# An Introduction to Thermodynamics and Statistical Physics by Piero Olla (auth.)

By Piero Olla (auth.)

This textbook deals a sophisticated undergraduate or preliminary graduate point advent to subject matters resembling kinetic conception, equilibrium statistical mechanics and the idea of fluctuations from a contemporary standpoint. the purpose is to supply the reader with the mandatory instruments of likelihood concept and thermodynamics (especially the thermodynamic potentials) to allow next learn at complicated graduate point. while, the ebook deals a bird's eye view on arguments which are usually passed over broadly speaking curriculum classes. additional positive aspects contain a spotlight at the interdisciplinary nature of the topic and in-depth dialogue of different interpretations of the concept that of entropy. whereas a few familiarity with easy innovations of thermodynamics and likelihood thought is thought, this doesn't expand past what's often got in easy undergraduate curriculum courses.

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Extra resources for An Introduction to Thermodynamics and Statistical Physics

Example text

Cells in -space identified by coordinates = (y1 , y2 , y3 , . . , yN ) and = (y2 , y1 , y3 , . . , yN ), would differ in fact only by exchange of the states of particles 1 and 2. If the two particles are identical, the two states will be physically indistinguishable. A partition that considers and as separate, would have therefore the same meaning as trying to subdivide the discrete elements of a discrete sample space. In order to obtain an appropriate partition, we must paste together all the elementary cells, that differ only by a permutations of the yk ’s.

In fact, it would describe a possible realization of the fluctuating density profile. This will allow us to recover our intuitive idea of density as a deterministic macroscopic quantity. A characteristic of systems in turbulent conditions, is sensitive dependence on initial conditions: any small change in the initial conditions (possibly even at the level of the microscopic state ), will lead to a different fluctuation pattern in the system. The statistical properties of the fluctuations, though, will remain unaffected.

We can proceed by Taylor expansion. Since xˆ = 0, the Taylor expansion of Zxˆ , does not contain the linear term: 1 Zxˆ (j) = 1 − σx2 j2 + o(j2 ). 71) Hence, substituting into Eq. 70), and using σX2N = Nσx2 : Z(J) = lim ZYN (j) = lim N→∞ N→∞ 1− j2 + o(N −1 ) 2N N = exp(−j2 /2). 73) as claimed. The relevance of this result to the random walk dynamics, described in Sect. 1, should be apparent. If we attach a time label tk = k t to each random variable xk , and take μx = 0, the sum XN will be precisely the displacement of a random walker, that, in the time tN = N t, has performed N independent steps xk .