Research Article | Open Access

Volume 2017 |Article ID 4925914 | https://doi.org/10.1155/2017/4925914

Oluwaseun Adeyeye, Zurni Omar, "Solving Nonlinear Fourth-Order Boundary Value Problems Using a Numerical Approach: th-Step Block Method", International Journal of Differential Equations, vol. 2017, Article ID 4925914, 9 pages, 2017. https://doi.org/10.1155/2017/4925914

# Solving Nonlinear Fourth-Order Boundary Value Problems Using a Numerical Approach: th-Step Block Method

Revised29 Aug 2017
Accepted03 Oct 2017
Published29 Oct 2017

#### Abstract

Nonlinear boundary value problems (BVPs) are more tedious to solve than their linear counterparts. This is observed in the extra computation required when determining the missing conditions in transforming BVPs to initial value problems. Although a number of numerical approaches are already existent in literature to solve nonlinear BVPs, this article presents a new block method with improved accuracy to solve nonlinear BVPs. A -step block method is developed using a modified Taylor series approach to directly solve fourth-order nonlinear boundary value problems (BVPs) where is the order of the differential equation under consideration. The schemes obtained were combined to simultaneously produce solution to the fourth-order nonlinear BVPs at points iteratively. The derived block method showed improved accuracy in comparison to previously existing authors when solving the same problems. In addition, the suitability of the -step block method was displayed in the solution for magnetohydrodynamic squeezing flow in porous medium.

#### 1. Introduction

Boundary value problems (BVPs) arise in several branches of science ranging from physical sciences to engineering. There has been commendable progress in solving problems associated with nonlinear ordinary differential equations (ODEs) involving boundary conditions in recent years. These ODEs are sometimes needed to fulfil certain boundary conditions at more than one point of the independent variable which will result in the problem known as two-point boundary value problem. Two-point nonlinear BVPs often cannot be solved by analytical methods and thus finding approximate solutions for these problems becomes essential.

This article considers the following special type of nonlinear boundary value problem: with the boundary conditions where is a continuous function on and the parameters and for are constants.

A variety of methods have been introduced to solve (1) such as shooting methods, splines methods, finite difference methods, finite element methods, differential transform methods, and collocation methods . Recently, the adoption of various families of linear multistep method (LMM) for numerically approximating higher order ODEs has been proposed. However, some LMMs cannot directly solve these higher order ODEs and thus require reduction to a system of first-order ODEs. In some cases, the accuracy of LMMs is low such as the case of predictor-corrector methods which incur high computational rigour. This computational rigour involves the derivation of separate predictors for each grid point of the LMM as seen in the work of Kayode and Adeyeye  and Kayode and Obarhua . These drawbacks caused for the introduction of block methods which were first proposed by Milne  as a means to obtain starting values for predictor-corrector methods. This concept was also further explored by Sarafyan .

Block methods differ from alternate approaches such as differential transform method and collocation method. This is because the formulation of block methods is an evaluation of the linear multistep method at different grid points to generate a family of methods that can be applied to produce approximate solutions of ODEs at each grid point simultaneously. This advantage was mentioned by Lambert  among other advantages which include being self-starting, permitting easy change of step-length, and being less expensive in terms of function evaluations. Block methods also yield better accuracy when applied to numerical problems.

This article introduces a -step block method where is the order of the differential equation. The block method is developed using a modification of the conventional Taylor series expansions approach by Lambert . This derivation is shown in Section 2 of this article while Section 3 considers certain numerical examples and their results to show the accuracy of the block method.

#### 2. Methodology

Lambert  highlighted three main approaches for developing LMMs. These include interpolation, numerical integration, and Taylor series expansions. This article adopts the Taylor series expansion approach for LMMs to develop the block method. However, certain modifications were introduced since Lambert  focused on first-order methods whereas this article develops a block method for fourth-order ODEs. Therefore, the approach was made suitable to develop block methods and not LMMs alone, hence the name modified Taylor series approach.

##### 2.1. Derivation of the th-Step Block Method Using Modified Taylor Series Approach

The algorithm described below is used to show the steps involved in deriving the -step block method using the modified Taylor series approach where is the order of the differential equation.

Algorithm 1.
Start.
Step 1. Obtain the coefficients of the initial multistep scheme: .
Step 2. Obtain the coefficients of the additional schemes: Step 3. Derive the coefficients of the derivative schemes: where .
Step 4. Combine schemes obtained in Steps , , and above to form a system of equations with matrix form equivalent where and , , ,
Step 5. Adopt matrix inverse approach to system of equations in Step to obtain the expected block method.
Stop.

In Algorithm 1, , , and and are constants with defined in Step of Algorithm 1.

Note that in Step of Algorithm 1, the expected are . The -values can take forms and -values not chosen will be used as evaluation points when developing the additional methods in Step .

Steps of Algorithm 1 require expanding individual terms using Taylor Series expansion such as Substituting these expansions in individual equations and equating coefficients of presents the resulting expressions in matrix form where Note that Algorithm 1 will not successfully obtain the required block method if matrix is singular. Thus, the nonsingularity of the resulting matrices is discussed.

##### 2.2. Nonsingularity of Resulting Matrices

The matrix in (7) is a square matrix with which follows from the theorems below.

Theorem 2. Suppose that is a square matrix with a row where every entry is zero, or a column where every entry is zero. Then .

Theorem 3. Suppose that is a square matrix with two equal rows, or two equal columns. Then .

With respect to Theorem 2, since the matrix does not have a row or column where every entry is zero, then its inverse exists. On the other hand, matrix has no equal rows or columns which further affirms that its inverse exists.

Theorems 2 and 3 are sufficient conditions to show that the inverse of the resulting matrix will always exist. In addition, the case of linear dependency is considered as defined in the following theorems.

Theorem 4. If matrix has linearly dependent columns, then .

Theorem 5. The rank of a matrix equals the maximum number of linearly independent column vectors. The matrix has the same number of linearly independent row vectors as it has linearly independent column vectors

Thus, Theorem 5 is tested for resulting matrices obtained in developing the -step block method to show that exists.

##### 2.3. Specification of the th-Step Block Method

Following Algorithm 1, the specification of the -step block method is as follows. From Step , the initial multistep scheme for the -step block method in terms of is Now, considering (9), the individual terms are expanded using Taylor series expansion as defined in (6). The resulting expansions are substituted back in (9) and rewritten in matrix form , where where matrix has which implies that there are no linearly dependent columns or rows and the inverse exists. This follows from the theorems in Section 2.2 showing that the matrix is nonsingular. Therefore, the scheme in (9) is obtained using matrix inverse method and substituting the value of as Following the subsequent steps of Algorithm 1, the specification of the -step block method is as follows:

##### 2.4. Order and Stability Properties of the th-Step Block Method

To ensure convergence of the block method, its consistency and zero-stability need to be investigated. This follows from Fatunla (1988) which states that a linear multistep method is convergent iff it is consistent and zero-stable.

Starting with the consistency property, a linear multistep method is consistent if it has order . Thus, the order of the -step block method is investigated.

With reference to the definition in Lambert , Henrici , and Butcher , the order and error constant of the -step block method follow Definition 6.

Definition 6. The linear operator associated with LMM is defined as On expanding and to obtain where the method is said to be of order if , and is the error constant.

The integrators of the block method (12) are of order six methods with the error constants, obtained as , , , , and , respectively. Having order , the consistency of the block method is affirmed.

Moving on to the second criterion for convergence which is the zero-stability of the block method. Note that this is the most important stability property a good numerical method should possess as it ensures convergence. The key word “zero” is based on the stability phenomenon in terms of convergence in the limit as step-size () tends to zero.

Therefore, to test the zero-stability of the -step block method, the integrators are normalized to give the first characteristic polynomial as with identity matrixThe roots of satisfy . Hence, the -step block method is zero-stable.

#### 3. Results and Discussion

This section tests the -step block method on some nonlinear problems. The numerical results are shown in Tables 13 and Figures 13.

 Exact solution Computed solution Error  Error (-step block method) 0.0 0.00000000000 0.00000000000 0.1 0.09531017980 0.09531018728 0.0002954265 0.2 0.18232155679 0.18232159067 0.0008719341 0.3 0.26236426447 0.26236434273 0.0014096072 0.4 0.33647223662 0.33647237832 0.0017352146 0.5 0.40546510811 0.40546533420 0.0017810699 0.6 0.47000362925 0.47000394905 0.0015577013 0.7 0.53062825106 0.53062863602 0.0011349902 0.8 0.58778666490 0.58778704558 0.0006286279 0.9 0.64185388617 0.64185415218 0.0001902154 1.0 0.69314718056 0.69314718056
 Exact solution Computed solution Error  Error (-step block method) 0.0 0.00000000000 0.00000000000 0.1 0.01981000000 0.01981000000 0.0004095 0.2 0.07712000000 0.07712000000 0.0025752 0.3 0.16623000000 0.16623000000 0.0066432 0.4 0.27904000000 0.27904000000 0.0115595 0.5 0.40625000000 0.40625000000 0.0156708 0.6 0.53856000000 0.53856000000 0.0173246 0.7 0.66787000000 0.66787000000 0.0154706 0.8 0.78848000000 0.78848000000 0.0102612 0.9 0.89829000000 0.89829000000 0.0036517 1.0 1.00000000000 1.00000000000
 Exact solution Error [] Error (-step block method) 0.0 0 0 0 0.1 0.085233703438701791 0.2 0.171320454429454980 0.3 0.259121838110931650 0.4 0.349516600242079760 0.5 0.443409441985037010 0.6 0.541740074458440520 0.7 0.645492623682151550 0.8 0.755705480041236500 0.9 0.873481690845957730 1.0 1.000000000000000000

Example 7. Consider the following nonlinear boundary value problem : with boundary conditions The exact solution of Example 7 is . The obtained numerical results for this problem are presented in Table 1 with . The maximum absolute error obtained by the -step block method is which is more accurate than the maximum error of by Mustafa et al. . The graphical comparison between exact and computed solution is shown in Figure 1.

Example 8. Consider the following nonlinear boundary value problem : with boundary conditions The exact solution of Example 8 is