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An efficient approach for the numerical solution of fifth-order KdV equations

  • Hijaz Ahmad , Tufail A. Khan and Shao-Wen Yao EMAIL logo
Published/Copyright: July 22, 2020

Abstract

The main aim of this article is to use a new and simple algorithm namely the modified variational iteration algorithm-II (MVIA-II) to obtain numerical solutions of different types of fifth-order Korteweg-de Vries (KdV) equations. In order to assess the precision, stability and accuracy of the solutions, five test problems are offered for different types of fifth-order KdV equations. Numerical results are compared with the Adomian decomposition method, Laplace decomposition method, modified Adomian decomposition method and the homotopy perturbation transform method, which reveals that the MVIA-II exceptionally productive, computationally attractive and has more accuracy than the others.

MSC 2010: 35Q53

1 Introduction

Nonlinear partial differential equations (PDEs) have turned into a helpful tool for delineating a large number of physical problems that arise in many fields of mathematics and science, including fluid dynamics, chemical physics, hydrodynamics, fluid mechanics, heat and mass transfer, solid-state physics, chemical kinematics, plasma physics, etc. [1,2,3,4,5,6,7,8,9,10,11,12]. The Korteweg-de Vries (KdV) equation is a nonlinear PDE and assumes a significant role with numerous applications, for example, in illustration of magneto-acoustic waves, ion-acoustic waves, nonlinear LC circuit’s waves and shallow water waves in plasmas. This equation has experienced a few extensions and modifications leading to numerous types of KdV equations showing up in three, five, seven or more order forms deferential equations. However, some notable types of them are specifically noteworthy in physical phenomena. These types of the KdV equation of fifth order are as follows:

  1. The Sawada-Kotera (SK) equation [13]:

    (1) u t + 45 u 2 u x + 15 u x 2 u x 2 + 15 u 3 u x 3 + 5 u x 5 = 0 .

  2. The Caudrey-Dodd-Gibbon (C-D-G) equation [14]:

    (2) u t + 180 u 2 u x + 30 u x 2 u x 2 + 30 u 3 u x 3 + 5 u x 5 = 0 .

  3. The Lax equation [15,16]:

    (3) u t + 30 u 2 u x + 30 u x 2 u x 2 + 10 u 3 u x 3 + 5 u x 5 = 0 .

  4. The fifth-order KdV equation [17]:

    (4) u t + u u x u 3 u x 3 + 5 u x 5 = 0 .

  5. The Kawahara equation [18]:

    (5) u t + u u x + 3 u x 3 5 u x 5 = 0 .

The fifth-order KdV equation, also called generalized KdV, is the indispensable model for numerous physical phenomena in quantum mechanics and nonlinear optics. Its general solution is not known while the exact solution for the particular case of solitary waves of the KdV equation of fifth order is reported in [19]. To solve this model numerically, several methods can be found in the literature, such as Wazwaz [16] has employed the tanh and sine-cosine methods, Djidjeli et al. [20] used finite difference schemes, Chun [21] utilized the Exp-function method, Abbasbandy and Zakaria [22] employed the homotopy analysis method for soliton solution, Goswami et al. [18] applied homotopy perturbation transform method (HPTM), Kaya [23,24] used the decomposition method as well as Adomian decomposition method, Rafei and Ganji [25] provided a comparative study of homotopy perturbation method and Adomian decomposition method, Lei et al. used the homogeneous balance method [26], Helal and Mehanna [27] provided a comparative investigation of the Adomian decomposition method and Crank-Nicholas method, Handibag and Karande [17] have developed the Laplace decomposition method and Bakodah [28] has used the modified Adomian decomposition method.

The main objective of this article is to employ modified variational iteration algorithm-II (MVIA-II) for the numerical treatment of nonlinear PDEs in physical sciences and engineering via the KdV equation of fifth order. The article is prepared as follows. In Section 2, MVIA-II is described. In Section 3, five different forms of KdV equations have been investigated to show the applicability and precision of the modified algorithm, and in Section 4, conclusions are given.

2 Implementation of MVIA-II

In this section, we will describe MVIA-II for finding the numerical solutions of fifth-order KdV equations. Suppose the generalized form of the KdV equation of fifth order:

(6) u t + α u 3 u x 3 + β u x 2 u x 2 + γ u 2 u x + 5 u x 5 = 0 ,

where γ , β and α are nonzero arbitrary constants. Approximate solution u k + 1 ( x ) of Eq. (6) for given initial condition u 0 ( x ) can be acquired as follows:

(7) u k + 1 ( x , t , h ) = u 0 ( x , t , h ) + h 0 t λ ( η ) u k ( x , η , h ) ( η ) + α u k ( x , η , h ) 3 u k ( x , η , h ) ( x ) 3 ˜ + β u k ( x , η , h ) ( x ) 2 u k ( x , η , h ) ( x ) 2 ˜ + γ u k 2 ( x , η , h ) u k ( x , η , h ) ( x ) ˜ + 5 u k ( x , η , h ) ( x ) 5 ˜ d η ,

where λ and h are two unknown parameters, the first one is known as the Lagrange multiplier [29], while the second one is an auxiliary parameter, which was used to accelerate the convergence in different methods [30,31,32,33,34,35,36,37], but we are the first to use it in VIA-II. The Lagrange multiplier can be found by taking δ on both sides of the recurrence relation (7) w.r.t. the variable u k ( x ) ,

(8) δ u k + 1 ( x , t , h ) = δ u 0 ( x , t , h ) + h δ 0 t λ ( η ) u k ( x , η , h ) ( η ) + α u k ( x , η , h ) 3 u k ( x , η , h ) ( x ) 3 ˜ + β u k ( x , η , h ) ( x ) 2 u k ( x , η , h ) ( x ) 2 ˜ + γ u k 2 ( x , η , h ) u k ( x , η , h ) ( x ) ˜ + 5 u k ( x , η , h ) ( x ) 5 ˜ d η ,

where u k ( x , η , h ) ˜ is a restricted term, i.e., δ u k ( x , η , h ) ˜ = 0 . The significant value of λ ( η ) can be identified by making use of the variation theory [38,39,40]. While h is an auxiliary term which is utilized to ensure convergence of numerically obtained solution ideally by limiting norm 2 of residual error over the space of the given system. The ideal decision of this h improves the precision and proficiency of the algorithm. For the optimal solution of the auxiliary parameter, we define a residual function for the approximated solution:

(9) r p ( x , t , h ) = u p ( x , t , h ) ( t ) + α u p ( x , t , h ) 3 u p ( x , t , h ) ( x ) 3 + β u p ( x , t , h ) ( x ) 2 u p ( x , t , h ) ( x ) 2 + γ u p 2 ( x , t , h ) u p ( x , t , h ) ( x ) + 5 u p ( x , t , h ) ( x ) 5 ,

where p is the number of approximation. The square of function (9) for the pth-order approximation with respect to parameter h for ( x , t ) ϵ [ a , b ] × [ a , b ] is

(10) 1 ( b + 1 ) 2 i = a b j = a b ( r p ( i , j , h ) ) 2 1 / 2 .

The lowest value of function (10) occurs at the point which should be selected and that will be the value of auxiliary parameter h. In small domains where the standard VIA-II gives high-order accuracy, this technology gives the value of h as 1. Putting values of both the parameters in the recurrence relation, we get an iterative formula:

(11) u k + 1 ( x , t , h ) = u 0 ( x , t , h ) + h 0 t λ ( η ) α u k ( x , η , h ) 3 u k ( x , η , h ) ( x ) 3 + β u k ( x , η , h ) ( x ) 2 u k ( x , η , h ) ( x ) 2 + γ u k 2 ( x , η , h ) u k ( x , η , h ) ( x ) + 5 u k ( x , η , h ) ( x ) 5 d η .

Introducing with a proper initial guess, one can get the different approximations by utilizing the iterative structure (11). This gives an exact solution u ( x ) , when

(12) u ( x ) = lim k u k ( x ) .

This algorithm is named as MVIA-II. We utilize this modified algorithm for the solution of the different types of KdV equations of fifth order, which is able to provide numerical results for nonlinear and linear problems, in a direct way very accurately.

3 Numerical examples

In this section, MVIA-II is employed for the numerical treatment of the SK equation, C-D-G equation, a fifth-order KdV equation, Lax equation and Kawahara equation. Numerical and graphical results gained from the modified algorithm are very encouraging, empowering, noteworthy and significant. Illustrated test problems revealed the effectiveness and power of the suggested algorithm.

3.1 Test Problem 1

The SK equation (1) having the following initial condition

(13) u ( x , 0 ) = 2 k 2 sech 2 [ k ( x c ) ] ,

and the exact solution of SK equation (1) with condition (13) was given by [28]

(14) u ( x , t ) = 2 k 2 sech 2 [ k ( x 16 k 4 t c ) ] .

The numerical solutions for Test Problem 3.1 using MVIA-II are reported in Table 1 of SK equation (1). To show the efficiency and applicability of the proposed algorithm in comparison with adomian decomposition method (ADM) [24] and modified ADM [28], the absolute errors are reported in Tables 1 and 2 for various values of t and x and k = 0.01 and c = 0.0 . A full agreement between the results of MVI-II and exact solution is observed, which confirms the validity of the proposed algorithm. In comparison with [24,28] results, one can ensure that the results of MVIA-II are more efficient and reliable. Results are also shown through illustrations in Figure 1 at different times t and for different x, while the behaviour of approximate and exact solutions can be seen in Figure 2.

Table 1

Numerical results of SK equation (1) in terms of absolute error using MVIA-II for Test Problem 3.1

x/t t = 0.2 t = 0.4 t = 5.0
MVIA-II [28] MVIA-II [28] MVIA-II [28]
2 0 1.54499 × 10−18 2.71050 × 10−20 4.52654 × 10−18 2.71050 × 10−20 6.6435 × 10−15
4 2.71050 × 10−20 5.36680 × 10−18 5.42101 × 10−20 1.12757 × 10−17 8.13151 × 10−20 1.32874 × 10−14
6 2.71050 × 10−20 1.13841 × 10−17 2.71050 × 10−20 2.02746 × 10−17 8.13151 × 10−20 1.99336 × 10−14
8 2.71050 × 10−20 1.97054 × 10−17 5.42101 × 10−20 3.15232 × 10−17 5.42101 × 10−20 2.65820 × 10−14
10 5.42101 × 10−20 3.02492 × 10−17 5.42101 × 10−20 4.50486 × 10−17 5.42101 × 10−20 3.32327 × 10−14
Table 2

Comparison of numerical solutions of SK equation (1) for Test Problem 3.1

x/t t = 0.2 t = 0.4 t = 0.5
MVIA-II [24] MVIA-II [24] MVIA-II [24]
0.1 0 9.59980 × 10−16 2.71050 × 10−20 1.91996 × 10−15 8.13151 × 10−20 6.6435 × 10−15
0.2 2.71050 × 10−20 1.91996 × 10−15 8.13151 × 10−20 3.83989 × 10−14 5.42101 × 10−20 1.32874 × 10−14
0.3 2.71050 × 10−20 2.87980 × 10−15 2.71050 × 10−20 5.75966 × 10−14 5.42101 × 10−20 1.99336 × 10−14
0.4 2.71050 × 10−20 3.83973 × 10−15 2.71050 × 10−20 7.67932 × 10−14 5.42101 × 10−20 2.65820 × 10−14
0.5 2.71050 × 10−20 4.79941 × 10−15 5.42101 × 10−20 9.59871 × 10−14 5.42101 × 10−20 3.32327 × 10−14
Figure 1 
                  The exact and numerical solutions of Test Problem 3.1 for 
                        
                           
                           
                              t
                              =
                              10
                           
                           t=10
                        
                      and 
                        
                           
                           
                              k
                              =
                              0.01
                           
                           k=0.01
                        
                     .
Figure 1

The exact and numerical solutions of Test Problem 3.1 for t = 10 and k = 0.01 .

Figure 2 
                  Approximate (MVIA-II) (left) and exact (right) solutions of Test Problem 3.1 for 
                        
                           
                           
                              k
                              =
                              0.01
                           
                           k=0.01
                        
                      in space-time graph form.
Figure 2

Approximate (MVIA-II) (left) and exact (right) solutions of Test Problem 3.1 for k = 0.01 in space-time graph form.

3.2 Test Problem 2

The C-D-G equation (22) having the initial condition:

(15) u ( x , 0 ) = k 2 e k x ( 1 + e k x ) 2 ,

and the exact solution of equation (22) with condition (15) was given by [28]

(16) u ( x , t ) = k 2 e k ( x k 4 t ) ( 1 + e k ( x k 4 t ) ) 2 .

The obtained numerical results from the MVIA-II for Test Problem 3.2 are reported in Table 3. Results by MVIA-II show improvement over previous ones reported by [28]. Comparison of approximate and exact solutions is also shown through curves in Figure 3 for k = 0.01 and time t = 10 . While Figure 4 shows the behaviour of the approximate and exact solution, which indicates that the proposed algorithm is a powerful mathematical tool for getting an accurate numerical solution of the C-D-G equation. The results of the proposed algorithm (MVIA-II) are compared with the results of the modified Adomian decomposition method [28]. It is well defined from the numerical and graphical results that the performance of the proposed algorithm is more precise.

Table 3

Comparison of numerical solutions of C–D–G equation (22) for Test Problem 3.2

x/t t = 0.4 t = 0.8 t = 5.0
MVIA-II [28] MVIA-II [28] MVIA-II [28]
2 6.77626 × 10−21 3.11957 × 10−21 6.77626 × 10−21 1.7893 × 10−20 1.01643 × 10−20 2.48025 × 10−18
4 3.38813 × 10−21 2.70138 × 10−21 6.77626 × 10−21 2.01336 × 10−20 1.35525 × 10−20 5.70681 × 10−18
6 3.38813 × 10−21 5.50257 × 10−21 0 3.91461 × 10−20 1.01643 × 10−20 8.93996 × 10−18
8 6.77626 × 10−21 4.74685 × 10−21 6.77626 × 10−21 5.79897 × 10−20 6.77626 × 10−21 1.21797 × 10−17
10 3.38813 × 10−21 7.21049 × 10−21 6.77626 × 10−21 5.63358 × 10−20 3.38813 × 10−21 1.54092 × 10−17
Figure 3 
                  The exact and numerical solutions’ comparison of Test Problem 3.2 for 
                        
                           
                           
                              k
                              =
                              0.01
                           
                           k=0.01
                        
                      and time 
                        
                           
                           
                              t
                              =
                              10
                           
                           t=10
                        
                     .
Figure 3

The exact and numerical solutions’ comparison of Test Problem 3.2 for k = 0.01 and time t = 10 .

Figure 4 
                  Approximate (MVIA-II) (left) and exact (right) solutions of Test Problem 3.2 for 
                        
                           
                           
                              k
                              =
                              0.01
                           
                           k=0.01
                        
                     .
Figure 4

Approximate (MVIA-II) (left) and exact (right) solutions of Test Problem 3.2 for k = 0.01 .

3.3 Test Problem 3

The Lax equation (3) having the following initial condition

(17) u ( x , 0 ) = 2 k 2 [ 2 3 tanh 2 k ( x c ) ] ,

and the exact solution of Lax’s equation with condition (17) was given by [28]

(18) u ( x , t ) = 2 k 2 [ 2 3 tanh 2 k ( x 56 k 4 t c ) ] .

From the numerical results of Test Problem 3.3 using MVIA-II reported in Table 4, we conclude that the obtained results are in quite agreement with the exact and with the results given in [28]. Figure 5 shows the comparison of approximate and exact solutions of Test Problem 3.3 for t = 10 and k = 0.01 .

Table 4

Comparison of numerical solutions of Lax equation (3) for Test Problem 3.3

x/t t = 0.2 t = 0.8 t = 5.0
MVIA-II [28] MVIA-II [28] MVIA-II [28]
2 5.75976 × 10−14 5.76197 × 10−14 2.30320 × 10−13 2.30342 × 10−13 1.42090 × 10−12 1.42104 × 10−12
4 1.15193 × 10−13 1.15281 × 10−13 4.60638 × 10−13 4.60727 × 10−13 2.84181 × 10−12 2.84213 × 10−12
6 1.72787 × 10−13 1.72985 × 10−13 6.90954 × 10−13 6.91153 × 10−13 4.26271 × 10−12 4.26326 × 10−12
8 2.30378 × 10−13 2.30730 × 10−13 9.21269 × 10−13 9.21621 × 10−13 5.68361 × 10−12 5.68444 × 10−12
10 2.87968 × 10−13 2.88518 × 10−13 1.15158 × 10−12 1.15213 × 10−12 7.10452 × 10−12 7.10566 × 10−12
Figure 5 
                  Exact and approximate solutions’ comparison of Test Problem 3.3 for 
                        
                           
                           
                              t
                              =
                              10
                           
                           t=10
                        
                      and 
                        
                           
                           
                              k
                              =
                              0.01
                           
                           k=0.01
                        
                     .
Figure 5

Exact and approximate solutions’ comparison of Test Problem 3.3 for t = 10 and k = 0.01 .

3.4 Test Problem 4

Consider the following KdV equation of fifth order (4)

(19) u t + u u x u 3 u x 3 + 5 u x 5 = 0 ,

having the initial condition:

(20) u ( x , 0 ) = e x ,

and the exact solution of this fKdV equation (4) with condition (20) was given by [18]

(21) u ( x , t ) = e x t .

The numerical simulation for different values of times t and x of Test Problem 3.4 using the MVIA-II is reported in Table 5. In the Tables 5 and 6, the comparison of numerical results of the MVIA-II, Laplace decomposition method [17] and homotopy perturbation transform method [18] for Test Problem 3.4 has been carried out. Comparison of numerical results is shown in Table 5 in terms of absolute errors for second-, fourth- and sixth-order approximations, as the order of approximation increases, the order of accuracy increases and results converge to exact solution and to show the efficiency and reliability of the MVIA-II for different values of x and t, the comparison of errors with a well-known method for nonlinear PDEs, the Laplace decomposition method, is shown in Table 6.

Table 5

Comparison of numerical solutions of fKdV equation (4) for Test Problem 3.4

x/t E 2 E 4 E 6
MVIA-II [18] MVIA-II [18] MVIA-II [18]
0.1 4.03000 × 10−7 4.03001 × 10−7 2.03168 × 10−10 2.04000 × 10−10 4.85726 × 10−14 2.00000 × 10−12
0.2 3.14615 × 10−6 3.14615 × 10−6 6.39581 × 10−9 6.39800 × 10−9 6.14129 × 10−12 8.00000 × 10−12
0.3 1.03656 × 10−5 1.03656 × 10−5 4.77886 × 10−8 4.77890 × 10−8 1.03657 × 10−10 1.03000 × 10−10
0.4 2.39942 × 10−5 2.39942 × 10−5 1.98186 × 10−7 1.98186 × 10−7 7.67223 × 10−10 7.66000 × 10−10
0.5 4.57809 × 10−5 4.57809 × 10−5 5.95330 × 10−7 5.95333 × 10−7 3.61487 × 10−9 3.61500 × 10−9
Table 6

Comparison of absolute errors of the MVIA-II and the Laplace decomposition method for sixth approximation

t/x x = 1.0 x = 1.5 x = 2.5
MVIA-II [17] MVIA-II [17] MVIA-II [17]
0.01 0 4.38198 × 10−11 0 3.32204 × 10−10 3.55271 × 10−15 4.25802 × 10−9
0.02 8.88178 × 10−16 7.22467 × 10−11 8.88178 × 10−16 5.47711 × 10−10 3.55271 × 10−15 7.02029 × 10−9
0.03 1.19904 × 10−14 1.19114 × 10−10 1.95399 × 10−14 9.03023 × 10−10 4.97379 × 10−14 1.15745 × 10−8
0.04 8.79296 × 10−14 1.96387 × 10−10 1.43884 × 10−13 1.48883 × 10−9 3.94351 × 10−13 1.90831 × 10−8
0.05 4.19220 × 10−13 3.23787 × 10−10 6.90114 × 10−13 2.45467 × 10−9 1.87405 × 10−12 3.14627 × 10−8

3.5 Test Problem 5

Consider the Kawahara equation (5)

(22) u t + u u x + 3 u x 3 5 u x 5 = 0 ,

having the initial condition:

(23) u ( x , 0 ) = 105 169 sech 4 x x 0 2 13 ,

and the exact solution of equation (5) with condition (23) was given by [18]

(24) u ( x , t ) = 105 169 sech 4 1 2 13 x x 0 + 36 t 169 .

To demonstrate the applicability and efficiency of our modified algorithm, we report the absolute errors for different values of x and t in Table 7. From the tabulated data and Figure 6, one can observe that the numerical and exact solutions by MVIA-II for the Kawahara equation (5) for different time levels with x = 6.0 are in good agreement for second-order approximation.

Table 7

Comparison of numerical solutions of Kawahara equation (5) for Test Problem 3.5

x/t MVIA-II [18]
0.1 2.96656 × 10−8 1.64944 × 10−6
0.2 2.37783 × 10−7 6.67437 × 10−6
0.3 8.04066 × 10−7 1.51864 × 10−5
0.4 1.90959 × 10−6 2.72977 × 10−5
0.5 3.73681 × 10−6 4.31169 × 10−5
Figure 6 
                  Absolute erorr graph for the Test Problem 3.5.
Figure 6

Absolute erorr graph for the Test Problem 3.5.

4 Conclusion

This article shows that the MVIA-II is very proficient, reliable and practically well suited for use in finding new traveling wave solutions for the higher order differential equations of KdV type. The reliability and accuracy of the method, as well as the decrease in the size of computational work, give this modified algorithm a more extensive pertinency. The results demonstrate that the algorithm is reliable and effective and gives more accurate solutions. This modified algorithm makes easy the computational work for solving nonlinear problems that emerge in science and engineering, and results of high degree accuracy can be obtained in few iterations as compared to earlier methods, and we hope that the obtained results will be useful for further studies in scientific materials science and engineering.

Acknowledgments

Hijaz Ahmad thanks Prof. Dr. Ji-Huan He for appreciation and continuous helpful discussions and advice. All the authors thank the Editor-in-Chief and unknown referees for the fruitful comments and significant remarks that helped them in improving the quality and readability of the paper, which led to a significant improvement of the paper. This work was supported by the National Natural Science Foundation of China (No. 71601072) and the Key Scientific Research Project of Higher Education Institutions in Henan Province of China (No. 20B110006).

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Received: 2019-11-15
Revised: 2020-04-28
Accepted: 2020-05-22
Published Online: 2020-07-22

© 2020 Hijaz Ahmad et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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