3) can be replaced by min q,i,j,l, q,ij l }, min q,i,j,l, q,ij l } and min q 3,i,j,l, q 3,ij l }, respectively. This yields a sharper version of (5.), (5.3), which is then combined with (.7). Now, to get the desired a posteriori error estimate of Theorem., it remains to show the trilinear interpolation estimate q q I C [ h i ( + D U ij l ) } + max j=,...,n i,l=0,...,n max i=,...,n j,l=0,...,n τ j ( + D U ij l ) } + max j=,...,n i,l=0,...,n k l ( + D 3 U ij l ) }]. This estimate follows from q q I = [q q I ] + [q I (q I ) I ] + [q II (q II ) I3 ] combined with the observation that q/x s C( + D s U ij l ) in each mesh

15 5 Table 6.. Bakhvalov mesh, λ = 3: maximum norm error e and the efficiency constant e/η for the upper error estimator η. ε = 0 ε = 0 ε = 0 3 ε = 0 k, k = 4,..., 0 N e e/η e e/η e e/η e e/η 6.74e-.80e-.7e-.3e-.73e-.3e-.73e-.3e e-3.8e- 4.3e-3.8e- 4.36e-3.8e- 4.38e-3.8e- 64.7e-3.86e-.08e-3.6e-.09e-3.6e-.0e-3.7e- 8.96e-4.87e-.70e-4.6e-.74e-4.6e-.75e-4.6e- Table 6.. Bakhvalov mesh, λ = 3: upper maximum norm error estimator η, its components η, η, η 3, and its efficiency constant e/η. ε = 0 ε = 0 k, k = 4,..., 0 N η η η 3 = η e/η η η η 3 = η e/η e- 7.79e- 9.64e-.80e-.0e-.07e-.33e-.3e- 3.9e-.4e-.48e-.8e- 3.0e- 3.09e- 3.4e-.8e e e-3 6.9e-3.86e- 8.6e-3 8.5e e-3.7e e-4.55e-3.58e-3.87e-.3e-3.3e-3.8e-3.6e- cell (x [i ], x [i] ) (x[j ], x [j] ) (x[l ] 3, x [l] 3 ); see [, Comment.5] for a similar argument. 6. Numerical results Our main result, the maximum norm a posteriori error estimate of Theorem., can be rewritten as (6.) e := U I u Cη, η := maxη 0, η, η, η 3 }, η n := max max h i=,...,n i M (n),ij l} ; max τ i=0,...,n j M (n),ij l} ; max k i=0,...,n l M (n) 3,ij l} }, j=0,...,n l=0,...,n 3 j=,...,n l=0,...,n 3 j=0,...,n l=,...,n 3 for n = 0,,, 3. Here we use C = C ln ( + ε/κ ), M (),ij l := min D U i,j,l, D U ij l }, M (),ij l := min D U i,j,l, D U ij l }, M () 3,ij l := min D 3U i,j,l, D 3U ij l }, and for s =,, 3, we also use M (0) () s,ij l =, M s,ij l := D s U ij l, M (3) s,ij l := ε D s DsU ij l. Note that the quantities η n involve M (n), n =,, 3, which can be viewed as discrete analogues of (possibly scaled) nth-order derivatives. We give numerical results on a priori chosen meshes to illustrate the efficiency of the upper maximum norm error estimator η in (6.) and its particular components η n, n = 0,,, 3. We are also interested in which of η n is the principal component in η if any. We shall compute the errors e and, more importantly, the quantities η, e/η, η n, e/η n and then examine their dependence on ε, numbers of mesh nodes and particular meshes.

16 6 Table 6.3. Uniform mesh: maximum norm error e and the efficiency constant e/η for the component η of the upper maximum norm error estimator η. ε = 0 ε = 0 3 ε = 0 4 ε = 0 k, k = 5,..., 0 N e e/η e e/η e e/η e e/η e- 8.0e- 9.90e- 9.99e- 9.99e-.0e+0.00e+0.0e e- 5.3e- 9.80e- 9.87e- 9.99e-.00e+0.00e+0.00e e-.86e- 9.43e- 9.53e- 9.99e- 9.99e-.00e+0.00e e-.00e- 8.4e- 8.5e- 9.97e- 9.98e-.00e+0.00e+0 Table 6.4. Uniform mesh: the components η and η 3 of the upper maximum norm error estimator η and the efficiency constant e/η for η. ε = 0 4 ε = 0 7 ε = 0 0 N η η 3 = η e/η η η 3 = η e/η η η 3 = η e/η 6 9.9e- 6.0e+.0e+0 9.9e- 6.0e+5.0e+0 9.9e- 6.0e+8.0e e- 3.e+.00e e- 3.e+5.00e e- 3.e+8.00e e-.56e+ 9.99e- 9.99e-.56e+5.00e e-.56e+8.00e e- 7.8e+ 9.98e-.00e+0 7.8e+4.00e+0.00e+0 7.8e+7.00e+0 We let ε = 0 k, k =,..., 0 and N = k } 9 k=5, with N = N = N 3 = N. Two tensor-product meshes are considered: a variant of the layer-adapted mesh by Bakhvalov [4] and a simple uniform mesh; see Tables For ε ε, our Bakhvalov-type mesh is defined by x [i] = x[i] = x[i] 3 := ϕ(i/n), i = 0,,..., N, where ϕ(t) := ελ ln [b/(b t)] for t [0, θ], ϕ() :=, and ϕ(t) is continuous on [0, ] and linear on [θ, ]. We use the constants b = /, ε = b/λ, and θ = b ελ. The constant λ will be specified later. For ε > ε, the Bakhvalov mesh is defined to be a simple uniform mesh. Note that a suitable Bakhvalov-type layeradapted mesh yields ε-uniform second-order accuracy [4, 8]. Besides, we expect efficient adaptive algorithms to generate meshes that are similar, in some sense, to a Bakhvalov mesh, as in [0, 6 and Figure ]. As a test problem, we use linear problem (3.) with p(x) := and f(x) such that the exact solution is given by u(x) = ( cos( πx ) µ(x ) )( x µ(x ) )( x 3 µ(x 3 ) ), µ(t) = e t/ε e /ε e /ε. Note that this u(x) exhibits boundary and corner layers. In Tables 6. and 6., we give numerical results for the Bakhvalov mesh with λ = 3. Under this choice of λ, the mesh yields ε-uniform second-order accuracy in the maximum norm, so, roughly speaking, one would like to be able to construct similar adaptive meshes. Examining Table 6., we observe agreement with our theoretical estimate (6.). Not only does e/η stabilize, but it becomes close to the linear interpolation error constant /8 =.5e. The components η n of η can be compared when examining Table 6.. For ε = 0 k, k =,..., 0, we observe that η η 3 = η. Furthermore, for ε 0 we have η η η 3, while for ε = 0 the quantity η is dominated by η and η 3. The quantity η 0 is not given, as it is negligible (and known a priori).

17 7 Table 6.5. Bakhvalov mesh, λ = : maximum norm error e, upper maximum norm error estimator η, its components η, η, η 3, and its efficiency constant e/η. ε = 0 5 ε = 0 0 N e η η η 3 = η e/η e η η η 3 = η e/η 6.75e- 5.49e- 3.77e- 4.36e- 4.0e-.4e- 5.49e- 3.77e- 4.83e- 4.64e- 3.0e-.47e-.67e-.3e- 4.8e-.3e-.47e-.58e-.43e- 5.40e e- 3.78e e-3.05e- 5.9e- 7.06e- 3.79e e-3.e- 5.8e- 8.75e- 9.6e-4 4.e-3 5.6e- 5.34e- 3.65e- 9.6e e e- 6.08e- Numerical results for uniform meshes are given in Tables 6.3 and 6.4. On these meshes, the boundary layers are not resolved and e = O(). This is correctly identified by η = η 3 blowing up even more significantly than e. Note that the component η also correctly indicates that the method is inaccurate, but, unlike η 3, it remains bounded. Furthermore, η better reflects the actual errors since e/η const =.0 in Table 6.4. Table 6.5 gives numerical results for the Bakhvalov mesh, but now with λ =. Thus the condition λ >, which implies ε-uniform second-order accuracy for our test problem [4, 8], is violated. Hence the errors slightly increase as ε 0. In this case, we observe that η is too small compared to η and e. In summary, our numerical results suggest that the error estimator η correctly indicates whether or not the method is ε-uniformly accurate. We also note that the quantity η = η 3 may blow up (see Table 6.4), while the component η is sometimes too optimistic (see Table 6.5). The component η seems the most relevant estimator for the actual error e. In particular, η does not blow up, like η 3, and hence seems a more suitable error indicator in possible adaptive mesh construction. We finally note that our conclusions agree with the numerical results in two dimensions [9]. References [] M. Ainsworth and I. Babuška, Reliable and robust a posteriori error estimating for singularly perturbed reaction-diffusion problems, SIAM J. Numer. Anal., 36 (999), [] T. Apel, Anisotropic finite elements: Local estimates and applications, Teubner, Stuttgart, 999. [3] I. Babuška and T. Strouboulis, The finite element method and its reliability, Clarendon Press, Oxford University Press, New York, 00. [4] N.S. Bakhvalov, Towards optimization of methods for solving boundary value problems in the presence of a boundary layer, Zh. Vychisl. Mat. i Mat. Fiz., 9 (969), (in Russian). [5] I. Boglaev, On monotone iterative methods for a nonlinear singularly perturbed reactiondiffusion problem, J. Comput. Appl. Math., 6 (004), [6] M.J. Castro-Diaz, F.F. Hetch, B. Mohammadi and O. Pironneau, Anisotropic unstructed mesh adaptation for flow simulations, Internat. J. Numer. Methods Fluids, 5 (997), [7] L. Chen, P. Sun, and J. Xu, Optimal anisotropic simplicial meshes for minimizing interpolation errors in L p-norm, Math. Comp., 76 (007), [8] C. Clavero, J.L. Gracia and E. Riordan, A parameter robust numerical method for a two dimensional reaction-diffusion problem, Math. Comp., 74 (005), [9] E.F. D Azevedo, Optimal triangular mesh generation by coordinate transformation, SIAM J. Sci. Statist. Comput., (99), [0] E.F. D Azevedo and R.B. Simpson, On optimal interpolation triangle incidences, SIAM J. Sci. Statist. Comput., 0 (989),

18 8 [] V. Dolejší and J. Felcman, Anisotropic mesh adaptation for numerical solution of boundary value problems, Numer. Methods Partial Differential Equations, 0 (004), [] K. Eriksson, An adaptive finite element method with efficient maximum norm error control for elliptic problems, Math. Models Methods Appl. Sci., 4 (994), [3] D. Gilbarg and N.S. Trudinger, Elliptic partial differential equations of second order, Springer-Verlag, Berlin, 998. [4] J. Guzmán, D. Leykekhman, J. Rossmann and A. H. Schatz, Hölder estimates for Green s functions on convex polyhedral domains and their applications to finite element methods, Numer. Math., (009), 43. [5] W.G. Habashi, J. Dompierre, Y. Bourgault, D. Ait-Ali-Yahia, M. Fortin and M.-G. Vallet, Anistopic mesh adaptation: towards user independent, mesh independent and solver independent CFD. Part I: General principles, Internat. J. Numer. Methods Fluids, 3 (000), [6] N. Kopteva, Maximum norm a posteriori error estimates for a one-dimensional convectiondiffusion problem, SIAM J. Numer. Anal., 39 (00), [7] N. Kopteva, Maximum norm a posteriori error estimates for a one-dimensional singularly perturbed semilinear reaction-diffusion problem, IMA J. Numer. Anal., 7 (007), [8] N. Kopteva, Maximum norm error analysis of a d singularly perturbed semilinear reactiondiffusion problem, Math. Comp., 76 (007), [9] N. Kopteva, Maximum norm a posteriori error estimate for a d singularly perturbed reaction-diffusion problem, SIAM J. Numer. Anal., 46 (008), [0] N. Kopteva and M. Stynes, A robust adaptive method for a quasilinear one-dimensional convection-diffusion problem, SIAM J. Numer. Anal., 39 (00), [] G. Kunert and R. Verfürth, Edge residuals dominate a posteriori error estimates for linear finite element methods on anisotropic triangular and tetrahedral meshes, Numer. Math., 86 (000), [] G. Kunert, Robust a posteriori error estimation for a singularly perturbed reaction-diffusion equation on anisotropic tetrahedral meshes. A posteriori error estimation and adaptive computational methods, Adv. Comput. Math., 5 (00), [3] O.A. Ladyzhenskaya and N.N. Ural tseva, Linear and quasilinear elliptic equations, Academic Press, New York, 968. [4] T. Linß, Maximum-norm error analysis of a non-monotone FEM for a singularly perturbed reaction-diffusion problem, BIT, 47 (007), [5] T. Linß and M. Stynes, The SDFEM on Shishkin meshes for linear convection-diffusion problems, Numer. Math., 87 (00), [6] J.J. H. Miller, E. O Riordan and G.I. Shishkin, Solution of Singularly Perturbed Problems with ε-uniform Numerical Methods Introduction to the Theory of Linear Problems in One and Two Dimensions, World Scientific, Singapore, 996. [7] R.H. Nochetto, Pointwise a posteriori error estimates for elliptic problems on highly graded meshes, Math. Comp., 64 (995),. [8] A.D. Polyanin, Handbook of linear partial differential equations for engineers and scientists, CRC Press, London, 00. [9] J. Remacle, X. Li, M.S. Shephard and J.E. Flaherty, Anisotropic adaptive simulation of transient flows using discountinuous Galerkin methods, Internat. J. Numer. Methods Engrg., 6 (005), [30] A.H. Schatz and L.B. Wahlbin, On the finite element method for singularly perturbed reaction-diffusion problems in two and one dimensions, Math. Comp., 40 (983),