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The aim of this paper is to derive a numerical scheme for Troesch’s problem and to overcome the difficulty which faces the existing numerical methods when considering the Troesch’s problem with large values of λ. A logarithmic finite difference method is derived for solving the Troesch’s problem. The method is very simple and works well for arbitrarily large values of the Troesch’s parameter. To test the proposed method, we have used a wide range of the Troesch’s parameter λ. A comparison with some existing methods is given. The numerical results show the robustness and the superiority of the proposed scheme over most of the existing numerical methods for the Troesch’s problem.

Troesch’s problem arises in the investigation of the confinement of plasma column by radiation pressure and is defined by

u ″ = λ sinh ( λ u ) (1)

subject to the boundary conditions

u ( 0 ) = 0 and u ( 1 ) = 1 (2)

The main difficulty associated with Troesch’s problem is the boundary layer near x = 1 . Accordingly, many researchers try to solve this equation numerically, some of these methods are: Sinc-Collocation method [

This paper is devoted to derive a logarithmic finite difference method as a new method to solve the Troesch’s problem for arbitrarily large parameter λ.

The paper remaining of this paper is organized as follows. In Section 2, we derive the numerical scheme as well as a fourth order finite difference scheme. Section 3 is devoted for the numerical results and comparisons with some existing methods. Concluding remarks are given in Section 4.

In order to derive the numerical method for solving Troesch’s, we present the following definition.

Definition: The inverse sine hyperbolic function is defined by

s i n h − 1 x = l n ( x + x 2 + 1 ) , x ∈ ℝ (3)

and its derivative is defined by

d d x ( sinh − 1 x ) = 1 1 + x 2 . (4)

By using the previous definition, the Troesch’s problem (1) can be written as

u = 1 λ sinh − 1 ( λ u ″ ) = 1 λ ln ( λ u ″ + ( λ u ″ ) 2 + 1 ) , (5)

which is the main point in this paper.

Using uniform mesh including ( n + 1 ) points 0 = x 0 < x 1 < x 2 < ⋯ < x n = 1 , such that x i = i h , i = 0 , 1 , ⋯ , n , where h = 1 n , α − 1 = λ h 2 . We denote the exact and the numerical solutions respectively by u ( x i ) and U i at the grid point x i . Now by using the second order central finite difference approximation for the second derivative

u ″ ( x i ) ≃ 1 h 2 ( U i + 1 − 2 U i + U i − 1 ) (6)

into the Equation (5), this will lead us to the logarithmic finite difference method

ln [ α ( U i + 1 − 2 U i + U i − 1 ) + [ α ( U i + 1 − 2 U i + U i − 1 ) ] 2 + 1 ] − λ U i = 0 , i = 1 , 2 , ⋯ , n − 1 (7)

subject to the boundary conditions

U 0 = 0 and U n = 1. (8)

The resulting system in (7) represents a nonlinear tridiagonal system in the unknown solution { U i } i = 1 n − 1 . The details of the logarithmic can be given displayed by the following algorithm.

Algorithm 1. Logarithmic method

1) For i = 1 using (7) and the boundary condition U 0 = 0 , we obtain

ln [ α ( U 2 − 2 U 1 ) + [ α ( U 2 − 2 U i ) ] 2 + 1 ] − λ U 1 = 0 (9)

2) For i = 2 , 3 , ⋯ , n − 2 we use

ln [ α ( U i + 1 − 2 U i + U i − 1 ) + [ α ( U i + 1 − 2 U i + U i − 1 ) ] 2 + 1 ] − λ U i = 0 , i = 2 , ⋯ , n − 2 (10)

3) For i = n − 1 using (7) and the boundary condition U n = 1 to obtain

ln [ α ( 1 − 2 U n − 1 + U n − 2 ) + [ α ( 1 − 2 U n − 1 + U n − 2 ) ] 2 + 1 ] − λ U n − 1 = 0 (11)

Newton’s method is used to solve the nonlinear system which can be described in the following way.

Algorithm 2. Newton’s method

1) For s = 0 , 1 , 2 , ⋯ .

2) Solve the tridiagonal linear system

J Z = F ( U (s) )

for the unknown vector Z using Crout’s method. The elements of the function F i ( U i ) and the Jacobian matrix J are defined as follows

F i ( U i ) = l n [ α ( U i + 1 − 2 U i + U i − 1 ) + [ α ( U i + 1 − 2 U i + U i − 1 ) ] 2 + 1 ] − λ U i = 0, i = 1, ⋯ , n − 1 (12)

and the Jacobian matrix has the tridiagonal structure

J = [ b 1 c 1 a 2 b 2 c 2 a i b i c i a n − 2 b n − 2 c n − 2 a n − 1 b n − 1 ]

with elements defined by

a i = α [ α ( U i + 1 − 2 U i + U i − 1 ) ] 2 + 1 , i = 2 , 3 , ⋯ , n − 1

b i = − λ − 2 α [ α ( U i + 1 − 2 U i + U i − 1 ) ] 2 + 1 , i = 1 , 2 , 3 , ⋯ , n − 1

c i = α [ α ( U i + 1 − 2 U i + U i − 1 ) ] 2 + 1 , i = 1 , 2 , 3 , ⋯ , n − 2

3) Update your solution by using

U ( s + 1 ) = U ( s ) − Z

4) Repeat steps (1)-(3) till the following condition

| U ( s + 1 ) − U ( s ) | ∞ ≤ ϵ

is satisfied. The constant ϵ is assumed to be small.

The initial guess vector is taken as the linear interpolation between the given boundary conditions, and has the following form

U i ( 0 ) = x i = i h , i = 1 , 2 , ⋯ , n − 1

The previous method is of second order accuracy and it works for wide range values of λ.

A fourth order finite difference method can be used for solving the Troche’s method for limited values of λ. This method can be given as follows.

By using the forth order approximation of the second derivative

u ″ ( x i ) ≃ δ 2 1 + 1 12 δ 2 U i , (13)

where δ 2 U i = U i − 1 − 2 U i + U i + 1

Using this approximation and after some manipulation we will end with the following scheme

U i − 1 − 2 U i + U i + 1 = ω [ sinh ( λ U i − 1 ) + 10 sinh ( λ U i ) + sinh ( λ U i + 1 ) ] , (14)

for i = 1,2, ⋯ , n − 1, where w = 1 12 λ h 2 , (15)

U 0 = 0 and U n = 1 (16)

The resulting method (14) is of fourth order accuracy. The numerical solution { U i } i = 1 n − 1 in this case can be also obtained by solving the nonlinear tridiagonal system. Newton’s method is used to solve this system as we did before.

To test the efficiency of the proposed method, we choose h = 0.0005 and ϵ = 10 − 12 , for the moderate values of λ = 0.5 , 1 , 5 , 10 . Comparison with some existing methods is given in Tables 1-5. In

For large values of λ, we choose h = 0.0001 . In ^{6}, the method won in this case as well and the results are given in

Finally, we tested the fourth order method and make some comparison with the logarithmic method, we have noticed that, the results produced by this method are quite good for the small values of λ only, see

x | Logarithmic method | FDM [ | Homotopy [ | Doha [ | Exact |
---|---|---|---|---|---|

0.1 | 0.0959443493 | 0.0959443492 | 0.0959395656 | 0.0959443493 | 0.0959443493 |

0.2 | 0.1921287477 | 0.1921287476 | 0.1921193244 | 0.1921287477 | 0.1921287477 |

0.3 | 0.2887944010 | 0.2887944007 | 0.2887806940 | 0.2887944009 | 0.2887944009 |

0.4 | 0.3861848465 | 0.3861848462 | 0.3861675428 | 0.3861848464 | 0.3861848464 |

0.5 | 0.4845471649 | 0.4845471645 | 0.4845274183 | 0.4845471647 | 0.4845471647 |

0.6 | 0.5841332486 | 0.5841332482 | 0.5841127822 | 0.5841332484 | 0.5841332484 |

0.7 | 0.6852011485 | 0.6852011481 | 0.6851822495 | 0.6852011483 | 0.6852011483 |

0.8 | 0.7880165228 | 0.7880165225 | 0.7880018367 | 0.7880165227 | 0.7880165227 |

0.9 | 0.8928542162 | 0.8928542161 | 0.8928462193 | 0.8928542161 | 0.8928542161 |

x | Logarithmic method | FDM [ | Homotopy [ | Doha [ | Exact |
---|---|---|---|---|---|

0.1 | 0.0846612572 | 0.0846612556 | 0.0843817004 | 0.0846612566 | 0.0846612565 |

0.2 | 0.1701713594 | 0.1701713565 | 0.1696207644 | 0.1701713585 | 0.1701713582 |

0.3 | 0.2573939098 | 0.2573939059 | 0.2565929224 | 0.2573939084 | 0.2573939080 |

0.4 | 0.3472228573 | 0.3472228528 | 0.3462107378 | 0.3472228556 | 0.3472228551 |

0.5 | 0.4405998376 | 0.4405998333 | 0.4394422743 | 0.4405998361 | 0.4405998351 |

0.6 | 0.5385344007 | 0.5385343971 | 0.5373300622 | 0.5385343987 | 0.5385343980 |

0.7 | 0.6421286118 | 0.6421286094 | 0.6410104651 | 0.6421286100 | 0.6421286091 |

0.8 | 0.7526080962 | 0.7526080954 | 0.7517335467 | 0.7526080957 | 0.7526080939 |

0.9 | 0.8713625212 | 0.8713625215 | 0.8708835371 | 0.8713625206 | 0.8713625196 |

x | Logarithmic method | B-spline [ | Collocation [ | Doha [ | Fortran code [ |
---|---|---|---|---|---|

0.2 | 0.01075346 | 0.01002027 | 0.00762552 | 0.01078872 | 0.01075342 |

0.4 | 0.03320065 | 0.03099793 | 0.03817903 | 0.03338672 | 0.03320051 |

0.8 | 0.25821762 | 0.24170496 | 0.23252435 | 0.25956596 | 0.25821664 |

0.9 | 0.45506203 | 0.42461830 | 0.44624551 | 0.45706638 | 0.45506034 |

x | Logarithmic method | Temimi [ | Scott [ |
---|---|---|---|

0.1 | 0.0000421216 | 0.0000421119 | 0.0000421118 |

0.2 | 0.0001299941 | 0.0001299641 | 0.0001299639 |

0.3 | 0.0003592878 | 0.0003589784 | 0.0003589779 |

0.4 | 0.0009781262 | 0.0009779027 | 0.0009779014 |

0.5 | 0.0026596252 | 0.0026590204 | 0.0026590172 |

0.6 | 0.0072305680 | 0.0072289310 | 0.0072289247 |

0.7 | 0.0196685008 | 0.0196640631 | 0.0196640603 |

0.8 | 0.0537425197 | 0.0537303294 | 0.0537303296 |

0.9 | 0.1521512477 | 0.1521140764 | 0.1521140787 |

x | Logarithmic method | Temimi [ |
---|---|---|

0.1 | 0.0000000000 | 0.0000000000 |

0.6 | 0.0000000000 | 0.0000000000 |

0.8 | 0.0000004122 | 0.0000004096 |

0.9 | 0.0001663062 | 0.0001652505 |

0.95 | 0.0033431273 | 0.0033218844 |

0.97 | 0.0111942916 | 0.0111219726 |

0.98 | 0.0208631539 | 0.0207221628 |

0.99 | 0.0414467260 | 0.0411119440 |

1.0 | 1.0 | 1.0 |

λ = 100 | λ = 150 | λ = 200 | |||
---|---|---|---|---|---|

x | Logarithmic method | x | Logarithmic method | x | Logarithmic method |

0.0 | 0.0000000000 | 0.0 | 0.0000000000 | 0.0 | 0.0000000000 |

0.8 | 0.0000000000 | 0.8 | 0.0000000000 | 0.8 | 0.0000000000 |

0.9 | 0.0000018373 | 0.9 | 0.0000000083 | 0.9 | 0.0000000000 |

0.95 | 0.0002726798 | 0.95 | 0.0000150234 | 0.95 | 0.0000009314 |

0.97 | 0.0020165050 | 0.98 | 0.0013534673 | 0.98 | 0.0003757441 |

0.98 | 0.0055113791 | 0.995 | 0.0139850466 | 0.995 | 0.0079377249 |

0.99 | 0.0156381379 | 0.998 | 0.0206481079 | 0.998 | 0.0168600022 |

0.999 | 0.0623708204 | 0.999 | 0.0362793031 | 0.999 | 0.0243867532 |

1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |

λ = 300 | λ = 1000 | ||
---|---|---|---|

x | Logarithmic method | x | Logarithmic method |

0.0 | 0.0000000000 | 0.0 | 0.0000000000 |

0.8 | 0.0000000000 | 0.98 | 0.0000000000 |

0.9 | 0.0000000000 | 0.99 | 0.0000002099 |

0.95 | 0.0000000042 | 0.995 | 0.0000310894 |

0.97 | 0.0000017119 | 0.996 | 0.0000844853 |

0.98 | 0.0000343798 | 0.997 | 0.0002297760 |

0.99 | 0.0006910751 | 0.998 | 0.0006287443 |

0.995 | 0.0031516136 | 0.995 | 0.0018073693 |

1.0 | 1.0 | 1.0 | 1.0 |

λ | x | Logarithmic method |
---|---|---|

10^{4} | 0.9999 | 0.0009901753 |

10^{5} | 0.9999 | 0.0000760075 |

10^{6} | 0.9999 | 0.0000052983 |

λ | Fourth order method | Logarithmic method |
---|---|---|

0.5 | 0.8928542161 | 0.8928542162 |

1.0 | 0.8713625198 | 0.8713625212 |

5.0 | 0.4550600270 | 0.4550601074 |

10.0 | 0.1521129902 | 0.1521512477 |

In this work, we have derived a logarithmic finite difference method to overcome the difficulty in solving the Troesch’s problem for large values of λ. This progress is very important, since all existing methods were trying to obtain the numerical solution for Troesch’s problem for large values of λ. The logarithmic method which we have derived is able and succeeds to get the numerical solution of Troesch’s problem for λ = 0.5 , 5 , 10 , ⋯ , 10 6 . To recap things, a new logarithmic finite difference method is derived and can provide the numerical solutions for large values of λ. I think that, to the best of our knowledge, the method and some of the given results are published for the first time.

Ismail, M.S. and Al-Basyoni, K.S. (2018) A Logarithmic Finite Difference Method for Troesch’s Problem. Applied Mathematics, 9, 550-559. https://doi.org/10.4236/am.2018.95039