1 Introduction

Superconvergence of the gradient for the finite element approximation is a phenomenon whereby the convergent order of the derivatives of the finite element solutions exceeds the optimal global rate. Up to now, superconvergence is still an active research topic (see [16]). Recently, we studied the superconvergence patch recovery (SPR) technique introduced by Zienkiewicz and Zhu [79] for the linear tetrahedral element and proved pointwise superconvergent property of the recovered gradient by SPR. For the linear tetrahedral element, Chen and Wang [10] also discussed superconvergent properties of the gradients by SPR and obtained superconvergence results of the recovered gradients in the average sense of the L 2 -norm. In addition, Chen [11] and Goodsell [12] derived superconvergence estimates of the recovered gradient by the L 2 -projection technique and the average technique, respectively. This article will use the SPR technique to obtain a superconvergence estimate for the gradient of the tensor-product linear triangular prism element. In this article, we shall use the letter C to denote a generic constant which may not be the same in each occurrence and also use the standard notations for the Sobolev spaces and their norms.

2 General elliptic boundary value problem and finite element discretization

We consider the model problem

Lu i , j = 1 3 j ( a i j i u)+ i = 1 3 a i i u+ a 0 u=fin Ω,u=0on Ω.
(2.1)

Here Ω= [ 0 , 1 ] 2 ×[0,1]= Ω x y × Ω z R 3 is a rectangular block with boundary Ω consisting of faces parallel to the x-, y-, and z-axes. We also assume that the given functions a i j , a i W 1 , (Ω), a 0 L (Ω), and f L 2 (Ω). In addition, we write 1 u= u x , 2 u= u y , and 3 u= u z , which are usual partial derivatives.

To discretize the problem, one proceeds as follows. The domain Ω is firstly partitioned into subcubes of side h, and each of these is then subdivided into two triangular prisms. We denote by { T h } these uniform partitions as above. Thus Ω ¯ = e T h e ¯ . Obviously, we can write e=D×L (see Figure 1), where D and L represent a triangle parallel to the xy-plane and a one-dimensional interval parallel to the z-axes, respectively.

Figure 1
figure 1

Triangular prisms partition. This figure gives how to partition the domain Ω. The domain Ω is firstly partitioned into subcubes of side h, and each of these is then subdivided into two triangular prisms.

We introduce a tensor-product linear polynomial space denoted by , that is,

q(x,y,z)= ( i , j , k ) I a i j k x i y j z k , a i j k R,qP,

where P= P x y P z , P x y stands for the linear polynomial space with respect to (x,y), and P z is the linear polynomial space with respect to z. The indexing set ℐ satisfies I={(i,j,k)|i,j,k0,i+j1,k1}. Let Π x y e be the linear interpolation operator with respect to (x,y)D, and let Π z e be the linear interpolation operator with respect to zL. Thus we may define the tensor-product linear interpolation operator by Π e : H 0 1 (e)P(e). Obviously, Π e = Π x y e Π z e = Π z e Π x y e . In addition, the weak form of problem (2.1) is

a(u,v)=(f,v)v H 0 1 (Ω),
(2.2)

where

a(u,v) Ω ( i , j = 1 3 a i j i u j v + i = 1 3 a i i u v + a 0 u v ) dxdydz,

and

(f,v)= Ω fvdxdydz.

Define the tensor-product linear triangular prism finite element space by

S 0 h (Ω)= { v H 0 1 ( Ω ) : v | e P ( e ) e T h } .

Thus, the finite element method of problem (2.2) is to find u h S 0 h (Ω) such that

a( u h ,v)=(f,v)v S 0 h (Ω).

Moreover, from the definitions of Π e and S 0 h (Ω), we may define a global tensor-product linear interpolation operator Π: H 0 1 (Ω) S 0 h (Ω). Here (Πu) | e = Π e u. As for this interpolation operator, the following Lemma 2.1 holds (see [13]).

Lemma 2.1 For Πu and u h , the tensor-product linear interpolant and the tensor-product linear triangular prism finite element approximation to u, respectively, we have the supercloseness estimate

| u h Πu | 1 , , Ω C h 2 | ln h | 4 3 u 3 , , Ω .
(2.3)

3 Gradient recovery and superconvergence

For v S 0 h (Ω), we consider a SPR scheme of ∇v. We denote by R h the SPR-recovery operator for ∇v and begin by defining the point values of R h v at the element nodes. After the recovered values at all nodes are obtained, we construct a tensor-product linear interpolation by using these values, namely SPR-recovery gradient R h v. Obviously, R h v ( S 0 h ( Ω ) ) 3 .

Let us first assume that N is an interior node of the partition T h , and denote by ω the element patch around N containing 12 triangular prisms. Under the local coordinate system centered N, we let Q i ( x i , y i , z i ) be the barycenter of a triangular prism e i ω, i=1,2,,12. Obviously, i = 1 12 ( x i , y i , z i )=(0,0,0). SPR uses the discrete least-squares fitting to seek linear vector function p ( P 1 ( ω ) ) 3 such that

i = 1 12 [ p ( Q i ) v ( Q i ) ] q( Q i )=(0,0,0)q P 1 (ω),
(3.1)

where v S 0 h (Ω). We define R h v(N)=p(0,0,0). If N is a boundary node, we calculate R h v(N) by linear extrapolation from the values of R h v already obtained at two neighboring interior nodes, N 1 and N 2 (with diagonal directions being used for edge nodes and corner nodes) (see Figure 2). Namely,

R h v(N)=2 R h v( N 1 ) R h v( N 2 ).
Figure 2
figure 2

N , a boundary node. N 1 and N 2 are interior nodes. We can calculate R h v(N) by linear extrapolation from the values of R h v already obtained at two neighboring interior nodes, N 1 and N 2 .

Lemma 3.1 Let ω be the element patch around an interior node N, and u W 3 , (ω). For Πu S 0 h (Ω) the interpolant to u, we have

|u(N) R h Πu(N)|C h 2 u 3 , , ω .
(3.2)

Proof Choose v=Πu and set q=1 in (3.1) to obtain i = 1 12 p( Q i )= i = 1 12 Πu( Q i ). Therefore,

R h Π u ( N ) 1 12 i = 1 12 Π u ( Q i ) = p ( 0 , 0 , 0 ) 1 12 i = 1 12 p ( x i , y i , z i ) = 1 12 i = 1 12 [ 1 p ( 0 , 0 , 0 ) x i + 2 p ( 0 , 0 , 0 ) y i + 3 p ( 0 , 0 , 0 ) z i ] = ( 0 , 0 , 0 ) .

That is,

R h Πu(N)= 1 12 i = 1 12 Πu( Q i ).
(3.3)

Further,

1 12 i = 1 12 Π u ( Q i ) u ( N ) = 1 12 i = 1 12 ( Π u u ) ( Q i ) + 1 12 i = 1 12 [ u ( Q i ) u ( N ) ] = 1 12 i = 1 12 ( Π u u ) ( Q i ) + 1 12 i = 1 12 [ 1 u ( N ) x i + 2 u ( N ) y i + 3 u ( N ) z i ] + r ( u ) ,
(3.4)

where 1 12 i = 1 12 [ 1 u(N) x i + 2 u(N) y i + 3 u(N) z i ]=(0,0,0), and the high-order term r(u) satisfies |r(u)|C h 2 | u | 3 , , ω .

In (3.4), we write f(u)= 1 12 i = 1 12 (Πuu)( Q i ). For every u P 2 (ω), it is not difficult to verify f(u)=(0,0,0). Thus, by the Bramble-Hilbert lemma [14], we have |f(u)|C h 2 | u | 3 , , ω . Therefore,

| 1 12 i = 1 12 Πu( Q i )u(N)|C h 2 | u | 3 , , ω .
(3.5)

Combining (3.3) and (3.5), we obtain the result (3.2). □

Lemma 3.2 For Πu S 0 h (Ω) the tensor-product linear interpolant to u, the solution of (2.2), and R h the SPR recovery operator, we have the superconvergent estimate

|u R h Πu | 0 , , Ω C h 2 u 3 , , Ω .
(3.6)

Proof Denote by F: e ˆ e an affine transformation. Obviously, there exists an element e T h , using the triangle inequality and the Sobolev embedding theorem [15], and (3.3), such that

| u R h Π u | 0 , , Ω = | u R h Π u | 0 , , e C h 1 | u ˆ R h Π u ˆ | 0 , , e ˆ C h 1 [ | u ˆ | 0 , , e ˆ + | R h Π u ˆ | 0 , , e ˆ ] C h 1 [ | u ˆ | 0 , , χ ˆ + | Π u ˆ | 1 , , χ ˆ ] C h 1 u ˆ 3 , , χ ˆ ,

where χ ˆ is a small patch of elements surrounding the triangular prism, e ˆ . Due to the fact that for u ˆ quadratic over χ ˆ ,

u ˆ R h Π u ˆ =(0,0,0)in  χ ˆ ,

so, from the Bramble-Hilbert lemma [14],

|u R h Πu | 0 , , Ω C h 1 | u ˆ | 3 , , χ ˆ C h 2 |u | 3 , , Ω ,

which completes the proof of the result (3.6). Finally, we give the main result in this article. □

Theorem 3.1 For u h S 0 h (Ω) the tensor-product linear triangular prism finite element approximation to u, the solution of (2.2), and R h the SPR recovery operator, we have the superconvergence estimate

|u R h u h | 0 , , Ω C h 2 | ln h | 4 3 u 3 , , Ω .
(3.7)

Proof Using the triangle inequality, we have

| u R h u h | 0 , , Ω | R h ( u h Π u ) | 0 , , Ω + | u R h Π u | 0 , , Ω | u h Π u | 1 , , Ω + | u R h Π u | 0 , , Ω ,

which combined with (2.3) and (3.6) completes the proof of the result (3.7). □