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Research article

Assessing the stability of the reservoir rim in moraine deposits for a mega RCC dam

  • Diamer Basha Dam is an under-construction, 272-meter-high, roller compacted concrete (RCC) dam on the Indus River in Pakistan. Once constructed, it will be the world's highest RCC gravity dam with a 105-kilometer-long reservoir. Most of the reservoir lies in unstable moraine deposits with steep slopes. Events like saturation during reservoir filling, alternate wetting, drawdown during reservoir operation, or a seismic event could trigger a large mass movement of these slopes into the reservoir to disrupt the dam functionality. This work identified the 15 most vulnerable slide areas using digital slope maps, elevation maps, and satellite imagery. Deterministic slope stability analysis was carried out on the identified sections under various stages of reservoir operation for static and seismic loading, using pseudo-static and dynamic analysis approaches. Probabilistic analysis was then performed using Monte Carlo simulation. The findings showed that most moraine deposits would collapse under reservoir filling, rapid drawdown, or seismic activity. Following the assessments, landslide susceptibility maps were generated, and an assessment of potential impacts, including the generation of dynamic waves, reservoir blockage, increased sediment loads, and reduced reservoir storage capacity, was also performed.

    Citation: Khalid Ahmad, Umair Ali, Khalid Farooq, Syed Kamran Hussain Shah, Muhammad Umar. Assessing the stability of the reservoir rim in moraine deposits for a mega RCC dam[J]. AIMS Geosciences, 2024, 10(2): 290-311. doi: 10.3934/geosci.2024017

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  • Diamer Basha Dam is an under-construction, 272-meter-high, roller compacted concrete (RCC) dam on the Indus River in Pakistan. Once constructed, it will be the world's highest RCC gravity dam with a 105-kilometer-long reservoir. Most of the reservoir lies in unstable moraine deposits with steep slopes. Events like saturation during reservoir filling, alternate wetting, drawdown during reservoir operation, or a seismic event could trigger a large mass movement of these slopes into the reservoir to disrupt the dam functionality. This work identified the 15 most vulnerable slide areas using digital slope maps, elevation maps, and satellite imagery. Deterministic slope stability analysis was carried out on the identified sections under various stages of reservoir operation for static and seismic loading, using pseudo-static and dynamic analysis approaches. Probabilistic analysis was then performed using Monte Carlo simulation. The findings showed that most moraine deposits would collapse under reservoir filling, rapid drawdown, or seismic activity. Following the assessments, landslide susceptibility maps were generated, and an assessment of potential impacts, including the generation of dynamic waves, reservoir blockage, increased sediment loads, and reduced reservoir storage capacity, was also performed.



    Nonlinear partial differential equations play an important part in various branches of science such as fluid mechanics, solid state physics, plasma physics and quantum mechanics. The coupled Schrödinger-KdV equations are put forward to model nonlinear dynamics of one-dimensional Langmuir and ion-acoustic waves in a system of coordinates moving at the ion-acoustic speed [18,19]. In detail, we consider the system [9]

    iϵut+puxxqvus|u|2u=0,(x,t)R×(0,T], (1.1)
    vt+αvxxx+(βvm+ρ|u|2)x=0,(x,t)R×(0,T], (1.2)
    u(x,t)=u(x+l,t),v(x,t)=v(x+l,t),(x,t)R×(0,T], (1.3)
    u(x,0)=φ(x),v(x,0)=ϕ(x),xR, (1.4)

    where i=1, m is a positive integer, p,q,s,ϵ,α,β,ρ are real constants with p0 and ϵ,α0. The complex-valued function u and the real-valued function v describe electric field of Langmuir oscillations and low-frequency density perturbation, respectively. The initial functions φ and ϕ are given l-periodic functions. Hence, it suffices to take a single period [0,l]. For Eqs (1.1) and (1.2), there are four physical invariants to be considered:

    The number of plasmons

    I1=l0|u(x,t)|2dx. (1.5)

    The number of particles

    I2=l0v(x,t)dx. (1.6)

    The energy of oscillations

    I3=l0[qβm+1vm+1+pρ|ux|2+qρv|u|2+sρ2|u|4qα2(vx)2]dx, (1.7)

    and the momentum

    I4=l0[qv22ρϵIm(uˉux)]dx. (1.8)

    According to [2], these invariants may connect closely to accurate behaviors in time. Extensive numerical studies have been presented for the coupled Schödinger-KdV equations in the last decade, such as the finite element method [3], radial basis function (RBF) collocation method [4], decomposition [5], variational iteration [6], exponential time differencing three-layer implicit scheme(ETDT-P) [7], homotopy perturbation [8], and fourth-order conservative compact finite difference scheme [9] and so on.

    In the aspect of compact difference scheme, which is well known for the narrower stencils, i.e., fewer neighboring nodes it uses, and have less truncation error comparing with typical finite difference schemes. A variety of fourth-order compact methods have been employed solving partial differential equations [9,12,13,14,21,22,23,24,25,28,29]. Furthermore, Wang [26] proposed a conservative eighth-order compact difference scheme for the nonlinear Schrödinger equation. In [27], Chen and Chen presented a conservative eighth-order compact difference scheme for the Klein-Gordon-Schrödinger equations. Motivated by ideas in [26,27], this article aims to construct a new general difference scheme which can deal with the conservativeness of the invariants and convergence theorem easily. In detail, there are following three advantages:

    (ⅰ) The proposed scheme is compact, linearized, decoupled.

    (ⅱ) The proposed scheme preserves several invariants in discrete sense.

    (ⅲ) The operator form of scheme is novel and can be easily generalized from the fourth-order compact method to the eight-order method for solving other equations.

    The rest of the paper is organized as follows. In Section 2, we introduce an eighth-order conservative compact finite difference scheme and apply it to solve the coupled Schödinger-KdV equations numerically. The discrete conservation properties of the proposed nonlinear scheme is analyzed and the convergence theorem of the linearized scheme is established in Section 3. Numerical experiments are presented in Section 4. Finally, a brief conclusion is given in Section 5.

    The domain Ω={(x,t)|0xl,0tT} is discretized into grids described by the set {xj,tn} of nodes, in which xj=jh,j=0,1,,J=l/h and tn=nτ,n=0,1,,N=T/τ, where h and τ are discretization parameters. Briefly, let unj=u(xj,tn), vnj=v(xj,tn) and Ωh={x0,x1,,xJ}. For more convenient discussion, define the following difference operators and notations:

    δtunj=un+1junjτ,δ2xunj=unj+12unj+unj1h2,δxunj=unj+1unjh,δ¯xunj=unjunj1h,δˆxunj=unj+1unj12h,un+12j=un+1j+unj2,(|u|2)n+12j=|unj|2+|un+1j|22,A1unj=(1+5h242δ2x)unj=142(5unj1+32unj+5unj+1),A2unj=(1+31h2252δ2x)unj=1252(31unj1+190unj+31unj+1),B1unj=(1+20h270δ2x+h470δ2xδ2x)unj=170(unj2+16unj1+36unj+16unj+1+unj+2),B2unj=(1+780h23780δ2x+23h43780δ2xδ2x)unj=13780(23unj2+688unj1+2358unj+688unj+1+23unj+2),Junj=(1+h24δ2x)unj=14(unj1+2unj+unj+1).

    About the approximate formulas of the first and second-order spatial derivatives at all nodes (with periodic boundary conditions) with the eighth-order accuracy, we have the following lemma. Note that we denote uj=u(xj,t)x or simply denote uj=(ux)j in the following lemma. Similarly, the notations uj and uj are the same meaning.

    Lemma 1. [1] For u and u, we have the following approximate formulas

    uj2+16uj1+36uj+16uj+1+uj+2=56h(5uj232uj1+32uj+1+5uj+2)+O(h8), (2.1)
    23uj2+688uj1+2358uj+688uj+1+23uj+2=15h2(31uj2+128uj1318uj+128uj+1+31uj+2)+O(h8). (2.2)

    For the convenience to discrete and analyse the equations, we need to rewrite the relations (2.1) and (2.2) to the operator's forms.

    Lemma 2. By the definition of the operators above, we have

    B1uj=A1δˆxuj+O(h8), (2.3)
    B2uj=A2δ2xuj+O(h8), (2.4)
    B1B2uj=A1A2δˆxδ2xuj+O(h8). (2.5)

    Proof. Assume that there is an operator A1uj=λ1uj1+λ2uj+λ1uj+1 such that

    B1uj=A1δˆxuj+O(h8). (2.6)

    By computation and the definition of operators above, we have λ1=5/42 and λ2=32/42. Hence, A1=A1 and (2.3) holds. (2.4) can be proved similarly. At last, combining (2.3) and (2.4), (2.5) follows directly.

    We note that Lemma 2 shows the discrete scheme has the eighth-order accuracy if we use the operators A1, A2, B1 and B2 or their combinations to discrete the corresponding derivative values at nodes.

    In the temporal discretization, we need to evaluate the function values at mid-nodes ((n+12)-nodes). The following lemma is necessary to ensure to approximate the function values at mid-nodes by values at n- and (n+1)-nodes, which can be obtained by Taylor's expansion.

    Lemma 3. For any smooth function g(t) and mN, we have

    (g(tn+12))mψ(g(tn),g(tn+1))=O(τ2), (2.7)

    where tn+12=tn+tn+12 and

    ψ(u,v)=1m+1mk=0ukvmk. (2.8)

    Proof. By using Taylor's expansion, we have

    g(tn+1)=g(tn+12)+τ2g(tn+12)+O(τ2), (2.9)
    g(tn)=g(tn+12)τ2g(tn+12)+O(τ2), (2.10)
    (g(tn+1))z=(g(tn+12))z+τ2z(g(tn+12))z1(g(tn+12))+O(τ2), (2.11)
    (g(tn))z=(g(tn+12))zτ2z(g(tn+12))z1(g(tn+12))+O(τ2), (2.12)

    where zN. Let k<m2,kN, from (2.11) and (2.12), we can obtain

    (g(tn))k(g(tn+1))mk+(g(tn))mk(g(tn+1))k=(g(tn))k(g(tn+1))k[(g(tn))m2k+(g(tn+1))m2k]=[(g(tn+12))2k+O(τ2)][2(g(tn+12))m2k+O(τ2)]=2(g(tn+12))m+O(τ2). (2.13)

    Plugging (2.13) into (2.8), (2.7) immediately follows.

    Denote the approximations of unj and vnj by Unj and Vnj, respectively. Ignoring the truncation error terms in Eqs (2.3)–(2.5) and (2.7), we obtain the following implicit compact scheme with truncation error O(τ2+h8) by using the Crank-Nicolson method for temporal discretization and Lemmas 2 and 3:

    iϵB2(δtUnj)+pA2δ2xUn+12jqB2(Vn+12jUn+12j)sB2((|U|2)n+12jUn+12j)=0, (2.14)
    B1B2(δtVnj)+αA1A2δˆxδ2xVn+12j+βB2A1δˆxψ(Vnj,Vn+1j)+ρB2A1δˆx(|U|2)n+12j=0, (2.15)
    Unj=Unj+J,Vnj=Vnj+J,n=0,1,,N,j=1,2,,J, (2.16)
    U0j=φ(xj),V0j=ϕ(xj). (2.17)

    The schemes (2.14 and 2.15) are nonlinear and gotten by discretizing the temporal derivative with the Crank-Nicolson method, which has the second-order O(τ2) and discretizing the special derivatives with the operators B1 and B1B2 for (1.1) and (1.2), respectively, which has the eighth-order O(h8) by Lemma 2.

    As to the linearized form of (2.14 and 2.15), we will discuss in the next section.

    Let Hp(Ωh)={u|u={uj},j=0,1,,Janduj=uj+J} denote the space of periodic real- or complex-valued grid functions defined on Ωh with period J. The discrete inner product and the corresponding discrete L2-norm on the grid function space Hp(Ωh) are defined as

    u,w=Jj=1uj¯wjh,||u||=u,u,

    where ¯w denotes the conjugate of w. Norm ||δ2xu||2=δ2xu,δ2xu is well-defined with periodic condition (uj=uj±J) and the discrete L- and H1-norm are defined as

    ||u||=max1jJ|uj|,||u||1=||u||2+||δ¯xu||2.

    The following lemmas can be easily proved:

    Lemma 4. For any grid functions u,wHp(Ωh), we have

    δxu,w=u,δ¯xw,δˆxu,w=u,δˆxw,δ2xu,w=δ¯xu,δ¯xw=δxu,δxw=u,δ2xw,A1u,w=u,A1w=u,w5h242δ¯xu,δ¯xw,A2u,w=u,A2w=u,w31h2252δ¯xu,δ¯xw,B1u,w=u,B1w=u,w20h270δ¯xu,δ¯xw+h470δ2xu,δ2xw,B2u,w=u,B2w=u,w780h23780δ¯xu,δ¯xw+23h43780δ2xu,δ2xw,Ju,w=u,Jw=u,wh24δ¯xu,δ¯xw.

    Lemma 5. For any grid functions uHp(Ωh), we have

    Re(δˆxu,u)=Re(δˆxA1u,u)=Re(δˆxB11A1u,u)=Re(δˆxB12A2u,u)=0.

    Lemma 6. [20] For any grid functions uHp(Ωh), we have

    ||δ¯xu||2h||u||,||u||h12||u||,||u||2ε||δ¯xu||2+(1ε+1l)||u||2ε>0.

    Lemma 7. [12] For a real circulant matrix A=C(b0,b1,,bn1), all eigenvalues of A are given by

    f(μk),k=0,1,2,,n1,

    where f(x)=b0+b1x+b2x2++bn1xn1, and μk=cos(2kπn)+isin(2kπn).

    For the compact schemes (2.14) and (2.15), we have the following conservative properties in the discrete sense. The process of proof is similar to [9]. Since it still has some difference, so for the convenience to read, we give the detail of proof as following:

    Theorem 1. The compact schemes (2.14) and (2.15) preserve the discrete conservation laws of the numbers of plasmons and particles, i.e.,

    ||Un||2=||U0||2 (3.1)

    and

    Jj=1Vnjh=Jj=1V0jh, (3.2)

    where Un=(Un1,Un2,,UnJ)T.

    Proof. Setting Gn is a vector with the component

    Gnj=qVn+12jUn+12j+s(|U|2)n+12jUn+12j, (3.3)

    then (2.14) can be written as

    iϵδt(B2Unj)+pA2δ2xUn+12j=B2Gnj. (3.4)

    Computing the inner product , on both sides of Eq (3.4) with Un+12, A12Gn, δt(A12Un), A12δ2xGn and δt(A12δ2xUn), respectively, and applying Lemma 4, we obtain

    iϵδt(B2Un),Un+12pA2δ¯xUn+12,δ¯xUn+12=B2Gn,Un+12= Gn,Un+1220h270δ¯xGn,δ¯xUn+12+h470δ2xGn,δ2xUn+12, (3.5)
    iϵδt(B2Un),A12Gnpδ¯xUn+12,δ¯xGn=B2Gn,A12Gn, (3.6)
    iϵδt(B2Un),δt(A12Un)pδ¯xUn+12,δt(δ¯xUn)=B2Gn,δt(A12Un). (3.7)
    iϵδt(B2Un),A12δ2xGn+pδ2xUn+12,δ2xGn=B2Gn,A12δ2xGn, (3.8)
    iϵδt(B2Un),δt(A12δ2xUn)+pδ2xUn+12,δt(δ2xUn)=B2Gn,δt(A12δ2xUn). (3.9)

    By the Hermitian property of inner product and multiplying Eqs (3.7) and (3.9) by iϵ, we can obtain

    iϵδt(A12Un),B2Gn=ϵ2δt(A12Un),δt(B2Un)ipϵδt(δ¯xUn),δ¯xUn+12. (3.10)
    iϵδt(A12δ2xUn),B2Gn=ϵ2δt(A12δ2xUn),δt(B2Un)+ipϵδt(δ2xUn),δ2xUn+12. (3.11)

    By Lemma 4 and Eqs (3.6), (3.8), (3.10) and (3.11), it follows that

    ϵ2δt(A12Un),δt(B2Un)ipϵδt(δ¯xUn),δ¯xUn+12=pδ¯xUn+12,δ¯xGn+B2Gn,A12Gn. (3.12)
    ϵ2δt(A12δ2xUn),δt(B2Un)+ipϵδt(δ2xUn),δ2xUn+12=pδ2xUn+12,δ2xGn+B2Gn,A12δ2xGn. (3.13)

    Multiplying by p, 20h270 and h470 in Eqs (3.5), (3.12) and (3.13), respectively, and eliminating the term δ¯xUn+12,δ¯xGn, we obtain

    ipϵδt(B2Un),Un+12+i20h2pϵ70δ¯xUn+12,δt(δ¯xUn)ih4pϵ70δ2xUn+12,δt(δ2xUn)+20h2ϵ270δt(B2Un),δt(A12Un)+h4ϵ270δt(B2Un),δt(A12δ2xUn)pGn,Un+1220h270A12Gn,B2Gnh470A12δ2xGn,B2Gnp2A2δ¯xUn+12,δ¯xUn+12=0. (3.14)

    From the definition of Gn we can see that Gn,Un+12 is a real number. Hence, the imaginary part of (3.14) is zero, i.e.,

    Re(δt(B2Un),Un+12+20h270δ¯xUn+12,δt(δ¯xUn)h470δ2xUn+12,δt(δ2xUn))=0. (3.15)

    Applying Lemma 4 in (3.15), we can obtain immediately that

    ||Un+1||2=||Un||2.

    Computing the inner product , on both sides of Eq (2.15) with 1:=(1,1,,1)THp(Ωh), we can obtain

    δt(B1B2Vn),1+αA1A2δˆxδ2xVn+12,1+βB2A1δˆxψ(Vn,Vn+1),1+ρB2A1δˆx(|U|2)n+12,1=0, (3.16)

    where

    (|U|2)n+12:=((|U|2)n+121,(|U|2)n+122,,(|U|2)n+12J),

    using the periodic conditions, one can have the equation

    δt(B1B2Vn),1=0,

    i.e.,

    B1B2Vn+1,1=B1B2Vn,1. (3.17)

    With the periodic conditions, (3.2) immediately satisfies. The proof is finished.

    Hereinafter we define

    Un:=(Un1,Un2,,UnJ)T,(Un).2:=((Un1)2,(Un2)2,,(UnJ)2)T,

    and

    Un.Vn:=(Un1Vn1,Un2Vn2,,UnJVnJ)T,ψ(Un,Vn):=1m+1mk=0(Un).k.(Vn).(mk).

    The compact schemes (2.14) and (2.15) are equivalent to the following matrix equations:

    iϵB2(δtUn)+pA2δ2xUn+12qB2(Vn+12.Un+12)sB2((|U|2)n+12.Un+12)=0, (3.18)
    B1B2(δtVn)+αA1A2δˆxδ2xVn+12+βB2A1δˆxψ(Vn,Vn+1)+ρB2A1δˆx(|U|2)n+12=0, (3.19)

    where A1, A2, B1 and B2 are J×J matrices corresponding to the operators A1, A2, B1 and B2, respectively,

    A1=142(32505532500532550532),A2=1252(19031031311903100311903131031190),
    B1=170(3616101161636161011163616000110001636161101163616161011636),
    B2=13780(2358688230236886882358688230232368823586880002323000688235868823230236882358688688230236882358).

    By the properties of circulant matrices, we can see that matrices A1, A2, B1 and B2 are circulant symmetric positive definite [10]. Let A11, A12, B11 and B12 denote inverse operators of A1, A2, B1 and B2, respectively. The matrices corresponding to the operators δ2x, δˆx, A11, A12, B11 and B12 are also circulant, therefore, they commute under multiplication.

    The compact schemes (2.14) and (2.15) can be equivalently written as

    iϵ(δtUnj)+pB12A2δ2xUn+12j=qVn+12jUn+12j+s(|U|2)n+12jUn+12j, (3.20)
    δtVnj+δˆx(αB11B12A1A2δ2xVn+12j+βB11A1ψ(Vnj,Vn+1j)+ρB11A1(|U|2)n+12j)=0. (3.21)

    which can be obtained by multiplying B12 and B11B12 in both side of (2.14) and (2.15), respectively.

    By applying Lemma 7, we can obtain the following result:

    Lemma 8. [14] For any grid function uHp(Ωh), we have the inequalities

    3263||u||2B12A2u,u10526||u||2,135||u||2A11B1u,u2111||u||2.

    Define

    |||u|||2Q=B12A2u,u,|||u|||2P=A11B1u,u.

    Lemma 8 shows that ||||||Q and ||||||P are norms on Hp(Ωh) equivalent to the discrete L2-norm ||||. For the proof of the following theorem, we want the relations (3.20) and (3.21).

    Theorem 2. The compact schemes (2.14) and (2.15) preserve the energy of oscillations in discrete sense, i.e.,

     qβm+1Jj=1(Vn+1j)m+1h+pρ|||δ¯xUn+1|||2Q+qρJj=1Vn+1j|Un+1j|2h+sρ2||Un+1||4L4qα2|||δ¯xVn+1|||2Q= qβm+1Jj=1(V0j)m+1h+pρ|||δ¯xU0|||2Q+qρJj=1V0j|U0j|2h+sρ2||U0||4L4qα2|||δ¯xV0|||2Q (3.22)

    where

    ||U||4L4=Jj=1|Uj|4h.

    Proof. Computing the inner product , on both sides of Eq (3.20) with δtUn, we can obtain the following equation with the commutativity under multiplication of circulant matrices:

    iϵδtUn,δtUnpB12A2δ¯xUn+12,δtδ¯xUn=qVn+12.Un+12,δtUn+s(|U|2)n+12.Un+12,δtUn. (3.23)

    Then taking the real part of Eq (3.23), we obtain

    p2τ(|||δ¯xUn+1|||2Q|||δ¯xUn|||2Q)= q2τ(Vn+12.Un+1,Un+1Vn+12.Un,Un) +s2τ((|U|2)n+12.Un+1,Un+1(|U|2)n+12.Un,Un). (3.24)

    Multiplying Eq (3.24) with 2τ and summing from 0 to n, we obtain

     p|||δ¯xUn+1|||2Q+s2||Un+1||4L4+qVn+12,|Un+1|.2q2nk=1Vk+1Vk1,|Uk|.2= p|||δ¯xU0|||2Q+s2||U0||4L4+qV12,|U0|.2. (3.25)

    Setting Wn is a vector with the component

    Wnj=αB12A2δ2xVn+12j+βψ(Vnj,Vn+1j)+ρ(|U|2)n+12j,

    then Eq (3.21) can be written as

    δtVnj+δˆxB11A1Wnj=0. (3.26)

    Computing the inner product , on both sides of Eq (3.26) with Wn and applying Lemma 5, we can obtain

    αδtVn,B12A2δ2xVn+12+βδtVn,ψ(Vn,Vn+1)+ρδtVn,(|U|2)n+12=0. (3.27)

    It follows from definition (2.8) that

    δtVn,ψ(Vn,Vn+1)=1(m+1)τ(Jj=1(Vn+1j)m+1hJj=1(Vnj)m+1h).

    It is easy to see that

    δtVn,B12A2δ2xVn+12=12τ(|||δ¯xVn+1|||2Q|||δ¯xVn|||2Q).

    Multiplying Eq (3.27) with 2τ and summing from 0 to n, we have

     α|||δ¯xVn+1|||2Q2βm+1Jj=1(Vn+1j)m+1hρnk=0Vk+1Vk,|Uk+1|.2+|Uk|.2= α|||δ¯xV0|||2Q2βm+1Jj=1(V0j)m+1h. (3.28)

    Since

     nk=0Vk+1Vk,|Uk+1|.2+|Uk|.2= nk=1Vk+1Vk1,|Uk|.2+Vn+1Vn,|Un+1|.2+V1V0,|U0|.2,

    the Eq (3.28) becomes

     α|||δ¯xVn+1|||2Q2βm+1Jj=1(Vn+1j)m+1hρnk=1Vk+1Vk1,|Uk|2ρVn+1Vn,|Un+1|2= α|||δ¯xV0|||2Q2βm+1Jj=1(V0j)m+1h+ρV1V0,|U0|2. (3.29)

    Multiplying Eqs (3.25) and (3.29) with ρ and q2, respectively, and subtracting the results, (3.22) follows immediately.

    In the following convergence analysis, we will take the symbol C as a general positive constant independent of h and τ, not necessarily the same at different occurrences. We assume that there is a positive constant Y such that the exact solutions u and v of the coupled system satisfy

    max{||un||,||unt||,||vn||,||vnt||}Y,0nN. (3.30)

    Let Y0=2(Y+1)2 and define a smooth function Ψ(r,s)C(R2) as

    Ψ(r,s)={ψ(r,s),r2+s2Y0,0,r2+s2Y0+1. (3.31)

    Since schemes (2.14) and (2.15) are nonlinear, we change it into the following linearized compact scheme to reduce computational cost:

    iϵB2(U0jU0jτ)+p2A2δ2x(U0j+U0j)qB2(V0jU0j)=sB2(|U0j|2U0j), (3.32)
    B1B2(V0jV0jτ)+α2A1A2δˆxδ2x(V0j+V0j)+βB2A1δˆxψ(V0j,V0j)=ρB2A1δˆx|U0j|2, (3.33)
    iϵB2(δtUnj)+pA2δ2xUn+12jqB2(ˆVnjˆUnj)=sB2(^(|U|2)njˆUnj), (3.34)
    B1B2(δtVnj)+αA1A2δˆxδ2xVn+12j+βB2A1δˆxΨ(Vnj,Vnj)=ρB2A1δˆx^(|U|2)nj, (3.35)

    where ˆU0=(U0+U0)/2, ˆUn=3Un/2Un1/2, and Un=2UnUn1 for n1.

    We can prove that the temporal and spatial convergence rates of the linearized compact schemes (3.34) and (3.35) are second- and eighth-order, respectively.

    Lemma 9. Let {yn} be a nonnegative real sequence, c a nonnegative constant, d and τ are positive constants. If

    yn+1c+dτni=0yiforn0,

    then

    yn+1(c+dτy0)edτ(n+1)forn0.

    Theorem 3. Suppose that u,vC4(0,T;C11(R)) are the exact solutions to Eqs (1.1) and (1.2), h8τ1=o(1), and that assumption (3.30) holds. Let U and V be the solutions of (3.34) and (3.35). Then there exists a constant C=C(Y,T) such that

    max0<nN{||unUn||1+||vnVn||1}C(τ2+h8),

    for h and τ sufficiently small.

    Proof. Let Enu=unUn and Env=vnVn. By Eqs (1.1), (1.2), (3.34) and (3.35), and ignoring the subindex j, we obtain

    iϵB2(δtEnu)+pA2δ2xEn+12u=qB2Tn1+sB2Tn2+rnu, (3.36)
    B1B2(δtEnv)+αA1A2δˆxδ2xEn+12v=βB2A1δˆxTn3ρB2A1δˆxTn4+rnv, (3.37)

    where

    Tn1=ˆvn.ˆunˆVn.ˆUn,Tn2=^(|u|2)n.ˆun^(|U|2)n.ˆUn,Tn3=Ψ(vn,vn)Ψ(Vn,Vn),Tn4=^(|u|2)n^(|U|2)n.

    By the assumption (3.30) and definition (3.31), one can see that Ψ((vn,vn+1))=ψ((vn,vn+1)), and hence, the truncation errors rnu and rnv are such that rnu=O(τ2+h8) and rnv=O(τ2+h8). Under the smoothness assumption of u and v, we have

    δtrnu=O(τ2+h8)andδtrnv=O(τ2+h8).

    From (3.36) and (3.37), we can obtain the following equations:

    iϵδtEnu+pB12A2δ2xEn+12u=qTn1+sTn2+Rnu, (3.38)
    A11B1(δtEnv)+αB12A2δˆxδ2xEn+12v=βδˆxTn3ρδˆxTn4+Rnv, (3.39)

    where Rnu=B12rnu and Rnv=B12A11rnu.

    We use the induction argument as in [15,16,17] to estimate the error bounds. To obtain our error estimate, we assume that there exists a constant h0>0 such that, for 0<hh0,

    max{||Enu||,||Env||,||δtEn1u||,||δtEn1v||}1,1nk. (3.40)

    Since E^0_u = E^0_v = 0 , it is easy to see that

    \begin{align} \lvert \lvert E^1_u \lvert \rvert_1 \le C(\tau^2 + h^8) \quad {\text{and}} \quad \lvert \lvert E^1_v \lvert \rvert_1 \le C(\tau^2 + h^8). \end{align}

    For n \ge 1 , by computing the inner product \left < \cdot, \cdot \right > on both sides of (3.38) with E^{n+\frac{1}{2}}_u . we can obtain following equation by Lemma 4:

    \begin{align} i \epsilon \left < \delta_t E^n_u , E^{n+\frac{1}{2}}_u \right > -p \left < \mathcal{B}_2^{-1} \mathcal{A}_2 \delta_{\overline{x}} E^{n+\frac{1}{2}}_u,\delta_{\overline{x} } E^{n+\frac{1}{2}}_u \right > = \\q\left < T^n_1,E^{n+\frac{1}{2}}_u \right > +s \left < T^n_2,E^{n+\frac{1}{2}}_u \right > +\left < R^n_u,E^{n+\frac{1}{2}}_u \right > . \end{align} (3.41)

    Taking the imaginary part of (3.41), we can obtain the inequality

    \begin{align} \frac{\epsilon}{2\tau}\{ \lvert \lvert E^{n+1}_u \lvert \rvert^2 - \lvert \lvert E^{n}_u \lvert \rvert^2 \} \le \frac{q^2}{2} \lvert \lvert T^n_1 \lvert \rvert^2 + \frac{s^2}{2} \lvert \lvert T^n_2 \lvert \rvert^2 +\frac{1}{2} \lvert \lvert R^n_u \lvert \rvert^2 +\frac{3}{2} \lvert \lvert E^{n+\frac{1}{2}}_u \lvert \rvert^2. \end{align} (3.42)

    By computing the inner product \left < \cdot, \cdot \right > on both sides of (3.39) with E^{n+\frac{1}{2}}_v . we can obtain following equation by Lemma 4:

    \begin{align} \begin{split} \ &\left < \mathcal{A}_1^{-1} \mathcal{B}_1 (\delta_t E^n_v), E^{n+\frac{1}{2}}_v \right > - \alpha \left < \mathcal{B}_2^{-1} \mathcal{A}_2 \delta_{\hat{x}} \delta_{\overline{x}} E^{n+\frac{1}{2}}_v, \delta_{\overline{x}} E^{n+\frac{1}{2}}_v \right > \\ = \ &\beta \left < T^n_3 , \delta_{\hat{x}} E^{n+\frac{1}{2}}_v \right > + \rho \left < T^n_4 , \delta_{\hat{x}} E^{n+\frac{1}{2}}_v \right > + \left < R^n_v , E^{n+\frac{1}{2}}_v \right > . \end{split} \end{align} (3.43)

    Since

    \begin{gather} T^n_1 = \hat{E}^n_v . \hat{u}^n + \hat{E}^n_u . \hat{v}^n - \hat{E}^n_u . \hat{E}^n_v, \\ T^n_2 = (\widehat{\lvert u \rvert^{.2}})^n . \hat{E}^n_u + \left[2{\text{Re}}(\widehat{\overline{u} . E_u})^n-(\widehat{\lvert E_u \rvert^{.2}})^n \right] . \hat{u}^n - \left[2{\text{Re}}(\widehat{\overline{u} . E_u})^n-(\widehat{\lvert E_u \rvert^{.2}})^n \right] . \hat{E}^n_u, \\ T^n_4 = (\widehat{\overline{u} . E_u})^n +(u . \widehat{\overline{E_u}})^n - (\widehat{\lvert E_u \rvert^{.2}})^n, \end{gather}

    we can have the inequality

    \begin{align} \lvert \lvert T^n_1 \lvert \rvert^2 + \lvert \lvert T^n_2 \lvert \rvert^2 + \lvert \lvert T^n_3 \lvert \rvert^2 + \lvert \lvert T^n_4 \lvert \rvert^2 \le C( \lvert \lvert E^{n-1}_u \lvert \rvert^2 + \lvert \lvert E^{n}_u \lvert \rvert^2 + \lvert \lvert E^{n-1}_v \lvert \rvert^2 + \lvert \lvert E^{n}_v \lvert \rvert^2 ). \end{align} (3.44)

    By Lemma 5, Eq (3.43) and inequality (3.44), we have

    \begin{align} \begin{split} &\frac{1}{2\tau} \{ \lvert \lvert \lvert E^{n+1}_v \lvert \lvert \rvert^2_P - \lvert \lvert \lvert E^{n}_v \lvert \lvert \rvert^2_P \} \le C \{ \lvert \lvert E^{n-1}_u \lvert \rvert^2 + \lvert \lvert E^{n}_u \lvert \rvert^2 + \lvert \lvert E^{n-1}_v \lvert \rvert^2 \} \\ &+ C \{ \lvert \lvert E^{n}_v \lvert \rvert^2 + \lvert \lvert E^{n+1}_v \lvert \rvert^2 + \lvert \lvert \delta_{\overline{x}} E^n_v \lvert \rvert^2 + \lvert \lvert \delta_{\overline{x}} E^{n+1}_v \lvert \rvert^2 + \lvert \lvert R^n_v \lvert \rvert^2 \}. \end{split} \end{align} (3.45)

    Summing inequalities (3.42) and (3.45) side by side, and using inequality (3.44), we can have following inequality with E^0_u = E^0_v = 0 :

    \begin{align} \epsilon \lvert \lvert E^{k+1}_u \lvert \rvert^2 + \lvert \lvert \lvert E^{k+1}_v \lvert \lvert \rvert^2_P \le C \tau \sum\limits_{n = 1}^{k+1} \{ \lvert \lvert E^n_u \lvert \rvert^2 + \lvert \lvert E^n_v \lvert \rvert^2 + \lvert \lvert \delta_{\overline{x}} E^n_v \lvert \rvert^2 +\lvert \lvert R^{n-1}_u \lvert \rvert^2 +\lvert \lvert R^{n-1}_v \lvert \rvert^2 \}. \end{align} (3.46)

    By Computing the inner product \left < \cdot, \cdot \right > on both sides of (3.38) with \delta_t E^n_u . we can obtain following equation by Lemma 4:

    \begin{align} i \epsilon \left < \delta_t E^n_u,\delta_t E^n_u \right > -p \left < \mathcal{B}_2^{-1} \mathcal{A}_2 \delta_{\overline{x}} E^{n+\frac{1}{2}}_u,\delta_{\overline{x}} \delta_t E^n_u \right > = q \left < T^n_1,\delta_t E^n_u \right > +s \left < T^n_2,\delta_t E^n_u \right > + \left < R^n_u,\delta_t E^n_u \right > . \end{align} (3.47)

    Taking the real part of (3.47) and summing from 0 to k , we can obtain

    \begin{align} \begin{split} \frac{p}{2\tau} \lvert \lvert \lvert \delta_{\overline{x}} E^{k+1}_u \lvert \lvert \rvert^2_Q = \ & -q {\text{Re}} \left(\sum\limits_{n = 0}^k \left < T^n_1,\delta_t E^n_u \right > \right) -s {\text{Re}} \left(\sum\limits_{n = 0}^k \left < T^n_2,\delta_t E^n_u \right > \right) - {\text{Re}} \left(\sum\limits_{n = 0}^k \left < R^n_u,\delta_t E^n_u \right > \right) \\ : = \ & M^k_1 + M^k_2 + M^k_3. \end{split} \end{align} (3.48)

    By using a method of summation by parts together with assumptions (3.30) and (3.40), we have the inequalities

    \begin{align} \lvert M^k_1 \rvert + \lvert M^k_2 \rvert &\le C \sum\limits_{n = 1}^k \{ \lvert \lvert E^n_u \lvert \rvert^2 +\lvert \lvert E^n_v \lvert \rvert^2 \} +\frac{C}{\tau} \lvert \lvert E^{k+1}_u \lvert \rvert^2, \\ \lvert M^k_3 \rvert &\le C \sum\limits_{n = 1}^k \{ \lvert \lvert E^n_u \lvert \rvert^2 +\lvert \lvert \delta_t R^{n-1}_u \lvert \rvert^2 \} +\frac{C}{\tau} \lvert \lvert E^{k+1}_u \lvert \rvert^2 +\frac{C}{\tau} \lvert \lvert R^{k}_u \lvert \rvert^2. \end{align}

    By (3.48) and the above estimates, we can obtain

    \begin{align} \lvert \lvert \lvert \delta_{\overline{x}} E^{k+1}_u \lvert \lvert \rvert^2_Q \le C\{ \lvert \lvert E^{k+1}_u \lvert \rvert^2 +\lvert \lvert R^{k}_u \lvert \rvert^2 \} +C\tau \sum\limits_{n = 1}^k \{ \lvert \lvert E^n_u \lvert \rvert^2 +\lvert \lvert E^n_v \lvert \rvert^2 + \lvert \lvert \delta_t R^{n-1}_u \lvert \rvert^2 \}. \end{align} (3.49)

    For any real-valued grid function f , an operator \Theta is defined by

    \begin{align} \Theta f_j = \sum\limits_{k = 1}^{j-1} f_k h + \frac{h}{2} f_j, \quad j = 1,2,\ldots,J, \quad \Theta f_0 = \sum\limits_{k = 1}^{J-1} f_k h + \frac{h}{2} f_J, \end{align} (3.50)

    with \Theta f_j = \Theta f_{j+J} . The following results can be easily proved:

    \begin{gather} \delta^2_x \Theta f_j = \delta_{\hat{x}} f_j, \quad \delta_{\hat{x}} \Theta f_j = \frac{1}{4} (f_{j-1}+2f_j +f_{j+1}) = \mathcal{J} f_j, \end{gather} (3.51)
    \begin{gather} \left < f,\Theta f \right > = \sum\limits_{j = 1}^J f_j \cdot \Theta f_j h = \frac{1}{2} \left(\sum\limits_{j = 1}^J f_j h \right)^2 \ge 0, \end{gather} (3.52)
    \begin{gather} \lvert \lvert \Theta f \lvert \rvert^2 \le \frac{l^2}{2} \lvert \lvert f \lvert \rvert^2. \end{gather} (3.53)

    Then define a matrix {\textbf{J}} corresponding to the operator \mathcal{J} , i.e.,

    \begin{align} {\textbf{J}} = \frac{1}{4}\begin{pmatrix} 2 & 1 & 0 & \cdots & 1 \\ 1 & 2 & 1 & \ddots & \vdots \\ 0 & \ddots & \ddots & \ddots & 0 \\ \vdots & \ddots & 1 & 2 & 1 \\ 1 & \cdots & 0 & 1 & 2 \\ \end{pmatrix}_{J \times J}. \end{align}

    It's obvious that {\textbf{J}} is invertible and {\textbf{J}}^{-1} is circulant symmetric positive definite as the scale J of matrix is an odd integer. By computing the inner product \left < \cdot, \cdot \right > on both sides of (3.39) with \delta_t (\mathcal{J}^{-1} \Theta E_v)^n and applying Lemma 4, (3.51) and (3.52), we can obtain

    \begin{align} \begin{split} \ &\left < \mathcal{J}^{-1} \mathcal{A}^{-1}_1 \mathcal{B}_1 (\delta_t E^n_v) ,\delta_t (\Theta E_v)^n \right > +\alpha \left < \mathcal{B}^{-1}_2 \mathcal{A}_2 \delta_{\overline{x}} E^{n+\frac{1}{2}}_v ,\delta_t \delta_{\overline{x}} E^n_v \right > \\ = \ & \beta \left < T^n_3,\delta_t E^n_v \right > + \rho \left < T^n_4,\delta_t E^n_v \right > +\left < R^n_v,\delta_t (\mathcal{J}^{-1} \Theta E_v)^n \right > . \end{split} \end{align} (3.54)

    Since {\textbf{J}} , {\textbf{A}}_1 , {\textbf{B}}_1 are circulant symmetric positive definite, so there exists {\textbf{G}} such that {\textbf{J}}^{-1} {\textbf{A}}^{-1}_1 {\textbf{B}}_1 = {\textbf{G}} {\textbf{G}}^T . By (3.51) and (3.52), we can have

    \begin{align} \begin{split} \ &\left < \mathcal{J}^{-1} \mathcal{A}^{-1}_1 \mathcal{B}_1 (\delta_t E^n_v) ,\delta_t (\Theta E_v)^n \right > = \left < \delta_t (G E_v)^n,\delta_t(\Theta(G E_v))^n \right > \\ = \ & \frac{1}{2} \left(h \sum\limits_{j = 1}^J \delta_t (G E_v)^n_j \right)^2 : = C^n \ge 0. \end{split} \end{align} (3.55)

    Summing Eq (3.54) from 0 to k together with (3.55), we can obtain

    \begin{align} \begin{split} \sum\limits_{n = 0}^k C^n + \frac{\alpha}{2\tau} \lvert \lvert \lvert \delta_{\overline{x}} E^{k+1}_v \lvert \lvert \rvert^2_Q = \ & \beta \sum\limits_{n = 0}^k \left < T^n_3,\delta_t E^n_v \right > + \rho \sum\limits_{n = 0}^k \left < T^n_4,\delta_t E^n_v \right > +\sum\limits_{n = 0}^k \left < R^n_v,\delta_t (\mathcal{J}^{-1} \Theta E_v)^n \right > \\ : = \ & M^k_4 + M^k_5 + M^k_6. \end{split} \end{align} (3.56)

    By using a method of summation by parts together with assumptions (3.30) and (3.40), we have the inequalities

    \begin{align} & \lvert M^k_4 \rvert + \lvert M^k_5 \rvert \le C \sum\limits_{n = 1}^k \{ \lvert \lvert E^n_v \lvert \rvert^2 + \lvert \lvert E^n_u \lvert \rvert^2 \} + \frac{C}{\tau} \lvert \lvert E^{k+1}_v \lvert \rvert^2, \\ & \lvert M^k_6 \rvert \le C \sum\limits_{n = 1}^k \{ \lvert \lvert \mathcal{J}^{-1} \Theta E_v^n \lvert \rvert^2 +\lvert \lvert \delta_t R^{n-1}_v \lvert \rvert^2 \} + \frac{C}{\tau} \lvert \lvert \mathcal{J}^{-1} \Theta E_v^{k+1} \lvert \rvert^2 + \frac{C}{\tau} \lvert \lvert R^{k}_v \lvert \rvert^2. \end{align}

    Noticing that \mathcal{J}({\textbf{I}}- \frac{h^2}{4} \delta^2_x + \frac{h^4}{16} \delta^2_x \delta^2_x -\frac{h^6}{64} \delta^2_x \delta^2_x \delta^2_x) = {\textbf{I}}-\frac{h^8}{256} \delta^2_x \delta^2_x \delta^2_x \delta^2_x , we have

    \begin{align} \mathcal{J}^{-1} = {\textbf{I}}- \frac{h^2}{4} \delta^2_x + \frac{h^4}{16} \delta^2_x \delta^2_x -\frac{h^6}{64} \delta^2_x \delta^2_x \delta^2_x +O(h^8). \end{align}

    By using Lemma 6 and (3.53), the above inequality can be written as

    \begin{align} \lvert M^k_6 \rvert \le C \sum\limits_{n = 1}^k \{ \lvert \lvert E_v^n \lvert \rvert^2 +\lvert \lvert \delta_t R^{n-1}_v \lvert \rvert^2 \} + \frac{C}{\tau} \lvert \lvert E_v^{k+1} \lvert \rvert^2 + \frac{C}{\tau} \lvert \lvert R^{k}_v \lvert \rvert^2, \end{align}

    Multiplying Eq (3.56) with 2\tau , we can obtain

    \begin{align} \lvert \lvert \lvert \delta_{\overline{x}} E^{k+1}_v \lvert \lvert \rvert^2_Q \le C \tau \sum\limits_{n = 1}^k \{ \lvert \lvert E_u^n \lvert \rvert^2 +\lvert \lvert E_v^n \lvert \rvert^2 + \lvert \lvert \delta_t R^{n-1}_v \lvert \rvert^2 \} + C\{ \lvert \lvert E_v^{k+1} \lvert \rvert^2 +\lvert \lvert R_v^k \lvert \rvert^2 \}. \end{align} (3.57)

    Since the norms \lvert \lvert \cdot \lvert \rvert , \lvert \lvert \lvert \cdot \lvert \lvert \rvert_P , and \lvert \lvert \lvert \cdot \lvert \lvert \rvert_Q are equivalent, we can have the following inequality by summing (3.46), (3.49) and (3.57):

    \begin{align} \begin{split} \lvert \lvert E^{k+1}_u \lvert \rvert^2_1 + \lvert \lvert E^{k+1}_v \lvert \rvert^2_1 &\le C \{ \lvert \lvert R_u^k \lvert \rvert^2 + \lvert \lvert R_v^k \lvert \rvert^2 \} \\ & +C \tau \sum\limits_{n = 1}^{k+1} \{ \lvert \lvert E_v^n \lvert \rvert^2_1 +\lvert \lvert E_u^n \lvert \rvert^2 + \lvert \lvert \delta_t R^{n-1}_u \lvert \rvert^2 +\lvert \lvert \delta_t R^{n-1}_v \lvert \rvert^2 + \lvert \lvert R^{n-1}_u \lvert \rvert^2+\lvert \lvert R^{n-1}_v \lvert \rvert^2 \}. \end{split} \end{align} (3.58)

    Taking \tau sufficiently small and applying Lemmas 8 and 9, we can obtain

    \begin{align} \lvert \lvert E^{k+1}_u \lvert \rvert^2_1 + \lvert \lvert E^{k+1}_v \lvert \rvert^2_1 \le C(\tau^4 +h^{16} ). \end{align} (3.59)

    Moreover, we need to confirm the inequality in (3.40) holds for n = k+1 to complete our proof. We can get the following inequalities by Lemma 6:

    \begin{align} &\lvert \lvert E^{k+1}_u \lvert \rvert_{\infty} \le C \lvert \lvert E^{k+1}_u \lvert \rvert_1 \le C(Y^*, h_0 ,T)(\tau^2 +h^8), \\ &\lvert \lvert \delta_t E^{k}_u \lvert \rvert_{\infty} \le \tau^{-1} \lvert \lvert E^{k+1}_u-E^{k}_u \lvert \rvert_{\infty} \le C(Y^*, h_0 ,T)(\tau +h^8 \tau^{-1}), \end{align}

    and similar inequalities hold for \lvert \lvert E^{k+1}_v \lvert \rvert_{\infty} and \lvert \lvert \delta_t E^{k}_u \lvert \rvert_{\infty} . Then it's easy to see that the inequalities above hold for n = k+1 when h^8 \tau^{-1} = o(1) , i.e., h^8 \tau^{-1} \rightarrow 0 as h \rightarrow 0 , and taking h sufficiently small, which implies that assumption (3.40) is valid for n = k+1 . The proof is finished.

    Corollary 1. By applying Lemma 6, we can obtain the following optimal order convergence rate under the same conditions in Theorem 3:

    \begin{align} \max\limits_{0 < n \le N} \{ \lvert \lvert u^n -U^n \lvert \rvert_{\infty} + \lvert \lvert v^n -V^n \lvert \rvert_{\infty} \} \le C(\tau^2 +h^8). \end{align}

    In this section, some numerical examples are presented to illustrate the conservative properties and eighth-order accuracy of the proposed compact scheme. The ultimate compact schemes (3.32)–(3.35) can be written as the following linear matrix equations:

    \begin{align} C_1 U^{0*}& = D_1 U^0 +E_1(U^0,V^0), \\ C_2 V^{0*} & = D_2 V^0 +E_2(U^0,V^0), \\ C_1 U^{n+1} & = D_1 U^n +F_1(\widehat{(\lvert U \rvert^{.2})}^{n},\hat{U}^n,\hat{V}^n), \\ C_2 V^{n+1} & = D_2 V^n +F_2(V^n,V^{n*},\widehat{(\lvert U \rvert^{.2})}^{n}), \end{align}

    where E_1 , E_2 , F_1 and F_2 are nonlinear terms. Our numerical experiments are conducted using Matlab (R2019b). The invariants I_1, I_2, I_3 and I_4 are tested by the discrete formulations:

    \begin{gather} I_{1h}^n = h \sum\limits_{j = 1}^J \lvert U^n_j \rvert^2, \quad I_{2h}^n = h \sum\limits_{j = 1}^J V^n_j, \\ I_{3h}^n = h \sum\limits_{j = 1}^J \left( \frac{q \beta}{m+1} (V^n_j)^{m+1} + p \rho \lvert \mathcal{B}_1^{-1} \mathcal{A}_1 \delta_{\hat{x}} U^n_j \rvert^2 +q \rho V^n_j \lvert U^n_j \rvert^2 + \frac{s \rho}{2} \lvert U^n_j \rvert^4 - \frac{q \alpha}{2} (\mathcal{B}_1^{-1} \mathcal{A}_1 \delta_{\hat{x}} V^n_j)^2 \right), \\ I_{4h}^n = h \sum\limits_{j = 1}^J \left( q(V^n_j)^2 - 2 \rho \epsilon Im(U^n_j \mathcal{B}_1^{-1} \mathcal{A}_1 \delta_{\hat{x}} \overline{U}^n_j) \right), \end{gather}

    and the errors of invariants are defined as

    \begin{align} E_1 = \lvert I_{1h}^n -I_{1h}^0 \rvert, \quad E_2 = \lvert I_{2h}^n -I_{2h}^0 \rvert, \quad E_3 = \lvert I_{3h}^n -I_{3h}^0 \rvert, \quad E_4 = \lvert I_{4h}^n -I_{4h}^0 \rvert. \end{align}

    Moreover, the accuracy of the proposed scheme is tested by the discrete L^2 - norm (\lvert \lvert u-U \lvert \rvert + \lvert \lvert v-V \lvert \rvert) and L^{\infty} - norm (\lvert \lvert u-U \lvert \rvert_{\infty} + \lvert \lvert v-V \lvert \rvert_{\infty}) .

    Example 1. [8] We consider the following Cauchy problem:

    \begin{align} & i u_t+u_{xx}- v u = 0,\quad (x,t) \in R \times (0,T], \\ & v_t+ v_{xxx}+(3 v^2+ \lvert u \rvert^2)_x = 0, \quad (x,t) \in R \times (0,T], \\ & u(x,0) = \varphi(x) , \quad v(x,0) = \phi(x),\quad x \in R, \end{align}

    whose exact solutions are given by u(x, t) = {\text{exp}}(i(x+t/4)) and v(x, t) = 3/4 . we then compute the equations with h = \pi/20 and \tau = 0.001 in the spatial interval [0, 2\pi] . The errors of the numerical invariants at different time are listed in Table 1, which indicates that the proposed compact scheme preserves the conservation properties. Table 2 shows that the convergence rate of the proposed compact scheme is eighth-order in space.

    Table 1.  Errors of invariants at different time: h = \pi/20 , \tau = 0.001 .
    t E_1 E_2 E_3 E_4
    1 3.73346E-11 1.11910E-13 9.03455E-12 7.48273E-11
    5 1.86450E-10 5.69322E-13 4.50811E-11 3.73753E-10
    10 3.72820E-10 1.32072E-12 8.96581E-11 7.47615E-10

     | Show Table
    DownLoad: CSV
    Table 2.  Convergence rates at different time: h = \pi/10 , \tau = 0.1 .
    t h \tau L^2-error {\text{Rate}} L^\infty-error {\text{Rate}}
    2 h \tau 1.09158E-03 4.35476E-04
    h/2 \tau/16 4.80890E-06 7.82649 1.91847E-06 7.82649
    h/4 \tau/256 1.89240E-08 7.98935 7.54999E-09 7.98927
    5 h \tau 2.94823E-03 1.17617E-03
    h/2 \tau/16 1.20760E-05 7.93156 4.81764E-06 7.93156
    h/4 \tau/256 4.73303E-08 7.99517 1.88923E-08 7.99439
    10 h \tau 6.04262E-03 2.41066E-03
    h/2 \tau/16 2.41903E-05 7.96460 9.65142E-06 7.96447
    h/4 \tau/256 1.02970E-07 7.87605 4.42506E-08 7.76890

     | Show Table
    DownLoad: CSV

    Example 2. [3] We consider the following coupled equations:

    \begin{align} & i \epsilon u_t+ \frac{3}{2}u_{xx}- \frac{1}{2} v u = 0,\quad (x,t) \in R \times (0,T], \\ & v_t+ \frac{1}{2}v_{xxx}+\frac{1}{2}( v^2+ \lvert u \rvert^2)_x = 0, \quad (x,t) \in R \times (0,T], \end{align}

    with exact solutions

    \begin{align} &u(x,t) = -\frac{6\sqrt{3}c}{5} \frac{{\text{tanh}}(\xi)}{{\text{cosh}}(\xi)}{\text{exp}} \left( ic \left( \left( \frac{3}{20\epsilon}-\frac{\epsilon c}{6} \right) t-\frac{\epsilon}{3}x \right) \right), \\ &v(x,t) = -\frac{9c}{5} \frac{1}{{\text{cosh}}^2(\xi)}, \quad \xi = \sqrt{c/10}(x+ct), \end{align}

    where c is an arbitrary positive constant. In addition, we set the artificial boundary conditions u(a, t) = u(b, t) = 0 and v(a, t) = v(b, t) = 0 to satisfy the physical condition that \lvert u \rvert and v tend to zero as \lvert x \rvert \to \infty . Our simulations are conducted by taking \epsilon = 1 , the traveling wave speed c = 0.45 and initial conditions

    \begin{align} &u(x,0) = -\frac{6\sqrt{3}c}{5} \frac{{\text{tanh}}(\xi)}{{\text{cosh}}(\xi)}{\text{exp}}(ic(-\frac{\epsilon}{3}x)), \\ &v(x,0) = -\frac{9c}{5} \frac{1}{{\text{cosh}}^2(\xi)}, \quad \xi = \sqrt{c/10}(x+ct). \end{align}

    Table 3 lists the numerical solutions at t = 0.001 , with h = 0.25 , \tau = 0.00001 and [a, b] = [-30, 30] , where the scheme MECS expands [a, b] to [-150,150] for reducing boundary truncation error. Compared with the numerical results obtained by the fourth-order compact scheme (FCS) in [9] and exponential time differencing three-layer implicit scheme with Padé approximation (ETDT-P) in [7]. We can see that the eighth-order compact scheme (ECS) and modified eighth-order compact scheme (MECS) give better approximations. In addition, MECS gives much more accurate error estimate than ECS, which is caused by boundary truncation error. The numerical solution profiles of \lvert U \rvert and V , as well as the contours in Figure 1 show that the waves traveling with a speed c = 0.45 keep the shape and hight, which are in good agreement with the exact solutions.

    Table 3.  Comparison of numerical solutions with exact solutions and other methods: t = 0.001 , \tau = 0.00001 , h = 0.25 .
    x {\text{MECS}} {\text{ECS}} {\text{FCS}} {\text{ETDT-P}} {\text{Exact solution}}
    Im U -20 3.7904E-03 3.7904E-03 3.7904E-03 3.7904E-03 3.7904E-03
    -10 2.1428E-01 2.1428E-01 2.1428E-01 2.1428E-01 2.1428E-01
    0 -3.013332E-09 -3.013332E-09 -3.0140E-09 -2.4973E-09 -3.013332E-09
    10 2.1424E-01 2.1424E-01 2.1424E-01 2.1424E-01 2.1424E-01
    20 3.7915E-03 3.7915E-03 3.7915E-03 3.7915E-03 3.7915E-03
    \lvert \lvert {\text{Im}}E_u \lvert \rvert 5.1605E-14 1.5738E-05 1.4412E-05 3.8279E-05
    {\text{Re}} U -20 -2.6597E-02 -2.6597E-02 -2.6597E-02 -2.6597E-02 -2.6597E-02
    -10 1.5188E-02 1.5188E-02 1.5188E-02 1.5188E-02 1.5188E-02
    0 -8.928390E-05 -8.928390E-05 -8.928328E-05 -8.9282E-05 -8.928390E-05
    10 -1.5200E-02 -1.5200E-02 -1.5200E-02 -1.5200E-02 -1.5200E-02
    20 2.6592E-02 2.6592E-02 2.6592E-02 2.6592E-02 2.6592E-02
    \lvert \lvert {\text{Re}}E_u \lvert \rvert 3.9746E-14 9.7531E-05 8.0273E-05 7.5941E-06
    V -20 -6.6886E-04 -6.6886E-04 -6.6886E-04 -6.6886E-04 -6.6886E-04
    -10 -4.5256E-02 -4.5256E-02 -4.5256E-02 -4.5256E-02 -4.5256E-02
    0 -8.1000E-01 -8.1000E-01 -8.1000E-01 -8.1000E-01 -8.1000E-01
    10 -4.5239E-02 -4.5239E-02 -4.5239E-02 -4.5239E-02 -4.5239E-02
    20 -6.6861E-04 -6.6861E-04 -6.6861E-04 -6.6861E-04 -6.6861E-04
    \lvert \lvert E_v \lvert \rvert 7.6034E-14 1.1311E-06 7.2736E-07 1.0331E-07

     | Show Table
    DownLoad: CSV
    Figure 1.  Numerical solution profiles of \lvert U \rvert and V (a and b) and the contours(c and d): t \in [0, 30] , [a, b] = [-70, 30] , h = 0.5 , \tau = 0.001 .

    Example 3. [11] We consider the following coupled equations:

    \begin{align} & i u_t+ u_{xx}- \sigma v u + \lvert u \rvert^2 u = 0,\quad (x,t) \in R \times (0,T], \\ & v_t+ v_{xxx}+\frac{1}{2}( v^2- \sigma \lvert u \rvert^2)_x = 0, \quad (x,t) \in R \times (0,T], \end{align}

    with exact solutions

    \begin{gather} u(x,t) = {\text{exp}}(i(\omega t +c x/2))\frac{\sqrt{2C^*(1+6\sigma)}}{{\text{cosh}}(\sqrt{C^*}(x-ct))}, \quad C^* = c^2/4+\omega^2, \\ v(x,t) = \frac{12C^*}{{\text{cosh}}^2(\sqrt{C^*}(x-ct))}, \quad 2c = 1+\sqrt{1+\frac{\sigma}{3}(1+6\sigma)}, \end{gather}

    where \sigma \in (-1/6, 0) and \omega \in R . Set the artificial boundary conditions u(a, t) = u(b, t) = 0 and v(a, t) = v(b, t) = 0 . Our simulations are conducted by taking \sigma = -1/12 , \omega = 0 , [a, b] = [-40, 70] , the traveling wave speed c = (1+\sqrt{71/72})/2 and initial conditions

    \begin{align} u(x,0) = {\text{exp}}(i c x/2)\frac{\sqrt{2C^*(1+6\sigma)}}{{\text{cosh}} \left( \sqrt{C^*}x \right) }, \quad C^* = c^2/4+\omega^2, \\ v(x,0) = \frac{12C^*}{{\text{cosh}}^2 \left( \sqrt{C^*} x \right) }, \quad 2c = 1+\sqrt{1+\frac{\sigma}{3}(1+6\sigma)}. \end{align}

    The errors of the numerical invariants at different times are listed in Table 4, which indicates that the proposed compact scheme preserves the conservation properties. Table 5 shows that the convergence rate of the proposed compact scheme is eighth-order in space. The numerical solution profiles of \lvert U \rvert and V , as well as the contours in Figure 2 show that the waves traveling with a speed c = 0.99652 keep the shape and hight, which are in good agreement with the exact solutions.

    Table 4.  Errors of invariants at different time: h = 0.1 , \tau = 0.001 .
    t E_1 E_2 E_3 E_4
    1 1.35891E-13 8.96330E-10 1.65457E-10 3.32290E-10
    5 7.79488E-13 9.67230E-08 8.24029E-10 1.65427E-09
    10 1.53033E-12 2.48881E-07 1.64719E-09 3.30639E-09

     | Show Table
    DownLoad: CSV
    Table 5.  Convergence rates at different time: h = 1 , \tau = 0.1 .
    t h \tau L^2-error {\text{Rate}} L^\infty-error {\text{Rate}}
    1 h \tau 2.83547E-02 1.60049E-02
    h/2 \tau/16 8.47660E-05 8.38589 5.63783E-05 8.14915
    h/4 \tau/256 3.28192E-07 8.01280 2.20134E-07 8.00062
    5 h \tau 7.81002E-02 3.78102E-02
    h/2 \tau/16 2.60905E-04 8.22566 1.49546E-04 7.98205
    h/4 \tau/256 1.01440E-06 8.00675 5.81734E-07 8.00601
    10 h \tau 1.44349E-01 7.50822E-02
    h/2 \tau/16 4.75731E-04 8.24520 2.60189E-04 8.17277
    h/4 \tau/256 1.84463E-06 8.01067 1.00971E-06 8.00947

     | Show Table
    DownLoad: CSV
    Figure 2.  Numerical solution profiles of \lvert U \rvert and V (a and b) and the contours(c and d): t \in [0, 30] , h = 0.25 , \tau = 0.001 .

    In this paper, we propose an eighth-order compact finite difference scheme by constructing several circulant symmetric positive definite matrices to obtain the numerical solution of coupled Schrödinger-KdV equations. The performance of proposed compact scheme is evaluated by conservation properties and error estimate. Numerical examples demonstrate the better performance of the proposed compact scheme in accuracy compared with FCS and ETDT-P given in [7,9]. Since the matrices have good properties, we can discuss the possibility that the proposed compact scheme can be applied to other equations such as nonlinear Dirac equation [21], generalized Rosenau-RLW equation [22], Klein-Gordon-Schrödinger equation [23], coupled Gross-Pitaevskii equations [24] and regularized long wave equation [25].

    This work was supported by National Natural Science Foundation of China (No. 11471092).

    The authors declare no conflicts of interest.



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