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Showing posts from February, 2009

### Moving Walls and Maximizing Microstates: It's Obvious. Not!

This installment of “It’s Obvious. Not!” looks at: Book: “Statistical Mechanics” Edition: second Authors: R.K. Pathria Publisher: Elsevier Butterworth Heinemann Page: 14 Section 1.3 follows the derivation laid out in 1.2 , but with a variable volume to arrive at: As in 1.2, a parameter is defined that must be equalized: The above subscript notation indicates that N and E are held constant and that V is equal to its equilibrium value. The basic formula of thermodynamics is then stated as: Immediately after which, it is stated that: But how? First, remembering that N and E are constant, rewrite the basic formula above as: Now, using the expression from section 1.2 relating the micro and macro states: We can write: However, so,

### Cool Math Tricks: Pulling Linear Factors out of Binomial Sums

While doing my statistical mechanics homework today I arrived at a sum that looked like Sums like this come up frequently when you're working with random walks, or flipping coins, or counting states of quantum mechanical systems where only two energies are allowed, or in any other number of contrived situations. It's almost a good looking sum because everything to the right of the factor of m looks like the sum for a binomial expansion : which simply evaluates to: It turns out that there's an easy way to get the factor of m out of the sum and get on with your life! First notice that: so that the sum can be re-written as: So, we rather handily got rid of the factor of m. The extra factor of p can be taken outside of the sum since it has nothing to do with the summation index m. Furthermore, the order of summation and differentiation can be interchanged to arrive at: Now the sum actually is a binomial expansion and after simplifying and performing the derivat

### Maximizing Microstates: It's Obvious! Not!

This installment of “It’s Obvious. Not!” looks at: Book: “Statistical Mechanics” Edition: second Authors: R.K. Pathria Publisher: Elsevier Butterworth Heinemann Page: 12 The idea in Pathria is to start with a function for the number of microstates as a function of energy and then maximize it to study the implications of a system in equilibrium, (the maximum number of microstates). Pathria skips a few steps in the differentiation and simplification. There shown below to help me and others along. Have fun! Starting with Maximize with respect to . Keep in mind that is a function of : Use the chain rule of differentiation to expand the second partial derivative: The last derivative term simplifies to -1: So, to maximize we have: Now, consider the differentiation of a function . The chain rule gives: Applying this to our result above, we get: