- #1
lemoz
- 2
- 0
Frequency dependant material simulation
Program debyeformulation;
Const
size=200;
Var
dz: array[0..size-1] of real;
ez: array[0..size-1] of real;
hx: array[0..size-2] of real;
ga: array[0..size-1] of real;
gb: array[0..size-1] of real;
gc: array[0..size-1] of real;
ix: array[0..size-1] of real;
sx: array[0..size-1] of real;
Time,maxTime,i:integer;
dx,dt,epsz,epsilon,sigma:real;
t0, spread, pulse:real;
tau,hi ;
ezl1,ezl2,ezh1, ezh2:real;
monfichier:text;
begin
assign(monfichier,'debyeformul100.txt');
rewrite(monfichier);
dx :=0.01; dt:=dx/6e8; epsz:=8.8e-12;
for i:=0 to size-1 do
begin
ga[i]:=1.0; gb[i]:=0.0; gc[i]:=0.0; dz[i]:=0.0; ez[i]:=0.0; hx[i]:=0.0; ix[i]:=0.0; sx[i]:=0.0;
end;
ezl1:=0.0; ezl2:=0.0; ezh1:=0.0; ezh2:=0.0;
epsilon:=2.0; sigma:=0.01; hi:=2.0; tau:=0.001;
for i:=100 to size-1 do
begin
ga[i]:=1.0/(epsilon+sigma*dt/epsz+hi*dt/tau);
gb[i]:= sigma*dt/epsz;
gc[i]:= hi*dt/tau;
end;
for i:=0 to size-1 do
begin
writeln(i,'',ga[i]:6:2,'',gb[i]:6:2,'',gc[i]:6:2);
end;
maxTime:=100; t0:=50.0; spread:=10.0;
for Time:=0 to maxTime-1 do
begin
for i:=1 to size-2 do
begin
dz[i]:=dz[i]+0.5*(hx[i-1]-hx[i]);
end;
pulse:=exp(-0.5*(Time-t0)*(Time-t0)/spread);
dz[5]:=dz[5]+pulse;
for i:=1 to size-2 do
begin
ez[i]:=ga[i]*(dz[i]-ix[i]-sx[i]);
ix[i]:=ix[i]+gb[i]*ez[i];
sx[i]:= exp(-dt/tau) *sx[i]+gc[i]*ez[i];
end;
ez[0]:= ezl2;
ezl2:= ezl1;
ezl1:= ez[1];
ez[size-1]:= ezh2;
ezh2 :=ezh1
ezh1 :=ez[size-2] ;
for i:=0 to size-1 do
begin
hx[i]:=hx[i]+0.5*(ez[i]-ez[i+1]);
end;
end;
for i:=0 to size-1 do
begin
write(monfichier,i,' ',ez[i]:1:3);
writeln(monfichier);
end;
close(monfichier);
readln;
end.
The choice of programming language depends on your familiarity and the specific requirements of your simulation. Commonly used languages for such simulations include Python, MATLAB, and C++. Python is user-friendly and has powerful libraries like NumPy and SciPy. MATLAB is excellent for matrix computations and has built-in functions for wave simulations. C++ is highly efficient for computationally intensive tasks.
The basic equations include the wave equation, which is typically a second-order partial differential equation (PDE). For a frequency dependent material, the material properties such as permittivity, permeability, and conductivity can vary with frequency, leading to a modified wave equation. The general form of the wave equation is ∂²u/∂t² = c²∇²u, where c is the wave speed. For frequency dependent materials, c can be a function of frequency.
Boundary conditions are crucial for accurately simulating wave propagation. Common boundary conditions include Dirichlet (fixed value) and Neumann (fixed gradient) conditions. For a one-dimensional simulation, you can set these at the edges of your simulation domain. For example, u(0,t) = A for a Dirichlet condition at the start, or ∂u/∂x|_(x=0) = B for a Neumann condition. Absorbing boundary conditions like Perfectly Matched Layer (PML) can also be used to minimize reflections.
To account for frequency dependence, you can use a Fourier Transform approach. Transform the wave equation into the frequency domain using the Fourier Transform, solve the equation for each frequency component, and then transform the solution back to the time domain using the Inverse Fourier Transform. This allows you to handle the frequency-dependent material properties effectively.
Yes, there are several libraries and tools that can assist in wave simulation. For Python, libraries like NumPy, SciPy, and FiPy are useful. MATLAB has built-in functions for solving PDEs and wave equations. For more specialized tasks, you might consider using finite element method (FEM) libraries such as FEniCS or MOOSE. These tools can simplify the implementation and help with numerical stability and accuracy.