How to numerically simulate molecular diffusion

In summary: Update figure figure; % Compute relative amount of particles on the right side right_side = right(x, y); % Update figure figure; % Close the window windowcls; end
  • #1
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I want to simulate molecular diffusion in MATLAB.

The idea is as follows:

http://www.math.chalmers.se/Math/Grundutb/GU/MVG300/V11/Laborationer/anim1.jpg

The picture represents a gas container and the red part is a barrier which the gas molecules cannot pass through except for the small area above it. The molecules are on the left side at the beginning, and moves RANDOMLY and we will animate this with sequences of pictures in a loop - and after one iteration the molecules position will uppdate.

After 1000 iterations:

http://www.math.chalmers.se/Math/Grundutb/GU/MVG300/V11/Laborationer/anim2.jpg

(x, y) is the initial position of the molecules and the new position is (x + dx, y + dy) where dx = randomnumber, dy = randomnumber.

The particles will hit the barrier and the containers wall, and we will simulate it as follows: If(x + dx, y + dy) is outside the container or within the barrier then we place the particle on the wall. ASay that (x + dx, y + dy) = (-0.21, 0.74).We let the particles position to be (0, 0.74) (analog in other cases).

When the naimation is done, a new window will open and in this the relative amount of particles on the left side will plot as a function of the iterations.

Something like this:

http://www.math.chalmers.se/Math/Grundutb/GU/MVG300/V11/Laborationer/anim3.jpg


Question:
How do I write a MATLAB program diff_anim with above ideas? diff_anim invokes with two parameters diff_anim(no_of_part, no_of_iterations). no_of_part is the amount of particles and no_of_iterations is thje amount of iterations. Particles initial position should randomly generate in the intervall 0 <= x <= 0.9, 0 <= y <= 1.
 
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  • #2
0. % diff_anim.mfunction diff_anim(no_of_part, no_of_iterations) % Initialize the figure figure; % Generate 'no_of_part' random initial positions x = 0.9 * rand(1, no_of_part); y = 1.0 * rand(1, no_of_part); % Iterate the diffusion process for iter = 1:no_of_iterations % Generate random displacements in (x,y) direction dx = 2*rand(1, no_of_part)-1; dy = 2*rand(1, no_of_part)-1; % Move particles x = x + dx; y = y + dy; % Check that particles are not outside the container or in the barrier x(x > 0.9) = 0.9; x(x < 0) = 0; y(y > 1.0) = 1.0; y(y < 0) = 0; % Remove particles in barrier x(y > 0.7) = 0; y(y > 0.7) = 0; % Animate clf; hold on; % Draw container and barrier rectangle('Position', [0, 0, 0.9, 1], 'Facecolor', [0.9 0.9 0.9]); rectangle('Position', [0, 0.7, 0.9, 0.3], 'Facecolor', [1 0 0]); % Plot particles plot(x, y, '.', 'MarkerSize', 10); axis([0 0.9 0 1]); drawnow; % Update particle positions x = x + dx; y = y + dy; end % Compute relative amount of particles on the left side left_side =
 

1. What is molecular diffusion?

Molecular diffusion is the process by which molecules move from an area of high concentration to an area of low concentration. This movement is driven by random thermal energy and is essential for many biological and chemical processes.

2. Why is it important to numerically simulate molecular diffusion?

Numerical simulation allows us to study and understand the behavior of molecular diffusion in complex systems, such as biological cells or chemical reactions. It also allows us to make predictions and design experiments to test our hypotheses.

3. How can molecular diffusion be simulated numerically?

Molecular diffusion can be simulated using mathematical models, such as the diffusion equation, which describes the time and space dependence of the diffusion process. These models can be solved using numerical methods, such as finite difference or finite element methods.

4. What are the challenges of numerically simulating molecular diffusion?

One of the main challenges is accurately representing the complex behavior of molecules at a microscopic level. This requires a high level of computational power and careful consideration of parameters such as diffusion coefficients and boundary conditions.

5. What are the applications of numerical simulation of molecular diffusion?

Numerical simulation of molecular diffusion has many applications in various fields, including drug delivery, material design, and environmental studies. It can also be used to study the behavior of biological systems, such as cell signaling and protein interactions.

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