Algebraic Type Approximation to the Blasius Velocity Profile
For the Blasius velocity profile we propose a simple algebraic type approximate function which is uniformly accurate over the whole region. Moreover, for further improvement a correction method based on a weight function is introduced. The availability of the proposed method is shown by the result of numerical experiments.
We consider the well-known Blasius problem subject to the boundary conditions The so-called Blasius function describes the stream on the boundary layer over a flat plate. There are lots of analytical approximation methods to the Blasius function such as the variational iteration method [1–6], the Adomian decomposition method [7–9], and the homotopy analysis method [10–12]. Recently, spectral methods based on orthogonal functions have been applied in approximation of solutions for the nonlinear boundary value problems like the Blasius problem [13–17]. In addition, numerical solutions of the nonlinear differential equations for the boundary layer problems such as Falkner-Skan equations, including the Blasius equation as a special case, have been studied by many researchers [18–25].
Concerning the streamwise velocity profile , we note an approximate analytical solution proposed in the literature  of the form where and are determined by the known properties of the Blasius function at the wall and far from the wall, respectively. The parameter is chosen by minimizing the residual function Recently, Savaş  introduced another approximate analytical solution for the streamwise velocity profile as for the constants or .
In the next section, motivated by the analytical solutions (3) and (5), we propose another algebraic type approximate analytical solution for the velocity profile as given by (6) and explore its properties with a method to determine the parameters therein. In Section 3, by using an appropriate weight function, we introduce a correction method to improve the accuracy of the presented approximation. Moreover, for further improvement we employ an auxiliary term which appropriately reflects the error of the presented approximation. Some numerical experiments are performed to demonstrate the efficiency of the presented method.
2. Approximation to the Velocity Profile
To approximate the velocity profile directly we suggest an algebraic type analytical function as for a constant and an exponent . We note that satisfies the boundary conditions and given in (2), and its derivative is Since , we may set which is a well-known Blasius constant . In addition, the velocity profile has an inversion of a simple form as
The related approximation to the Blasius stream function can be obtained by the formula In fact, using the symbolic computational software Mathematica (version 9), one can find the analytical form of as where is the hypergeometric function  whose series expansion is and is the shifted factorial defined by with .
For an appropriate parameter in we may choose a value at which the -norm of the residual function , is minimized. To find one can use a package, Mathematica, for example, and we will obtain the local minimum in at the value .
Figure 1 shows the errors of the presented approximate velocity profile, , with integers and near the value . The error means difference between and the numerical solution for the velocity profile which is regarded as an exact solution. By numerical experiments for various values of , we can see that the accuracy of becomes better far from the wall as goes large while it becomes better near as goes small.
3. Improvement by a Weighted Average
In order to improve the accuracy of the proposed approximate velocity profile over the whole region, we introduce a weighted average for , where is a weight function defined as for and . It follows that for with . Moreover, it should be noticed that for a sufficiently large and thus This implies that the point plays the role of a threshold between two approximate velocity profiles and . On the other hand, the related approximate stream function can be obtained by numerical integration in the equation
Referring to Figure 1 for the cases of and , we may take in (16) which is a center of the points and at which and , respectively, have the maximum absolute errors. Thick lines in Figure 2 indicate errors (i.e., differences from the numerical solution) of the corrected approximate stream function and the velocity profile with and in the weight function . We can see that the maximum error is about 0.01 in the velocity profile and about 0.02 in the stream function. Comparing these with the errors of the approximate stream functions and in Figure 2(a) and the velocity profiles and in Figure 2(b), one can find distinct improvement of the corrected velocity profile defined in (15).
In practice, by numerical experiments, we can find better case of parameters like , for example, which results in more accurate approximation with the maximum errors about 0.003 and 0.005 in the velocity profile and the stream function, respectively. However, this choice of the parameters looks rather ambiguous. Thus, for development of plausible further improvement, we refer to the correction method proposed in the literature  which uses an auxiliary term reflecting the error of the presented approximation. First, observing the behavior of the error given in Figure 2, for example, we can have the numerical values of the critical points and of . Then, to approximate appropriately, we suggest a function of the form where The value of in (20) can be determined by the condition which implies that We consider a corrected approximation One may expect that the accuracy of goes higher as becomes closer to the error .
For example, for the case of , , we can evaluate numerical values of the constants , , , and as given in Table 1. Figure 3 shows errors of and the velocity profile indicated by thick lines, compared with those of and indicated by thin lines, for the parameters . Additionally, dashed lines indicate Savas's approximations and . We can find that and with have the maximum errors about 0.002 and 0.005, respectively. This implies that the correction method (23) can highly improve the proposed method (15) as a result.
Conflict of Interests
The author declares that there is no conflict of interests regarding the publication of this paper.
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF-2013R1A1A4A03005079).
J. Biazar, M. Gholami Porshokuhi, and B. Ghanbari, “Extracting a general iterative method from an Adomian decomposition method and comparing it to the variational iteration method,” Computers & Mathematics with Applications, vol. 59, no. 2, pp. 622–628, 2010.View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
W. Bao and J. Shen, “A generalized-Laguerre-Hermite pseudospectral method for computing symmetric and central vortex states in Bose-Einstein condensates,” Journal of Computational Physics, vol. 227, no. 23, pp. 9778–9793, 2008.View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
K. Parand, M. Dehghan, A. R. Rezaei, and S. M. Ghaderi, “An approximation algorithm for the solution of the nonlinear Lane-Emden type equations arising in astrophysics using Hermite functions collocation method,” Computer Physics Communications, vol. 181, no. 6, pp. 1096–1108, 2010.View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
K. Parand, M. Dehghan, and A. Taghavi, “Modified generalized Laguerre function tau method for solving laminar viscous flow: the Blasius equation,” International Journal of Numerical Methods for Heat & Fluid Flow, vol. 20, no. 6-7, pp. 728–743, 2010.View at: Publisher Site | Google Scholar | MathSciNet
S. J. Liao, “A non-iterative numerical approach for the two-dimensional viscous flow problems governed by the Falkner-Skan equation,” International Journal for Numerical Methods in Fluids, vol. 35, pp. 495–518, 2001.View at: Google Scholar
R. Beals and R. Wong, Special Functions, Cambridge University Press, New York, NY, USA, 1st edition, 2010.View at: MathSciNet