Derivation of Grand Canonical Ensemble from scratch?

In summary, the conversation discusses the grand canonical partition function in statistical physics and the desire to derive various quantities using combinatoric arguments. The question is whether there is a way to proceed for the grand canonical partition function, with a specific example given for a universe with a fixed temperature and a single species of particle. The reply suggests an alternate derivation of the Grand Canonical Assembly from the viewpoint of information theory and inference, and the person asking the question finds answers in some statistical physics books.
  • #1
tim_lou
682
1
I've been studying and thinking about statistical physics for a couple days now... and what bothers me is the grand canonical partition function. Namely that for a system with fixed chemical potential and energy [itex]\epsilon_i[/itex] the probability of having [itex]N_i[/itex] particle in that state is proportional to:

[tex]P\sim \exp[-\beta(\epsilon_i -\mu N_i)][/tex]

I completely understand the derivation from book, basically it invokes the thermodynamic identity. (the dS= bla bla and what not)

However, I really want to view statistical physics in a whole different view point. I want to be able to derive P, V, S, E, and chemical potential using basic combinatoric argument and prove that the chemical potential is indeed G/N (for non-interacting particles).

I've read some other books and understand where the Boltzmann statistics comes in. Basically, the books use combinatorics to show that for classical particles, the multiplicity is:
[tex]\Omega=\prod_i \frac{g_i^{N_i}}{N_i!}[/tex]

and from that, and a couple energy and particles constrain, Boltzmann statistics naturally comes in. However, is there a similar way to proceed for the grand canonical partition function?

Let me make this question precise:

Suppose that we have the universe at a fixed temperature, T, and there is only one species of particle and the university has N of these particles (N is not fixed). Further suppose that there are fixed number of states of energy, and for each energy [itex]\epsilon_i[/tex] we have degeneracy [itex]g_i[/itex] (both are fixed constants).

suppose the multiplicity is given by:
[tex]\Omega=\prod_i \frac{g_i^{N_i}}{N_i!}[/tex]

we look at the sub system (of the universe) that has energy [itex]\epsilon_i[/itex], what is the probability that this system has [tex]N_i[/tex] particles? how would the chemical potential come in?

I know that the energy of the universe must be constant:
[tex]E=\sum_i \epsilon_i N_i[/tex]

Do I need further constrains on the problem or what?
 
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  • #2
For an alternate derivation of the Grand Canonical Assembly from the viewpoint of information theory and inference, including derivation of the quantities you are interested in, see

Jaynes, E. T., 1957, `Information Theory and Statistical Mechanics,' Phys. Rev., 106, 620.

http://bayes.wustl.edu/etj/articles/theory.1.pdf"
 
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  • #3
Thank you for the reply, marcusl, though I did not understand a lot of the things in the article you posted. However, I have found some answers from some statistical physics books I found.

the answer is very simple... basically instead of partition the whole universe under one parameter, one partitions the universe using [itex]\epsilon_i[/itex] and [itex]N_i[/tex]... and the result follows naturally.
 

Related to Derivation of Grand Canonical Ensemble from scratch?

1. What is the Grand Canonical Ensemble?

The Grand Canonical Ensemble is a statistical mechanical ensemble used to describe the behavior of a system in equilibrium with both a heat reservoir and a particle reservoir. It is used to calculate the properties of a system at a given temperature, volume, and chemical potential.

2. Why is the Grand Canonical Ensemble important?

The Grand Canonical Ensemble allows us to study systems that are not in a closed system, but rather exchange energy and particles with their surroundings. This is important for understanding real-world systems, such as gases and solutions, which are constantly exchanging particles and energy with their surroundings.

3. How is the Grand Canonical Ensemble derived?

The Grand Canonical Ensemble is derived using statistical mechanics principles and the Boltzmann distribution. By considering the probability of different microstates of a system, we can determine the most likely distribution of particles and energy in a system at a given temperature and chemical potential.

4. What is the difference between the Grand Canonical Ensemble and other ensembles?

The Grand Canonical Ensemble differs from other ensembles, such as the Canonical and Microcanonical ensembles, in that it allows for fluctuations in the number of particles in the system. This makes it more suitable for studying systems in contact with a particle reservoir.

5. What are some applications of the Grand Canonical Ensemble?

The Grand Canonical Ensemble is commonly used in the study of gases, solutions, and other systems where particles can enter or leave the system. It is also used in the study of phase transitions and critical phenomena.

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