Research Article  Open Access
ChienChang Yen, "A Numerical Convolution Representation of Potential for a Disk Surface Density in 3D", Journal of Applied Mathematics, vol. 2014, Article ID 693537, 12 pages, 2014. https://doi.org/10.1155/2014/693537
A Numerical Convolution Representation of Potential for a Disk Surface Density in 3D
Abstract
This study presents a numerical convolution representation of a potential induced from a disk of surface density, which has often been investigated in the center region of galaxies. The advantage of this representation is to release the softening length for the body method and artificial boundary conditions for the spectral methods. With the help of fast Fourier transform, the computational complexity is only , where is the number of zones in one dimension. Numerical results show an almost second order of accuracy on a Cartesian coordinate system. A comparison study also demonstrates that this method can calculate the potential for disk surface density based on the uniform grids.
1. Introduction
Let us consider the Poisson equation with singular disk surface density [1–3] where,,, and are position, gravitational constant, Dirac symbol, and density, respectively. Hereafter, we setwithout loss of generality.
One possible method of solving (1) is to discretize the partial differential equation (1) using the finite difference method [4–6]. This discretization is where,, andfor, based on the uniform mesh gridswith the mesh sizes,, andin,, anddirections, respectively. In this approach, the values of the potential on imposed boundaries should be calculated and a fully 3D calculation must be performed. Any numerical solutions of this partial differential problem involveunknowns, whereis the number of zones in one dimension. The linear complexity of this approach is at least, for example, [7]. It seems unfeasible that a thin gaseous disk problem can directly solve the partial differential equation.
The other approach for solving (1) uses the integral form. The solution to (1) can be represented in terms of the fundamental solution,, where as The difficulties encountered in the numerical approach for solving (1) or (5) are related to the extent of the domain. The spectral method uses trigonometric bases functions and the artificial periodic boundary conditions [8]. However, the potential is not periodic in reality. A direct calculation using thebody method [1] has the total amount of complexity based on the numberof mesh zones. For thebody method based on a uniform grid and using the FFT technique, the numerical complexity can be reduced fromto. In other words, this produces a fast algorithm of linear complexity. Unfortunately, this method has a drawback, which is to introduce the softening parameter to overcome the singular integration wherecontains the origin.
The proposed method does not require the artificial periodic boundary condition and softening parameter and further improves the accuracy of thebody method. This study uses the uniform and logarithmic grid generations in Cartesian and polar coordinates, respectively. The other treatment is linear approximation for the surface density on the cell. This ensures that the proposed method is of second order in Cartesian coordinates. This study uses two examples, adisk [9] and its variation, to show numerically that the method achieves secondorder accuracy. A comparison study also demonstrates that the proposed method is suitable for potentials on a thin disk.
Before the end of the section, another approach for an improvement ofbody method is the fast multipole methods (FMM) [1, 10] which decomposes the region into local interactions and farfield interactions for every position. The FMM is efficient for fully threedimensional calculations under the assumption that the far field is smooth. The FFM also relies on the ability to manipulate local multipole expansions for every box in the tree hierarchy [11–13]. The proposed presentation based on the uniform grids is without the assumption of smoothness in the farfield region and does not require extra efforts on the tree hierarchy.
This paper is organized as follows. Sections 2.1 and 2.2 present the numerical methods for Cartesian and spherical coordinates, respectively. Section 4.1 presents the order of accuracy of the proposed methods as verified by a family of finite disks[9]. Section 4.2 presents a comparison with thebody calculation. Finally, we conclude this study in Section 5.
2. Methods
2.1. Cartesian Coordinates
Assume that the numerical computation domainfor some number, which contains the support of the surface density, and further discretize the region uniformly as follows. Given a positive integer, define,,, and, where. Next, define the center of the cellasand, where. Hence, the domainis discretized into thecells.
We recall thebody method with fast Fourier transform (FFT). Represent the potential functionof (1) as Where According to (2), the potential is Calculate (8) using a numerical approach for the planeassuming that the support of the surface density is compact. Based on a restriction to the calculation of potentials on theplane, derive the formulae for all. Equation (8) is equal to The potential atfor thebody method is approximated by That is, The approximated integral is of the first order and the fundamental function is singular whenever. Thebody method applies a softening parameter technique forwith, to avoid the singularity.
To improve the calculation of (11), approximate the surface densityonin (8) linearly by whereandandare constant in the cell. The error of the discretization is. If a higher order accuracy is required, additional terms in the Taylor expansion in (13) can be considered.
Let By substituting (13) into (9), the potential can be approximated by where
Fortunately, the integrals of (14) can be obtained analytically with the help of the following simple formulae: The valueis then equal to where the notation. The calculations ofandare split into two parts by the identity and, respectively. This leads to
The forms of,, andin (16) are a type of convolution and the computational complexity can be reduced towith the help of FFT in theanddirections. In thedirection, the computational complexity is. This implies that the total complexity of this method isandfor the planeand the whole 3D space, respectively.
2.2. Spherical and Polar Coordinates
While the method appears to be secondorderaccurate for discretization on a Cartesian grid, it loses secondorder accuracy using spherical polar coordinates. An algorithm for solving the Poisson equation in spherical polar coordinates has been used extensively in protostellar collapse and fragmentation calculations, where often a thin disk formed at the central core of the collapsing cloud [14]. The algorithm implemented in [15] is subsequently modified, see [16], to deal with filamentary density distributions. This method in [16] employed finite differences for the radial dependence of the potential and spherical harmonics for the angular dependence, resulting in a secondorder method through convergence testing.
We are going to develop our approach for spherical coordinates, which can be reduced to polar coordinates (). The singular integral disappears, but the high order of accuracy is not attained because there is no explicit form for the integral of an elliptic function. Although the proposed calculation of potential has some drawbacks, it is still better thanbody calculations and the method in [17].
The support of the surface density is compact, contained in a region for some number. Confine the discretization on the radial region in logarithmic form and the azimuthal region uniformly as follows to achieve a fast calculation. Given a positive integer, define,,,,,,, andwhere. The effect ofis to refine the mesh. Here, the proposed method for polar coordinates is of first order because a singular integration occurs (see below). Furthermore, the region is discretized into thecells,for. For, the cellsdo not cover the origin and extra cellsshould be included. To simplify notation, denote,.
The potential functionof (1) under the assumptionin spherical coordinates can be expressed as where
The surface densityonin (20) can be linearly approximated by whereandandare constants in the cell. Equation (22) is the Taylor expansion in two dimensions and it follows that the error of the discretization is.
In this section, let Similarly for,, andon the central region . The termin (24) is for the formulation of a convolution type. According to (20) and (22), where Equation (27) can then be rewritten as
Define, whereis the dimensionless radius. The evaluation of (23), (24), and (25) can be obtained with the help of the following simple integrals:
The definitions ofandlead to Calculate the value of the integral The last integral in this equation is an elliptic integral, and this study uses a trapezoidal rule for its evaluation. This numerical approach approximates the valueas follows: where the notation. Similarly,
3. Convergence Results
Let us denoteas the area of the regioninand discuss the convergence of the proposed method. The integrals of over a region and, for some constant, are whereis a constant. Furthermore, if, then this integral is.
Lemma 1. If two compact disk densities and satisfy then their corresponding potentials and , respectively, satisfy whereis a positive constant and depends on the supports ofand.
Proof. Let the supports ofandbe contained in a finite region. Following (5) and (35), it yields that Sinceis finite and the lemma is obtained.
The order of accuracy between analytic and numerical solutions can be observed from the truncated errors of the disk surfaceand truncated density,
The potential calculated numerically is denoted byand the analytic solution is. We estimate
We deduce the following theorem by aforementioned argument and Lemma 1.
Theorem 2. If the truncated errors of the Taylor series of the disk surface densityare, whereis the mesh size in one dimension and, then the errors between the numerical and analytic potential also are .
4. Numerical Results
This study verifies that the proposed method achieved is of secondorder accuracy through the following two examples: adisk [9] and a nonaxisymmetric diskconsisting of two superposeddisks. Thedisk has the surface density whereandis a given constant. The corresponding potential is where
4.1. Order of Accuracy
This study investigates the order of accuracy of the proposed method in norms,, andto demonstrate the convergence in total variation, energy, and pointwise senses, respectively. These norms for a functionon a domainare defined as Without loss of generality for studying the order of accuracy, set the computational domain, and . Setfor adisk simulation andfor adisk simulation, wherewithand. Figure 1 presents the profiles of the surface density, the potential onplane and the residual between analytic and numerical solutions for. In Table 1, columnrepresents the error between the analytic and numerical solutions innorm,, and. Columnin Table 1 represents the order of accuracy as measured byfortoand similarly for. These errors show that the proposed method achieves nearly secondorder accuracy. More precisely, the order of convergence is approximatelyor.

(a)
(b)
(c)
(d)
(e)
(f)
Continue to use thedisk as an example and a unit diskas the computational domain to investigate the potential in polar coordinates. Set the value. Figure 2 presents the profiles of the surface density, potential, and the difference between analytic and numerical solutions for. According to Table 2, the order of accuracy is only approximately 1. Although the surface density at the origin is smooth, the singular elliptic integral introduces significant error there.

(a)
(b)
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4.2. A Comparison Study
Start with And introduce a softening parameterto approximate For polar coordinates [1], the value ofcan be approximated by