Solving Diffusion Problem Homework Statement

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In summary, the conversation discusses the concept of steady state concentration and how it is affected by the fluxes due to diffusion and potential. The equations for these fluxes are discussed and it is suggested to set them equal to each other in order to solve for the concentration. The potential is also converted into a force by taking the gradient, and a comparison is made to Einstein's solution for Brownian motion. An application of this concept to calculating the density of the Earth's atmosphere is also mentioned.
  • #1
superwolf
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Homework Statement



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2. The attempt at a solution

a) The steady state concentration is the concentration when t --> infitine, right? How can I find that when I don't know c(x, 0)?
 
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  • #2
You have two fluxes, one due to diffusion, the other due to the potential. In steady state, these cancel each other. So try writing those equations down and solving them.
 
  • #3
Flux due to diffusion = [tex]D\frac{dc}{dx}[/tex], right?

How about the flux due to the potential? Is that simply [tex]ax^2[/tex]?

It that is true, I get [tex]c=\frac{a}{3D}x^3[/tex]

I still don't know what to do with b) and c) though...
 
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  • #4
No, you need to convert the potential into a force. You do that by taking the gradient.

Now is it the case that [tex]D\frac{dc}{dx}[/tex] is a force? If it is, then maybe the next step would be to set these forces equal, diffusion and that from the potential. As far as getting a force from diffusion, would that have something to do with "pressure"? I hope there is something in your text or notes that will further you on this.

By the way, I think Einstein was the one who originally solved this question for Brownian motion. One of the applications is to calculate the density of the Earth's atmosphere as a function of altitude. In this case, the potential is due to gravity, which is balanced against diffusion. (I.e. that's why we can breathe at even high altitude places a couple miles above the lowest points on the planet.) Here's an article:
http://psas.pdx.edu/RocketScience/PressureAltitude_Derived.pdf
 
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FAQ: Solving Diffusion Problem Homework Statement

How do I approach solving a diffusion problem?

Solving a diffusion problem involves breaking down the problem into smaller, more manageable steps. First, identify the type of diffusion problem (e.g. Fickian or non-Fickian). Then, determine the boundary conditions and initial conditions. Next, use the appropriate diffusion equation (e.g. Fick's first or second law) to solve for the concentration profile. Finally, check your solution for accuracy and make any necessary adjustments.

What is the difference between Fick's first and second law?

Fick's first law describes the diffusion of a solute in a stationary medium, while Fick's second law takes into account the diffusion of a solute in a moving medium. Fick's first law assumes a constant diffusion coefficient, while Fick's second law allows for the diffusion coefficient to vary with time and position.

How do I handle non-Fickian diffusion in a problem?

Non-Fickian diffusion refers to situations where the solute does not diffuse according to Fick's laws. This can occur when there are other factors at play, such as chemical reactions or convection. In these cases, the appropriate diffusion equation must be used (e.g. Stefan-Maxwell equation for multicomponent diffusion) and additional information about the system may be needed to solve the problem.

What are some common assumptions made in solving diffusion problems?

Some common assumptions made in solving diffusion problems include: a dilute solution, constant diffusion coefficient, steady-state conditions, and one-dimensional diffusion. These assumptions may not always hold true in real-world situations, so it is important to carefully consider the problem and make any necessary adjustments to the assumptions.

How can I check the accuracy of my solution for a diffusion problem?

One way to check the accuracy of your solution is to compare it to experimental data or previous solutions for similar problems. You can also check if your solution satisfies the boundary and initial conditions, as well as any physical constraints (e.g. concentration cannot be negative). Additionally, you can perform sensitivity analysis by varying the input parameters to see how they affect the solution.

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