Helmholtz equation appears from general conservation laws of physics and can be interpreted as wave equations. Helmholtz equation is widely applied in the scientific and engineering design problem. Many methods have been proposed for solving the Helmholtz equations, such as finite difference method  , finite element method    , spectral method   and other methods    . However, the computational cost of the finite element method increases greatly for large wave number problems. Additionally, boundary element method is limited to constant-coefficients problems. Finite difference schemes provide the simplest and least expensive avenue for achieving high-order accuracy. Some high order algorithms are proposed in     . In this paper, we derive a fourth-order finite difference scheme using 19 points for solving the three-dimensional Helmholtz equation.
The discretization of the fully three-dimensional Helmholtz equation contains a large number of unknowns and requires considerable memory space. The time and space complexity increase exponentially as the grid number increases. In the meantime, to maintain a given accuracy, the mesh must be refined as the wave number increases. Some parallel algorithms are presented in   . However, this kind of parallel algorithms cannot settle the conflict between the grid number and the performance of the computer hardware.
Fast Fourier transform is a powerful technique for solving the Helmholtz equation both in two and three dimensions   . However, fast algorithm in  requires much computational cost. In light of this, we propose a fast algorithm for solving the three-dimensional Helmholtz equation. The fast operator applies inexpensive transformation to break the large discretization matrix into small and independent systems. Therefore, the equation in the whole region is divided into some small equations in the vertical direction. Meanwhile, the algorithm saves much memory space and requires less computational time due to the sparsity of the fast operator. The problem is reduced on the aperture by introducing a Gaussian elimination and the Neumann boundary condition in the vertical direction.
The paper is outlined as follows. In Section 2, a fourth-order finite difference method for the Helmholtz equation is derived. In Section 3 and Section 4, a fast algorithm is proposed by the Fourier transformation and Gaussian elimination. Two numerical experiments of the fast fourth-order algorithm are presented in Section 5. The paper is concluded in Section 6.
2. Fourth-Order Finite Difference Method
The model problem is described as follows
in the cubic domain with Neumann boundary condition
where k is the wave number and is one of the planes of domain. and are known function. The Helmholtz equation is approximated by a fourth-order finite difference discretization with and the partition .
The 19-points finite difference stencil with h yields the following linear system
where and are standard second order central difference operator and is the fourth-order finite difference solution of Equation (1).
Moreover, we can write Equation (3) in the matrix form
the symbol represents the Kronecker product. and are identity matrices, the subscripts denote their dimension. and are and tridiagonal matrices respectively. and are the boundary parts of and F.
3. Fast Algorithm for Three-Dimensional Helmholtz Equation
and are all tridiagonal Toeplitz matrices. Fourier-sine transformation can be applied to these matrices for accelerating the algorithm. Multiplying discrete Fourier-sine transformation matrices and on the both side of and , we have
and can be defined in the similar way.
Therefore, multiplying on both side of Equation (4), we have
The sparse structure of is given in Figure 1 when
Figure 1. The sparse structure of with .
, where means the number of the unknowns. Hence, the above equation can be transformed into a block tridiagonal matrix based on the structure of the fast operator. Equation (5) can be simplified as
In this paper, we take
as the top surface of the domain and it can be extended to the general situations. Since the solutions on the other surfaces are already known, we need to extract
which contains the parts of
Next, we use the Gaussian elimination with a row partial pivoting to solve Equation (7).
First of all, constructing a LU-decomposition for , i.e. , we have
Since is nonsingular, multiplying on both side of Equation (8), we can obtain
Consequently, the last equation of Equation (9) can be derived
where is the last element of , is the last element of , and is the last element of . Combining equations analogously to Equation (10), we have
4. Discretization of Neumann Boundary Condition
The fourth-order finite difference discretization of Equation (2) can be expressed as
Using the fourth-order substitution of we can derive
or the matrix form
Multiplying on both side of Equation (12), we can obtain
Moreover, replacing l with in Equation (3), we have
and the matrix form
Multiplying on both side of Equation (15), there follows
Eliminating from Equation (13) gives
Combining Equation (11) and Equation (17) and derive a linear system
Finally, after deriving , we can obtain by substituting in Equation (7). Multiplying , we can get the numerical solution of the 3D Helmholtz equation.
5. Numerical Experiments
In this section, two numerical experiments are presented to test the validity and efficiency of the proposed method. Both experiments are implemented on MATLAB. All the equations are solved by the BiCG method. Equations in the two examples are solved in a cube .
Example 1. Consider the following problem
and the corresponding Neumann boundary condition can be calculated.
Table 1 fully corroborates the theoretical design rate of the convergence for the proposed method. We can see that a good accuracy (10−7) is achieved with a small number of grid points (16 - 32 in each direction). In the case of space complexity of , the sparsity of Fourier operator accelerates the speed for solving the three-dimensional Helmholtz equation. Moreover, the comparison of the computational time of three times Fourier transformation and twice Fourier transformation are given in Table 1. Here and represent two different transform operators. As we can see from Table 1, the algorithm proposed in this paper saves much computational time and makes it possible to solve the equation with large grid number. Meanwhile, we give the numerical solutions of Equation (19) in the whole domain and numerical solution on the face in Figure 2 and Figure 3 respectively.
with the exact solution
We give the figures of the numerical solutions U with different wave number in Figure 4 and Figure 5. As shown in Figure 4 and Figure 5, the solutions of the Helmholtz equation are highly oscillating for large wave number.
Table 1. Convergence rate and comparisons of computational time (s) for solving Example 1 with different operators.
Figure 2. The numerical solutions of Equation (19) with .
Figure 3. The numerical solutions of Equation (19) on the face with .
Figure 4. The numerical solutions of Equation (20) with (left) and (right).
Figure 5. The numerical solutions of Equation (20) with (left) and (right).
We propose a fast-high order method for solving the 3D Helmholtz equation with Neumann boundary condition. Fourier operator is used to generate block-tridiagonal structure of the discretization of the Helmholtz equation. Moreover, by using the Gaussian elimination in the vertical direction, the Helmholtz equation is reduced into a linear system in the layer of the domain. The validity and efficiency of the method are tested by two numerical experiments.
This research was supported by the Nature Science Foundation of Hebei Province (No. A2016502001) and the Fundamental Research Funds for the Central Universities (No. 2018MS129).
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