MDL: some restructuring in ADI_scheme.org
This commit is contained in:
Marco De Lucia
Max Luebke
parent
d54fe25cac
commit
8b4f1aae46
@ -1,114 +1,11 @@
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#+TITLE: Adi 2D Scheme
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#+TITLE: Numerical solution of diffusion equation in 2D with ADI Scheme
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#+LaTeX_CLASS_OPTIONS: [a4paper,10pt]
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#+LATEX_HEADER: \usepackage{fullpage}
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#+LATEX_HEADER: \usepackage{amsmath}
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#+OPTIONS: toc:nil
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* Input
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- =c= $\rightarrow c$
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- containing current concentrations at each grid cell for species
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- size: $N \times M$
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- row-major
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- =alpha= $\rightarrow \alpha$
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- diffusion coefficient for both directions (x and y)
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- size: $N \times M$
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- row-major
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- =boundary_condition= $\rightarrow bc$
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- Defines closed or constant boundary condition for each grid cell
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- size: $N \times M$
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- row-major
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* Internals
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- =A_matrix= $\rightarrow A$
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- coefficient matrix for linear equation system implemented as sparse matrix
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- size: $((N+2)\cdot M) \times ((N+2)\cdot M)$ (including ghost zones in x direction)
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- column-major (not relevant)
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- =b_vector= $\rightarrow b$
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- right hand side of the linear equation system
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- size: $(N+2) \cdot M$
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- column-major (not relevant)
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- =x_vector= $\rightarrow x$
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- solutions of the linear equation system
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- size: $(N+2) \cdot M$
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- column-major (not relevant)
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* Calculation for $\frac{1}{2}$ timestep
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** Symbolic addressing of grid cells
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[[./grid.png]]
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** Filling of matrix $A$
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- row-wise iterating with $i$ over =c= and =\alpha= matrix respectively
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- addressing each element of a row with $j$
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- matrix $A$ also containing $+2$ ghost nodes for each row of input matrix $\alpha$
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- $\rightarrow offset = N+2$
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- addressing each object $(i,j)$ in matrix $A$ with $(offset \cdot i + j, offset \cdot i + j)$
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*** Rules
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$s_x(i,j) = \frac{\alpha(i,j)*\frac{t}{2}}{\Delta x^2}$ where $x$ defining the domain size in x direction.
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For the sake of simplicity we assume that each row of the $A$ matrix is addressed correctly with the given offset.
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**** Ghost nodes
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$A(i,-1) = 1$
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$A(i,N) = 1$
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**** Inlet
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$A(i,j) = \begin{cases}
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1 & \text{if } bc(i,j) = \text{constant} \\
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-1-2*s_x(i,j) & \text{else}
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\end{cases}$
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$A(i,j\pm 1) = \begin{cases}
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0 & \text{if } bc(i,j) = \text{constant} \\
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s_x(i,j) & \text{else}
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\end{cases}$
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** Filling of vector $b$
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- each elements assign a concrete value to the according value of the row of matrix $A$
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- Adressing would look like this: $(i,j) = b(i \cdot (N+2) + j)$
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- $\rightarrow$ for simplicity we will write $b(i,j)$
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*** Rules
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**** Ghost nodes
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$b(i,-1) = \begin{cases}
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0 & \text{if } bc(i,0) = \text{constant} \\
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c(i,0) & \text{else}
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\end{cases}$
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$b(i,N) = \begin{cases}
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0 & \text{if } bc(i,N-1) = \text{constant} \\
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c(i,N-1) & \text{else}
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\end{cases}$
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*** Inlet
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$p(i,j) = \frac{\Delta t}{2}\alpha(i,j)\frac{c(i-1,j) - 2\cdot c(i,j) + c(i+1,j)}{\Delta x^2}$[fn:1]
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$b(i,j) = \begin{cases}
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bc(i,j).\text{value} & \text{if } bc(i,N-1) = \text{constant} \\
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-c(i,j)-p(i,j) & \text{else}
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\end{cases}$
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[fn:1] $p$ is called =t0_c= inside code
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** Finite differences with nodes as cells' centres
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*** The explicit FTCS scheme as in PHREEQC
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* Finite differences with nodes as cells' centres
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The 1D diffusion equation is:
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@ -118,17 +15,22 @@ The 1D diffusion equation is:
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& = \alpha \frac{\partial^2 C}{\partial x^2}
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\end{align}
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We discretize it following a Forward Time, Centered Space finite
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difference scheme where the nodes correspond to the centers of a grid
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such as:
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We aim at numerically solving [[eqn:1]] on a spatial grid such as:
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[[./grid_pqc.pdf]]
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The left boundary is defined on $x=0$ while the center of the first
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cell is in $x=dx/2$, with $dx=L/n$.
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cell - which are the points constituting the finite difference nodes -
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is in $x=dx/2$, with $dx=L/n$.
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We discretize [[eqn:1]] as following, for each index i in 1, \dots, n-1
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and assuming constant $\alpha$:
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** The explicit FTCS scheme (as in PHREEQC)
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We start by discretizing [[eqn:1]] following an explicit Euler scheme and
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specifically a Forward Time, Centered Space finite difference.
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For each cell index $i \in 1, \dots, n-1$ and assuming constant
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$\alpha$, we can write:
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#+NAME: eqn:2
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\begin{equation}\displaystyle
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@ -142,8 +44,8 @@ left cell boundary) and then repeat the differentiation to get the
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second derivative of $C$ on the the cell centre $i$.
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This discretization works for all internal cells, but not for the
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boundaries. To properly treat them, we need to account for the
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discrepancy in the discretization.
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domain boundaries ($i=0$ and $i=n$). To properly treat them, we need
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to account for the discrepancy in the discretization.
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For the first (left) cell, whose center is at $x=dx/2$, we can
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evaluate the left gradient with the left boundary using such distance,
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@ -193,29 +95,30 @@ C_n^{j+1} = C_n^{j} + \frac{\alpha \cdot \Delta t}{\Delta x^2} \cdot (C^j_{n-1}
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A similar treatment can be applied to the BTCS implicit scheme.
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*** implicit BTCS
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** Implicit BTCS scheme
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First, we define the Backward time difference:
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First, we define the Backward Time difference:
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\begin{equation}
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\frac{\partial C }{\partial t} = \frac{C^j_i - C^{j-1}_i}{\Delta t}
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\frac{\partial C^{j+1} }{\partial t} = \frac{C^{j+1}_i - C^{j}_i}{\Delta t}
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\end{equation}
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Second the spatial derivative approximation:
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Second the spatial derivative approximation, evaluated at time level $j+1$:
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\begin{equation}
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\frac{\partial^2 C }{\partial t} = \frac{\frac{C^{j}_{i+1}-C^{j}_{i}}{\Delta x}-\frac{C^{j}_{i}-C^{j}_{i-1}}{\Delta x}}{\Delta x}
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\frac{\partial^2 C^{j+1} }{\partial x^2} = \frac{\frac{C^{j+1}_{i+1}-C^{j+1}_{i}}{\Delta x}-\frac{C^{j+1}_{i}-C^{j+1}_{i-1}}{\Delta x}}{\Delta x}
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\end{equation}
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Taking the 1D diffusion equation from [[eqn:1]] and substituting each term by the
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equations given above leads to the following equation:
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\begin{equation}\displaystyle
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\frac{C_i^{j} -C_i^{j-1}}{\Delta t} = \alpha\frac{\frac{C^{j}_{i+1}-C^{j}_{i}}{\Delta x}-\frac{C^{j}_{i}-C^{j}_{i-1}}{\Delta x}}{\Delta x}
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\end{equation}
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Since we are not able to solve this system w.r.t unknown values in $C^{j-1}$ we
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are shifting each j by 1 to $j \to (j+1)$ and $(j-1) \to j$ which leads to:
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# \begin{equation}\displaystyle
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# \frac{C_i^{j+1} -C_i^{j}}{\Delta t} = \alpha\frac{\frac{C^{j+1}_{i+1}-C^{j+1}_{i}}{\Delta x}-\frac{C^{j+1}_{i}-C^{j+1}_{i-1}}{\Delta x}}{\Delta x}
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# \end{equation}
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# Since we are not able to solve this system w.r.t unknown values in $C^{j-1}$ we
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# are shifting each j by 1 to $j \to (j+1)$ and $(j-1) \to j$ which leads to:
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\begin{align}\displaystyle
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\frac{C_i^{j+1} - C_i^{j}}{\Delta t} & = \alpha\frac{\frac{C^{j+1}_{i+1}-C^{j+1}_{i}}{\Delta x}-\frac{C^{j+1}_{i}-C^{j+1}_{i-1}}{\Delta x}}{\Delta x} \nonumber \\
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@ -249,3 +152,113 @@ Substituting with the new variable $s_x$ and reordering of terms leads to the eq
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\begin{equation}\displaystyle
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-C^j_0 = s_x \cdot C^{j+1}_1 + (2s_x) \cdot l + (-1 - 3s_x) \cdot C^{j+1}_0
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\end{equation}
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*TODO*
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- Right boundary
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- Tridiagonal matrix filling
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#+LATEX: \clearpage
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* Old stuff
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** Input
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- =c= $\rightarrow c$
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- containing current concentrations at each grid cell for species
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- size: $N \times M$
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- row-major
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- =alpha= $\rightarrow \alpha$
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- diffusion coefficient for both directions (x and y)
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- size: $N \times M$
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- row-major
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- =boundary_condition= $\rightarrow bc$
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- Defines closed or constant boundary condition for each grid cell
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- size: $N \times M$
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- row-major
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** Internals
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- =A_matrix= $\rightarrow A$
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- coefficient matrix for linear equation system implemented as sparse matrix
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- size: $((N+2)\cdot M) \times ((N+2)\cdot M)$ (including ghost zones in x direction)
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- column-major (not relevant)
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- =b_vector= $\rightarrow b$
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- right hand side of the linear equation system
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- size: $(N+2) \cdot M$
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- column-major (not relevant)
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- =x_vector= $\rightarrow x$
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- solutions of the linear equation system
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- size: $(N+2) \cdot M$
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- column-major (not relevant)
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** Calculation for $\frac{1}{2}$ timestep
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** Symbolic addressing of grid cells
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[[./grid.png]]
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** Filling of matrix $A$
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- row-wise iterating with $i$ over =c= and =\alpha= matrix respectively
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- addressing each element of a row with $j$
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- matrix $A$ also containing $+2$ ghost nodes for each row of input matrix $\alpha$
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- $\rightarrow offset = N+2$
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- addressing each object $(i,j)$ in matrix $A$ with $(offset \cdot i + j, offset \cdot i + j)$
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*** Rules
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$s_x(i,j) = \frac{\alpha(i,j)*\frac{t}{2}}{\Delta x^2}$ where $x$ defining the domain size in x direction.
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For the sake of simplicity we assume that each row of the $A$ matrix is addressed correctly with the given offset.
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**** Ghost nodes
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$A(i,-1) = 1$
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$A(i,N) = 1$
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**** Inlet
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$A(i,j) = \begin{cases}
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1 & \text{if } bc(i,j) = \text{constant} \\
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-1-2*s_x(i,j) & \text{else}
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\end{cases}$
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$A(i,j\pm 1) = \begin{cases}
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0 & \text{if } bc(i,j) = \text{constant} \\
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s_x(i,j) & \text{else}
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\end{cases}$
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** Filling of vector $b$
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- each elements assign a concrete value to the according value of the row of matrix $A$
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- Adressing would look like this: $(i,j) = b(i \cdot (N+2) + j)$
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- $\rightarrow$ for simplicity we will write $b(i,j)$
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*** Rules
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**** Ghost nodes
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$b(i,-1) = \begin{cases}
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0 & \text{if } bc(i,0) = \text{constant} \\
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c(i,0) & \text{else}
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\end{cases}$
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$b(i,N) = \begin{cases}
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0 & \text{if } bc(i,N-1) = \text{constant} \\
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c(i,N-1) & \text{else}
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\end{cases}$
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*** Inlet
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$p(i,j) = \frac{\Delta t}{2}\alpha(i,j)\frac{c(i-1,j) - 2\cdot c(i,j) + c(i+1,j)}{\Delta x^2}$
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\noindent $p$ is called =t0_c= inside code
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$b(i,j) = \begin{cases}
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bc(i,j).\text{value} & \text{if } bc(i,N-1) = \text{constant} \\
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-c(i,j)-p(i,j) & \text{else}
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\end{cases}$
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