Dynamical systems entropy production

Evans and Baranyai [51, 52] have explored what they describe as a nonlinear generalization of Prigogine s principle of minimum entropy production. In their theory the rate of (first) entropy production is equated to the rate of phase space compression. Since phase space is incompressible under Hamilton s equations of motion, which all real systems obey, the compression of phase space that occurs in nonequilibrium molecular dynamics (NEMD) simulations is purely an artifact of the non-Hamiltonian equations of motion that arise in implementing the Evans-Hoover thermostat [53, 54]. (See Section VIIIC for a critical discussion of the NEMD method.) While the NEMD method is a valid simulation approach in the linear regime, the phase space compression induced by the thermostat awaits physical interpretation even if it does turn out to be related to the rate of first entropy production, then the hurdle posed by Question (3) remains to be surmounted. 

The plan of this chapter is the following. Section II gives a summary of the phenomenology of irreversible processes and set up the stage for the results of nonequilibrium statistical mechanics to follow. In Section III, it is explained that time asymmetry is compatible with microreversibility. In Section IV, the concept of Pollicott-Ruelle resonance is presented and shown to break the time-reversal symmetry in the statistical description of the time evolution of nonequilibrium relaxation toward the state of thermodynamic equilibrium. This concept is applied in Section V to the construction of the hydrodynamic modes of diffusion at the microscopic level of description in the phase space of Newton s equations. This framework allows us to derive ab initio entropy production as shown in Section VI. In Section VII, the concept of Pollicott-Ruelle resonance is also used to obtain the different transport coefficients, as well as the rates of various kinetic processes in the framework of the escape-rate theory. The time asymmetry in the dynamical randomness of nonequilibrium systems and the fluctuation theorem for the currents are presented in Section VIII. Conclusions and perspectives in biology are discussed in Section IX. 

It is most remarkable that the entropy production in a nonequilibrium steady state is directly related to the time asymmetry in the dynamical randomness of nonequilibrium fluctuations. The entropy production turns out to be the difference in the amounts of temporal disorder between the backward and forward paths or histories. In nonequilibrium steady states, the temporal disorder of the time reversals is larger than the temporal disorder h of the paths themselves. This is expressed by the principle of temporal ordering, according to which the typical paths are more ordered than their corresponding time reversals in nonequilibrium steady states. This principle is proved with nonequilibrium statistical mechanics and is a corollary of the second law of thermodynamics. Temporal ordering is possible out of equilibrium because of the increase of spatial disorder. There is thus no contradiction with Boltzmann s interpretation of the second law. Contrary to Boltzmann s interpretation, which deals with disorder in space at a fixed time, the principle of temporal ordering is concerned by order or disorder along the time axis, in the sequence of pictures of the nonequilibrium process filmed as a movie. The emphasis of the dynamical aspects is a recent trend that finds its roots in Shannon s information theory and modem dynamical systems theory. This can explain why we had to wait the last decade before these dynamical aspects of the second law were discovered. 

The foundation of irreversible thermodynamics is the concept of entropy production. The consequences of entropy production in a dynamic system lead to a natural and general coupling of the driving forces and corresponding fluxes that are present in a nonequilibrium system. 

Note, however, that the concept of the entropy production rate is of critical importance for analyzing the evolution of systems that are close to equilibrium rather than of dynamic systems, which are described by rigid kinetic schemes with time deterministic behavior ( dynamic machines ). 

As mentioned above, the time-integral of the dissipation function takes on the value of the extensive generalised entropy production, 2, over a period, t under suitable circumstances. The main requirement is that the dynamics satisfies the condition know as the adiabatic incompressibility of phase space . In this case, 2, = —JtFefiV, where is the dissipative flux caused by the field, F, p = IKk T) where T is the temperature of the corresponding initial system and V is the volume of the system. An example where such a relation can be applied is if a molten salt at equilibrium was exposed to a constant electric field. In that case the entropy production would be directly proportional to the current induced, and the FR would describe the probability that it would be observed to flow in the + ve or — ve... 

We have obtained several interesting results from the theorem If the period of the external transformation is much longer than the relaxation time, then thermodynamic entropy production is proportional to the ratio of the period and relaxation time. The relaxation time is proportional to the inverse of the Kolmogorov-Sinai entropy for small strongly chaotic systems. Thermodynamic entropy production is proportional to the inverse of the dynamical entropy [11]. On the other hand, thermodynamic entropy production is proportional to the dynamical entropy when the period of the external transformation is much shorter than the relaxation time. Furthermore, we found fractional scaling of the excess heat for long-period external transformations, when the system has longtime correlation such as 1 /fa noise. Since excess heat is measured as the area of a hysteresis loop [12], these properties can be confirmed in experiments. 

The area of the hysteresis loop is proportional to jT, so it increases for greater T. The thermodynamic entropy production is proportional to the dynamical entropy production in this case [22, 23]. In a large chaotic system, we can expect less thermodynamic entropy production for weak diffusion. 

The fluctuation theorem deals with fluctuations. Since the statistics of fluctuations will be different in different statistical ensembles, the fluctuation theorem is a set of closely related theorems. Also some theorems consider nonequilibrium steady-state fluctuations, while others consider transient fluctuations. One of the fluctuation theorems state that in a time-reversible dynamic system in contact with constant temperature heat bath, the fluctuations in the time-averaged irreversible entropy productions in a nonequilibrium steady state satisfy Eqn (15.49) (Evans and Searles, 2002). 

The transient fluctuation theorem is applied to the transient response of a system. It bridges the microscopic and macroscopic domains and links the time-reversible and irreversible description of processes. In transient fluctuations, the time averages are calculated from a zero time with the known initial distribution function until a finite time. The initial distribution function may be, for example, one of the equilibrium distribution functions of statistical mechanics. So, for arbitrary averaging times, the transient fluctuation theorems are exact. The transient fluctuation theorem describes how irreversible macroscopic behavior evolves from time-reversible microscopic dynamics as either the observation time or the system size increases. It also shows how the entropy production can be related to the forward and backward dynamical randomness of the trajectories or paths of systems as characterized by the entropies per unit time. 

Crooks stationary fluctuation theorem relates entropy production to the dynamical randomness of the stochastic processes. Therefore, it relates the statistics of fluctuations to the nonequilibrium thermodynamics through the entropy production estimations. The theorem predicts that entropy production will be positive as either the system size or the observation time increases and the probability of observing an entropy production opposite to that dictated by the second law of thermodynamics decreases exponentially. 

We shall apply equations in section 14.2.1 in a study of the transport coeiS-cients. The system was studied by molecular dynamics simulations to provide molecular insight. The entropy production from eq 14.17 is... 

This equation shows that in isothennal biochemical reaction cycles, the entropy of the system changes because of the heat dissipation rate <(dis exchanged with the surrounding and the rate of free energy dissipation P due to entropy production. This equation also indicates the dissipative character of biochemical cycles. Dynamic equations similar to Eq. (11.5) can also be written for enthalpy and Gibbs ftee energy changes... 

The first term on the right-hand side is the ordinary first entropy. It is negative and represents the cost of the order that is the constrained static state x. The second term is also negative and is quadratic in the coarse velocity. It represents the cost of maintaining the dynamic order that is induced in the system for a nonzero flux x. The third and fourth terms sum to a positive number, at least in the optimum state, where they cancel with the second term. As will become clearer shortly, they represent the production of first entropy as the system returns to equilibrium, and it is these terms that drive the flux. 

The reason we employ two rather distinct methods of inquiry is that neither, by itself, is free of open methodological issues. The method of molecular dynamics has been extensively applied, inter alia, to cluster impact. However, there are two problems. One is that the results are only as reliable as the potential energy function that is used as input. For a problem containing many open shell reactive atoms, one does not have well tested semiempirical approximations for the potential. We used the many body potential which we used for the reactive system in our earlier studies on rare gas clusters containing several N2/O2 molecules (see Sec. 3.4). The other limitation of the MD simulation is that it fails to incorporate the possibility of electronic excitation. This will be discussed fmther below. The second method that we used is, in many ways, complementary to MD. It does not require the potential as an input and it can readily allow for electronically excited as well as for charged products. It seeks to compute that distribution of products which is of maximal entropy subject to the constraints on the system (conservation of chemical elements, charge and... 

And equilibrium always carries the day. Equilibrium may be held at bay for a time, and systems may even enter a metastable state, but ultimately, equilibrium rules. Chemical reactions are able to respond to and adjust to lower energy and maximize entropy because chemical reactions are reversible and dynamic. Being reversible and dynamic means that chemical reactions can be quite pliable and can be coaxed into producing more products or reverting to reactants. Such manipulations are obligatory in chemical industry and the chemical lab, but they are more important than that. Equilibrium concerns us all, every day. As we live and breathe. 

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