1. Introduction
The modeling of numerous phenomena in diverse scientific fields leads us to consider conventional or fractional boundary value problems of time dependent differential equations on a modeling domain such as the first and second type boundary value problems to heat equation or diffusion equation. For example, the Brownian motion problem in statistics is modeled by heat equation via the Fokker–Planck equation (Adriaan Fokker [
1] and Max Planck [
2]). It is also named as the Kolmogorov forward equation, who discovered the concept in 1931, see in [
3] independently. The stock market fluctuations represent one of the several important real-world applications of the mathematical model of Brownian motion. It was first given in the PhD thesis titled as “The theory of speculation”, by Louis Bachelier (see Mandelbrot and Hudson [
4]) in 1900.
Another representative sample of problems that mathematical modeling brings about the heat equation is the image processing problems appearing through many applied sciences from archaeology to zoology. Examples of archaeological investigations include a camcorder for 3D underwater reconstruction of archeological objects in the study of Meline et al. [
5]. Furthermore, a recent investigation by Woźniak and Polap [
6] gave soft trees with neural components as image processing technique for archeological excavations. In zoology, a study of image reconstruction problem by the application of magnetic resonance imaging was given by Ziegler et al. [
7] and in medical sciences as medical image reconstruction was studied in Zeng [
8]. Furthermore, tomography, and medical and industrial applications are archetypal examples where substantial mathematical manipulation is required. In some cases, the aim is humble denoising or de-blurring. Witkin [
9] and Koenderink [
10] gave the modeling of blurring of an image by the heat equation. Later, a problem of solving the reverse heat equation known as de-blurring is studied in Rudin et al. [
11] and Guichard and Morel [
12].
Additionally, in mathematical biology, Wolpert [
13,
14] gave a phenomenological concept of pattern formation and differentiation known as positional information. The pre-programming of the cells for reacting to a chemical concentration and differentiate accordingly, into different kinds of cells such as cartilage cells was proposed. Afterwards, the animal coat patterns, pattern formation on growing domains as alligators, snakes and bacterial patterns were modeled by reaction diffusion equations in Murray [
15]. Furthermore, therein, gliomas or glioblastomas, which are highly diffusive brain tumors, are analyzed and a mathematical model for the spatiotemporal dynamics of tumor growth was developed. Therefore, the basic model in dimensional form was given by the diffusion equation
where
is the number of cells at a position
and time
represents the net rate of growth of cells including proliferation and death (or loss), and
diffusional flux of cells taken
, where
(distance
/time) is the diffusion coefficient of cells in brain tissue and
is the gradient operator.
In general, finding analytical solutions of these modeled problems is a difficult task or even not possible. Approximations are needed when a mathematical model is switched to a numerical model. Finite difference methods (FDM) are a class of numerical techniques for solving differential equations that each derivative appearing in the partial differential equation has to be replaced by a suitable divided difference of function values at the chosen grid points, see Grossman et al. [
16]. In the last decade, the use of advanced computers has led to the widespread use of FDM in modern numerical analysis. For example, recently, a study on fractional diffusion equation-based image denoising model using Crank–Nicholson and Grünwald Letnikov difference schemes (CN–GL) have been given in Abirami et al. [
17]. Another example is the most recent investigation by Buranay and Nouman [
18] in which computation of the solution to heat equation
on special polygons, where
and
f is the heat source by using implicit schemes defined on hexagonal grids was given. Therein, under some smoothness assumptions of the solution, two implicit methods were developed both on two layers with 14-point that has convergence orders of
and
accordingly to the solution on the grids. Besides the solution of the modeled problem, the high accurate computation of the derivatives of the solution are fundamental to determine some important phenomena of the considered model problem. Such as for the diffusion problem (
1) the functions
and
gives the rate of change of the cells and diffusional flux of cells, respectively.
In the literature, exhaustive studies exist for the approximation of the derivatives of the solution to Laplace’s equation under some smoothness conditions of the boundary functions and compatibility conditions. For the 2D Laplace equation, research was conducted by Volkov [
19] and Dosiyev and Sadeghi [
20]. For the 3D Laplace equation on a rectangular parallelepiped, studies were given by Volkov [
21] and Dosiyev and Sadeghi [
22], and recently by Dosiyev and Abdussalam [
23], and Dosiyev and Sarikaya [
24].
For the heat equation, the derivative of the solution of one-dimensional heat equation with respect to the space variable was given in Buranay and Farinola [
25]. Within this paper, two implicit schemes were developed that converge to the corresponding exact spatial derivative with
and
accordingly. Most recently, in Buranay et al. [
26] numerical methods using implicit schemes with hexagonal grids approximating the derivatives of the solution of (
2) on a rectangle has been given. The smoothness condition
in the Hölder space was required and uniform convergence on the grids to the respective spatial derivatives of
of accuracy for
was proved.
In regard to the equilateral triangulation with a regular hexagonal support, we remark the research by Barrera et al. [
27] where a new class of quasi-interpolant was constructed which has remarkable properties such as high order of regularity and polynomial reproduction. Furthermore, on the Delaunay triangulation, we mention the study by Guessab [
28] that approximations of differentiable convex functions on arbitrary convex polytopes were given. Further, optimal approximations were computed by using efficient algorithms accessed by the set of barycentric coordinates generated by the Delaunay triangulation.
The motivation of the contributions of this research is the need of highly accurate and time-efficient numerical algorithms that compute the derivatives of the solution
to the heat Equation (
2). The achievements of this study are summarized as follows.
The first type boundary value problem (Dirichlet problem) for the heat Equation (
2) on a rectangle
D is considered. The smoothness of the solution
u is taken from the Hölder space
, where
and
also
denote the closure of
respectively. At the first stage, an implicit scheme on hexagonal grids given in Buranay and Nouman [
18] with
order of accuracy is used to approximate the solution
The step sizes
h and
are taken for the spatial variables
and
, respectively, while
is taken for the time variable. An analogous implicit method is also given to approximate the derivative of the solution with respect to time.
At the second stages, computation of the first-order spatial derivatives and second-order mixed derivatives involving time derivatives of the solution
of (
2) are developed. When
uniform convergence of the approximate derivative to the exact derivatives
and
with order
of accuracy on the hexagonal grids are proved.
Numerical examples are given and for the solution of the obtained algebraic linear systems preconditioned conjugate gradient method is used. The incomplete block matrix factorization of the
M-matrices given in Buranay and Iyikal [
29] (see also Concus et al. [
30], Axelsson [
31]) is applied for the preconditioning.
2. Hexagonal Grid Approximation of the Heat Equation and the Rate of Change by Using Fourth Order Accurate Difference Schemes
Let
be a rectangle, where we require
to be multiple of
. Next, let
,
, be the sides of
D that starting from the side
are labeled in anticlockwise direction. Furthermore, the boundary of
D is shown by
. Further, we indicate the closure of
D by
. Let
and
with the lateral surface
and
is the closure of
. Let
s be a non-integer positive number,
be the Banach space of functions
that are continuous in
together with all derivatives of the form
with bounded norm
where
further,
for
are defined as
Volkov gave the differentiability properties of solutions of boundary value problems for the Laplace and Poisson equations on rectangle in the study [
32]. On cylindrical domains with smooth boundary, the differentiability properties of solutions of the parabolic equations were given in Ladyženskaja et al. [
33] and Friedman [
34]. On regions with edges, Azzam and Kreyszig studied the smoothness of solutions of parabolic equations for the Dirichlet problem in [
35] and for the mixed boundary value problem in [
36].
2.1. Dirichlet Problem of Heat Equation and Difference
Problem: Stage 1
Our interest is the following problem for the heat equation:
where
is positive constant. This problem is framed as first type (Dirichlet) boundary value problem.
We assume that the initial and boundary functions
,
, respectively, also the heat source function
possess the necessary smoothness and satisfy the conditions that the problem (
11) has unique solution
We define hexagonal grids on
D with the step size
such that
, and
is positive integer and present this set by
as
Let
be the set of nodes on the interior of
, and let
=
be the
vertex of
D,
Further, we denote by
and
the set of interior nodes whose distance from the boundary is
, thus the hexagon is irregular hexagon with a ghost point that emerges through the left (
) or right (
) side of the rectangle, respectively. The illustration of the exact solution at the irregular hexagons with a ghost point at time levels
,
t and
is given in
Figure 1.
Further, we indicate by
and
. Moreover, let
Next, we give the set of interior hexagonal points and the lateral surface points by
respectively. Let
and
and
. Furthermore,
and
is the closure of
We denote the center of the hexagon by
and
is the pattern of the hexagon consisting the neighboring points
Furthermore, the exact solution at the neighboring points
for the time moment
is presented by
, while
is the value on the boundary point as given:
where
and the value of
if
and
if
. Moreover,
present the numerical solution at the same space coordinates of
and
accordingly for time moments
. We also use the following notations in
Table 1 to denote the values and partial derivatives of the heat source function
f and
with respect to the space variables.
For computing numerically the solution of the BVP
we use the following difference problem given in Buranay and Arshad [
18] and call this Stage 1
.
where
are the initial and boundary functions in (
11), respectively, also
and
2.2. Dirichlet Problem for the Rate of Change and Difference
Problem: Stage 1
Further, for the computation of , we construct the next boundary value problem denoted by which defines the rate of change function
where
and
are the initial and boundary functions in (
11).
Assuming , fourth-order accurate implicit schemes for the solution of the BVP is proposed with the following difference problem. This stage is called Stage 1.
Stage 1 where the operators
and
are presented in (
20)–(
24), respectively, and
2.3. Matrices and Convergence of Finite Difference Schemes in Stage 1 and Stage 1
Let
and
,
and
be real matrices. We denote by
(
) if
(
) for all
. Also
(
) if
(
). Analogous notation is also used for the vectors. Further, let
w be a vector with coordinates
the vector with coordinates
is denoted by
For a fixed time level
we present the Equations (
17) and (
28) in matrix form with
N unknown interior grid points
labeled using standard ordering as
respectively, where
and
and
and
is the neighboring topology matrix,
are diagonal matrices with entries
respectively (see Buranay and Arshad [
18]).
Lemma 1. (Buranay and Arshad [18]) (a) The matrices and in (31) are symmetric positive definite (spd) matrices (b) is spd matrix and .
Lemma 2. The matrix in (31) is nonsingular matrix for Proof. Taking into consideration Lemma 1, the matrix
is a spd matrix. Further, using the Equations (
32)–(
35),
is strictly diagonally dominant matrix with positive diagonal entries. Furthermore, off-diagonal entries are non-positive for
Therefore, it is nonsingular
matrix. □
From (
17) and (
36) the error function (
36) satisfies the following system as given in Buranay and Arshad: [
18]
where
and
and
are presented in (
17). Analogously, using (
28) and (
37) the error function (
37) satisfies the following system:
where
and
, and
are the given functions in (
27), (
29) and (
30) respectively.
Further, the following systems are considered:
for
where
and
are given functions. The algebraic systems (
44) and (
45) at a fixed time level
may be given in matrix representation as
accordingly. In these equations,
and the matrices
and
are given in (
32).
Lemma 3. Let the solutions of (46) and (47) be presented by and , respectively, for . Iffor then Proof. From Lemma 2, when
the matrix
is nonsingular
matrix therefore,
Furthermore, from (
32)
and using (
48) it follows that
and
Further, assuming
and from induction we achieve
which gives
for
Next, assume that
using (
46)–(
50), and by induction it follows that
□
Remark 1. Writing the implicit schemes on hexagonal grids for the problems (17) and (28) in the canonical form it follows that the maximum principle holds when Further, Lemma 3 is the consequence of comparison theorem (see Chapter 4, Section 4.2 Theorem 1 and Theorem 2 in Samarskii [37]) applied to the systems (44) and (45). Theorem 1. For the solution of the system (38) and (41) when the following pointwise error estimations hold true:respectively, whereand and andand u is the solution of BVP and is the function giving the distance from the considered hexagonal grid point to the surface of Proof. We give the proof of (
57) by considering the auxiliary system
and the majorant functions
which
satisfy the following difference problem for
respectively.
Therefore, difference problems (
62) and (
66) in matrix form are
accordingly, and
and
are as given in (
32) and
and
satisfying
and
and
for
Using that
on
and
on
and on the basis of Lemma 3 we obtain
The proof of (
58) is analogous and follows from Lemma 3 by taking the majorant functions
where
is as given in (
60). □
5. Experimental Investigations
The proposed fourth order two stage implicit methods are applied on two test problems such that for the first example the exact solution is known. However, for the second example the exact solution is not given. We take
, and
Further, Mathematica is used for the realization of the algorithms in machine precision. Also we used preconditioned conjugate gradient method with the preconditioning approach given in Buranay and Iyikal [
29] (see also Concus et al. [
30] and Axelsson [
31]). We define the following:
is the given fourth order method for the computation , respectively.
is the given fourth-order method for the computation , seriatim.
presents the for one time level spend by the method accordingly.
shows the for one time level spend by the method , respectively.
Furthermore,
,
and
are the computed grid functions obtained by the methods
and
, accordingly for
and
where
are positive integers. The error function
on the set
obtained by
,
for
is presented by
while the error function resulting by the methods
,
are shown with
, respectively. Furthermore,
Further, we denote the order of convergence of the approximate solution
and
to the functions
and
obtained by using the fourth-order implicit method
by
Furthermore, the order of convergence of the approximate solutions
and
to their corresponding exact solutions
and
obtained by
are given by
We remark that the computed values of (
161) and (
162) are
showing the fourth order convergence of the given methods in
and linear convergence in
5.1. Test Problem Example 1
Equations are given as follows:
where
present the heat source and the exact solution respectively and we take
. For the Example 1,
Table 2 demonstrates
and
achieved by
respectively while
Table 3 shows
and
taken by the method
accordingly.
Table 2 and
Table 3 justify the theoretical results given such that the approximate solutions
and
converge to the corresponding exact functions
and
and
with fourth order in spatial variables and first order in time for
. Moreover, the last two rows in
Table 2 and
Table 3 demonstrate that the order of convergence is also
when
.
Figure 2 and
Figure 3 illustrate the grid functions
and
,
respectively, when
obtained by the corresponding method
for the Example 1.
Figure 4 and
Figure 5 demonstrate the grid functions
,
and
for
respectively, for
achieved by applying the corresponding method
for the Example 1.
5.2. Test Problem Example 2
Equations are given as follows:
The heat source function is
The problem in Example 2 is a benchmark problem such that the solution is not provided. An analogous problem with zero heat source was also considered in Henner et al. [
39]. By applying the proposed methods
we obtain the approximate solutions
and
accordingly at every time level for the considered values
,7 and
.
Table 4 and
Table 5 present
and
, respectively, at the grid points
and
and the corresponding order of convergence
for
at the grid point
given as
By the same way
Table 6 and
Table 7 show
and
, respectively, at the the considered grids and the corresponding convergence orders
for
at the point
defined as
The computed solutions
and
achieved by using the corresponding two stage method
are demonstrated in
Figure 6 and
Figure 7 for the time levels
and
.
Figure 8 and
Figure 9 illustrate the approximate solutions
and
taken by using the respective two stage method
for time levels
and
.