Journal of Computational Methods in Physics

Volume 2016 (2016), Article ID 3698251, 5 pages

http://dx.doi.org/10.1155/2016/3698251

## On the Use of Recursive Evaluation of Derivatives and Padé Approximation to Solve the Blasius Problem

School of Computing, University of North Florida, Jacksonville, FL 32224, USA

Received 25 October 2015; Accepted 30 December 2015

Academic Editor: Mikhail Tokar

Copyright © 2016 Asai Asaithambi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The Blasius problem is one of the well-known problems in fluid mechanics in the study of boundary layers. It is described by a third-order ordinary differential equation derived from the Navier-Stokes equation by a similarity transformation. Crocco and Wang independently transformed this third-order problem further into a second-order differential equation. Classical series solutions and their Padé approximants have been computed. These solutions however require extensive algebraic manipulations and significant computational effort. In this paper, we present a computational approach using algorithmic differentiation to obtain these series solutions. Our work produces results superior to those reported previously. Additionally, using increased precision in our calculations, we have been able to extend the usefulness of the method beyond limits where previous methods have failed.

#### 1. Introduction

The boundary value problem described by is called the Blasius problem [1]. It is one of the well-known problems in fluid mechanics in the study of boundary layers.

Blasius [1], Howarth [2], and Asaithambi [3] provide direct analytical and numerical treatments of (1). For instance, with , Blasius [1] obtained the series solution with and representing the unknown . For this case (), Howarth [2] computed the numerical value . Liao [4] takes the approach of the Homotopy Analysis Method (HAM) and obtains “an explicit, totally analytic approximate solution” for the Blasius problem with .

Asaithambi [3] solved numerically the problem corresponding to using Taylor series and shooting and obtained . Using suitable substitutions, Fang et al. [5] have shown that it suffices to consider the case . Using Fang et al. [5], it is easy to see that the value corresponds to . Thus the numerical value corresponding to Asaithambi [3] will be which is in agreement with Howarth [2]. Thus, for the rest of this paper, we will consider only the case corresponding to .

Using the transformation Wang [6] reduced the problem (1) (with ) to subject to the boundary conditions It has been recently reported [7, 8] that Crocco [9] had used transformation (4) already in the 1940s. Since no closed-form solutions are available in general, the search for efficient series and numerical solutions to (5)-(6) has been an active research topic.

Ahmad [7] obtains a series solution for (5) as where ’s satisfy with , , and . Expanded through the first few terms, this solution looks like This series appears to be identical to the one obtained by Wang [6] who used Adomian decomposition. All of these researchers who obtained the series in terms of then proceeded to solve for by satisfying the boundary condition . This, in essence, is the approach known as* shooting* in numerical analysis.

Ahmad [7] obtains an increasing sequence of approximations to and a decreasing sequence and concludes that satisfies . Hashim [10] and Ahmad and Albarakati [11] use a Padé approximation to improve upon the results of Wang [6] and Ahmad [7], respectively, but report that as the number of terms in the series solution increases, the accuracy of the produced by the corresponding Padé approximation suffers.

In this paper, we develop a shooting method that successfully obtains Taylor series expansions of arbitrarily large orders by computing exact derivatives, not approximations to the derivatives, directly by using recursive formulas derived from the differential equation itself. Also, our approach does not require the use of symbolic manipulation packages as it does not involve extensive algebraic manipulations. Finally, by using increased precision, we are able to obtain superior Padé approximants to the solution as well.

#### 2. Method of Solution

As with any shooting method, we convert the two-point boundary-value problem to an initial-value problem and determine an appropriate set of boundary conditions at one end so that the boundary condition at the other end is satisfied. To be specific, we will solve (5)-(6) by supplying the initial value and a suitable value for to obtain a solution for which the boundary condition in (6) is satisfied.

Accordingly, we begin by letting and obtain the solution of (5)-(6) as Taylor series expansion of degree . In other words, we first solve the initial value problem subject to the conditions In order to indicate its dependence on both and , we denote the solution thus obtained as . Now, if we let then we wish to obtain for which .

Our method will begin by obtaining the th-order Taylor series solution of (10)-(11) in the form in which the notation has been used. The quantity is called the th Taylor coefficient for around . Thus, we wish to obtain the Taylor coefficients for for arbitrary .

For this purpose in relation to this problem, we will use the following formulas.(1)For two functions and satisfying , (2)For any two functions and , The formulas (15)-(16) are examples of algorithmic/automatic differentiation formulas, which may be used to evaluate successive Taylor coefficients recursively without deriving symbolic expressions for the corresponding derivatives. Note that these formulas evaluate the* exact* derivatives and are not comparable to any finite-difference formulas (numerical), nor are they formulas that require the use of symbolic manipulation systems. A more extensive yet preliminary treatment of automatic differentiation (recursive evaluation of Taylor coefficients) may be found in Moore [12] and other references cited there.

##### 2.1. Evaluation of Taylor Coefficients

We rewrite (10)-(11) as an equivalent system in the form Using (15)-(16) in (17) yields, for , In (18), we have introduced a temporary variable to make the presentation of the calculation easier to understand. The initial conditions and in (17) now take the form Also, note that the Taylor coefficients for the function are given by Therefore, with an initial “guess” for , it is possible to compute the numerical values of and for arbitrary recursively. A quick examination of the calculations for the present problem shows that for . In other words, the only nonzero terms in the Taylor series are those that correspond to powers of that are multiples of 3 (the constant term , the term, the term, etc.).

##### 2.2. Removing the Dependence on the Initial Guess for

We realized that it will be useful to explicitly separate the dependence of these coefficients on so that the series we obtain may be compared with those obtained previously. As we proceeded to do this, we discovered an alternate approach to solving for .

As a clarification for this, let us rewrite (9) as Comparing (21) with (9) term by term yields, for instance, Next, comparing (21) with (13) yields, We were able to verify this by running the calculations with initial guesses for and . In both cases, we obtained the same values for the , as in (22).

##### 2.3. Solving for

Suppose we obtain Taylor coefficients of orders through ; then we can compute for using (23). In order to impose the condition , we truncate the series in (21) after , set , and set the resulting summation to zero. We obtain Dividing throughout by , setting , defining in (24), and denoting the resulting polynomial as , we obtain Since in (25) is a polynomial equation, it can be solved easily using Newton’s method.

Alternatively, with , we can obtain a Padé approximant for as and then solve instead. Standard numerical algorithms are available [13] for obtaining the Padé approximant (26), and they involve the solution of linear systems of order .

Once the root for which or is obtained, then the value of is computed as .

This approach is the same as Ahmad and Albarakati’s [11]. However, as our experimentation with arbitrarily large orders has revealed, our results are much superior and extend far beyond the limits encountered by [11] where their method failed to produce results.

#### 3. Numerical Results and Discussion

In this section, we present our results and compare them with those obtained by Ahmad [7] and by Ahmad and Albarakati [11]. It must be noted that the index values these authors have used are not the same as the index we have used for the order of the Taylor series, but they are closely related. In order to make things clearer, we will tabulate the index of our method corresponding to the indexes used in the previous works.

Ahmad [7] computes an increasing sequence converging to the desired value of and then a decreasing sequence also converging to and concludes that satisfies . The index used in [7] satisfies , where is the order of the Taylor series obtained by our method. For instance, when is used in [7], the corresponding Taylor series is of order 6. Table 1 shown indicates that our results for obtained by solving are in excellent agreement with the increasing sequence of [7].