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A comparison study of two modified analytical approach for the solution of nonlinear fractional shallow water equations in fluid flow

  • In this study, a comparison between the modified homotopy analysis transform method (MHATM) and residual power series method (RPSM) have been given for solving time-fractional coupled shallow water equations (SWEs). The time-fractional coupled SWEs are a system of PDEs that describe the flow below a pressure surface in a fluid is considered. Rigorous convergence analysis and error estimated have been exhibited for both the featured methods. The results obtained by MHATM and RPSM are then compared with well-known exact solutions. To show the effectiveness and advantage of the featured techniques the numerical simulation of coupled SWEs has been represented graphically with tabulated data. However, the results indicate that MHATM provides more accurate value than RPSM for solving fractional coupled SWEs.

    Citation: Sunil Kumar, Amit Kumar , Zaid Odibat, Mujahed Aldhaifallah, Kottakkaran Sooppy Nisar. A comparison study of two modified analytical approach for the solution of nonlinear fractional shallow water equations in fluid flow[J]. AIMS Mathematics, 2020, 5(4): 3035-3055. doi: 10.3934/math.2020197

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  • In this study, a comparison between the modified homotopy analysis transform method (MHATM) and residual power series method (RPSM) have been given for solving time-fractional coupled shallow water equations (SWEs). The time-fractional coupled SWEs are a system of PDEs that describe the flow below a pressure surface in a fluid is considered. Rigorous convergence analysis and error estimated have been exhibited for both the featured methods. The results obtained by MHATM and RPSM are then compared with well-known exact solutions. To show the effectiveness and advantage of the featured techniques the numerical simulation of coupled SWEs has been represented graphically with tabulated data. However, the results indicate that MHATM provides more accurate value than RPSM for solving fractional coupled SWEs.


    Recent research focused on the applications of the Fractional Calculus (FC) in various fields which includes the modeling and analysis of complex real-world problems. Very recently, numerous papers appeared with the various type of applications of fractional calculus [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22]. Further, computational aspects of various problems can be found in [23,24,25,26].

    The SWEs are a system of PDEs that illustrate the flow below a pressure surface in a fluid, the motion of water bodies and flow in vertically well-mixed water bodies. The general characteristics of shallow water flows are that the vertical dimension is much smaller than the typical horizontal scale, the fluid is homogeneous and incompressible, the flow is steady, and the pressure distribution is hydrostatic. The SWEs can be utilized to model the hydrodynamics of lakes, ocean currents, tidal flats, coastal zones and to study dredging feasibility. Differently, it can also be used to investigate several physical phenomena[27]. Many geophysical flows are modelled by variants of the SWEs.

    The popular form of SWEs can be derived from the Benny system. The Benny equations are characterized as [28]

    u(x,y,t)t+u(x,y,t)u(x,y,t)tu(x,y,t)yy0u(x,τ,t)ydτ+h(x,t)x=0,h(x,t)t+xy0u(x,τ,t)ydτ=0, (1.1)

    where h(x,t), u(x,y,t) denotes the free surface and the horizontal velocity component respectively and y is the rigid bottom. If the horizontal velocity component u is independent of the height h the (1.1) reduced to the equation system in the classical water theory corresponding to the case of irrational motion. The Equivalent wave motion is the coupled SWEs as

    Dth(x,t)+u(x,t)Dxh(x,t)+h(x,t)Dxu(x,t)=0,Dtu(x,t)+u(x,t)Dxu(x,t)+Dxh(x,t)=0, (1.2)

    subject to the ICs

    h(x,0)=f(x),u(x,0)=g(x). (1.3)

    In this article, let us suppose the time-fractional order coupled SWEs of the form

    Dλth(x,t)+u(x,t)Dxh(x,t)+h(x,t)Dxu(x,t)=0,Dβtu(x,t)+u(x,t)Dxu(x,t)+Dxh(x,t)=0. (1.4)

    subject to the initial conditions

    h(x,0)=f(x),u(x,0)=g(x). (1.5)

    Here 0<λ1 and 0<β1 are the parameters representing the orders of the fractional time derivative. The fractional derivative is considered in the Caputo sense [29,30].

    Recently, Kumar [31] gave the solutions of time-fractional nonlinear SWEs by using the HPM. The essential target of this work is to present a comparative study between the HATM [32,33,34] with modification and RPSM [35,36,37,38,39] through the solution of fractional SWEs (1.4). The MHATM is a combination of LTM and the HAM [40,41,42,43,44] with homotopy polynomials [45]. Where as RPSM is an analytical method based on power series expansion without linearization, perturbation or discretization. The benefits of the RSPM as compared to the other classical power series techniques is that the RPSM does not require any conversion while switching from the low-order to the higher-order. Also, it can switch from simple linearity to complex nonlinearity. This means that the RSPM can apply directly to the problems by considering a suitable initial guess approximation.

    Let us assume the time-fractional SWEs (1.4) with the ICs

    h(x,0)=19(x22x+1)andu(x,0)=23(1x). (2.1)

    In this case, the exact solution of Eq (1.4) for standard motion, i.e. λ=1 and β=1, is given by [28]

    h(x,t)=(x1)29(t1)2andu(x,t)=2(x1)3(t1). (2.2)

    Further, starting with the ICs

    f0(x)=h(x,0)=19(x22x+1),g0(x)=u(x,0)=u(x,t)=2(x1)3(t1), (2.3)

    and with the kth1 and kth2 residual functions for SWEs as

    Reshk1(x,t)=λhk1tλ+uk1hk1x+hk1uk1x,k1=1,2,3,...,Resuk2(x,t)=βuk2tβ+uk1uk1x+hk1x,k2=1,2,3,.... (2.4)

    In addition, taking into account those forms of f0(x) and g0(x) and using (2.4), the kth truncated series of the multiple FPS expansion of h(x,t) and u(x,t) at t=0 should be

    hk(x,t)=f(x)+f1(x)tλΓ(1+λ)+kn=2fn(x)tnλΓ(1+nλ),k=2,3,4,...,uk(x,t)=g(x)+g1(x)tβΓ(1+β)+kn=2gn(x)tnβΓ(1+nβ),k=2,3,4,..., (2.5)

    respectively. To ascertain the first unknown coefficients, f1(x) and g1(x), in the expansion of (2.5), substitute the 1st truncated series h1(x) and u1(x) into the 1-st residual functions given in (2.4), to obtain

    Resh1(x,t)=λh1tλ+u1h1x+h1u1x,Resu1(x,t)=βu1tβ+u1u1x+h1x. (2.6)

    But since h1(x,t)=f(x)+f1(x)tλΓ(1+λ) and u1(x,t)=g(x)+g1(x)tβΓ(1+β). Then Eq (2.6) leads to the following result:

    Resh1(x,t)=f1(x)+(g(x)+g1(x)tβΓ(1+β))(f(x)+f1(x)tλΓ(1+λ))x+(f(x)+f1(x)tλΓ(1+λ))(g(x)+g1(x)tβΓ(1+β))x,Resu1(x,t)=g1(x)+(g(x)+g1(x)tβΓ(1+β))(g(x)+g1(x)tβΓ(1+β))x+(f(x)+f1(x)tλΓ(1+λ)). (2.7)

    Now, based on the result of (2.5) for n=1, the substitution of t=0 through (2.7) yields

    f1(x)=294x9+2x29,g1(x)=232x3. (2.8)

    Therefore, the 1st RPS approximate solution of (1.4) can be represented as

    h1(x,t)=19(x22x+1)+(294x9+2x29)tλΓ(1+λ),u1(x,t)=23(1x)(232x3)tλΓ(1+λ). (2.9)

    In a similar way, the second unknown coefficients f2(x) and g2(x) can be obtained by substituting the 2nd truncated series h2(x,t)=f(x)+f1(x)tλΓ(1+λ)+f2(x)t2λΓ(1+2λ) and u2(x,t)=g(x)+g1(x)tβΓ(1+β)+g2(x)t2βΓ(1+2β) of (2.5) into 2-nd residual functions Resh2(x,t)=λh2tλ+u2h2x+h2u2x and Resu2(x,t)=βu2tβ+u2u2x+h2x of (2.4) to get the following discretized form:

    Resh2(x,t)=f1(x)+g(x)f(x)+g(x)f(x)+(f2(x)+gf1(x)+f1g(x))tλΓ(1+λ)+(g1f(x)+f1g1(x))tβΓ(1+β)+(g(x)f2(x)+f2(x)g(x))t2λΓ(1+2λ)+(g2(x)f(x)+f(x)g2)t2βΓ(1+2β)+(g1f1+f1g1(x))tλ+βΓ(1+λ)Γ(1+β)+(g1f2+f2g1(x))t2λ+βΓ(1+2λ)Γ(1+β)+(g2f1+f1g2(x))tλ+2βΓ(1+λ)Γ(1+2β)+(g2f2+f2g2(x))t2λ+2βΓ(1+2λ)Γ(1+2β)Resu2(x,t)=g1(x)+g(x)g(x)+f(x)+(g2(x)+gg1(x)+g1g"(x))tβΓ(1+β)+f1(x)tλ1+λ+f2(x)t2λ1+2λ+(gg2+g1g1(x)+g2g(x))t2βΓ(1+2β)+(g1g2+g2g1(x))t3βΓ(1+3β)+(g2g2(x))t4βΓ(1+4β)+(2f1(x)g2(x)+2f2(x)g1(x))t3βΓ(1+3β)+2f2(x)g2(x)t4βΓ(1+4β). (2.10)

    Now, operating Dλt one time on both sides of (2.7) gives the λth time fractional derivative of Resh2(x,t) and Resu2(x,t). Then, from (2.5) when n=2, substituting t=0 through (2.10) yields

    f2(x)=234x3+2x23,g2(x)=434x3. (2.11)

    Hence, the 2nd RPS approximate solution of (1.4) of the form

    h2(x,t)=19(x22x+1)+(294x9+2x29)tλΓ(1+λ)+(234x3+2x23)t2λΓ(1+2λ),u2(x,t)=23(1x)(232x3)tβΓ(1+β)+(434x3)t2βΓ(1+2β). (2.12)

    By applying the same steps for n=3, we get following form of f3(x) and g3(x)

    f3(x)=20940x9+20x29,g3(x)=32932x9. (2.13)

    In fact, from (2.13) and based on Preceding results for f0(x), g0(x), f1(x), g1(x), f2(x) and g2(x) the 3rd RPS approximation solution of Eq (1.4) will be ready to summarized as follows

    h3(x,t)=19(x22x+1)+(294x9+2x29)tλΓ(1+λ)+(234x3+2x23)t2λΓ(1+2λ)+(20940x9+20x29)t3λΓ(1+3λ),u3(x,t)=23(1x)(232x3)tβΓ(1+β)+(434x3)t2βΓ(1+2β)+(32932x9)t3βΓ(1+3β). (2.14)

    Continuing in this approach, the rest of the components of fn(x) and gn(x) for n4, can be completely acheived and the series solution is thus completely determined. Finally, the solution of Eq (1.4) is given by

    h(x,t)=n=0fn(x)tnλΓ(1+nλ),u(x,t)=n=0gn(x)tnβΓ(1+nβ) (2.15)

    Convergence study and error estimate

    Theorem 1. Let us take the coupled fractional differential equation (1.4) with the initial conditions given by (2.1) and assume that Dλth(x,t) and Dβtu(x,t) be the Caputo derivative with Dλth(x,t),Dβtu(x,t)C([0,M]×[0,L]), where C([0,M]×[0,L]) be the set of all continuous functions over the interval [0,M]×[0,L], then the approximate solutions ˜h(x,t) and ˜u(x,t) of the coupled fractional differential equation (1.4) are

    ˜h(x,t)Nn=0Antnλand˜u(x,t)Nn=0Bntnβ,

    where

    An=Dnλh(x,t0)Γ(nλ+1)andBn=Dnβu(x,t0)Γ(nβ+1).

    Furthermore, value δ, where 0δt so that the errors E1N(x,t) and E2N(x,t) on the Banach space (C[0,M]×[0,L],.) for the approximate solutions ˜h(x,t) and ˜u(x,t) have the form

    E1N(x,t)=Sup0xM,0tL|D(N+1)λh(x,0+)Γ((N+1)λ+1)tnλ|and
    E2N(x,t)=Sup0xM,0tL|D(N+1)βu(x,0+)Γ((N+1)β+1)tnβ|respectively,ifδ0+

    Proof. First part of the proof is follows for the approximate solution ˜h(x,t).

    In this case, the error term:

    E1N(x,t)=h(x,t)˜h(x,t),

    where

    h(x,t)=n=0Dnλh(x,0)Γ(nλ+1)tnλand˜h(x,t)=Nn=0Dnλh(x,0)Γ(nλ+1)tnλ.

    For 0<α<1,

    JnλtDnλth(x,t)J(n+1)λtD(n+1)λth(x,t)=Jnλt(Dnλth(x,t)JλtDλt(Dnλth(x,t)))=Jnλt(Dnλth(x,0)),=Dnλh(x,0)Γ(nλ+1)tnλ.

    The N-th order approximation for h(x,t) is

    ˜h(x,t)=Nn=0Dnλh(x,0)Γ(nλ+1)tnλ=Nn=0JnλtDnλth(x,t)J(n+1)λtD(n+1)λth(x,t),using above=h(x,t)Nn=0J(n+1)λtD(n+1)λth(x,t).

    Therefore, we have the following error term

    E1N(x,t)=h(x,t)˜h(x,t),=Nn=0J(n+1)λtD(n+1)λth(x,t)=J(N+1)λtD(N+1)λth(x,t)=1Γ((N+1)λ)t0D(N+1)λh(x,ζ)(tζ)1(N+1)λdζ=D(N+1)λh(x,δ)Γ((N+1)λ)t0dζ(tζ)1(N+1)λ,In view of the integral mean value theorem=D(N+1)λh(x,δ)Γ((N+1)λ)t(N+1)λ.

    Now, the error term on the Banach space (C[0,M]×[0,L],.) is

    E1N(x,t)=Sup0xM,0tL|h(x,t)˜h(x,t)|=Sup0xM,0tL|D(N+1)λh(x,δ)Γ((N+1)λ)t(N+1)λ|
    =Sup0xM,0tL|D(N+1)λh(x,0+)Γ((N+1)λ)t(N+1)λ|asδ0+,

    As N,E1N(x,t)0, hence h(x,t) can be approximate as

    h(x,t)=n=0Dnλh(x,0)Γ(nλ+1)tnλNn=0Dnλh(x,0)Γ(nλ+1)tnλ=˜h(x,t),

    with the error term E1N(x,t).

    Following the similar argument, for the approximate solution ˜u(x,t) we can also find the error E2N(x,t)=u(x,t)˜u(x,t).

    Here, we consider a general fractional partial differential equation for the discussion of MHATM method as

    Dλth(x,t)+R[x]h(x,t)+N[x]h(x,t)=g(x,t),t>0,xR,0<λ1, (3.1)

    where R[x], N[x], g(x,t) and h(x,t) is defined as above.

    Now methodology discussed in [32,33], applied to Eq (3.1) we obtained the mth-order deformation equation

    hm(x,t)=(χm+)hm1(1χm)j1i=0tih(i1)(0)+L1(1sλL(Rm1[t]hm1(t)+m1k=0Pk(h0,h1,,hm)g(x,t),)) (3.2)

    where Pk are the homotopy polynomial.

    The expression in nonlinear operator form has been modified in homotopy analysis transforms method for the convenience. That is, the nonlinear term N[x,t]h(x,t) is expanded in terms of homotopy polynomials as

    N[h(x,t)]=N(m1k=0hm(x,t))=m=0Pmhm. (3.3)

    Next, from the Eq (3.2), we find the various hm(x,t) for m1 and the series solution of Eq (3.1) is thus entirely determined

    h(x,t)=m=0hm(x,t). (3.4)

    Applying the Laplace transform (LT) on both the sides of (1.4), we get

    sλL[h(x,t)]sλ1h(x,0)+L[2uhxhux]=0,sβL[u(x,t)]sβ1u(x,0)+L[uux+hx]=0. (3.5)

    After some simplification and applying the inverse Laplace transform on (3.5), we have

    h(x,t)=19(x22x+1)+L1(sλL[uhx+hux]),u(x,t)=2(1x)3+L1(sβL[uux+hx]). (3.6)

    Next, for this case the system of nonlinear operator as follows:

    N[ϕ(x,t;q)]=L[ϕ(x,t;q)]19(x22x+1)+sλL[Φϕx+ϕΦx],N[Φ(x,t;q)]=L[ϕ(x,t;q)]2(1x)3+sβL[2Φ(Φ)x+ϕx]. (3.7)

    Which leads to the mthorder deformation equations as

    L[hm(x,t)χmhm1(x,t)]=Rm(hm1,x,t),L[um(x,t)χmum1(x,t)]=Rm(um1,x,t). (3.8)

    Applying the inverse Laplace transform to both sides of (3.8) yields

    hm(x,t)=χmhm1(x,t)+qL1[Rm(hm1,x,t)],um(x,t)=χmum1(x,t)+qL1[Rm(um1,x,t)], (3.9)

    where

    Rm(hm1,x,t)=L[hm1(x,t)](1χm)19(x22x+1)+sλ[2Pm+ P1m],Rm(um1,x,t)=L[um1(x,t)](1χm)23(1x)+sβ[(P2m+(um1)x], (3.10)

    Pm, P1m, and P2m are the homotopy polynomials given as

    Pm=1Γm[mqmN[(qΦ(x,t;q))(qϕ(x,t;q))x]]q=0,P1m=1Γm[mqmN[(qϕ(x,t;q))(qΦ(x,t;q))x]]q=0,P2m=1Γm[mqmN[(qΦ(x,t;q))(qΦ(x,t;q))x]]q=0, (3.11)
    ϕ(t;q)=ϕ0+qϕ1+q2ϕ2+q3ϕ3+...,Φ(t;q)=Φ0+qΦ1+q2Φ2+q3Φ3+.... (3.12)

    The solutions of mthorder deformation Eq (3.8) becomes

    hm(x,t)=(χm+)hm1(1χm)19(x22x+1)+L1(sλ[Pm+P1m]),um(x,t)=(χm+)um1(1χm)23(1x)+L1(sβ[(P2m+(um1)x]). (3.13)

    By putting the initial approximation (2.1) into the iterative scheme (3.13), we successively obtain

    h1=tλΓ(1+λ)29(x1)2,u1=tβΓ(1+β)23(1x),h2=(1+)tλΓ(1+λ)29(x1)2+2t2λΓ(1+2λ)49(x1)2+2tλ+βΓ(1+λ+β)29(x1)2,u2=(1+)tβΓ(1+β)23(1x)+2t2βΓ(1+2β)89(1x)+2tλ+βΓ(1+λ+β)49(1x).

    Similarly way the remaining term of the series hm(x,t) and um(x,t), for m3 can be completely achieved. Finally, the solution of Eq (1.4) can be given in the form

    h(x,t)=m=0hm(x,t),u(x,t)=m=0um(x,t). (3.14)

    In this subsection, we examine the convergence analysis and error estimate of the MHATM for (1.4) with respect to the initial condition (2.1)

    Theorem 2. Suppose that hm(x,t),um(x,t),h(x,t) and u(x,t) be defined in Banach space (C[0,1],.). Then the series solution {hm(x,t)}m=0 and {um(x,t)}m=0 given by (3.14) convergence to the solutions of (1.4), if there exist 0<μ<1, such that hn+1μhn and un+1μun, for nN.

    Proof. We have (C[0,1],.) is the Banach space of all continuous functions on [0,1] with the norms,

    h(x,t)=maxx,t[0,1]|h(x,t)|andu(x,t)=maxx,t[0,1]|u(x,t)|.

    Define that {Sn} is the sequence of partial sum as,

    S0=h0(x,t),S1=h0(x,t)+h1(x,t),S2=h0(x,t)+h1(x,t)+h2(x,t),...Sm=h0(x,t)+h1(x,t)+h2(x,t)+...+hm(x,t).

    It is sufficient to show that {Sm}m=0 is a Cauchy sequence in Banach space (C[0,1],.). For m,nN,mn, we have

    SmSn=(SmSm1)+(Sm1Sm2)+...+(Sn+1Sn)(SmSm1)+(Sm1Sm2)+...+(Sn+1Sn)=hm(x,t)+hm1(x,t)+...+hn(x,t)μmu0(x,t)+μm1u0(x,t)+...+μn+1u0(x,t)=1μmn1μμn+1u0(x,t).

    Since 0<μ<1, we have 1μmn<1; then,

    SmSnμn+11μmaxu0(x,t).

    Since u0(x,t) is bounded,

    limm,nSmSn=0.

    Therefore {Sm}m=0 is a Cauchy sequence in the Banach space (C[0,1],.), so the series solution defined in (3.14), converges. Similarly, we can show for u(x,t) case. This completes the proof.

    Theorem 3. The maximum absolute truncation error of the series solution Eq (3.14) for Eq (1.4) w.r.to the initial conditions 2.1 is estimated to be

    |h(x,t)mi=0hi(x,t)|μn+11μh0(x,t)and|u(x,t)mi=0ui(x,t)|μn+11μu0(x,t).

    Proof. From theorem 4.1, for mn we have

    SmSn=1μmn1μμn+1u0(x,t). (3.15)

    Now, as m then Smu(x,t). So,

    |u(x,t)Sn|μn+11μμn+1u0(x,t). (3.16)

    Since 0<μ<1, we have 1μmn<1. Therefore the above inequality becomes,

    |h(x,t)mi=0hi(x,t)|μn+11μh0(x,t).

    Similarly we can show the inequality

    |u(x,t)mi=0ui(x,t)|μn+11μu0(x,t).

    This complete the proof.

    In this section, comparison of RPSM and MHATM are made in a systematic fashion through different graphical representation and tabulated data.

    The geometrical behaviour of the obtained solutions of Eq (1.4) are compare by depicted through 3D Figures 15 of the 5th order MHATM, 5th order RPSM and the exact solution represented by the Eq (2.2). The scenario of subfigures reveals that their surface graphic and profile are almost the same even if for different values of α. Figure 6 explore the comparison of the approximate solution received by RPSM and MHATM method with consideration to exact solutions at time instance t=0.5 when λ=β=1. Figures indicate that a high level of accuracy has been attained between the exact solution and the solutions obtained by MHATM and RPSM.

    Figure 1.  The surfaces show (a) the numerical approximate solution of h(x,t) by MHATM, (b) the numerical approximate solution of h(x,t) by RPSM and (c) the exact solution of h(x,t) when λ=1,β=1.
    Figure 2.  The surfaces show (a) the numerical approximate solution of u(x,t) by MHATM, (b) the numerical approximate solution of u(x,t) by RPSM and (c) the exact solution of u(x,t) when λ=1,β=1.
    Figure 3.  The surfaces show (a) the numerical approximate solution of h(x,t) by MHATM, (b) the numerical approximate solution of h(x,t) by RPSM, (c) the numerical approximate solution of u(x,t) by MHATM and (d) the numerical approximate solution of u(x,t) by RPSM when λ=0.9,β=0.9.
    Figure 4.  The surfaces show (a) the numerical approximate solution of h(x,t) by MHATM, (b) the numerical approximate solution of h(x,t) by RPSM, (c) the numerical approximate solution of u(x,t) by MHATM and (d) the numerical approximate solution of u(x,t) by RPSM when λ=0.8,β=0.8.
    Figure 5.  The surfaces show (a) the numerical approximate solution of h(x,t) by MHATM, (b) the numerical approximate solution of h(x,t) by RPSM, (c) the numerical approximate solution of u(x,t) by MHATM and (d) the numerical approximate solution of u(x,t) by RPSM when λ=0.7,β=0.7.
    Figure 6.  The surfaces show (a) comparison of five term MHATM solution and five term RPSM solution with regard to exact solution of h(x,t) at time instance t=0.5 and (b) comparison of five term MHATM solution and five term RPSM solution with regard to exact solution of u(x,t) at time instance t=0.5.

    Figure 7 indicating the numerical simulations for comparison of the absolute error for RPSM and MHATM solutions. Even if both the present methods are reliable and efficient, Figure 6 guarantee plausibility to consider MHATM give more accurate than RPSM solutions for fractional SWEs.

    Figure 7.  The surfaces show (a) absolute error E5(h)=|h(x,t)h5(x,t)| by MHATM, (b) absolute error E5(h)=|h(x,t)h5(x,t)| by RPSM, (c) absolute error E5(u)=|u(x,t)u5(x,t)| by MHATM and (d) absolute error E5(u)=|u(x,t)u5(x,t)| by RPSM.

    Figure 8 shows the comparison of the approximate analytical solutions acheived by RPSM and MHATM for λ=0.7, λ=0.8, λ=0.9 and λ=1. Also for the β=0.7, β=0.8, β=0.9 and β=1.

    Figure 8.  The surfaces show (a) plot of h5(x,t) versus time t for different values of λ using MHATM, (b) plot of h5(x,t) versus time t for different values of λ using RPSM, (c) plot of u5(x,t) versus time t for different values of β using MHATM and (d) Plot of u5(x,t) versus time t for different values of β using RPSM when =1.

    The comparison of results between proposed methods RPSM and MHATM at different points of x and t using the parameters c=12,k=1,b=9 and =1 presented in Table 1.

    Table 1.  The comparison of the absolute error in the solution of fractional SWEs using five term approximation for MHATM and RPSM at different points of x and t with c=12,k=1,b=9 and =1 for λ=1,β=1.
    (x, t) |hexacthHATM| |uexactuHATM| |hexacthRPSM| |uexactuRPSM|
    (0.1, 0.1) 7.11111×107 6.66667×107 8.27052×105 9.15580×105
    (0.1, 0.2) 5.22000×105 4.80000×105 9.40010×104 1.02467×104
    (0.1, 0.3) 6.96269×104 6.24857×104 4.66383×103 4.95146×103
    (0.2, 0.1) 5.61866×107 5.92593×107 6.53415×105 7.97169×105
    (0.2, 0.2) 4.12444×105 4.26667×105 7.42537×104 8.83779×104
    (0.2, 0.3) 5.50139×104 5.55429×104 3.68358×103 4.26263×103
    (0.3, 0.1) 4.30178×107 5.18519×107 5.00213×105 6.78757×105
    (0.3, 0.2) 3.15778×105 3.73333×105 5.68320×104 7.42887×104
    (0.3, 0.3) 4.21200×104 4.86000×104 2.88188×103 3.57380×103

     | Show Table
    DownLoad: CSV

    At the mth-order of approximation, the exact square residual error are:

    Δhm=1010(N[mi=0hi(x,t)])2dxdtandΔum=1010(N[mi=0ui(x,t)])2dxdt (4.1)

    where N[h(x,t)]=λhtλ+uhx+hux and N[u(x,t)]=βutβ+uux+hx.

    Next, for the convenience point of view, we also introduced the averaged residual error defined by [47]

    Ehm=1k21k1j=1k1l=1(N[mi=0hi(jΔx,lΔt)])2andEum=1k21k1j=1k1l=1(N[mi=0ui(jΔx,lΔt)])2 (4.2)

    where Δx=140k1,Δt=140k2,k1=k2=5 for SWEs. The optimal value of can be achieved by means of minimizing the so called averaged residual error Em defined by (4.2), Equivalent to the nonlinear algebraic equations Ehm=0 and Eum=0.

    Tables 2 and 3 display the comparison of the averaged residual error for the optimal value of with a different order of approximation. Also, the accuracy and validity of the MHATM technique can be demonstrated using the averaged residual error.

    Table 2.  Optimal value of for h(x,t).
    Order of approx. Optimal value of for λ=1 Optimal value of for λ=0.9 value of Em for λ=1 value of Em for λ=0.9
    1 -1.0472 -0.722061 2.95623×109 1.78708×107
    2 -0.99085 -0.989655 5.28515×1010 6.73821×1010
    3 -0.98990 -0.827603 6.32161×1010 3.20375×107

     | Show Table
    DownLoad: CSV
    Table 3.  Optimal value of for u(x,t).
    Order of approx. Optimal value of for β=1 Optimal value of for β=0.9 value of Em for β=1 value of Em for β=0.9
    1 -1.03090 -0.710627 1.20212×108 1.83569×106
    2 -1.02271 -1.04991 1.21163×1011 2.45324×106
    3 -1.06235 -1.03767 1.13253×1014 2.48388×106

     | Show Table
    DownLoad: CSV

    In this work, we have fruitfully applied MHATM and RPSM for solving time-fractional coupled SWEs. There are various features assumed for this equation are summarized as follows:

    (ⅰ) The key procedure of the new adaption in MHATM has decomposed the non-linear term N(u) into the sum of homotopy polynomial Pm, which helps for obtaining the rapid convergent of the series solution.

    (ⅱ) Further, the fractional coupled SWEs have been solved by using two independent analytic methods such as MHATM and RPSM.

    (ⅲ) We compare these two methods and show that the results of the MHATM method are in excellent agreement with results of the RPSM method and the obtained numerical solutions are present graphically which approves the validity of the MHATM and RPSM.

    (ⅳ) From the obtained results, it can be noted that, although both the featured techniques are reliable and efficient to handle the different nonlinear problems appearing in science and engineering, MHATM provides highly accurate numerical solution of fractional SWEs, in comparison with RPSM. The paper is concluded by observing that, MHATM is more efficient and accurate for solving the fractional coupled SWEs.

    The author K.S. Nisar thanks to Deanship of Scientific research (DSR), Prince Sattam bin Abdulaziz University, Saudi Arabia for providing facilities and support.

    The authors declare no conflict of interest in this paper.



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