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Optimal harvesting policy for the Beverton--Holt model

  • Received: 01 September 2015 Accepted: 29 June 2018 Published: 01 May 2016
  • MSC : Primary: 39A23, 92D25; Secondary: 39A30, 49K15.

  • In this paper, we establish the exploitation of a single population modeled by the Beverton--Holt difference equation with periodic coefficients. We begin our investigation with the harvesting of a single autonomous population with logistic growth and show that the harvested logistic equation with periodic coefficients has a unique positive periodic solution which globally attracts all its solutions. Further, we approach the investigation of the optimal harvesting policy that maximizes the annual sustainable yield in a novel and powerful way; it serves as a foundation for the analysis of the exploitation of the discrete population model. In the second part of the paper, we formulate the harvested Beverton--Holt model and derive the unique periodic solution, which globally attracts all its solutions. We continue our investigation by optimizing the sustainable yield with respect to the harvest effort. The results differ from the optimal harvesting policy for the continuous logistic model, which suggests a separate strategy for populations modeled by the Beverton--Holt difference equation.

    Citation: Martin Bohner, Sabrina Streipert. Optimal harvesting policy for the Beverton--Holt model[J]. Mathematical Biosciences and Engineering, 2016, 13(4): 673-695. doi: 10.3934/mbe.2016014

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  • In this paper, we establish the exploitation of a single population modeled by the Beverton--Holt difference equation with periodic coefficients. We begin our investigation with the harvesting of a single autonomous population with logistic growth and show that the harvested logistic equation with periodic coefficients has a unique positive periodic solution which globally attracts all its solutions. Further, we approach the investigation of the optimal harvesting policy that maximizes the annual sustainable yield in a novel and powerful way; it serves as a foundation for the analysis of the exploitation of the discrete population model. In the second part of the paper, we formulate the harvested Beverton--Holt model and derive the unique periodic solution, which globally attracts all its solutions. We continue our investigation by optimizing the sustainable yield with respect to the harvest effort. The results differ from the optimal harvesting policy for the continuous logistic model, which suggests a separate strategy for populations modeled by the Beverton--Holt difference equation.


    A Reinforced material is a composite building material consisting of two or more materials with different properties. The main objective of studies of reinforced materials is the prediction of their macroscopic behavior from the properties of their individual components as well as from their microstructural characteristics.

    The theory of ideal fiber-reinforced composites was initiated by Adkins and Rivlin [4] who studied the deformation of a structure reinforced with thin, flexible and inextensible cords, which lie parallel and close together in smooth surfaces. This theory was further developed by the authors in [44], [1], [2], [3], [45].

    The homogenization of elastic materials reinforced with highly contrasted inclusions has been considered by several authors in the two last decades (see for instance [6], [10], [17], [21], [18], and the references therein). The main result is that the materials obtained by the homogenization procedure have new elastic properties.

    The homogenization of structures reinforced with fractal inclusions has been considered by various authors, among which [39], [31], [40], [41], [12], [42], [13], and [14]. Lancia, Mosco and Vivaldi studied in [31] the homogenization of transmission problems across highly conductive layers of iterated fractal curves. In [40], Mosco and Vivaldi dealt with the asymptotic behavior of a two-dimensional membrane reinforced with thin polygonal strips of large conductivity surrounding a pre-fractal curve obtained after n-iterations of the contractive similarities of the Sierpinski gasket. In [39], they considered an analogous problem with the Koch curve. The same authors considered in [41] a two-dimensional domain reinforced by an increasing number of thin conductive fibers developing a fractal geometry and studied the spectral asymptotic properties of conductive layered-thin-fibers of fractal nature in [42].

    The homogenization of insulating fractal surfaces of Koch type approximated by three-dimensional insulating layers has been performed by Capitanelli et al. in [12], [13], and [14]. Due to the physical characteristics of the inclusions, singular energy forms containing fractal energies are obtained in these articles as the limit of non-singular full-dimensional energies. On the other hand, the effective properties of elastic materials fixed on rigid thin self-similar micro-inclusions disposed along two and three dimensional Sierpinski carpet fractals have been recently obtained in [20].

    In the present work, we consider the deformation of a three-dimensional elastic material reinforced with highly contrasted thin vertical strips constructed on horizontal iterated Sierpinski gasket curves. Our main purpose is to describe the macroscopic behavior of the composite as the width of the strips tends to zero, their material coefficients tend to infinity, and the sequence of the iterated Sierpinski gasket curves converges to the Sierpinski gasket in the Hausdorff metric.

    The asymptotic analysis of problems of this kind was previousely studied in [11], [26], [9], and [5], where the authors considered media comprising low dimensional thin inclusions or thin layers of higher conductivity or higher rigidity. The limit problems consist in second order transmission problems. Problems involving thin highly conductive fractal inclusions have been addressed in a series of papers (see for instance [39], [31], [12], [14], and [19]). The obtained mathematical models are elliptic or parabolic boundary value problems involving transmission conditions of order two on the interfaces. The homogenization of three-dimensional elastic materials reinforced by highly rigid fibers with variable cross-section, which may have fractal geometry, has been carried out in [21]. The authors showed that the geometrical changes induced by the oscillations along the fiber-cross-sections can provide jumps of displacement fields or stress fields on interfaces, including fractal ones, due to local concentrations of elastic rigidities. Note that the numerical approximation of second order transmission problems across iterated fractal interfaces has been considered in some few papers among which [32] and [15].

    Let us first consider the points A1=(0,0), A2=(1,0) and A3=(1/2,3/2) of the xy-plane. Let V0={A1A2A3} be the set of vertices of the equilateral triangle A1A2A3 of side one. We define inductively

    Vh+1=Vh(2hA2+Vh)(2hA3+Vh). (1)

    Let us set

    V=hNVh. (2)

    The Sierpinski gasket, which is denoted here by Σ, is then defined (see for instance [30]) as the closure of the set V, that is,

    Σ=¯V. (3)

    We define the graph Σh=(Vh,Sh), where Sh is the set of edges [p,q]; p,qVh, such that |pq|=2h, where |pq| is the Euclidian distance between p and q. The graph Σh is then the standard aproximation of the Sierpinski gasket, which means that the sequence (Σh)h converges, as h tends to , to the Sierpinski gasket Σ in the Hausdorff metric.

    The edges which belong to Sh can be rearranged as Skh; k=1,2,...,Nh, where Nh=3h+1.

    Let ω be a bounded domain in R2 with Lipschitz continuous boundary ω such that Σ¯ω and

    Σω=V0. (4)

    Let (εh)hN be a sequence of positive numbers, such that

    limhεh2h=0. (5)

    We define

    Tkh=(ωSkh)×(εh,εh) (6)

    and set

    Th=kIhT,kh, (7)

    where Ih={1,2,...,Nh}. Denoting |Th| the 2-dimensional measure of Th, one can see that

    |Th|=εh3h+12h. (8)

    Let Ω=ω×(1,1). We suppose that ΩTh is the reference configuration of a linear, homogeneous and isotropic elastic material with Lamé coefficients μ>0 and λ0. This means that the deformation tensor e(u)=(eij(u))i,j=1,2,3, with eij(u)=12(uixj+ujxi) for some displacement u, is linked to the stress tensor σ(u)=(σij(u))i,j=1,2,3 through Hooke's law

    σij(u)=λemm(u)δij+2μeij(u) ; i,j=1,2,3, (9)

    where the summation convention with respect to repeated indices has been used and will be used in the sequel, and δij denotes Kronecker's symbol. We suppose that Th is the reference configuration of a linear, homogeneous and isotropic elastic material with σh(u)=(σhij(u))i,j=1,2,3:

    σhij(u)=λhemm(u)δij+2μheij(u) ; i,j=1,2,3,

    with

    λh=ηhλ0 and μh=ηhμ0, (10)

    where λ0 and μ0 are positive constants and

    ηh=1εh(56)h. (11)

    The special scaling (10) and (11) of the Lamé -coefficients depend on the structural constants of Th. The choice of ηh is dictated by the lower bound inequality of assertion 3 of Proposition 6, which will play a crucial role in the asymptotic behavior of the energy Fh.

    We suppose that a perfect adhesion occurs between ΩTh and Th along their common interfaces. We suppose that the material in Ω is submitted to volumic forces with density fL2 (Ω,R3) and is held fixed on Ω. We define the energy functional Fh on L2(Ω,R3) through

    Fh(u)={ΩThσij(u)eij(u)dx+Thσhij(u)eij(u)dsdx3                  if  uH10(Ω,R3)H1(Th,R3),+             otherwise, (12)

    where ds is the one-dimensional Lebesgue measure on the line segments Skh; k=1,2,...,Nh. The equilibrium state in Ω is described by the minimization problem

    minuL2(Ω,R3)L2(Th,R3){Fh(u)2Ωf.udx}. (13)

    We use Γ-convergence methods (see for instance [5] and [16]) in order to describe the asymptotic behavior of problem (13) as h goes to . According to the critical term

    γ=limh(3h+12hlnεh), (14)

    which is associated with the size of the boundary layers taking place in the neighbourhoods of the fractal strips, we prove that if γ(0,+) then the effective energy of the composite is given by

    F(u,v)={Ωσij(u)eij(u)dx+μ0ΣdLΣ(¯v)+πμγHd(Σ)(ln2)2ΣA(s)(uv).(uv)dHd(s)               if  (u,v)H10(Ω,R3)×DΣ,E×L2Hd(Σ),+         otherwise, (15)

    where ¯v=(v1,v2), LΣ(¯v) is a quadratic measure-valued gradient form supported on Σ (see Proposition 1 in the next Section), Hd is the d-dimensional Hausdorff measure; d being the fractal dimension of Σ with

    d=ln3/ln2, (16)

    DΣ,E is the domain of the energy supported on the fractal Σ (see (27) in the next Section), and

    A(s)={Diag(1,2(1+κ),2(1+κ)) if n(s)=±(0,1),(7+κ4(1+κ)3(κ1)4(1+κ)03(κ1)4(1+κ)3κ+54(1+κ)0002(1+κ))if n(s)=±(32,12),(7+κ4(1+κ)3(1κ)4(1+κ)03(1κ)4(1+κ)3κ+54(1+κ)0002(1+κ))if n(s)=±(32,12), (17)

    where κ=3μ+λμ+λ and n(s) is the unit normal on sΣ.

    The effective energy (15) contains new degrees of freedom implying nonlocal effects associated with thin boundary layer phenomena taking place near the fractal strips and a singular energy term supported on the Sierpinski gasket Σ. The equilibrium of the fractal Σ is asymptotically described by a generalized Laplace equation which is related to the discontinuity of the effective stresses through the following relations (see Corollary 1):

    { [σα3|x3=0]Σ=πμγHd(Σ)(ln2)2Aαβ(s)(UβVβ)Hd on Σ,πμγ(ln2)2Aαβ(s)(UβVβ)=μ0ΔΣVα in Σα,β=1,2 (18)

    where ΔΣ is the Laplace operator on the Sierpinski gasket, that is, the second order operator in L2Hd(Σ,R2) defined by the form EΣ in Lemma 2.1 in the next Section under the Dirichlet condition Vα=0 on V0=Σω; α=1,2, μ0 is the effective shear modulus of the material occupying the fractal Σ,

    [σα3|x3=0]Σ=σα3|Σ×{0+}σα3|Σ×{0}α=1,2, (19)

    is the jump of σα3|x3=0 on ΣTm; {Tm}mN being the network of the interiors of the triangles which are contained in the Sierpinski gasket Σ (see Figure 2).

    Figure 1. 

    The graph Σh for h=0,1,2,3

    .
    Figure 2. 

    The network {Tm}mN where σα3|Σ×{0+} is the outward normal stress on ΣTm and σα3|Σ×{0} is the inward normal stress

    .

    If γ=+ then, for every (u,v)H10(Ω,R3)×DΣ,E×L2Hd(Σ), F(u,v)<u=v on Σ. In this case the energy supported on the structure is given by

    F(u)={Ωσij(u)eij(u)dx+μ0ΣdLΣ(¯u)              if  uH10(Ω,R3)(DΣ,E×L2Hd(Σ))+         otherwise. (20)

    If γ=0 the displacements u and v are independent. In this case the effective energy of the structure turns out to be

    F0(u)={Ωσij(u)eij(u)dx if  uH10(Ω,R3)+                    otherwise. (21)

    The paper is organized as follows: in Section 2 we introduce the energy form and the notion of a measure-valued local energy on the Sierpinski gasket Σ. Section 3 is devoted to compactness results which is useful for the proof of the main result. In Section 4 we formulate the main result of this work. Section 5 is consacred to the proof of the main result. This proof is developed in 3 Subsections: in the first Subsection we study the boundary layers at the interface matrix/strips, in the second Subsection we establish the first condition of the Γ-convergence property, and in the third Subsection we prove the second condition of the Γ -convergence property.

    In this Section we introduce the energy form and the notion of a measure-valued local energy (or Lagrangian) on the Sierpinski gasket. For the definition and properties of Dirichlet forms and measure energies we refer to [24], [35], and [37].

    For any function w:VR2 we define

    EhΣ(w)=(53)hp,qVh|pq|=2h|w(p)w(q)|2. (22)

    Let us define the energy

    EΣ(z)=limhEhΣ(z), (23)

    with domain D={z:VR2:EΣ(z)<}. Every function zD can be uniquely extended to be an element of C(Σ,R2), still denoted z. Let us set

    D={zC(Σ,R2):EΣ(z)<}, (24)

    where EΣ(z)=EΣ(zV). Then DC(Σ,R2)L2Hd(Σ,R2). We define the space DE as

    DE=¯D.DE, (25)

    where .DE is the intrinsic norm

    zDE={EΣ(z)+z2L2Hd(Σ,R2)}1/2. (26)

    The space DE is injected in L2Hd(Σ,R2) and is an Hilbert space with the scalar product associated to the norm (26).Let us now define the space

    DΣ,E={zDE:z(A1)=z(A2)=z(A3)=0}. (27)

    We denote EΣ(.,.) the bilinear form defined on DΣ,E×DΣ,E by

    EΣ(w,z)=12(EΣ(w+z)EΣ(w)EΣ(z))w,zDΣ,E. (28)

    One can see that

    EΣ(w,z)=limhEhΣ(w,z), (29)

    where

    EhΣ(w,z)=(53)hp,qVh|pq|=2h(w(p)w(q)).(z(p)z(q)). (30)

    The form EΣ(.,.) is a closed Dirichlet form in the Hilbert space L2Hd(Σ,R2) and, according to [25,Theorem 4.1], EΣ(.,.) is a local regular Dirichlet form in L2Hd(Σ,R2), which means that

    1. (local property) w,zDΣ,E with supp[w] and supp[z] are disjoint compact sets EΣ(w,z)=0,

    2. (regularity) DΣ,EC0(Σ,R2) is dense both in C0(Σ,R2) (the space of functions of C(Σ,R2) with compact support) with respect to the uniform norm and in DΣ,E with respect to the intrinsic norm (26).

    The second property implies that DΣ,E is not trivial (that is DΣ,E is not made by only the constant functions). Moreover, every function of DΣ,E possesses a continuous representative. Indeed, according to [36,Theorem 6.3. and example 71], the space DΣ,E is continuously embedded in the space Cβ(Σ,R2) of Hölder continuous functions with β=ln53/ln4.

    Now, applying [29,Chap. 6], we have the following result:

    Lemma 2.1. There exists a unique self-adjoint operator ΔΣ on L2Hd(Σ,R2) with domain

    DΔΣ={w=(w1w2)L2Hd(Σ,R2):ΔΣw=(ΔΣw1ΔΣw2)L2Hd(Σ,R2)}DΣ,E

    dense in L2Hd(Σ,R2), such that, for every wDΔΣ and zDΣ,E,

    EΣ(w,z)=Σ(ΔΣw).zdHdHd(Σ).

    Let us consider the sequence (νh)h of measures defined by

    νh=1Card(Vh)pVhδp, (31)

    where Card(Vh))=3h+1+32 is the number of verticies of Vh and δp is the Dirac measure at the point p. We have the following result:

    Lemma 2.2. The sequence (νh)h weakly converges in C(Σ) to the measure

    \begin{equation*} \nu = \boldsymbol{1}_{\Sigma }\left( s\right) \dfrac{d\mathcal{H}^{d}\left( s\right) }{\mathcal{H}^{d}\left( \Sigma \right) }\mathit{\text{,}} \end{equation*}

    where C\left( \Sigma \right) ^{\ast } is the topological dual of the space C\left( \Sigma \right) .

    Proof. Let \varphi \in C\left( \Sigma \right) . Then, according to the ergodicity result of [22,Theorem 6.1],

    \begin{equation*} \begin{array}{lll} \underset{h\rightarrow \infty }{\lim }\int_{\Sigma }\varphi \left( x\right) d\nu _{h} & = & \underset{h\rightarrow \infty }{\lim }\underset{p\in \mathcal{V}_{h}}{\sum \limits}\dfrac{\varphi \left( p\right) }{Card\left( \mathcal{ V}_{h}\right) } \\ & = & \dfrac{1}{\mathcal{H}^{d}\left( \Sigma \right) }\int_{\Sigma }\varphi \left( s,0\right) d\mathcal{H}^{d}\left( s\right) \text{.} \end{array} \end{equation*}

    We note that the approximating form \mathcal{E}_{\Sigma }^{h}\left( .,.\right) can be written as

    \begin{equation} \mathcal{E}_{\Sigma }^{h}\left( w,z\right) = \int_{\Sigma }\nabla _{h}w.\nabla _{h}z\text{ }d\nu _{h}\text{,} \end{equation} (32)

    where \nu _{h} is the measure defined in (31) and

    \begin{equation*} \nabla _{h}w.\nabla _{h}z\left( p\right) = \underset{q:\text{ }\left\vert p-q\right\vert = 2^{-h}}{\sum\limits }\frac{\left( w\left( p\right) -w\left( q\right) \right) }{\left\vert p-q\right\vert ^{\varkappa /2}}.\frac{\left( z\left( p\right) -z\left( q\right) \right) }{\left\vert p-q\right\vert ^{\varkappa /2}}\text{,} \end{equation*}

    where \varkappa is the unique positive number for which the sequence \left( \mathcal{E}_{\Sigma }^{h}\left( .,.\right) \right) _{h} has a non trivial limit (see [38] for more details). We note that, according to equality (22), \varkappa = \ln \dfrac{5}{3}/\ln 2 . We have the following result:

    Proposition 1. For every w,z\in \mathcal{D}_{\Sigma ,\mathcal{E}} , the sequence of measures \left( \mathcal{L}_{\Sigma }^{h}\left( w,z\right) \right) _{h} defined by

    \begin{equation*} \mathcal{L}_{\Sigma }^{h}\left( w,z\right) \left( A\right) = \int_{A\cap \Sigma }\nabla _{h}w.\nabla _{h}z\mathit{\text{}}d\nu _{h}\mathit{\text{,}}\forall A\subset \Sigma \mathit{\text{,}} \end{equation*}

    weakly converges in the topological dual C\left( \Sigma ,\mathbb{R} ^{2}\right) ^{\ast } of the space C\left( \Sigma ,\mathbb{R}^{2}\right) to a signed finite Radon measure \mathcal{L}_{\Sigma }\left( w,z\right) on \Sigma , called Lagrangian measure on \Sigma . Moreover,

    \begin{equation*} \mathcal{E}_{\Sigma }\left( w,z\right) = \int_{\Sigma }d\mathcal{L}_{\Sigma }\left( w,z\right) \mathit{\text{,}}\forall w,z\in \mathcal{D}_{\Sigma ,\mathcal{E}} \mathit{\text{.}} \end{equation*}

    Proof. The proof follows the lines of the proof of [23,Proposition 2.3] for the von Koch snowflake. Let us set, for every w\in \mathcal{D}_{\Sigma , \mathcal{E}} , \mathcal{L}_{\Sigma }^{h}\left( w\right) = \mathcal{L} _{\Sigma }^{h}\left( w,w\right) . We deduce from (23), (29), and (32) that \left( \mathcal{L}_{\Sigma }^{h}\left( w\right) \left( \Sigma \right) \right) _{h} is a uniformly bounded sequence. Then, observing that, for every w\in \mathcal{D}_{\Sigma , \mathcal{E}} and every \varphi e_{1}\in \mathcal{D}_{\Sigma ,\mathcal{E} }\cap C\left( \Sigma ,\mathbb{R}^{2}\right) , with e_{1} = \left( 1,0\right) ,

    \begin{equation} \int_{\Sigma }\varphi d\mathcal{L}_{\Sigma }^{h}\left( w\right) = \mathcal{E} _{\Sigma }^{h}\left( \varphi w,w\right) -\frac{1}{2}\mathcal{E}_{\Sigma }^{h}\left( \varphi e_{1},\left\vert w\right\vert ^{2}e_{1}\right) \text{,} \end{equation} (33)

    we deduce, taking into account the regularity of the form \mathcal{E} _{\Sigma }\left( .,.\right) , that

    \begin{equation} \underset{h\rightarrow \infty }{\lim }\int_{\Sigma }\varphi d\mathcal{L} _{\Sigma }^{h}\left( w\right) = \mathcal{E}_{\Sigma }\left( \varphi w,w\right) -\frac{1}{2}\mathcal{E}_{\Sigma }\left( \varphi e_{1},\left\vert w\right\vert ^{2}e_{1}\right) \text{.} \end{equation} (34)

    On the other hand, according to [33,Proposition 1.4.1], the energy form \mathcal{E}_{\Sigma }\left( w\right) , which is a Dirichlet form of diffusion type, admits the following integral representation:

    \begin{equation} \mathcal{E}_{\Sigma }\left( w\right) = \int_{\Sigma }d\mathcal{L}_{\Sigma }\left( w\right) \text{,} \end{equation} (35)

    where \mathcal{L}_{\Sigma }\left( w\right) is a positive Radon measure which is uniquely determined by the relation

    \begin{equation*} \int_{\Sigma }\varphi d\mathcal{L}_{\Sigma }\left( w\right) = \mathcal{E} _{\Sigma }\left( \varphi w,w\right) -\frac{1}{2}\mathcal{E}_{\Sigma }\left( \varphi e_{1},\left\vert w\right\vert ^{2}e_{1}\right) \text{, }\forall \varphi \in \mathcal{D}_{\Sigma ,\mathcal{E}}\cap C\left( \Sigma \right) \text{.} \end{equation*}

    Thus, combining with (34), the sequence \left( \mathcal{L} _{\Sigma }^{h}\left( w\right) \right) _{h} converges in the sense of measures to the measure \mathcal{L}_{\Sigma }\left( w\right) . Now, observing that

    \begin{equation*} \mathcal{L}_{\Sigma }^{h}\left( w,z\right) = \frac{1}{2}\left( \mathcal{L} _{\Sigma }^{h}\left( w+z\right) -\mathcal{L}_{\Sigma }^{h}\left( w\right) - \mathcal{L}_{\Sigma }^{h}\left( z\right) \right) \text{,} \end{equation*}

    we deduce that the sequence \left( \mathcal{L}_{\Sigma }^{h}\left( w,z\right) \right) _{h} weakly converges in C\left( \Sigma ,\mathbb{R} ^{2}\right) ^{\ast } to the measure \mathcal{L}_{\Sigma }\left( w,z\right) .

    In this Section we establish the compactness results which is very useful for the proof of the main homogenization result.

    Lemma 3.1. For every sequence \left( u_{h}\right) _{h} ; u_{h}\in H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \cap H^{1}\left( T_{h},\mathbb{ R}^{3}\right) , such that \sup_{h}F_{h}\left( u_{h}\right) <+\infty , we have

    1. \sup_{h}\left\Vert u_{h}\right\Vert _{H_{0}^{1}\left( \Omega ,\mathbb{ R}^{3}\right) }<+\infty ,

    2. \dfrac{1}{\left\vert T_{h}\right\vert }\int\nolimits_{T_{h}}\left \vert u_{h}\right\vert ^{2}dsdx_{3}\leq C\left\{ \left\Vert u_{h}\right\Vert _{L^{2}\left( \Omega ,\mathbb{R}^{3}\right) }^{2}-\dfrac{2^{h}}{3^{h+1}}\ln \varepsilon _{h}\right\} , where C is a positive constant independent of h .

    Proof. 1. Observing that

    \begin{equation*} F_{h}\left( u_{h}\right) \geq \int_{\Omega }\sigma _{ij}\left( u_{h}\right) e_{ij}\left( u_{h}\right) dx\text{,} \end{equation*}

    we have, using Korn's inequality (see for instance [43]), that

    \begin{equation} \underset{h}{\sup }\int\nolimits_{\Omega }\left\vert \nabla u_{h}\right\vert ^{2}dx < +\infty \text{.} \end{equation} (36)

    2. Let n^{k} be the unit normal to S_{h}^{k} ; k\in I_{h} . Then n^{k} = \pm \left( -\sqrt{3}/2,1/2\right) , n^{k} = \pm \left( \sqrt{3} /2,1/2\right) or n^{k} = \pm \left( 0,1\right) . Let us denote s,s^{\perp } the local coordinates defined by

    \begin{equation} \left( \begin{array}{c} s \\ s^{\perp } \end{array} \right) = \left( \begin{array}{cc} 1/2 & \sqrt{3}/2 \\ -\sqrt{3}/2 & 1/2 \end{array} \right) \left( \begin{array}{c} x_{1} \\ x_{2} \end{array} \right) \text{ if }S_{h}^{k}\perp \left( -\sqrt{3}/2,1/2\right) \text{,} \end{equation} (37)

    by

    \begin{equation} \left( \begin{array}{c} s \\ s^{\perp } \end{array} \right) = \left( \begin{array}{cc} -1/2 & \sqrt{3}/2 \\ \sqrt{3}/2 & 1/2 \end{array} \right) \left( \begin{array}{c} x_{1} \\ x_{2} \end{array} \right) \text{ if }S_{h}^{k}\perp \left( \sqrt{3}/2,1/2\right) \text{,} \end{equation} (38)

    and by

    \begin{equation} \left( \begin{array}{c} s \\ s^{\perp } \end{array} \right) = \left( \begin{array}{cc} 1 & 0 \\ 0 & 1 \end{array} \right) \left( \begin{array}{c} x_{1} \\ x_{2} \end{array} \right) \text{ if }S_{h}^{k}\perp \left( 0,1\right) \text{,} \end{equation} (39)

    where the symbol \perp represents the direction normal to the edge S_{h}^{k} . Let s_{h}^{k} = \left( s_{1h}^{k},s_{2h}^{k}\right) denotes the center of S_{h}^{k} ; k\in I_{h} , in the new coordinates. We define, for \theta \in \left[ 0,2\pi \right) and r\in \left( 0,\varepsilon _{h}/2\right) ,

    \begin{equation} u_{h}^{k}\left( x_{1}\left( s\right) ,x_{2}\left( s\right) ,r,\theta \right) = u_{h}\left( s,r\sin \theta +s_{2h}^{k},r\cos \theta \right) \text{.} \end{equation} (40)

    Then, according to (36), we have, for r_{1}\leq r_{2}<\varepsilon _{h}/2 and \theta \in \left[ 0,2\pi \right) ,

    \begin{equation} \underset{k\in I_{h}}{\sum \limits}\int_{S_{h}^{k}}\int\nolimits_{r_{1}}^{r_{2}} \left\vert \frac{\partial u_{h}^{k}\left( x_{1}\left( s\right) ,x_{2}\left( s\right) ,r,\theta \right) }{\partial r}\right\vert ^{2}rdrds\leq C\text{.} \end{equation} (41)

    Solving the Euler equation of the following one dimensional minimization problem:

    \begin{equation*} \min \left\{ \int\nolimits_{r_{1}}^{r_{2}}\left( \psi ^{\prime }\right) ^{2}rdr\text{: }\psi \left( r_{1}\right) = 0\text{, }\psi \left( r_{2}\right) = 1\right\} \text{,} \end{equation*}

    we deduce that, for every \theta \in \left[ 0,2\pi \right) ,

    \begin{equation} \left. \begin{array}{l} \ln \dfrac{r_{2}}{r_{1}}\int\nolimits_{r_{1}}^{r_{2}}\left\vert \dfrac{ \partial u_{h}^{k}\left( x_{1}\left( s\right) ,x_{2}\left( s\right) ,r,\theta \right) }{\partial r}\right\vert ^{2}rdr \\ { \ \ \ \ \ \ }\geq \left\vert u_{h}^{k}\left( x_{1}\left( s\right) ,x_{2}\left( s\right) ,r_{2},\theta \right) -u_{h}^{k}\left( x_{1}\left( s\right) ,x_{2}\left( s\right) ,r_{1},\theta \right) \right\vert ^{2}\text{.} \end{array} \right. \end{equation} (42)

    Then, using (41) and (42), we obtain that

    \begin{equation} \left. \begin{array}{r} \underset{k\in I_{h}}{\sum \limits}\int\nolimits_{S_{h}^{k}}\int\nolimits_{0}^{2 \pi }\left\vert u_{h}^{k}\left( x_{1}\left( s\right) ,x_{2}\left( s\right) ,r_{2},\theta \right) -u_{h}^{k}\left( x_{1}\left( s\right) ,x_{2}\left( s\right) ,r_{1},\theta \right) \right\vert ^{2}d\theta ds \\ \leq C\ln \dfrac{r_{2}}{r_{1}}. \end{array} \right. \end{equation} (43)

    Let us define

    \begin{equation} \digamma \left( r,\theta \right) = \underset{k\in I_{h}}{\sum \limits} \int\nolimits_{S_{h}^{k}}\left\vert u_{h}^{k}\left( x_{1}\left( s\right) ,x_{2}\left( s\right) ,r,\theta \right) \right\vert ^{2}ds\text{.} \end{equation} (44)

    We deduce from the inequality (43) that, for r_{1}\leq r_{2}<\varepsilon _{h}/2 ,

    \begin{equation} \int\nolimits_{0}^{2\pi }\digamma \left( r_{1},\theta \right) d\theta \leq C\left( \int\nolimits_{0}^{2\pi }\digamma \left( r_{2},\theta \right) d\theta +\ln \dfrac{r_{2}}{r_{1}}\right) \text{.} \end{equation} (45)

    Observing that, for k\in I_{h} and \theta _{0} fixed in \left[ 0,2\pi \right) ,

    \begin{equation*} \left. \begin{array}{r} \left\vert u_{h}^{k}\left( x_{1}\left( s\right) ,x_{2}\left( s\right) ,r,\theta \right) -u_{h}^{k}\left( x_{1}\left( s\right) ,x_{2}\left( s\right) ,r,\theta _{0}\right) \right\vert ^{2} \\ = \left\vert \int\nolimits_{\theta _{0}}^{\theta }\dfrac{\partial u_{h}^{k}}{ \partial \theta }\left( x_{1}\left( s\right) ,x_{2}\left( s\right) ,r,\phi \right) d\phi \right\vert ^{2} \\ \leq Cr\int\nolimits_{0}^{2\pi }\left\vert \dfrac{1}{r}\dfrac{\partial u_{h}^{k}}{\partial \theta }\left( x_{1}\left( s\right) ,x_{2}\left( s\right) ,r,\phi \right) \right\vert ^{2}rd\phi \text{,} \end{array} \right. \end{equation*}

    we deduce that

    \begin{equation} \left. \begin{array}{r} \underset{k\in I_{h}}{\sum \limits}\int\nolimits_{S_{h}^{k}}\int\nolimits_{0}^{2 \pi }\int\nolimits_{0}^{\varepsilon _{h}}\left\vert u_{h}^{k}\left( x^{\prime }\left( s\right) ,r,\theta \right) -u_{h}^{k}\left( x^{\prime }\left( s\right) ,r,\theta _{0}\right) \right\vert ^{2}drd\theta ds \\ { \ \ \ \ }\leq \text{ }C\varepsilon _{h}\underset{k\in I_{h}}{\sum \limits} \int\nolimits_{C_{h}^{k}}\left\vert \nabla u_{h}\right\vert ^{2}dx_{1}dx_{2}dx_{3} \\ \leq C\varepsilon _{h}\int\nolimits_{\Omega }\left\vert \nabla u_{h}\right\vert ^{2}dx\text{,} \end{array} \right. \end{equation} (46)

    where x^{\prime }\left( s\right) = \left( x_{1}\left( s\right) ,x_{2}\left( s\right) \right) , C_{h}^{k} is the cylinder of radius \varepsilon _{h} around the edge S_{h}^{k} . This estimate implies that

    \begin{equation} \begin{array}{ccc} \int\nolimits_{0}^{\varepsilon _{h}}\digamma \left( \rho ,\theta _{0}\right) drd\theta & \leq & C\left\{ \int\nolimits_{0}^{2\pi }\int\nolimits_{0}^{\varepsilon _{h}}\digamma \left( r,\theta \right) drd\theta +\varepsilon _{h}\right\} \text{.} \end{array} \end{equation} (47)

    Now, using (45) and (47), we deduce, by setting m_{h} = \dfrac{1}{\varepsilon _{h}}\dfrac{2^{h}}{3^{h+1}} , that, for \theta _{0} = 0 and \pi , and for every r_{2}\in \left[ a_{h},b_{h}\right] ; a_{h} = \dfrac{1}{4\sqrt{3}}\left( \dfrac{2}{3}\right) ^{h/2} and b_{h} = 2a_{h} ,

    \begin{equation*} \begin{array}{lll} m_{h}\int\nolimits_{T_{h}}\left\vert u_{h}\right\vert ^{2}dsdx_{3} & = & m_{h}\int\nolimits_{0}^{\varepsilon _{h}}\digamma \left( r,0\right) dr+m_{h}\int\nolimits_{0}^{\varepsilon _{h}}\digamma \left( r,\pi \right) dr \\ & \leq & Cm_{h}\left( \int\nolimits_{0}^{2\pi }\int\nolimits_{0}^{\varepsilon _{h}}\digamma \left( r,\theta \right) drd\theta +\varepsilon _{h}\right) \\ & \leq & C\left( m_{h}\int\nolimits_{0}^{\varepsilon _{h}}\left( \int\nolimits_{0}^{2\pi }\digamma \left( r_{2},\theta \right) d\theta +\ln \dfrac{r_{2}}{r}\right) dr+\dfrac{2^{h}}{3^{h+1}}\right) \\ & \leq & C\dfrac{2^{h}}{3^{h+1}}\left( \int\nolimits_{0}^{2\pi }\digamma \left( r_{2},\theta \right) d\theta +\ln \dfrac{r_{2}}{\varepsilon _{h}} +1\right) \\ & \leq & C\left( \left( \dfrac{2}{3}\right) ^{h/2}r_{2}\int\nolimits_{0}^{2\pi }\digamma \left( r_{2},\theta \right) d\theta +\dfrac{2^{h}}{3^{h+1}}\left( -\ln \varepsilon _{h}+1\right) \right) \text{.} \end{array} \end{equation*}

    Integrating with respect to r_{2} over the interval \left[ a_{h},b_{h} \right] , we obtain that

    \begin{equation*} \begin{array}{lll} \dfrac{1}{\left\vert T_{h}\right\vert }\int\nolimits_{T_{h}}\left\vert u_{h}\right\vert ^{2}dsdx_{3} & \leq & C\left( \int\nolimits_{a_{h}}^{b_{h}}\int\nolimits_{0}^{2\pi }\digamma \left( r,\theta \right) rdrd\theta -\dfrac{2^{h}}{3^{h+1}}\ln \varepsilon _{h}\right) \\ & \leq & C\left\{ \left\Vert u_{h}\right\Vert _{L^{2}\left( \Omega ,\mathbb{R }^{3}\right) }^{2}-\dfrac{2^{h}}{3^{h+1}}\ln \varepsilon _{h}\right\} \text{. } \end{array} \end{equation*}

    Let \mathcal{M}\left( \mathbb{R}^{3}\right) be the space of Radon measures on \mathbb{R}^{3} . We have the following result:

    Lemma 3.2. Let u_{h}\in L^{2}\left( \Omega ,\mathbb{R}^{3}\right) \cap L^{2}\left( T_{h},\mathbb{R}^{3}\right) , such that

    \begin{equation*} \sup\limits_{h}\dfrac{1}{\left\vert T_{h}\right\vert }\int\nolimits_{T_{h}}\left \vert u_{h}\right\vert ^{2}dsdx_{3} < +\infty \mathit{\text{.}} \end{equation*}

    Then, there exists a subsequence of \left( u_{h}\right) _{h} , still denoted \left( u_{h}\right) _{h} , such that

    \begin{equation*} u_{h}\dfrac{\boldsymbol{1}_{T_{h}}\left( x\right) }{\left\vert T_{h}\right\vert }dsdx_{3}\overset{\ast }{\underset{h\rightarrow \infty }{ \rightharpoonup }}v\boldsymbol{1}_{\Sigma }\left( s\right) \dfrac{d\mathcal{H }^{d}\left( s\right) \otimes \delta _{0}\left( x_{3}\right) }{\mathcal{H} ^{d}\left( \Sigma \right) }\mathit{\text{in}}\mathcal{M}\left( \mathbb{R} ^{3}\right) \mathit{\text{,}} \end{equation*}

    with v\left( s,0\right) \in L_{\mathcal{H}^{d}}^{2}\left( \Sigma , \mathbb{R}^{3}\right) .

    Proof. Let us consider the sequence of Radon measures \left( \vartheta _{h}\right) _{h} on \mathbb{R}^{3} defined by

    \begin{equation*} \vartheta _{h} = \frac{\boldsymbol{1}_{T_{h}}\left( x\right) }{\left\vert T_{h}\right\vert }dsdx_{3}\text{.} \end{equation*}

    Let x_{h}^{k} = \left( x_{1h}^{k},x_{2h}^{k}\right) denotes the center of S_{h}^{k} ; k\in I_{h} , in Cartesian coordinates. Then, using the ergodicity result of [22,Theorem 6.1], we have, for every \varphi \in C_{0}\left( \mathbb{R}^{3}\right) ,

    \begin{equation*} \begin{array}{lll} \underset{h\rightarrow \infty }{\lim }\int_{\mathbb{R}^{3}}\varphi \left( x\right) d\vartheta _{h} & = & \underset{h\rightarrow \infty }{\lim } \underset{k\in I_{h}}{\sum \limits}\dfrac{2}{3^{h+1}}\varphi \left( x_{h}^{k},0\right) \\ & = & \underset{h\rightarrow \infty }{\lim }\underset{k\in I_{h}}{\sum\limits } \dfrac{1}{N_{h}}\varphi \left( x_{h}^{k},0\right) \\ & = & \dfrac{1}{\mathcal{H}^{d}\left( \Sigma \right) }\int_{\Sigma }\varphi \left( s,0\right) d\mathcal{H}^{d}\left( s\right) \text{,} \end{array} \end{equation*}

    from which we deduce that \vartheta _{h}\overset{\ast }{\underset{ h\rightarrow \infty }{\rightharpoonup }}\vartheta , with

    \begin{equation*} \vartheta = \boldsymbol{1}_{\Sigma }\left( s\right) \dfrac{d\mathcal{H} ^{d}\left( s\right) \otimes \delta _{0}\left( x_{3}\right) }{\mathcal{H} ^{d}\left( \Sigma \right) }. \end{equation*}

    Let u_{h}\in L^{2}\left( \Omega ,\mathbb{R}^{3}\right) \cap L^{2}\left( T_{h},\mathbb{R}^{3}\right) , such that

    \begin{equation*} \sup\limits_{h}\dfrac{1}{\left\vert T_{h}\right\vert }\int\nolimits_{T_{h}}\left \vert u_{h}\right\vert ^{2}dsdx_{3} < +\infty \text{.} \end{equation*}

    As \vartheta _{h} \left( \mathbb{R}^{3}\right) = \dfrac{1}{\left\vert T_{h}\right\vert }\int\nolimits_{T_{h}}dsdx_{3} = 1 , we have

    \begin{equation*} \begin{array}{lll} \left\vert \int_{\mathbb{R}^{3}}u_{h}d\vartheta _{h}\right\vert ^{2} & \leq & \int_{\mathbb{R}^{3}}\left\vert u_{h}\right\vert ^{2}d\vartheta _{h} \\ & & \\ & = & \dfrac{1}{\left\vert T_{h}\right\vert }\int\nolimits_{T_{h}}\left \vert u_{h}\right\vert ^{2}dsdx_{3}\text{,} \end{array} \end{equation*}

    from which we deduce that the sequence \left( u_{h}\vartheta _{h}\right) _{h} is uniformly bounded in variation, hence \ast -weakly relatively compact. Possibly passing to a subsequence, we can suppose that the sequence \left( u_{h}\vartheta _{h}\right) _{h} converges to some \chi . Let \varphi \in C_{0}\left( \mathbb{R}^{3},\mathbb{R}^{3}\right) . Then, using Fenchel's inequality (also known as the Fenchel-Young inequality, see for instance [7]), we have

    \begin{equation*} \left. \begin{array}{l} \underset{h\rightarrow \infty }{\lim \inf }\dfrac{1}{2}\int_{\mathbb{R} ^{3}}\left\vert u_{h}\right\vert ^{2}d\vartheta _{h} \\ \geq \underset{h\rightarrow \infty }{\lim \inf }\left( \int_{\mathbb{R} ^{3}}u_{h}.\varphi d\vartheta _{h}-\dfrac{1}{2}\int_{\mathbb{R} ^{3}}\left\vert \varphi \right\vert ^{2}d\vartheta _{h}\right) \\ \geq \langle \chi ,\varphi \rangle -\dfrac{1}{2}\int_{\mathbb{R} ^{3}}\left\vert \varphi \right\vert ^{2}d\vartheta \text{.} \end{array} \right. \end{equation*}

    As the left hand side of this inequality is bounded, we deduce that

    \begin{equation*} \sup \left\{ \langle \chi ,\varphi \rangle \text{; }\varphi \in C_{0}\left( \mathbb{R}^{3},\mathbb{R}^{3}\right) \text{, }\int_{\Sigma }\left\vert \varphi \right\vert ^{2}\left( s,0\right) d\mathcal{H}^{d}\left( s\right) \leq 1\right\} < +\infty \text{,} \end{equation*}

    from which we deduce, according to Riesz' representation theorem, that there exists v such that \chi = v\left( s,x_{3}\right) \vartheta and v\left( s,0\right) \in L_{\mathcal{H}^{d}}^{2}\left( \Sigma ,\mathbb{R}^{3}\right) .

    Proposition 2. Let \left( u_{h}\right) _{h} ; u_{h}\in H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \cap H^{1}\left( T_{h},\mathbb{R}^{3}\right) , be a sequence, such that \sup_{h}F_{h}\left( u_{h}\right) <+\infty . There exists a subsequence, still denoted \left( u_{h}\right) _{h} , such that

    1. u_{h}\underset{h\rightarrow \infty }{\rightharpoonup }u H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) -weak,

    2. If \gamma \in \left( 0,+\infty \right) then

    \begin{equation*} u_{h}\dfrac{\boldsymbol{1}_{T_{h}}\left( x\right) }{\left\vert T_{h}\right\vert }dsdx_{3}\overset{\ast }{\underset{h\rightarrow \infty }{ \rightharpoonup }}v\left( s,0\right) \boldsymbol{1}_{\Sigma }\left( s\right) \dfrac{d\mathcal{H}^{d}\left( s\right) }{\mathcal{H}^{d}\left( \Sigma \right) }\mathit{\text{,}} \end{equation*}

    with v\left( s,0\right) \in L_{\mathcal{H}^{d}}^{2}\left( \Sigma ,\mathbb{R} ^{3}\right) .

    3. If \gamma \in \left( 0,+\infty \right) then, with \eta _{h} given in (11), we have \overline{v}\left( s,0\right) \in \mathcal{D} _{\Sigma ,\mathcal{E}} and

    \begin{equation*} \underset{h\rightarrow \infty }{\lim \inf }\mathit{\text{}}\int_{T_{h}}\sigma _{ij}^{h}\left( u_{h}\right) e_{ij}\left( u_{h}\right) dsdx_{3}\geq \mu _{0} \mathcal{E}_{\Sigma }\left( \overline{v}\right) . \end{equation*}

    Proof. 1. Thanks to Lemma 3.1 _{1} , one immediately obtains that, up to some subsequence, u_{h}\underset{h\rightarrow \infty }{\rightharpoonup }u \ H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) -weak.

    2. If \gamma \in \left( 0,+\infty \right) then, according to Lemma 3.1 _{2} and Lemma 3.2, one has, up to some subsequence,

    \begin{equation*} u_{h}\dfrac{\boldsymbol{1}_{T_{h}}\left( x\right) }{\left\vert T_{h}\right\vert }dsdx_{3}\overset{\ast }{\underset{h\rightarrow \infty }{ \rightharpoonup }}v\left( s,0\right) \boldsymbol{1}_{\Sigma }\left( s\right) \dfrac{d\mathcal{H}^{d}\left( s\right) }{\mathcal{H}^{d}\left( \Sigma \right) }\text{,} \end{equation*}

    with v\left( s,0\right) \in L_{\mathcal{H}^{d}}^{2}\left( \Sigma ,\mathbb{R} ^{3}\right) .

    3. One can easily check that

    \begin{equation} \left. \begin{array}{l} \int_{T_{h}}\sigma _{ij}^{h}\left( u_{h}\right) e_{ij}\left( u_{h}\right) dsdx_{3} \\ { \ \ \ \ }\geq 2\mu _{h}\left( \int_{T_{h}}\left( \left( e_{11}\left( u_{h}\right) \right) ^{2}+2\left( e_{12}\left( u_{h}\right) \right) ^{2}+\left( e_{22}\left( u_{h}\right) \right) ^{2}\right) dsdx_{3}\right) \text{.} \end{array} \right. \end{equation} (48)

    Computing the strain tensor in the local coordinates (37) and (38), observing that for S_{h}^{k}\perp \left( -\sqrt{3}/2,1/2\right) or S_{h}^{k}\perp \left( \sqrt{3}/2,1/2\right) the covariant derivative \dfrac{\partial u_{\alpha ,h}^{k}}{\partial s^{\perp }} = 0 on S_{h}^{k} ; \alpha = 1,2 , we obtain

    \begin{equation} \left. \begin{array}{l} \int_{S_{h}^{k}}\left( \left( e_{11}\left( u_{h}\right) \right) ^{2}+2\left( e_{12}\left( u_{h}\right) \right) ^{2}+\left( e_{22}\left( u_{h}\right) \right) ^{2}\right) ds \\ = \int_{S_{h}^{k}}\left( \dfrac{1}{4}\left( \dfrac{\partial u_{1,h}^{k}}{ \partial s}\right) ^{2}+\dfrac{3}{8}\left( \dfrac{\partial u_{2,h}^{k}}{ \partial s}\right) ^{2}\right) ds \\ \geq \dfrac{1}{4}\int_{S_{h}^{k}}\left( \left( \dfrac{\partial u_{1,h}^{k}}{ \partial s}\right) ^{2}+\left( \dfrac{\partial u_{2,h}^{k}}{\partial s} \right) ^{2}\right) ds\text{.} \end{array} \right. \end{equation} (49)

    For S_{h}^{k}\perp \left( 0,1\right) , since \dfrac{\partial u_{\alpha ,h}^{k}}{\partial x_{2}} = 0 on S_{h}^{k} ; \alpha = 1,2 , we have

    \begin{equation} \left. \begin{array}{l} \int_{S_{h}^{k}}\left( \left( e_{11}\left( u_{h}\right) \right) ^{2}+2\left( e_{12}\left( u_{h}\right) \right) ^{2}+\left( e_{22}\left( u_{h}\right) \right) ^{2}\right) ds \\ = \int_{S_{h}^{k}}\left( \dfrac{\partial u_{1,h}^{k}}{\partial x_{1}}\right) ^{2}+\dfrac{1}{2}\left( \dfrac{\partial u_{2,h}^{k}}{\partial x_{1}}\right) ^{2}ds \\ \geq \dfrac{1}{4}\int_{S_{h}^{k}}\left( \left( \dfrac{\partial u_{1,h}^{k}}{ \partial x_{1}}\right) ^{2}+\left( \dfrac{\partial u_{2,h}^{k}}{\partial x_{1}}\right) ^{2}\right) ds\text{.} \end{array} \right. \end{equation} (50)

    According to (48) and (10), we deduce from (49) and (50) that

    \begin{equation} \left. \begin{array}{l} \int_{T_{h}}\sigma _{ij}^{h}\left( u_{h}\right) e_{ij}\left( u_{h}\right) dsdx_{3} \\ \geq \dfrac{\mu _{h}}{2}\int_{T_{h}}\left( \dfrac{\partial u_{1,h}^{k}}{ \partial s}\right) ^{2}+\left( \dfrac{\partial u_{2,h}^{k}}{\partial s} \right) ^{2}dsdx_{3} \\ { \ }\geq 2^{h}\varepsilon _{h}\mu _{h}\underset{k\in I_{h}}{\sum \limits} \dfrac{1}{2\varepsilon _{h}}\int_{-\varepsilon _{h}}^{\varepsilon _{h}}\left( u_{\alpha ,h}\left( p^{k},x_{3}\right) -u_{\alpha ,h}\left( q^{k},x_{3}\right) \right) ^{2}dx_{3} \\ { \ } = 2^{h}\varepsilon _{h}\eta _{h}\mu _{0}\underset{k\in I_{h}}{\sum \limits }\dfrac{1}{2\varepsilon _{h}}\int_{-\varepsilon _{h}}^{\varepsilon _{h}}\left( u_{\alpha ,h}\left( p^{k},x_{3}\right) -u_{\alpha ,h}\left( q^{k},x_{3}\right) \right) ^{2}dx_{3} \\ { \ }\geq \mu _{0}\left( \dfrac{5}{3}\right) ^{h}\underset{\underset{ \left\vert p-q\right\vert = 2^{-h}}{p,q\in \mathcal{V}_{h}}}{\sum\limits }\left( \dfrac{1}{2\varepsilon _{h}}\int_{-\varepsilon _{h}}^{\varepsilon _{h}}\left( u_{\alpha ,h}\left( p,x_{3}\right) -u_{\alpha ,h}\left( q,x_{3}\right) \right) dx_{3}\right) ^{2}\text{.} \end{array} \right. \end{equation} (51)

    Let us set \overline{u}_{h} = \left( u_{1,h},u_{2,h}\right) and \widetilde{ \overline{u}}_{h} = \dfrac{1}{2\varepsilon _{h}}\int_{-\varepsilon _{h}}^{\varepsilon _{h}}\overline{u}_{h}\left( .,x_{3}\right) dx_{3} . We introduce the harmonic extension of \widetilde{\overline{u}}_{h}\mid _{ \mathcal{V}_{h}} obtained by the decimation procedure (see for instance [30,Proposition 1] and [8,Corollary1]):

    We define the function H_{h+1}\widetilde{\overline{u}}_{h}:\mathcal{V} _{h+1}\longrightarrow \mathbb{R}^{2} as the unique minimizer of the problem

    \begin{equation} \min \left\{ \mathcal{E}_{\Sigma }^{h+1}\left( w\right) \text{; }w:\mathcal{V }_{h+1}\longrightarrow \mathbb{R}^{2}\text{, }w = \widetilde{\overline{u}}_{h} \text{ on }\mathcal{V}_{h}\right\} \text{.} \end{equation} (52)

    Then \mathcal{E}_{\Sigma }^{h+1}\left( H_{h+1}\widetilde{\overline{u}} _{h}\right) = \mathcal{E}_{\Sigma }^{h}\left( \widetilde{\overline{u}} _{h}\right) . For m>h , we define the function H_{m}\widetilde{\overline{u }}_{h} from \mathcal{V}_{m} into \mathbb{R}^{2} by

    \begin{equation*} H_{m}\widetilde{\overline{u}}_{h} = H_{m}\left( H_{m-1}\left( ...\left( H_{h+1} \widetilde{\overline{u}}_{h}\right) \right) \right) \text{.} \end{equation*}

    For every m>h we have H_{m}\widetilde{\overline{u}}_{h}\mid _{\mathcal{V} _{h}} = \widetilde{\overline{u}}_{h}\mid _{\mathcal{V}_{h}} and

    \begin{equation} \mathcal{E}_{\Sigma }^{m}\left( H_{m}\widetilde{\overline{u}}_{h}\right) = \mathcal{E}_{\Sigma }^{h}\left( \widetilde{\overline{u}}_{h}\right) \text{.} \end{equation} (53)

    Now we define, for a fixed h\in \mathbb{N} , the function H\widetilde{ \overline{u}}_{h} on \mathcal{V}_{\infty } as follows. For p\in \mathcal{ V}_{\infty } , we choose m\geq h such that p\in \mathcal{V}_{m} and set

    \begin{equation} H\widetilde{\overline{u}}_{h}\left( p\right) = H_{m}\widetilde{\overline{u}} _{h}\left( p\right) \text{.} \end{equation} (54)

    As \underset{h}{\sup }\int_{T_{h}}\sigma _{ij}^{h}\left( u_{h}\right) e_{ij}\left( u_{h}\right) dsdx_{3}\mathcal{<\infty } , we have, according to (51) and (53),

    \begin{equation} \sup\limits_{h}\mathcal{E}_{\Sigma }\left( H\widetilde{\overline{u}}_{h}\right) = \sup\limits_{h}\mathcal{E}_{\Sigma }^{h}\left( \widetilde{\overline{u}}_{h}\right) < +\infty \text{,} \end{equation} (55)

    from which we deduce, using Section 2 , that H\widetilde{\overline{u}}_{h} has a unique continuous extension on \Sigma , still denoted H\widetilde{ \overline{u}}_{h} , and that the sequence \left( H\widetilde{\overline{u}} _{h}\right) _{h} is bounded in \mathcal{D}_{\Sigma ,\mathcal{E}} . Therefore, there exists a subsequence, still denoted \left( H\widetilde{ \overline{u}}_{h}\right) _{h} , weakly converging to some \overline{u} ^{\ast }\in \mathcal{D}_{\Sigma ,\mathcal{E}} , with

    \begin{equation} \mathcal{E}_{\Sigma }\left( \overline{u}^{\ast }\right) \leq \underset{ h\rightarrow \infty }{\text{ }\lim \inf }\mathcal{E}_{\Sigma }\left( H \widetilde{\overline{u}}_{h}\right) \leq \text{ }\underset{h\rightarrow \infty }{\lim \inf }\mathcal{E}_{\Sigma }^{h}\left( \widetilde{\overline{u}} _{h}\right) \text{.} \end{equation} (56)

    On the other hand, using Lemma 3.2, we have, for every \varphi \in C_{0}\left( \Sigma ,\mathbb{R}^{2}\right) ,

    \begin{equation*} \begin{array}{lll} \underset{h\rightarrow \infty }{\lim }\dfrac{1}{\mathcal{H}^{d}\left( \Sigma \right) }\int_{\Sigma }H\widetilde{\overline{u}}_{h}.\varphi d\mathcal{H} ^{d}\left( s\right) & = & \underset{h\rightarrow \infty }{\lim }\int_{ \mathbb{R}^{3}}\overline{u}_{h}.\varphi d\upsilon _{h} \\ & = & \dfrac{1}{\mathcal{H}^{d}\left( \Sigma \right) }\int_{\Sigma } \overline{v}\left( s,0\right) .\varphi d\mathcal{H}^{d}\left( s\right) \text{ ,} \end{array} \end{equation*}

    which implies that \overline{u}^{\ast }\left( s\right) = \overline{v}\left( s,0\right) . Therefore \overline{v}\left( s,0\right) \in \mathcal{D} _{\Sigma ,\mathcal{E}} and, according to (51) and (56),

    \begin{equation*} \underset{h\rightarrow \infty }{\lim \inf }\text{ }\int_{T_{h}}\sigma _{ij}^{h}\left( u_{h}\right) e_{ij}\left( u_{h}\right) dsdx_{3}\geq \mu _{0} \mathcal{E}_{\Sigma }\left( \overline{v}\right) \text{.} \end{equation*}

    In this Section we state the main result of this work. According to Proposition 2 we introduce the following topology \tau :

    Definition 4.1. We say that a sequence \left( u_{h}\right) _{h} ; u_{h}\in H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \cap H^{1}\left( T_{h},\mathbb{ R}^{3}\right) , \tau -converges to \left( u,v\right) if

    \begin{equation*} \left\{ \begin{array}{l} u_{h}\underset{h\rightarrow \infty }{\rightharpoonup }u \ \text{in } \ {\bf{H }}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \text{-weak,} \\ u_{h}\dfrac{\boldsymbol{1}_{T_{h}}\left( x\right) }{\left\vert T_{h}\right\vert }dsdx_{3}\overset{\ast }{\underset{h\rightarrow \infty }{ \rightharpoonup }}v\boldsymbol{1}_{\Sigma }\left( s\right) \dfrac{d\mathcal{H }^{d}\left( s\right) \otimes \delta _{0}\left( x_{3}\right) }{\mathcal{H} ^{d}\left( \Sigma \right) }\text{ in }\mathcal{M}\left( \mathbb{R} ^{3}\right) \text{,} \end{array} \right. \end{equation*}

    with v\equiv v\left( s,0\right) \in L_{\mathcal{H}^{d}}^{2}\left( \Sigma , \mathbb{R}^{3}\right) .

    Our main result in this work reads as follows:

    Theorem 4.2. If \gamma \in \left( 0,+\infty \right) then

    1. ( \lim \sup inequality) for every \left( u,v\right) \in H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \times \mathcal{D}_{\Sigma , \mathcal{E}}\times L_{\mathcal{H}^{d}}^{2}\left( \Sigma \right) there exists a sequence \left( u_{h}\right) _{h} ; u_{h}\in H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \cap H^{1}\left( T_{h},\mathbb{R}^{3}\right) , such that \left( u_{h}\right) _{h} \tau -converges to \left( u,v\right) and

    \begin{equation*} \underset{h\rightarrow \infty }{\lim \sup }F_{h}\left( u_{h}\right) \leq F_{\infty }\left( u,v\right) \mathit{\text{,}} \end{equation*}

    where F_{\infty } is the functional defined in (15),

    2. ( \lim \inf inequality) for every sequence \left( u_{h}\right) _{h} ; u_{h}\in H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \cap H^{1}\left( T_{h},\mathbb{R}^{3}\right) , such that \left( u_{h}\right) _{h} \tau -converges to \left( u,v\right) , we have \overline{v}\in \mathcal{D} _{\Sigma ,\mathcal{E}} and

    \begin{equation*} \underset{h\rightarrow \infty }{\lim \inf }F_{h}\left( u_{h}\right) \geq F_{\infty }\left( u,v\right) \mathit{\text{.}} \end{equation*}

    Before proving this Theorem, let us write the homogenized problem obtained at the limit as h\longrightarrow \infty .

    Corollary 1. Problem (13) admits a unique solution U_{h} which, under the hypothesis of Theorem 4.2, \tau -converges to \left( U,V\right) \in H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \times \mathcal{D}_{\Delta _{\Sigma }}\times L_{\mathcal{H}^{d}}^{2}\left( \Sigma \right) solution of the problem

    \left\{\begin{aligned} -\sigma_{i j, j}(U) &=f_{i} & & { in }\; \Omega, \\ -\mu_{0} \Delta_{\alpha, \Sigma}\left(V_{\alpha}\right) &=\frac{\pi \mu \gamma}{(\ln 2)^{2}} A_{\alpha \beta}(s)\left(U_{\beta}-V_{\beta}\right) ; \alpha, \beta=1,2, & & { in }\; \Sigma, \\ \left[\left.\sigma_{\alpha 3}\right|_{x_{3}=0}\right]_{\Sigma} &=\frac{\pi \mu}{\mathcal{H}^{d}(\Sigma)(\ln 2)^{2}} A_{\alpha \beta}(s)\left(U_{\beta}-V_{\beta}\right) \mathcal{H}^{d} & { on } \;\Sigma, \\ U_{3} &=V_{3} & { on }\; \Sigma, \\ U &=0 & & { on }\; \partial \Omega, \\ V_{\alpha} &=0 ; \alpha=1,2, & { on }\; \mathcal{V}_{0} . \end{aligned}\right. (57)

    Proof. One can easily check that problem (13) has a unique solution U_{h}\in H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \cap H^{1}\left( T_{h},\mathbb{R}^{3}\right) . Now, observing that

    \begin{equation*} F_{h}\left( U_{h}\right) -2\int_{\Omega }f.U_{h}dx\leq F_{h}\left( 0\right) = 0\text{,} \end{equation*}

    we deduce, using the fact that \underset{h\rightarrow \infty }{\lim }\eta _{h} = +\infty , the Korn inequality, and the Poincaré inequality, that

    \begin{equation*} \left. \begin{array}{l} \int_{\Omega }\left\vert \nabla U_{h}\right\vert ^{2}dx \\ \leq \int_{\Omega }\sigma _{ij}\left( U_{h}\right) e_{ij}\left( U_{h}\right) dx+\int_{T_{h}}\sigma _{ij}^{h}\left( U_{h}\right) e_{ij}\left( U_{h}\right) dsdx_{3} \\ \leq 2\left\Vert f\right\Vert _{L^{2}\left( \Omega ,\mathbb{R}^{3}\right) }\left\Vert U_{h}\right\Vert _{L^{2}\left( \Omega ,\mathbb{R}^{3}\right) }\leq C\left\Vert \nabla U_{h}\right\Vert _{L^{2}\left( \Omega ,\mathbb{R} ^{9}\right) }\text{,} \end{array} \right. \end{equation*}

    from which we deduce that \sup_{h}F_{h}\left( U_{h}\right) <+\infty . Then, using Proposition 2 and Theorem 4.2, we deduce, according to [16,Theorem 7.8]), that the sequence \left( U_{h}\right) _{h} \tau -converges to the solution \left( U,V\right) of the problem

    \begin{equation} \underset{\left( \xi ,\zeta \right) \in V}{\min }\left\{ \begin{array}{l} \int_{\Omega }\sigma _{ij}\left( \xi \right) e_{ij}\left( \xi \right) dx+\mu _{0}\int_{\Sigma }d\mathcal{L}_{\Sigma }\left( \zeta \right) \\ +\dfrac{\pi \mu \gamma }{\mathcal{H}^{d}\left( \Sigma \right) \left( \ln 2\right) ^{2}}\int\nolimits_{\Sigma }A\left( s\right) \left( \xi -\zeta \right) .\left( \xi -\zeta \right) d\mathcal{H}^{d}\left( s\right) \\ -2\int_{\Omega }f.\xi dx{ \ } \end{array} \right\} \text{,} \end{equation} (58)

    where V = H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \times \mathcal{D} _{\Delta _{\Sigma }}\times L_{\mathcal{H}^{d}}^{2}\left( \Sigma \right) . On the other hand, according to [27,Theorem 6], the trace of \xi \in H^{1}\left( \Omega ,\mathbb{R}^{3}\right) on \omega \cap \Sigma exists for \mathcal{H}^{d} -almost-every x\in \omega \cap \Sigma and belongs to the Besov space B_{d}^{2}\left( \Sigma ,\mathbb{R}^{3}\right) of functions \psi :\Sigma \longrightarrow \mathbb{R}^{3} such that

    \begin{equation} \text{ }\int_{\Sigma }\left\vert \psi \left( x\right) \right\vert ^{2}d \mathcal{H}^{d}\left( x\right) +\underset{\left\vert x-y\right\vert < 1}{ \int_{\Sigma }\int_{\Sigma }}\dfrac{\left\vert \psi \left( x\right) -\psi \left( y\right) \right\vert ^{2}}{\left\vert x-y\right\vert ^{2d}}d\mathcal{H }^{d}\left( x\right) d\mathcal{H}^{d}\left( y\right) < +\infty \text{.} \end{equation} (59)

    Then, according to Lemma 2.1, we obtain from (58), using for example [46,Theorems 3.1 and 3.3], that \overline{v}\in \mathcal{D}_{\Delta _{\Sigma }} and for every \left( \xi ,\zeta \right) \in H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \times \mathcal{D}_{\Sigma ,\mathcal{E}}\times L_{\mathcal{H}^{d}}^{2}\left( \Sigma \right) ,

    \begin{equation} \begin{array}{l} \int_{\Omega }\left( -\sigma _{ij,j}\left( U\right) -f_{i}\right) \xi _{i}dx-\dfrac{\mu _{0}}{\mathcal{H}^{d}\left( \Sigma \right) }\int_{\Sigma }\left( \Delta _{\alpha ,\Sigma }\overline{V}\right) \zeta _{\alpha }d \mathcal{H}^{d}\left( s\right) \\ +\dfrac{\pi \mu \gamma }{\mathcal{H}^{d}\left( \Sigma \right) \left( \ln 2\right) ^{2}}\int\nolimits_{\Sigma }A\left( s\right) \left( U-V\right) .\left( \xi -\zeta \right) d\mathcal{H}^{d}\left( s\right) \\ \\ -\langle \left[ \sigma _{i3}|_{x_{3} = 0}\right] _{\Sigma },\xi _{i}\rangle _{B_{-d}^{2}\left( \Sigma ,\mathbb{R}^{3}\right) ,B_{d}^{2}\left( \Sigma ,\mathbb{R}^{3}\right) } = 0\text{,} \end{array} \end{equation} (60)

    B_{-d}^{2}\left( \Sigma ,\mathbb{R}^{3}\right) being the dual space of B_{d}^{2}\left( \Sigma ,\mathbb{R}^{3}\right) (see [28,p. 291]). Since \left( V_{1},V_{2}\right) \in D_{\Sigma ,\mathcal{E}}\subset L_{ \mathcal{H}^{d}}^{2}\left( \Sigma ,\mathbb{R}^{2}\right) , the trace of U on \Sigma belongs to B_{d}^{2}\left( \Sigma ,\mathbb{R}^{3}\right) \subset L_{\mathcal{H}^{d}}^{2}\left( \Sigma ,\mathbb{R}^{3}\right) , and, according to Lemma 2.1, \Delta _{\alpha ,\Sigma } ; \alpha = 1,2 , is a second order operator in L_{\mathcal{H}^{d}}^{2}\left( \Sigma \right) defined by the form \mathcal{E}_{\Sigma } under the Dirichlet condition V_{\alpha } = 0 on \mathcal{V}_{0} ; \alpha = 1,2 , the transmission condition

    \begin{equation*} -\mu _{0}\Delta _{\alpha ,\Sigma }\left( V_{\alpha }\right) = \dfrac{\pi \mu \gamma }{\left( \ln 2\right) ^{2}}A_{\alpha \beta }\left( s\right) \left( U_{\beta }-V_{\beta }\right) \text{; }\alpha ,\beta = 1,2\text{, in }\Sigma \text{,} \end{equation*}

    in problem (57) is well posed.

    The proof of Theorem 4.2 is given in three steps.

    We consider here a local problem associated with boundary layers in the vicinity of the strips. We denote w^{m} ; m = 1,2 , the solution of the following boundary value problem:

    \begin{equation} \left\{ \begin{array}{rcll} \sigma _{ij,j}\left( w^{m}\right) \left( y\right) & = & 0 & \forall y\in \mathbb{R}^{2+}\text{; }i = 1,2\text{,} \\ w^{m}\left( y_{1},0\right) & = & e_{m} & \forall y_{1}\in \left] -1,1\right[ \text{,} \\ \sigma _{i2}\left( w^{m}\right) \left( y_{1},0\right) & = & 0 & \forall y_{1}\in \mathbb{R}\setminus \left] -1,1\right[ \text{,} \\ w_{m}^{m}\left( y\right) & = & -\dfrac{\ln \left\vert y\right\vert }{\ln 2} & \text{as }\left\vert y\right\vert \rightarrow \infty \text{, }y_{2} > 0\text{ ,} \\ \left\vert w_{p}^{m}\right\vert \left( y\right) & \leq & C & \text{for } \left\{ \begin{array}{l} p = 2\text{ if }m = 1\text{,} \\ p = 1\text{ if }m = 2\text{,} \end{array} \right. \end{array} \right. \end{equation} (61)

    where \mathbb{R}^{2+} = \left\{ y = \left( y_{1},y_{2}\right) \in \mathbb{R}^{2} \text{; }y_{2}>0\right\} and e_{m} = \left( \delta _{1m},\delta _{2m}\right) ; m = 1,2 . The displacement w^{m} ; m = 1,2 , which belongs to the space H_{loc}^{1}\left( \mathbb{R}^{2+},\mathbb{R}^{2}\right) , is given (see for instance [34] and [18]) by

    \begin{equation} \begin{array}{lll} w_{1}^{1}\left( y\right) & = & \dfrac{1}{4\pi \mu }\int\nolimits_{-1}^{1} \xi \left( t\right) \left( \begin{array}{l} -\left( 1+\kappa \right) \ln \left( \sqrt{\left( y_{1}-t\right) ^{2}+\left( y_{2}\right) ^{2}}\right) \\ +\dfrac{2\left( y_{2}\right) ^{2}}{\left( y_{1}-t\right) ^{2}+\left( y_{2}\right) ^{2}} \end{array} \right) dt\text{,} \\ w_{2}^{1}\left( y\right) & = & \dfrac{1}{4\pi \mu }\int\nolimits_{-1}^{1} \xi \left( t\right) \left( \begin{array}{l} -\left( 1-\kappa \right) \arctan \left( \dfrac{y_{2}}{y_{1}-t}\right) \\ +\dfrac{2y_{2}\left( y_{1}-t\right) }{\left( y_{1}-t\right) ^{2}+\left( y_{2}\right) ^{2}} \end{array} \right) dt \end{array} \end{equation} (62)

    and

    \begin{equation} \begin{array}{lll} w_{1}^{2}\left( y\right) & = & \dfrac{1}{4\pi \mu }\int\nolimits_{-1}^{1} \xi \left( t\right) \left( \begin{array}{l} \left( 1-\kappa \right) \arctan \left( \dfrac{y_{2}}{y_{1}-t}\right) \\ +\dfrac{2y_{2}\left( y_{1}-t\right) }{\left( y_{1}-t\right) ^{2}+\left( y_{2}\right) ^{2}} \end{array} \right) dt\text{,} \\ w_{2}^{2}\left( y\right) & = & \dfrac{1}{4\pi \mu }\int\nolimits_{-1}^{1} \xi \left( t\right) \left( \begin{array}{l} -\left( 1+\kappa \right) \ln \left( \sqrt{\left( y_{1}-t\right) ^{2}+\left( y_{2}\right) ^{2}}\right) \\ -\dfrac{2\left( y_{2}\right) ^{2}}{\left( y_{1}-t\right) ^{2}+\left( y_{2}\right) ^{2}} \end{array} \right) dt\text{,} \end{array} \end{equation} (63)

    where

    \begin{equation} \xi \left( t\right) = \left\{ \begin{array}{ll} \dfrac{4\mu }{\left( 1+\kappa \right) \ln 2}\dfrac{1}{\sqrt{1-t^{2}}} & \text{if }t\in \left] -1,1\right[ \text{,} \\ 0 & \text{otherwise.} \end{array} \right. \end{equation} (64)

    One can check that w^{m}\left( y\right) ; m = 1,2 , is also the solution of problem (61) posed in the half-plane \mathbb{R}^{2-} :

    \begin{equation*} \mathbb{R}^{2-} = \left\{ y = \left( y_{1},y_{2}\right) \in \mathbb{R}^{2}\text{ ; }y_{2} < 0\right\} . \end{equation*}

    We introduce the scalar problem

    \begin{equation} \left\{ \begin{array}{rcll} \Delta w\left( y\right) & = & 0 & \forall y\in \mathbb{R}^{2+}\text{; }i = 1,2 \text{,} \\ w\left( y_{1},0\right) & = & 1 & \forall y_{1}\in \left] -1,1\right[ \text{,} \\ \dfrac{\partial w}{\partial y_{2}}\left( y_{1},0\right) & = & 0 & \forall y_{1}\in \mathbb{R}\setminus \left] -1,1\right[ \text{,} \\ w\left( y\right) & = & -\dfrac{\ln \left\vert y\right\vert }{\ln 2} & \text{ as }\left\vert y\right\vert \rightarrow \infty \text{, }y_{2} > 0\text{.} \end{array} \right. \end{equation} (65)

    The solution of (65) is given by

    \begin{equation} w\left( y\right) = \dfrac{-1}{\pi \ln 2}\int\nolimits_{-1}^{1}\frac{\ln \left( \sqrt{\left( y_{1}-t\right) ^{2}+\left( y_{2}\right) ^{2}}\right) }{ \sqrt{1-t^{2}}}dt\text{.} \end{equation} (66)

    Observe that w\left( y\right) is also the solution of problem (65) posed in the half-plane \mathbb{R}^{2-} . We now state the following preliminary result in this section:

    Proposition 3. ([18,Proposition 7]). One has

    1. \underset{R\rightarrow \infty }{\lim }\dfrac{1}{\ln R} \int\nolimits_{B\left( 0,R\right) \cap {\bf{R}}^{2\pm }}\sigma _{ij}\left( w^{m}\right) e_{ij}\left( w^{l}\right) dy = \delta _{ml}\dfrac{ 2\mu \pi }{\left( 1+\kappa \right) \left( \ln 2\right) ^{2}} ; m,l = 1,2 ,

    2. \underset{R\rightarrow \infty }{\lim }\dfrac{1}{\ln R} \int\nolimits_{B\left( 0,R\right) \cap {\bf{R}}^{2\pm }}\left\vert \nabla w\right\vert ^{2}dy = \dfrac{\pi }{\left( \ln 2\right) ^{2}} , where B\left( 0,R\right) is a disc of radius R centred at the origin.

    Let r_{h} be a positive parameter, such that

    \begin{equation} \underset{h\rightarrow \infty }{\lim }2^{h}r_{h} = \underset{h\rightarrow \infty }{\lim }\dfrac{\varepsilon _{h}}{r_{h}} = 0\text{.} \end{equation} (67)

    We define the rotation \mathcal{R}\left( x_{h}^{k}\right) ; x_{h}^{k} = \left( x_{1h}^{k},x_{2h}^{k}\right) being the center of S_{h}^{k} in Cartesian coordinates, by

    \begin{equation} \mathcal{R}\left( x_{h}^{k}\right) = \left\{ \begin{array}{ll} Id_{\mathbb{R}^{3}} & \text{if }n^{k} = \pm \left( 0,1\right) \text{,} \\ & \\ \left( \begin{array}{ccc} 1/2 & \sqrt{3}/2 & 0 \\ -\sqrt{3}/2 & 1/2 & 0 \\ 0 & 0 & 1 \end{array} \right) & \text{if }n^{k} = \pm \left( -\sqrt{3}/2,1/2\right) \text{,} \\ \left( \begin{array}{ccc} -1/2 & \sqrt{3}/2 & 0 \\ \sqrt{3}/2 & 1/2 & 0 \\ 0 & 0 & 1 \end{array} \right) & \text{if }n^{k} = \pm \left( \sqrt{3}/2,1/2\right), \end{array} \right. \end{equation} (68)

    where n^{k} is the unit normal on S_{h}^{k} and Id_{\mathbb{R}^{3}} is the 3\times 3 identity marix. Let \varphi _{h}^{k} ; k\in I_{h} , be the truncation function defined on \mathbb{R}^{2} by

    \begin{equation} \varphi _{h}^{k}\left( x\right) = \left\{ \begin{array}{ll} \dfrac{4\left( r_{h}^{2}-R_{k,h}^{2}\left( x\right) \right) }{3r_{h}^{2}} & \text{if }r_{h}/2\leq R_{h}^{k}\left( x\right) \leq r_{h}\text{,} \\ 1 & \text{if }R_{h}^{k}\left( x\right) \leq r_{h}/2\text{,} \\ 0 & \text{if }R_{h}^{k}\left( x\right) \geq r_{h}\text{,} \end{array} \right. \end{equation} (69)

    where R_{h}^{k}\left( x\right) = \sqrt{\left( \left( x^{\prime }-x_{h}^{k}\right) .n^{k}\right) ^{2}+x_{3}^{2}} with x^{\prime } = \left( x_{1},x_{2}\right) . We define, for k\in I_{h} ,

    \begin{equation} D_{h}^{k}\left( r_{h}\right) = \left\{ \left( \left( x-x_{h}^{k}\right) .n^{k},x_{3}\right) \in \mathbb{R}^{2}\text{; }R_{h}^{k}\left( x\right) < r_{h}\text{, }\forall x\in \mathbb{R}^{3}\right\} \end{equation} (70)

    and the cylinder

    \begin{equation} Z_{h}^{k} = \mathcal{R}\left( x_{h}^{k}\right) S_{h}^{k}\times D_{h}^{k}\left( r_{h}\right) \text{.} \end{equation} (71)

    We then set

    \begin{equation} Z_{h} = \underset{k\in I_{h}}{\bigcup \limits}Z_{h}^{k}\text{.} \end{equation} (72)

    We define, the function w_{h}^{mk}\left( x\right) ; k\in I_{h} and m = 1,2,3 , by

    \begin{equation} w_{h}^{1k}\left( x\right) = \varphi _{h}^{k}\left( x\right) \mathcal{R}\left( x_{h}^{k}\right) \left( e_{1}-\frac{1}{\ln \varepsilon _{h}}\left( \begin{array}{c} 1-w\left( \dfrac{x_{3}}{\varepsilon _{h}},\dfrac{\left( x^{\prime }-x_{h}^{k}\right) .n^{k}}{\varepsilon _{h}}\right) \\ 0 \\ 0 \end{array} \right) \right) \text{,} \end{equation} (73)
    \begin{equation} w_{h}^{2k}\left( x\right) = \varphi _{h}^{k}\left( x\right) \mathcal{R}\left( x_{h}^{k}\right) \left( e_{2}-\frac{1}{\ln \varepsilon _{h}}\left( \begin{array}{c} 0 \\ 1-w_{1}^{1}\left( \dfrac{x_{3}}{\varepsilon _{h}},\dfrac{\left( x^{\prime }-x_{h}^{k}\right) .n^{k}}{\varepsilon _{h}}\right) \\ w_{2}^{1}\left( \dfrac{x_{3}}{\varepsilon _{h}},\dfrac{\left( x^{\prime }-x_{h}^{k}\right) .n^{k}}{\varepsilon _{h}}\right) \end{array} \right) \right) \end{equation} (74)

    and

    \begin{equation} w_{h}^{3k}\left( x\right) = \varphi _{h}^{k}\left( x\right) \mathcal{R}\left( x_{h}^{k}\right) \left( e_{3}-\frac{1}{\ln \varepsilon _{h}}\left( \begin{array}{c} 0 \\ w_{1}^{2}\left( \dfrac{x_{3}}{\varepsilon _{h}},\dfrac{\left( x^{\prime }-x_{h}^{k}\right) .n^{k}}{\varepsilon _{h}}\right) \\ 1-w_{2}^{2}\left( \dfrac{x_{3}}{\varepsilon _{h}},\dfrac{\left( x^{\prime }-x_{h}^{k}\right) .n^{k}}{\varepsilon _{h}}\right) \end{array} \right) \right) \text{,} \end{equation} (75)

    where e_{m} = \left( \delta _{1m},\delta _{2m},\delta _{3m}\right) ; m = 1,2,3 . We define the local perturbation w_{\varepsilon }^{m} ; m = 1,2,3 , on \Omega by

    \begin{equation} w_{h}^{m}\left( x\right) = w_{h}^{mk}\left( x\right) \text{, }\forall k\in I_{h}\text{, }\forall x\in \Omega \text{.} \end{equation} (76)

    We have the following result:

    Lemma 5.1. If \gamma \in \left( 0,+\infty \right) then, for every \Phi \in C^{1}\left( \overline{\Omega },\mathbb{R}^{3}\right) , we have

    \begin{equation*} \underset{h\rightarrow \infty }{\lim }\int\nolimits_{Z_{h}}\sigma _{ij}\left( w_{h}^{m}\Phi _{m}\right) e_{ij}\left( w_{h}^{l}\Phi _{l}\right) dx = \dfrac{\pi \mu \gamma }{\mathcal{H}^{d}\left( \Sigma \right) \left( \ln 2\right) ^{2}}\int\nolimits_{\Sigma }A\left( s\right) \Phi \left( s\right) .\Phi \left( s\right) d\mathcal{H}^{d}\left( s\right) \mathit{\text{,}} \end{equation*}

    where A\left( s\right) is the material matrix defined in (17).

    Proof. Let us introduce the change of variables

    \begin{equation*} \left\{ \begin{array}{lll} y_{1} & = & \dfrac{x_{3}}{\varepsilon _{h}}\text{,} \\ y_{2} & = & \dfrac{\left( x^{\prime }-x_{h}^{k}\right) .n^{k}}{\varepsilon _{h}}\text{,} \end{array} \right. \end{equation*}

    on Z_{h}^{k} ; k\in I_{h} . Then, using the smoothness of \Phi and Proposition 3, we have

    \begin{equation*} \left. \begin{array}{l} \underset{h\rightarrow \infty }{\lim }\int\nolimits_{Z_{h}}\sigma _{ij}\left( w_{h}^{m}\Phi _{m}\right) e_{ij}\left( w_{h}^{l}\Phi _{l}\right) dx \\ = \underset{h\rightarrow \infty }{\lim }\underset{k\in I_{h}}{\sum\limits } \int\nolimits_{Z_{h}^{k}}\sigma _{ij}\left( w_{h}^{mk}\right) e_{ij}\left( w_{h}^{lk}\right) \Phi _{m}\Phi _{l}dx \\ = \underset{h\rightarrow \infty }{\lim }\dfrac{3^{h+1}}{2^{h}\ln ^{2}\varepsilon _{h}}\int\nolimits_{D\left( 0,\frac{r_{h}}{\varepsilon _{h}} \right) \backslash D\left( 0,1\right) }\sigma _{ij}\left( w^{m}\right) e_{ij}\left( w^{l}\right) dy_{1}dy_{2} \\ \times \left( \underset{k\in I_{h}}{\sum \limits}\dfrac{1}{N_{h}}\left( \mathcal{R} \left( x_{h}^{k}\right) \Phi \right) _{m}\left( \mathcal{R}\left( x_{h}^{k}\right) \Phi \right) _{l}\left( x_{1h}^{k},x_{2h}^{k},0\right) \right) \\ = \dfrac{\pi \mu \gamma }{\mathcal{H}^{d}\left( \Sigma \right) \ln 2} \int\nolimits_{\Sigma }\left( B\mathcal{R}\left( s\right) \Phi \left( s\right) \right) _{m}\left( \mathcal{R}\left( s\right) \Phi \left( s\right) \right) _{l}d\mathcal{H}^{d}\left( s\right) \\ = \dfrac{\pi \mu \gamma }{\mathcal{H}^{d}\left( \Sigma \right) \left( \ln 2\right) ^{2}}\int\nolimits_{\Sigma }\mathcal{R}^{t}\left( s\right) B \mathcal{R}\left( s\right) \Phi \left( s\right) .\Phi \left( s\right) d \mathcal{H}^{d}\left( s\right) \text{,} \end{array} \right. \end{equation*}

    where B = Diag \left( 1,\dfrac{2}{\left( 1+\kappa \right) },\dfrac{2}{\left( 1+\kappa \right) }\right) and \mathcal{R}\left( s\right) is the rotation matrix defined by \mathcal{R}\left( s\right) = Id_{\mathbb{R} ^{3}} on the faces of \Sigma which are perpendicular to the vectors \pm \left( 0,1\right) , by \mathcal{R}\left( s\right) = \left( \begin{array}{ccc} 1/2 & \sqrt{3}/2 & 0 \\ -\sqrt{3}/2 & 1/2 & 0 \\ 0 & 0 & 1 \end{array} \right) on the faces of \Sigma which are perpendicular to the vectors \pm \left( -\sqrt{3}/2,1/2\right) , and \left( \begin{array}{ccc} -1/2 & \sqrt{3}/2 & 0 \\ \sqrt{3}/2 & 1/2 & 0 \\ 0 & 0 & 1 \end{array} \right) on the faces of \Sigma which are perpendicular to the vectors \pm \left( \sqrt{3}/2,1/2\right) . Then observing that

    \begin{equation*} \mathcal{R}^{t}\left( s\right) B\mathcal{R}\left( s\right) = \mathcal{R} \left( s\right) B\mathcal{R}\left( s\right) = A\left( s\right) \text{,} \end{equation*}

    we have the result.

    In this Subsection we prove the lim-sup condition of the \Gamma -convergence property stated in Theorem 4.2. Let p_{h}^{k} = \left( p_{h,1}^{k},p_{h,2}^{k}\right) , q_{h}^{k} = \left( q_{h,1}^{k},q_{h,2}^{k}\right) be the extremities of the line segment S_{h}^{k} . Let v\in C_{c}^{1}\left( \omega ,\mathbb{R}^{3}\right) . Then, we build the following sequence:

    \begin{equation} \begin{array}{lll} v_{1,h}^{k}\left( x^{\prime }\right) & = & v_{1}\left( x_{1h}^{k},x_{2h}^{k}\right) +2^{h}\zeta _{h}^{1,k}\left( x^{\prime }\right) \left\vert v_{1}\left( p_{h}^{k}\right) -v_{1}\left( q_{h}^{k}\right) \right\vert \text{,} \\ v_{2,h}^{k}\left( x^{\prime }\right) & = & v_{2}\left( x_{1h}^{k},x_{2h}^{k}\right) +2^{h}\zeta _{h}^{2,k}\left( x^{\prime }\right) \left\vert v_{2}\left( p_{h}^{k}\right) -v_{2}\left( q_{h}^{k}\right) \right\vert \text{,} \\ v_{3,h}^{k}\left( x^{\prime }\right) & = & v_{3}\left( x_{1h}^{k},x_{2h}^{k}\right) \text{,} \end{array} \end{equation} (77)

    for every x^{\prime }\in \omega , where, using the local coordinates (37) for S_{h}^{k}\perp \left( -\sqrt{3}/2,1/2\right) ,

    \begin{equation} \left\{ \begin{array}{lll} \zeta _{h}^{1,k}\left( x^{\prime }\right) & = & 2\sqrt{\mu _{h}}\dfrac{ s+p_{h,1}^{k}/2-p_{h,2}^{k}\sqrt{3}/2}{\sqrt{\lambda _{h}+2\mu _{h}}}\text{,} \\ \zeta _{h}^{2,k}\left( x^{\prime }\right) & = & \dfrac{2\left( s+p_{h,1}^{k}/2-p_{h,2}^{k}\sqrt{3}/2\right) }{\sqrt{3}}\text{,} \end{array} \right. \end{equation} (78)

    using the local coordinates (38) for S_{h}^{k}\perp \left( \sqrt{3 }/2,1/2\right) ,

    \begin{equation*} \left\{ \begin{array}{lll} \zeta _{h}^{1,k}\left( x^{\prime }\right) & = & \sqrt{2}\sqrt{\mu _{h}} \dfrac{s-p_{h,1}^{k}/2-p_{h,2}^{k}\sqrt{3}/2}{\sqrt{\lambda _{h}+2\mu _{h}}} \text{,} \\ \zeta _{h}^{2,k}\left( x^{\prime }\right) & = & \dfrac{\sqrt{2}\left( s-p_{h,1}^{k}/2-p_{h,2}^{k}\sqrt{3}/2\right) }{\sqrt{3}}\text{,} \end{array} \right. \end{equation*}

    and, using the local coordinates (39) for S_{h}^{k}\perp \left( 0,1\right) ,

    \begin{equation*} \left\{ \begin{array}{lll} \zeta _{h}^{1,k}\left( x^{\prime }\right) & = & \sqrt{\dfrac{\mu _{h}}{2}} \dfrac{\left( x_{1}-p_{h,1}^{k}\right) }{\sqrt{\lambda _{h}+2\mu _{h}}}\text{ ,} \\ \zeta _{h}^{2,k}\left( x^{\prime }\right) & = & \dfrac{\left( x_{1}-p_{h,1}^{k}\right) }{\sqrt{2}}\text{.} \end{array} \right. \end{equation*}

    Let us now introduce the intervals J_{h}^{p_{h}^{k}} and J_{h}^{q_{h}^{k}} centred at the points p_{h}^{k} and q_{h}^{k} respectively, such that

    \begin{equation} S_{h}^{k}\cap J_{h}^{p_{h}^{k}} = \left[ p_{h}^{k},p_{h}^{k}+{\bf{s}} _{h}\right) \text{, }S_{h}^{k}\cap J_{h}^{q_{h}^{k}} = \left( q_{h}^{k}- {\bf{s}}_{h},q_{h}^{k}\right] \text{,} \end{equation} (79)

    where {\bf{s}}_{h} = \left( \begin{array}{c} s_{h} \\ s_{h} \end{array} \right) , such that \underset{h\rightarrow \infty }{\lim }2^{h}s_{h} = 0 . Let \psi _{h}^{k} be a C_{c}^{\infty }\left( S_{h}^{k}\cup J_{h}^{p_{h}^{k}}\cup J_{h}^{q_{h}^{k}}\right) test-function, such that

    \begin{equation} \psi _{h}^{k} = \left\{ \begin{array}{ll} 1 & \text{on }\ S_{h}^{k}\backslash J_{h}^{p_{h}^{k}}\cup J_{h}^{q_{h}^{k}} \text{,} \\ 0 & \text{on }J_{h}^{p_{h}^{k}}\cup J_{h}^{q_{h}^{k}}\backslash \left( \left( p_{h}^{k},p_{h}^{k}+{\bf{s}}_{h}\right) \cup \left( q_{h}^{k}- {\bf{s}}_{h},q_{h}^{k}\right) \right) \text{.} \end{array} \right. \end{equation} (80)

    We define the test-function v_{h} by

    \begin{equation} v_{h} = \psi _{h}^{k}v_{h}^{k}\text{, }\forall k\in I_{h}\text{.} \end{equation} (81)

    We have the following convergences:

    Lemma 5.2. We have

    1. v_{h}\dfrac{\boldsymbol{1}_{T_{h}}\left( x\right) }{\left\vert T_{h}\right\vert }dsdx_{3}\overset{\ast }{\underset{h\rightarrow \infty }{ \rightharpoonup }}v\boldsymbol{1}_{\Sigma }\left( s\right) \dfrac{d\mathcal{H }^{d}\left( s\right) }{\mathcal{H}^{d}\left( \Sigma \right) } ,

    2. \underset{h\rightarrow \infty }{\lim }\int_{T_{h}}\sigma _{ij}^{h}\left( v_{h}\right) e_{ij}\left( v_{h}\right) dsdx_{3}\mathcal{ = \mu }_{0}\underset{h\rightarrow \infty }{\lim }\left( \dfrac{5}{3}\right) ^{h} \underset{\underset{\left\vert p-q\right\vert = 2^{-h}}{p,q\in \mathcal{V}_{h} }}{\underset{\alpha = 1,2}{\sum \limits}}\left\vert v_{\alpha }\left( p\right) -v_{\alpha }\left( q\right) \right\vert ^{2} .

    Proof. 1. Let \varphi \in C_{0}\left( \mathbb{R}^{3},\mathbb{R}^{3}\right) . We have

    \begin{equation*} \left. \begin{array}{r} \underset{h\rightarrow \infty }{\lim }\int_{\mathbb{R}^{3}}\varphi \left( x\right) .v_{h}\left( x^{\prime }\right) \dfrac{\boldsymbol{1}_{T_{h}}\left( x\right) }{\left\vert T_{h}\right\vert }dsdx_{3}\mathcal{ = }\underset{ h\rightarrow \infty }{\lim }\underset{k\in I_{h}}{\sum\limits }\dfrac{2v\left( x_{1h}^{k},x_{2h}^{k}\right) }{3^{h+1}}.\varphi \left( x_{1h}^{k},x_{2h}^{k},0\right) \\ +C\underset{h\rightarrow \infty }{\lim }\underset{k\in I_{h}}{\underset{ \underset{i = 1,2,3}{\alpha = 1,2}}{\sum\limits }}\dfrac{2}{3^{h+1}}\left\vert v_{\alpha }\left( p_{h}^{k}\right) -v_{\alpha }\left( q_{h}^{k}\right) \right\vert \varphi _{i}\left( x_{1h}^{k},x_{2h}^{k},0\right) \text{,} \end{array} \right. \end{equation*}

    where C is a positive constant independent of h . On the one hand we have

    \begin{equation*} \begin{array}{lll} \underset{h\rightarrow \infty }{\lim }\underset{k\in I_{h}}{\sum \limits}\dfrac{2}{ 3^{h+1}}v\left( x_{1h}^{k},x_{2h}^{k}\right) .\varphi \left( x_{1h}^{k},x_{2h}^{k},0\right) & = & \underset{h\rightarrow \infty }{\lim } \underset{k\in I_{h}}{\sum \limits}\dfrac{1}{N_{h}}v\left( x_{h}^{k}\right) .\varphi \left( x_{h}^{k},0\right) \\ & = & \dfrac{1}{\mathcal{H}^{d}\left( \Sigma \right) }\int_{\Sigma }v\left( s\right) .\varphi \left( s,0\right) d\mathcal{H}^{d}\left( s\right) \text{.} \end{array} \end{equation*}

    On the other hand, since

    \begin{equation*} \left\vert v_{\alpha }\left( p_{h}^{k}\right) -v_{\alpha }\left( q_{h}^{k}\right) \right\vert \leq C\left\vert p_{h}^{k}-q_{h}^{k}\right\vert \end{equation*}

    and \left\vert p_{h}^{k}-q_{h}^{k}\right\vert = 2^{-h} , we have

    \begin{equation*} \underset{h\rightarrow \infty }{\lim }\underset{k\in I_{h}}{\underset{ \underset{i = 1,2,3}{\alpha = 1,2}}{\sum \limits}}\dfrac{2}{3^{h+1}}\left\vert v_{\alpha }\left( p_{h}^{k}\right) -v_{\alpha }\left( q_{h}^{k}\right) \right\vert \varphi _{i}\left( x_{1h}^{k},x_{2h}^{k},0\right) = 0\text{.} \end{equation*}

    2. Computing tensors in local coordinates (37) and (38), we obtain, for S_{h}^{k}\perp \left( -\sqrt{3}/2,1/2\right) or S_{h}^{k}\perp \left( \sqrt{3}/2,1/2\right) ,

    \begin{equation*} \sigma _{ij}^{h}\left( v_{h}^{k}\right) e_{ij}\left( v_{h}^{k}\right) = \dfrac{\left( \lambda _{h}+2\mu _{h}\right) }{4}\left( \dfrac{\partial v_{1,h}^{k}}{\partial s}\right) ^{2}+\dfrac{3\mu _{h}}{4}\left( \dfrac{ \partial v_{2,h}^{k}}{\partial s}\right) ^{2}\text{,} \end{equation*}

    and if S_{h}^{k}\perp \left( 0,1\right) ,

    \begin{equation*} \sigma _{ij}^{h}\left( v_{h}^{k}\right) e_{ij}\left( v_{h}^{k}\right) = \left( \lambda _{h}+2\mu _{h}\right) \left( \dfrac{\partial v_{1,h}^{k}}{ \partial x_{1}}\right) ^{2}+\mu _{h}\left( \dfrac{\partial v_{2,h}^{k}}{ \partial x_{1}}\right) ^{2}\text{.} \end{equation*}

    Thus, according to (77)-(78), we obtain on each S_{h}^{k} ; k\in I_{h} ,

    \begin{equation*} \sigma _{ij}^{h}\left( v_{h}^{k}\right) e_{ij}\left( v_{h}^{k}\right) = \mu _{h}2^{2h}\left\{ \left\vert v_{1}\left( p_{h}^{k}\right) -v_{1}\left( q_{h}^{k}\right) \right\vert ^{2}+\left\vert v_{2}\left( p_{h}^{k}\right) -v_{2}\left( q_{h}^{k}\right) \right\vert ^{2}\right\} \text{,} \end{equation*}

    which implies that

    \begin{equation*} \left. \begin{array}{l} \underset{h\rightarrow \infty }{\lim }\int_{T_{h}}\sigma _{ij}^{h}\left( v_{h}\right) e_{ij}\left( v_{h}\right) dsdx_{3} \\ \mathcal{ = \mu }_{0}\underset{h\rightarrow \infty }{\lim }\eta _{h}\underset{ k\in I_{h},\alpha = 1,2}{\sum \limits}\varepsilon _{h}2^{h}\left\vert v_{\alpha }\left( p_{h}^{k}\right) -v_{\alpha }\left( q_{h}^{k}\right) \right\vert ^{2} \\ = \mathcal{\mu }_{0}\underset{h\rightarrow \infty }{\lim }\left( \dfrac{5}{3} \right) ^{h}\underset{k\in I_{h},\alpha = 1,2}{\sum\limits }\left\vert v_{\alpha }\left( p_{h}^{k}\right) -v_{\alpha }\left( q_{h}^{k}\right) \right\vert ^{2} \\ = \mathcal{\mu }_{0}\underset{h\rightarrow \infty }{\lim }\left( \dfrac{5}{3} \right) ^{h}\underset{\underset{\left\vert p-q\right\vert = 2^{-h}}{p,q\in \mathcal{V}_{h}}}{\underset{\alpha = 1,2}{\sum\limits }}\left\vert v_{\alpha }\left( p\right) -v_{\alpha }\left( q\right) \right\vert ^{2}\text{.} \end{array} \right. \end{equation*}

    We prove here the lim-sup condition of the \Gamma -convergence property stated in Theorem 4.2 _{1} .

    Proposition 4. If \gamma \in \left( 0,+\infty \right) then, for every \left( u,v\right) \in H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \times \mathcal{D}_{\Sigma ,\mathcal{E}}\times L_{\mathcal{H}^{d}}^{2}\left( \Sigma \right) , there exists a sequence \left( u_{h}\right) _{h} , such that u_{h}\in H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \cap H^{1}\left( T_{h},\mathbb{R}^{3}\right) , \left( u_{h}\right) _{h} \tau -converges to \left( u,v\right) , and

    \begin{equation*} \underset{h\rightarrow \infty }{\lim \sup }F_{h}\left( u_{h}\right) \leq F_{\infty }\left( u,v\right) \mathit{\text{.}} \end{equation*}

    Proof. Let \left( u,v\right) \in H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \times \mathcal{D}_{\Sigma ,\mathcal{E}}\times L_{\mathcal{H}^{d}}^{2}\left( \Sigma \right) . Let \left( u_{n},v_{n}\right) _{n} be a sequence in the space C_{c}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \times \left( C_{c}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \cap \mathcal{D}_{\Sigma , \mathcal{E}}\times L_{\mathcal{H}^{d}}^{2}\left( \Sigma \right) \right) such that u_{n}\underset{n\rightarrow \infty }{\longrightarrow }u \ H^{1}\left( \Omega ,\mathbb{R}^{3}\right) -strong, \overline{v}_{n} \underset{n\rightarrow \infty }{\longrightarrow }\overline{v} strongly with respect to the norm (26), and v_{3,n}\underset{n\rightarrow \infty } {\longrightarrow }v_{3} strongly with respect to the norm of L_{\mathcal{H} ^{d}}^{2}\left( \Sigma \right) . We define the sequence \left( u_{n,h}^{0}\right) _{h,n} by

    \begin{equation} u_{n,h}^{0} = u_{n}-w_{h}^{m}\left( \left( u_{n}\right) _{m}-\left( v_{n,h}\right) _{m}\right) \text{,} \end{equation} (82)

    where v_{n,h} is the test-function (81) associated with v_{n} , and w_{h}^{m} is the perturbation defined in (76). Then u_{n,h}^{0}\in H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \cap H^{1}\left( T_{h},\mathbb{R}^{3}\right) and, using Lemma 5.1, Lemma 5.2, and the fact that the measure \left\vert Z_{h}\right\vert of the set Z_{h} tends to zero as h tends to \infty , that \left( u_{n,h}^{0}\right) _{h} \tau -converges to \left( u_{n},v_{n}\right) as h tends to \infty .

    We have

    \begin{equation} \left. \begin{array}{l} F_{h}\left( u_{n,h}^{0}\right) = \int\nolimits_{\Omega \backslash Z_{h}}\sigma _{ij}\left( u_{n,h}^{0}\right) e_{ij}\left( u_{n,h}^{0}\right) dx \\ { \ \ \ \ }+\int\nolimits_{Z_{h}}\sigma _{ij}\left( u_{n,h}^{0}\right) e_{ij}\left( u_{n,h}^{0}\right) dx+\int\nolimits_{T_{h}}\sigma _{ij}^{h}\left( v_{n,h}\right) e_{ij}\left( v_{n,h}\right) dsdx_{3}\text{.} \end{array} \right. \end{equation} (83)

    We immediately obtain

    \begin{equation*} \underset{h\rightarrow \infty }{\lim }\int\nolimits_{\Omega \backslash Z_{h}}\sigma _{ij}\left( u_{n,h}^{0}\right) e_{ij}\left( u_{n,h}^{0}\right) dx = \int\nolimits_{\Omega }\sigma _{ij}\left( u_{n}\right) e_{ij}\left( u_{n}\right) dx\text{.} \end{equation*}

    Using Lemma 5.1, it follows that

    \begin{equation} \left. \begin{array}{l} \underset{h\rightarrow \infty }{\lim }\int\nolimits_{Z_{h}}\sigma _{ij}\left( u_{n,h}^{0}\right) e_{ij}\left( u_{n,h}^{0}\right) dx \\ = \underset{h\rightarrow \infty }{\lim }\int\nolimits_{Z_{h}}\sigma _{ij}\left( w_{h}^{m}\left( \left( u_{n}\right) _{m}-\left( v_{n,h}\right) _{m}\right) \right) e_{ij}\left( w_{h}^{m}\left( \left( u_{n}\right) _{m}-\left( v_{n,h}\right) _{m}\right) \right) \\ = \dfrac{\pi \mu \gamma }{\mathcal{H}^{d}\left( \Sigma \right) \ln 2} \int\nolimits_{\Sigma }A\left( s\right) \left( u_{n}-v_{n}\right) .\left( u_{n}-v_{n}\right) d\mathcal{H}^{d}\left( s\right) \end{array} \right. \end{equation} (84)

    and, using Lemma 5.2 and Proposition 1, we obtain

    \begin{equation*} \left. \begin{array}{l} \underset{h\rightarrow \infty }{\lim }\int\nolimits_{T_{h}}\sigma _{ij}^{h}\left( v_{n,h}\right) e_{ij}\left( v_{n,h}\right) dsdx_{3} \\ = \mathcal{\mu }_{0}\underset{h\rightarrow \infty }{\lim }\left( \dfrac{5}{3} \right) ^{h}\underset{\underset{\left\vert p-q\right\vert = 2^{-h}}{p,q\in \mathcal{V}_{h}}}{\underset{\alpha = 1,2}{\sum\limits }}\left\vert v_{\alpha ,n}\left( p,0\right) -v_{\alpha ,n}\left( q,0\right) \right\vert ^{2} \\ = \mu _{0}\mathcal{E}_{\Sigma }\left( \overline{v}_{n}\right) \\ = \mu _{0}\int_{\Sigma }d\mathcal{L}_{\Sigma }\left( \overline{v}_{n}\right) \text{.} \end{array} \right. \end{equation*}

    This yields

    \begin{equation} \left. \begin{array}{l} \underset{h\rightarrow \infty }{\lim }F_{h}\left( u_{n,h}^{0}\right) = \int\nolimits_{\Omega }\sigma _{ij}\left( u_{n}\right) e_{ij}\left( u_{n}\right) dx+\mu _{0}\int_{\Sigma }d\mathcal{L}_{\Sigma }\left( \overline{v}_{n}\right) \\ { \ \ \ \ \ \ \ \ \ \ \ }+\dfrac{\pi \mu \gamma }{\mathcal{H}^{d}\left( \Sigma \right) \ln 2}\int\nolimits_{\Sigma }A\left( s\right) \left( u_{n}-v_{n}\right) .\left( u_{n}-v_{n}\right) d\mathcal{H}^{d}\left( s\right) \\ { \ \ \ \ \ \ \ \ \ \ \ } = F_{\infty }\left( u_{n},v_{n}\right) \text{.} \end{array} \right. \end{equation} (85)

    The continuity of F_{\infty } implies that \lim_{n\rightarrow \infty }\lim_{h\rightarrow \infty }F_{h}\left( u_{n,h}^{0}\right) = F_{\infty }\left( u,v\right) . Then, using the diagonalization argument of [5,Corollary 1.18], we prove the existence of a sequence \left( u_{h}\right) _{h} = \left( u_{n\left( h\right) ,h}^{0}\right) _{h} : \underset {h\rightarrow \infty }{\lim }n\left( h\right) = +\infty , such that

    \begin{equation*} \underset{h\rightarrow \infty }{\lim \sup }F_{h}\left( u_{h}\right) \leq F_{\infty }\left( u,v\right) . \end{equation*}

    In this Subsection we prove the second assertion of Theorem 4.2.

    Proposition 5. If \gamma \in \left( 0,+\infty \right) , then, for every sequence \left( u_{h}\right) _{h} , such that u_{h}\in H_{0}^{1}\left( \Omega ,\mathbb{R}^{3}\right) \cap H^{1}\left( T_{h},\mathbb{ R}^{3}\right) and \left( u_{h}\right) _{h} \tau -converges to \left( u,v\right) , we have \overline{v}\in \mathcal{D}_{\Sigma ,\mathcal{E}} and

    \begin{equation*} \underset{h\rightarrow \infty }{\lim \inf }F_{h}\left( u_{h}\right) \geq F_{\infty }\left( u,v\right) \mathit{\text{.}} \end{equation*}

    Proof. Let \left( u_{h}\right) _{h} ; u_{h}\in H_{0}^{1}\left( \Omega ,\mathbb{R} ^{3}\right) \cap H^{1}\left( T_{h},\mathbb{R}^{3}\right) , such that \left( u_{h}\right) _{h} \tau -converges to \left( u,v\right) . We suppose that \sup_{h}F_{h}\left( u_{h}\right) <+\infty , otherwise the \lim \inf inequality is trivial. Then, owing to Proposition 2 and Proposition 1, we have that \overline{v}\in \mathcal{D}_{\Sigma , \mathcal{E}} and

    \begin{equation} \begin{array}{lll} \underset{h\rightarrow \infty }{\lim \inf }\text{ }\int_{T_{h}}\sigma _{ij}^{h}\left( u_{h}\right) e_{ij}\left( u_{h}\right) dsdx_{3} & \geq & \mu _{0}\mathcal{E}_{\Sigma }\left( \overline{v}\right) \\ & = & \mu _{0}\int_{\Sigma }d\mathcal{L}_{\Sigma }\left( \overline{v} \right) \text{.} \end{array} \end{equation} (86)

    Let \left( u_{n},v_{n}\right) _{n} \subset C_{c}^{1}\left( \Omega , \mathbb{R}^{3}\right) \times \left( C_{c}^{1}\left( \Omega ,\mathbb{R} ^{3}\right) \cap \mathcal{D}_{\Sigma ,\mathcal{E}}\times L_{\mathcal{H} ^{d}}^{2}\left( \Sigma \right) \right) , such that

    \begin{equation*} u_{n}\underset{n\rightarrow \infty }{\longrightarrow }u\ H^{1}\left( \Omega , \mathbb{R}^{3}\right) -\text{strong,} \end{equation*}

    \overline{v}_{n}\underset{n\rightarrow \infty }{\longrightarrow }\overline{v } strongly with respect to the norm (26), and v_{3,n}\underset{ n\rightarrow \infty }{\longrightarrow }v_{3} strongly with respect to the norm of L_{\mathcal{H}^{d}}^{2}\left( \Sigma \right) . Let \left( u_{n,h}^{0}\right) _{h,n} be the corresponding sequence defined in (82). We have from the definition of the subdifferentiability of convex functionals

    \begin{equation} \left. \begin{array}{c} \int\nolimits_{Z_{h}}\sigma _{ij}\left( u_{h}\right) e_{ij}\left( u_{h}\right) dx\geq \int\nolimits_{Z_{h}}\sigma _{ij}\left( u_{n,h}^{0}\right) e_{ij}\left( u_{n,h}^{0}\right) dx \\ { \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ }+2\int\nolimits_{Z_{h}} \sigma _{ij}\left( u_{n,h}^{0}\right) e_{ij}\left( u_{h}-u_{n,h}^{0}\right) dx\text{.} \end{array} \right. \end{equation} (87)

    Due to the structure of the sequence \left( u_{n,h}^{0}\right) _{h} , we have

    \begin{equation} \left. \begin{array}{r} \int\nolimits_{Z_{h}}\sigma _{ij}\left( u_{n,h}^{0}\right) e_{ij}\left( u_{h}-u_{n,h}^{0}\right) dx = \int\nolimits_{Z_{h}}\sigma _{ij}\left( u_{n}\right) e_{ij}\left( u_{h}-u_{n,h}^{0}\right) dx \\ -\int\nolimits_{Z_{h}}\sigma _{ij,j}\left( w_{h}^{m}\left( u_{n}-v_{n,h}\right) _{m}\right) \left( u_{h}-u_{n,h}^{0}\right) _{i}dx\text{ .} \end{array} \right. \end{equation} (88)

    Since \left\vert Z_{h}\right\vert tends to zero as h tends to \infty , it follows that

    \begin{equation} \underset{h\rightarrow \infty }{\lim }\int\nolimits_{Z_{h}}\sigma _{ij}\left( u_{n}\right) e_{ij}\left( u_{h}-u_{n,h}^{0}\right) dx = 0\text{.} \end{equation} (89)

    Using the definition of the perturbation w_{h}^{m} and the expressions (62), (63) and (66), we get

    \begin{equation} \begin{array}{l} \left\vert \int\nolimits_{Z_{h}}\sigma _{ij,j}\left( w_{h}^{m}\left( u_{n}-v_{n,h}\right) _{m}\right) \left( u_{h}-u_{n,h}^{0}\right) _{i}dx\right\vert \\ \quad \leq C_{n}^{m}\left( \int\nolimits_{Z_{h}}\left\vert \left( u_{h}-u_{n,h}^{0}\right) \right\vert ^{2}dx\right) ^{1/2}\left( 1+\left( \int\nolimits_{Z_{h}}\left\vert \nabla w_{h}^{m}\left( x\right) \right\vert ^{2}dx\right) ^{1/2}\right) \text{,} \end{array} \end{equation} (90)

    where C_{n}^{m} is a positive constant which may depend of n . Then, using Lemma 5.1, we obtain that

    \begin{equation} \underset{h\rightarrow \infty }{\lim }\int\nolimits_{Z_{h}}\sigma _{ij,j}\left( w_{h}^{m}\left( u_{n}-v_{n,h}\right) _{m}\right) \left( u_{h}-u_{n,h}^{0}\right) _{i}dx = 0\text{.} \end{equation} (91)

    We deduce from (84) that

    \begin{equation} \left. \begin{array}{l} \underset{h\rightarrow \infty }{\lim }\int\nolimits_{Z_{h}}\sigma _{ij}\left( u_{n,h}^{0}\right) e_{ij}\left( u_{n,h}^{0}\right) dx \\ { \ \ } = \dfrac{\pi \mu \gamma }{\mathcal{H}^{d}\left( \Sigma \right) \ln 2}\int\nolimits_{\Sigma }A\left( s\right) \left( u_{n}-v_{n}\right) .\left( u_{n}-v_{n}\right) d\mathcal{H}^{d}\left( s\right) \text{.} \end{array} \right. \end{equation} (92)

    On the other hand, as \left\vert Z_{h}\right\vert tends to zero as h tends to \infty , we have

    \begin{equation} \underset{h\rightarrow \infty }{\lim \inf }\int\nolimits_{\Omega \backslash Z_{h}}\sigma _{ij}\left( u_{h}\right) e_{ij}\left( u_{h}\right) dx\geq \int\nolimits_{\Omega }\sigma _{ij}\left( u\right) e_{ij}\left( u\right) dx \text{.} \end{equation} (93)

    We deduce from (86)-(93) that

    \begin{equation*} \begin{array}{lll} \underset{h\rightarrow \infty }{\lim \inf }F_{h}\left( u_{h}\right) & \geq & \int\nolimits_{\Omega }\sigma _{ij}\left( u\right) e_{ij}\left( u\right) dx+\mu _{0}\int_{\Sigma }d\mathcal{L}_{\Sigma }\left( \overline{v}\right) \\ & & +\dfrac{\pi \mu \gamma }{\mathcal{H}^{d}\left( \Sigma \right) \ln 2} \int\nolimits_{\Sigma }A\left( s\right) \left( u_{n}-v_{n}\right) .\left( u_{n}-v_{n}\right) d\mathcal{H}^{d}\left( s\right) \text{.} \end{array} \end{equation*}

    Letting n tend to \infty in the right hand side of the above inequality, we deduce that

    \begin{equation*} \begin{array}{lll} \underset{h\rightarrow \infty }{\lim \inf }F_{h}\left( u_{h}\right) & \geq & \int\nolimits_{\Omega }\sigma _{ij}\left( u\right) e_{ij}\left( u\right) dx+\mu _{0}\int_{\Sigma }d\mathcal{L}_{\Sigma }\left( \overline{v}\right) \\ & & +\dfrac{\pi \mu \gamma }{\mathcal{H}^{d}\left( \Sigma \right) \ln 2} \int\nolimits_{\Sigma }A\left( s\right) \left( u-v\right) .\left( u-v\right) d\mathcal{H}^{d}\left( s\right) \text{,} \end{array} \end{equation*}

    which is equivalent to

    \begin{equation*} \underset{h\rightarrow \infty }{\lim \inf }F_{h}\left( u_{h}\right) \geq F_{\infty }\left( u,v\right) \text{.} \end{equation*}

    This ends the proof of Theorem 4.2.

    [1] Appl. Math. Comput., 187 (2007), 873-882.
    [2] in Monographs in Inequalities, ELEMENT, Zagreb, Volume 9, 2015.
    [3] J. Differ. Equations Appl., 10 (2004), 851-868.
    [4] Fish & Fisheries Series, 1993.
    [5] Math. Biosci., 135 (1996), 111-127.
    [6] J. Biol. Dyn., 7 (2013), 86-95.
    [7] Birkhäuser Boston, Inc., Boston, MA, 2001.
    [8] Birkhäuser Boston, Inc., Boston, MA, 2003.
    [9] in Discrete dynamics and difference equations, World Sci. Publ., Hackensack, NJ, (2010), 189-193.
    [10] Int. J. Math. Comput., 26 (2015), 1-10.
    [11] in Difference equations, discrete dynamical systems, and applications, Springer-Verlag, Berlin-Heidelberg-New York, 150 (2015), 3-14.
    [12] Appl. Anal., 86 (2007), 1007-1015.
    [13] Nonlinear Anal., 71 (2009), e2173-e2181.
    [14] J. Math. Biol., 57 (2008), 413-434.
    [15] John Wiley & Sons, Inc., New York, 1990.
    [16] Appl. Math. Lett., 36 (2014), 19-24.
    [17] Math. Biosci., 152 (1998), 165-177.
    [18] Volume 8, Elsevier North-Holland, Inc., New York, NY, 1980.
    [19] Fisheries Research, 24 (1995), 3-8.
    [20] Academic Press, Inc., Boston, MA, 1991.
    [21] Int. J. Difference Equ., 7 (2012), 35-60.
    [22] J. Difference Equ. Appl., 11 (2005), 415-422.
    [23] J. Difference Equ. Appl., 20 (2014), 859-874.
    [24] Appl. Math. Optim., 31 (1995), 219-241.
    [25] Int. J. Difference Equ., 4 (2009), 115-136.
    [26] J. Math. Inequal., 5 (2011), 253-264.
    [27] Mar. Resour. Econ., 24 (2009), 147-169.
    [28] (Russian) (Chita) in Modeling of natural systems and optimal control problems, VO "Nauka'', Novosibirsk, (1993), 65-74.
    [29] Nouveaux Mémoires de l'Académie Royale des Sciences et Belles-Lettres de Bruxelles, 18 (1845), 1-42.
    [30] Adv. Dyn. Syst. Appl., 1 (2006), 113-120.
    [31] J. Math. Biol., 50 (2005), 663-682.
    [32] Nonlinear Anal. Real World Appl., 4 (2003), 639-651.
    [33] IEEE Trans. Automat. Cont., 40 (1995), 1779-1783.
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