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Journal of Applied Mathematics
Volume 2019, Article ID 3084394, 19 pages
https://doi.org/10.1155/2019/3084394
Research Article

A New Approximate Analytical Solutions for Two- and Three-Dimensional Unsteady Viscous Incompressible Flows by Using the Kinetically Reduced Local Navier-Stokes Equations

Department of Mathematics, College of Education for Pure Science, University of Basrah, Basrah, Iraq

Correspondence should be addressed to Assma J. Harfash; moc.oohay@4791jamssa

Received 13 October 2018; Accepted 9 December 2018; Published 1 January 2019

Academic Editor: Oluwole D. Makinde

Copyright © 2019 Abdul-Sattar J. Al-Saif and Assma J. Harfash. 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

In this work, the kinetically reduced local Navier-Stokes equations are applied to the simulation of two- and three-dimensional unsteady viscous incompressible flow problems. The reduced differential transform method is used to find the new approximate analytical solutions of these flow problems. The new technique has been tested by using four selected multidimensional unsteady flow problems: two- and three-dimensional Taylor decaying vortices flow, Kovasznay flow, and three-dimensional Beltrami flow. The convergence analysis was discussed for this approach. The numerical results obtained by this approach are compared with other results that are available in previous works. Our results show that this method is efficient to provide new approximate analytic solutions. Moreover, we found that it has highly precise solutions with good convergence, less time consuming, being easily implemented for high Reynolds numbers, and low Mach numbers.

1. Introduction

Many of the physical phenomena in fluid mechanics are formulated according to the unsteady viscous incompressible Navier-Stokes (INS) equations, which has the non-dimensional formula consisting of the momentum equations and the continuity equation [18]where is the physical time, is the velocity field, is the pressure, and is the Reynolds number (, where is the scale velocity field, is the characteristic length, and is the kinematic viscosity of the fluid).

Analytical and numerical solutions of INS equations are known difficulty because they are non-linear equations, and they do not find the time evolution equation for the pressure that must be determined by solving the Passion equation at each time step, which requires effort and time. Therefore, there are a lot of studies that have developed an alternative formula description of incompressible fluid flows. One of these alternative formulas is the kinetically reduced local Navier-Stokes (KRLNS) equations which was suggested in [1] for the thermodynamic description of incompressible fluid flows at low Mach numbers. The system of KRLNS equations issuch thatwhere is the Mach number ( is the ratio of the characteristic flow speed to the isentropic sound speed ), and is the grand potential. The time scale in INS equations related to that of KRLNS equations; .

All studies which have presented the KRLNS equations for simulation of unsteady incompressible viscous flow problems, used the numerical schemes for solving these equations. The KRLNS equations are proposed for the simulation of low Mach number flows in [2], and used the spectral element method to find the numerical solution of the three-dimensional Taylor Green vortex flow. In [3], two-dimensional KRLNS system is simplified and compared with a Chorin’s artificial compressibility method for steady state computation of the flow in a lid-driven cavity at various Reynolds numbers, the Taylor Green vortex flow is demonstrated that the KRLNS equations correctly describe the time evolution of the velocity and of the pressure, for this purpose, the explicit Mac Cormack scheme is used. In [5] the KRLNS equations are applied to two-dimensional simulation of doubly periodic shear layers and decaying homogeneous isotropic turbulence, to solve these equations have been used the central difference scheme for the spatial discretization in both advection and diffusion terms and four stages Runge-Kutta method for the time integration, the numerical results are compared with those obtained by the artificial compressibility method, the lattice Boltzmann method, and the pseudospectral method. Higher order difference approximations are used in [6] to find the solutions of the KRLNS equations which are applied for two-dimensional simulations of Womersley problem and doubly periodic shear layers.

The main purpose of this paper is to find new approximate analytical solutions for two- and three-dimensional unsteady viscous incompressible flow problems. To achieve this objective, the flow problems that are described by alternative formulas of Navier-stokes equations, which are named KRLNS (3) and (4), the reduced differential transform method (RDTM) is proposed. The reasons that encourage us to propose RDTM to solve the present problems are being an effective and efficient method to find approximate analytical solutions for nonlinear equations and we believe that it has been achieved for the first time in its study. Moreover, we extend the application of RDTM and compare its reliability and efficiency with other methods. New approximate analytical solutions for two- and three-dimensional unsteady viscous incompressible flows were found using RDTM. The results that we obtained are better than others, refer to the results in [4, 8] in accuracy, convergence, and CPU time.

The structure of this paper is organized as follows: In Section 2, we begin with some basic definitions and the use of the RDTM on the KRLNS equations. Section 3 explains the manner we adopted to discuss the convergence of the solutions. In Section 4, we apply this method to solve four flow problems of different dimensions in order to show its ability and efficiency in finding new approximate solutions. Section 5 introduces conclusions of the present work.

2. Reduced Differential Transform Method

The RDTM is an iterative procedure for obtaining a Taylor series solution of differential equations. This method is similar to the differential transform method which was first introduced by Zhou [9]. RDTM has been successfully used to many nonlinear problems [1019] since it does not require any parameter, discretization, linearization, or small perturbations; thus it reduces the size of computational work and is easily applicable.

The main idea of this method depends on the representation the function of two variables as a product of single-variable function, i.e., , then the function can be represented as

Definition 1. If function is analytic and differentiated continuously with respect to time and space , thenis called t-dimensional spectrum function of , and is the transformed this function.

Definition 2. The reduced differential inverse transform of is defined as

Then, the inverse transformation of the set of gives the terms approximation solution as follows:and the exact solution is

To show some basic properties of dimensional RDTM [18], we have to consider to be a vector of variables, and function is analytic and continuously differentiable with respect to time and space in the domain of interest, then the fundamental mathematical operations performed by RDTM are readily obtained and listed in Table 1.

Table 1: Reduced differential transformation.

For the application of this method with the KRLNS equations to find the approximate analytical solutions for INS equations, we referred to as (KRDTM) in this paper. In order to do that we suppose that , , and , where , and are the fluid velocity components in the , , and directions and , , , , and are t-dimensional spectrum functions of , , , , and , respectively, we getsuch thatwhere , , , , and . Then the exact solution is obtained as follows:where

3. Analysis of Convergence

The convergence of the approximate analytical solutions that are resulted from the application of RDTM to INS equations is discussed by relying on the approach followed in [20, 21].

Let us consider the Hilbert space define byand the norm Definesuch that .

We consider the INS equation in the following form:which is equivalent to the following form:where is the linear partial derivative with respect to t, is a nonlinear operator, is a linear operator, and is a general nonlinear operator involving both linear and nonlinear terms. According to RDTMand the solutionswhere . It is noted that the solutions by RDTM are equivalent to determining the sequencesuch that

The sufficient condition for convergence of the series solution is presented in the following theorems

Theorem 3. The series solution converges whenever there is such that , , and

Proof. We show that is a Cauchy sequence in the Hilbert space . For this reason, considerUsing triangle inequalitysince and , we get ; thus, we conclude that is a Cauchy sequence in the Hilbert space , then the series solution converges to some .

Theorem 4. Let be a nonlinear operator satisfies Lipschitz condition from a Hilbert space into and be the exact solution of INS equations. If the series solution converges, then it is converged to .

Proof. Let , and we haveTherefore, there is a unique solution of the problem (18) by the Banach fixed-point theorem. Now we should prove that converges to

Definition 5. For and , we definethen we can say that converges to the exact solution when and for all .

4. Test Problems

In this section, the KRDTM is applied to find approximate analytical solutions of four unsteady viscous incompressible flow problems, two of these problems have exact solutions and the others do not have the exact solutions. We applied KRDTM for each problem to get some approximate analytical solutions. Then the convergence of these solutions has been discussed theoretically and numerically. Finally, the results have been reviewed through some figures, which represent the velocity components and the vorticity functions, which satisfyand explain the time development with the enstrophy, which is defined aswhere is volume for three-dimension flow problems. Our results are computed by using various value of Reynolds numbers at some time levels. All our calculations are run by Maple 18 software.

(P1) is two-dimensional Taylor decaying vortices flow [4, 7, 8], which describes an initially periodical vortex structure convected by the flow field and exponentially decaying due to the viscous decaying. The exact solution of this problem that achieves (1) and (2) iswhen we used KRDTM for solving two-dimensional (1) and (2) equations, where , , , and ; we getsuch thatwhere , , , and ; the solutions are produced as follows:wheresuch thatandTo prove that the condition of the convergence of these solutions verified on the domain apply Definition 5such that . For example, if , , and such that , for all and in this domain, thenand if thenif and thenand if then

The errors measurements , , and -norm resulting from the application of KRDTM and the implicit central compact method (ICCM) in [8] for the computed velocity component with CPU time for various grids at time level , , and are tabulated in Table 2. In Table 3, -norm errors for are calculated at time level and for various Reynolds numbers. The contours of the vorticity and pressure are explained in Figure 1 at , , and . The comparisons of the computed and velocity components with the exact solution along the vertical and horizontal center lines at time levels , , and are shown in Figure 2. In Table 2, it can be noticed that the accuracy of new solutions and the size of the calculated errors of KRDTM are not often affected by the grid size which has been used in comparison with numerical results. Moreover, the results of KRDTM are better than ICCM [8]. Also, from this table, it is clearly shown that the KRDTM results in less computation time (CPU) than ICCM [8]. In Table 3, the same facts have been shown with various Reynolds numbers and grid spacing for -norm at , where in some cases the CPU time reached zero. The results are given in Figure 1 show that the profiles of vorticity and pressure as contour plot are equivalent and identical with other results in [4, 7, 8]. Moreover Figure 2 compares between the exact and new approximate analytical solutions, and the identical is confirmation of the efficiency of KRDTM in solving INS equations with good convergence for different time.

Table 2: The , , and -norm errors for of at and .
Table 3: The -norm errors for of at and .
Figure 1: Contours plots of vorticity and pressure of .
Figure 2: The comparison of the computed and with the exact solutions of .

(P2) is Kovasznay flow [4, 7, 8], which is the laminar flow of viscous fluid behind a two-dimensional grid, with -axis normal to the grid and the velocity field is assumed to be such that and , where and are the components of velocity; is the average velocity in the -direction. Thus, the two-dimensional INS equations with a periodicity in one direction may represent the wake of a two-dimensional grid the same as (1) with replacing the convective terms by , where refers to one in this test. When we solved two-dimensional equations (1) and (2) by using KRDTM for this test problem, we get the same equations (31), (32), and (33) with

The exact solution of the steady state of this problem [7] considers the initial conditions in this testwhere , is a reference pressure (an arbitrary constant), and in the test. The solutions produce similar solutions in (35), (36), and (37) withandThese solutions satisfy the conditions of convergence in the domain ,For example, if , such that , and , for all and , thenif thenand if then

In Tables 4 and 5, the -norm error for the computed velocity component at for some values of Reynolds numbers is compared with the numerical results of the upwind compact finite difference method (UCFDM) in [4]. We noticed that the accuracy of the results obtained from KRDTM is higher and better than the results of UCFDM for different values of Reynolds numbers. It is clear that the maximum CPU time for all cases is not more than 30.0s, and iterations number (3) of KRDTM is less than iterations number of UCFDM (number of iterations ). This shows that KRDTM is faster convergence and more accurate than UCFDM. The influence of the Reynolds number value on the computed vorticity and stream function which is satisfied