04.08.2013 Views

Statistical Mechanics - Physics at Oregon State University

Statistical Mechanics - Physics at Oregon State University

Statistical Mechanics - Physics at Oregon State University

SHOW MORE
SHOW LESS

You also want an ePaper? Increase the reach of your titles

YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.

1.5. THERMAL EQUILIBRIUM. 15<br />

the magnetiz<strong>at</strong>ion of the subsystems. Since the total magnetiz<strong>at</strong>ion does not<br />

change, the only independent variable is xA.<br />

If we assume th<strong>at</strong> the st<strong>at</strong>es of the subsystems are not correl<strong>at</strong>ed, we have<br />

the following result for the multiplicity functions:<br />

g(N, x) = <br />

g(NA, xA)g(NB, xB) (1.39)<br />

xA<br />

with xA and xB rel<strong>at</strong>ed as described above.<br />

Next, we introduce a short hand not<strong>at</strong>ion for the individual terms:<br />

t(xA) = g(NA, xA)g(NB, xB) (1.40)<br />

and notice th<strong>at</strong> this function is also sharply peaked around its maximum value<br />

when both NA and NB are large. For our model the multiplicity function is in<br />

th<strong>at</strong> case<br />

Therefore<br />

g(U) =<br />

t(xA) = 2<br />

<br />

1<br />

2<br />

π NANB<br />

N 1 −<br />

e<br />

<br />

2<br />

πN 2N 1 −<br />

e 2 x2N 2 (x2<br />

2<br />

ANA+x B NB) = t0e<br />

1 − 2 (x2<br />

2<br />

ANA+xB NB)<br />

(1.41)<br />

(1.42)<br />

If the number of particles is very large, xA is approxim<strong>at</strong>ely a continuous<br />

variable and the number of st<strong>at</strong>es is maximal when the deriv<strong>at</strong>ive is zero. It is<br />

easier to work with the log of the product in t, which is also maximal when the<br />

term is maximal. We find<br />

log(t(xA)) = log(t0)− 1<br />

2 (x2ANA+x 2 BNB) = log(t0)− 1<br />

2 x2ANA− 1<br />

(xN−xANA)<br />

2NB<br />

2 )<br />

(1.43)<br />

The deriv<strong>at</strong>ives are straightforward (thank you, logs)<br />

<br />

∂ log(t)<br />

= −xANA −<br />

∂xA<br />

NA<br />

(xANA − xN)<br />

NB<br />

(1.44)<br />

2 ∂ log(t)<br />

∂xA 2<br />

<br />

= −NA − N 2 A<br />

NB<br />

= − NNA<br />

NB<br />

(1.45)<br />

The second deriv<strong>at</strong>ive is neg<strong>at</strong>ive, and hence we do have a maximum when the<br />

first deriv<strong>at</strong>ive is zero. This is the case when<br />

or<br />

0 = −xANB − (xANA − xN) (1.46)<br />

xA = x = xB<br />

(1.47)

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!