No Arabic abstract
Statistical thermodynamics of small systems shows dramatic differences from normal systems. Parallel to the recently presented steady-state thermodynamic formalism for master equation and Fokker-Planck equation, we show that a ``thermodynamic theory can also be developed based on Tsallis generalized entropy $S^{(q)}=sum_{i=1}^N(p_i-p_i^q)/[q(q-1)]$ and Shiinos generalized free energy $F^{(q)}=[sum_{i=1}^Np_i(p_i/pi_i)^{q-1}-1]/[q(q-1)]$, where $pi_i$ is the stationary distribution. $dF^{(q)}/dt=-f_d^{(q)}le 0$ and it is zero iff the system is in its stationary state. $dS^{(q)}/dt-Q_{ex}^{(q)} = f_d^{(q)}$ where $Q_{ex}^{(q)}$ characterizes the heat exchange. For systems approaching equilibrium with detailed balance, $f_d^{(q)}$ is the product of Onsagers thermodynamic flux and force. However, it is discovered that the Onsagers force is non-local. This is a consequence of the particular transformation invariance for zero energy of Tsallis statistics.
Filtering theory gives an explicit models for the flow of information and thereby quantifies the rates of change of information supplied to and dissipated from the filters memory. Here we extend the analysis of Mitter and Newton from linear Gaussian models to general nonlinear filters involving Markov diffusions.The rates of entropy production are now generally the average squared-field (co-metric) of various logarithmic probability densities, which may be interpreted as Fisher information associate with Gaussian perturbations (via de Bruijns identity). We show that the central connection is made through the Mayer-Wolf and Zakai Theorem for the rate of change of the mutual information between the filtered state and the observation history. In particular, we extend this Theorem to cover a Markov diffusion controlled by observations process, which may be interpreted as the filter acting as a Maxwells Daemon applying feedback to the system.
This PhD thesis deals with the Markov picture of developed turbulence from the theoretical point of view. The thesis consists of two parts. The first part introduces stochastic thermodynamics, the second part aims at transferring the concepts of stochastic thermodynamics to developed turbulence. / Central in stochastic thermodynamics are Markov processes. An elementary example is Brownian motion. In contrast to macroscopic thermodynamics, the work done and the entropy produced for single trajectories of the Brownian particles are random quantities. Statistical properties of such fluctuating quantities are central in the field of stochastic thermodynamics. Prominent results are so-called fluctuation theorems which express the balance between production and consumption of entropy and generalise the second law. / Turbulent cascades of eddies are assumed to be the predominant mechanism of turbulence generation and fix the statistical properties of developed turbulent flows. An intriguing phenomenon of developed turbulence, known as small-scale intermittency, are violent small-scale fluctuations in flow velocity that exceed any Gaussian prediction. / In analogy to Brownian motion, it is demonstrated in the thesis how the assumption of the Markov property leads to a Markov process for the turbulent cascade that is equivalent to the seminal K62 model. In addition to the K62 model, it is demonstrated how many other models of turbulence can be written as a Markov process, including scaling laws, multiplicative cascades, multifractal models and field-theoretic approaches. Based on the various Markov processes, the production of entropy along the cascade and the corresponding fluctuation theorems is discussed. In particular, experimental data indicates that entropy consumption is linked to small-scale intermittency, and a connection between entropy consumption and an inverse cascade is suggestive.
We develop a method for producing estimates on the spectral gaps of reversible Markov jump processes with chaotic invariant measures, and we apply it to prove the Kac conjecture for hard sphere collision in three dimensions.
Recently the 14 moments model of Extended Thermodynamics for dense gases and macromolecular fluids has been considered and an exact solution, of the restrictions imposed by the entropy principle and that of Galilean relativity, has been obtained through a non relativistic limit. Here we prove uniqueness of the above solution and exploit other pertinent conditions such us the convexity of the function $h$ related to the entropy density, the problem of subsystems and the fact that the flux in the conservation law of mass must be the moment of order 1 in the conservation law of momentum. Also the solution of this last condition is here obtained without using expansions around equilibrium. The results present interesting aspects which were not suspected when only approximated solutions of this problem were known.
In this paper, we provide new proofs of the existence and the condensation of Bethe roots for the Bethe Ansatz equation associated with the six-vertex model with periodic boundary conditions and an arbitrary density of up arrows (per line) in the regime $Delta<1$. As an application, we provide a short, fully rigorous computation of the free energy of the six-vertex model on the torus, as well as an asymptotic expansion of the six-vertex partition functions when the density of up arrows approaches $1/2$. This latter result is at the base of a number of recent results, in particular the rigorous proof of continuity/discontinuity of the phase transition of the random-cluster model, the localization/delocalization behaviour of the six-vertex height function when $a=b=1$ and $cge1$, and the rotational invariance of the six-vertex model and the Fortuin-Kasteleyn percolation.