Self-Diffusion, two versions of mean square displacement

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In self-diffusion theory, two definitions of mean square displacement are presented: an integral form involving particle concentration and a summation form used in practical applications. The integral version is primarily utilized in theoretical derivations, while the summation version serves as an approximation in applied contexts. The discussion highlights the test-particle method, which models diffusion through the motion of test particles influenced by friction and random forces, as described by the Langevin equation. This method allows for Monte Carlo simulations to generate particle trajectories and calculate concentration. Ultimately, the connection between Monte Carlo statistics and the Fokker-Planck equation illustrates the deterministic nature of the diffusion process.
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Dear all,

in self diffusion theory you can see in different books, different definitions of the mean square displacement:

<r(t)^2> = \frac{1}{N} \int d\vec{r} \ \vec{r}(t)^2 c( \vec{r} ,t)
where c(r,t) is the particle concentration and N the number of particles.
or

<r(t)^2> = \frac{1}{N}\sum_i^N \vec{r}_i(t)^2

In all theoretical derivations, only the integral version is used whereas in practical / applied treatements of self diffusion, the sum version is used. This is why I assume, the sum version is an approximation to the integral version. But I don't see it.
derivator

Edit:
See for example:
the sum version (formula 2.2): http://ocw.mit.edu/courses/nuclear-...of-transport-fall-2003/lecture-notes/lec2.pdf
the integral version (formula 12.4): http://books.google.de/books?id=PTO...sion mean square&pg=PA124#v=onepage&q&f=false
 
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This is known as the test-particle method. The idea behind this method is to describe the diffusion of the particles, e.g., in an suspension by the motion of test particles under the influence of a friction force (the average force from many collisions of the fluid molecules of with the particle) and a fluctuating random force. This is the Langevin equation. This you do many times in a Monte Carlo simulation which gives trajectories (\vec{\xi}(t),\vec{\pi}(t)) in phase space. The concentration is then given by the test-particle ansatz,

c(t,\vec{x},\vec{p})=\frac{N}{N_e} \sum_{i=1}^{N_e} \delta^{(3)}[\vec{x}-\vec{\xi}_i(t)] \delta^{(3)}[\vec{p}-\vec{\pi}_i(t)],

where N is the number of suspended particles and N_e is the number of test particles, i.e., the ensemble size.

Of course the concentration as function of space alone is given by

c(t,\vec{x})=\int_{\mathbb{R}^3} \mathrm{d}^3 \vec{p} c(t,\vec{x},\vec{p}).

Now, if you plug in the test-particle ansatz for c(t,\vec{x}) into the equation for average quantities, you get your second equation.

The Monte Carlo statistics given by the Langevin equation, is by the way equivalent to the Fokker-Planck equation (or diffusion equation) for the concentration, which is a deterministic partial differential equation (transport equation).

The whole trick of test particles is that you can put them on a computer to solve the transport equation.
 
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