Possible to derive Boltzmann distribution using W, not lnW?

In summary, the conversation discusses the possibility of using the method of maximizing W instead of lnW in deriving the Boltzmann distribution. The Stirling approximation of W is also mentioned, along with the t equations obtained by maximizing W with the constraints of constant particle number and energy. However, it is mentioned that proceeding from this point has been unsuccessful in isolating ni and applying the constraints. It is questioned whether this method can be used or if lnW must be used instead, which should still result in the same distribution. The conversation suggests searching on scholar.google.com for further insight.
  • #1
Darren73
8
0
Hi all, in following the many available derivations of the Boltzmann distribution I was trying to do it by maximizing W, which is N choose n1,n2,...nt., instead of lnW, because it should give the same answer (since W is monotonically increasing with lnW, am I wrong?).

So given the two constraint equations of constant particle number and energy, [tex]
g=\sum_{i}n_{i}=N,

h=\sum_{i}n_{i}\epsilon_{i}=E
[/tex]And the Stirling approximation of W, [tex]
W=N^{N}n_{1}^{-n_{1}}n_{2}^{-n_{2}}...n_{t}^{-n_{t}}
[/tex]

And maximizing W with the above constraints (using Lagrange multipliers) gives the following t equations, [tex]
\frac{\partial W}{\partial n_{i}}-\alpha\frac{\partial g}{\partial n_{i}}-\beta\frac{\partial h}{\partial n_{i}}=0
[/tex]

Which gives,[tex]
\frac{\partial W}{\partial n_{i}}-\alpha-\beta\epsilon_{i}=0
[/tex] [tex]
\frac{\partial W}{\partial n_{i}}=C_{i}n_{i}^{n_{i}}\left(\ln n_{i}+1\right) [/tex] [tex]
C_{i}n_{i}^{n_{i}}\left(\ln n_{i}+1\right)=\alpha+\beta\epsilon_{i} [/tex]

Where Ci is some constant of the other nj's and N. Proceeding from this point has proven fruitless for me to isolate ni and apply the constraints. Does anyone know if this can be done? Or do you have to use lnW? It would seem odd to me if this cannot be done by maximizing W directly. And they should give the same distribution, namely [itex]n_{i}=N\exp -\beta \epsilon_{i} [/itex], correct?
 
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  • #2
Given this long without a reply, perhaps a search at scholar.google.com would yield some insight.
 

1. What is the Boltzmann distribution?

The Boltzmann distribution is a probability distribution used to describe the distribution of energy among particles in a system at a given temperature. It is derived from the principles of statistical mechanics and is a fundamental concept in thermodynamics and statistical physics.

2. What is W and how is it related to the Boltzmann distribution?

W, also known as the multiplicity or number of microstates, is a measure of the number of ways a system can be arranged while maintaining the same macroscopic properties. It is directly related to the Boltzmann distribution as it is used to calculate the probability of a system being in a particular microstate.

3. Can the Boltzmann distribution be derived using W instead of lnW?

Yes, it is possible to derive the Boltzmann distribution using W instead of lnW. This method is known as the combinatorial approach and is commonly used in introductory statistical mechanics courses. It involves counting the number of microstates for a given macrostate and then using this information to calculate the probability of each microstate.

4. What are the advantages of using the combinatorial approach to derive the Boltzmann distribution?

One advantage of using the combinatorial approach is that it provides a more intuitive understanding of the Boltzmann distribution. By counting the number of possible microstates, it becomes easier to see how the probability of a particular microstate is influenced by the number of particles and energy levels in the system. Additionally, this approach can be extended to more complex systems and can be used to derive other thermodynamic quantities such as entropy and free energy.

5. Are there any limitations to using W to derive the Boltzmann distribution?

While the combinatorial approach can be useful in understanding the Boltzmann distribution, it may not always be the most efficient method. For larger, more complex systems, it may be more practical to use the logarithmic approach using lnW. Additionally, the combinatorial approach assumes that all microstates are equally probable, which may not be the case in some systems. Therefore, it is important to carefully consider the assumptions and limitations when using this method to derive the Boltzmann distribution.

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