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Problems from nonequilibrium state

As the average relaxation time becomes even longer than 104 s long times must be allowed before the sample itself achieves equilibrium. Eventually this becomes impractical and the sample becomes a glass. The longest volume relaxation times exceed the patience of the experimenter and the sample is allowed to remain in the nonequilibrium state. However, the sample does not remain in the same state as time increases because it will still relax toward the equilibrium state. The fundamental assumption of stationarity of the fluctuations is then violated and interpretation of the PCS becomes a problem. Such considerations have not stopped people from collecting data in this regime45, but they do preclude a clean interpretation. [Pg.155]

Figures 4.44 and 4.45, best viewed in color, show a benign complication of the problem caused by the Lewis numbers. If, however, we reduce the Lewis number LeA further to 0.07, the system trajectories indicate periodic explosions of the underlying system throughout all time, and the trajectories do not converge to the steady state at all, even with what we thought to be proper feedback. The trajectory that these curves settle at is called a periodic attractor of the system in contradistinction to the earlier encountered point attractor of Figures 4.43 or 4.44, for example. A point attractor, or more accurately a fixed-point attractor, is a more commonly encountered steady state in chemical and biological engineering systems. It could be called a stationary nonequilibrium state to distinguish it from the stationary equilibrium states associated with closed or isolated batch processes. Figures 4.44 and 4.45, best viewed in color, show a benign complication of the problem caused by the Lewis numbers. If, however, we reduce the Lewis number LeA further to 0.07, the system trajectories indicate periodic explosions of the underlying system throughout all time, and the trajectories do not converge to the steady state at all, even with what we thought to be proper feedback. The trajectory that these curves settle at is called a periodic attractor of the system in contradistinction to the earlier encountered point attractor of Figures 4.43 or 4.44, for example. A point attractor, or more accurately a fixed-point attractor, is a more commonly encountered steady state in chemical and biological engineering systems. It could be called a stationary nonequilibrium state to distinguish it from the stationary equilibrium states associated with closed or isolated batch processes.
Now the technique provides the basis for simulating concentrated suspensions at conditions extending from the diffusion-dominated equilibrium state to highly nonequilibrium states produced by shear or external forces. The results to date, e.g., for structure and viscosity, are promising but limited to a relatively small number of particles in two dimensions by the demands of the hydrodynamic calculation. Nonetheless, at least one simplified analytical approximation has emerged [44], As supercomputers increase in power and availability, many important problems—addressing non-Newtonian rheology, consolidation via sedimentation and filtration, phase transitions, and flocculation—should yield to the approach. [Pg.94]

This agrees with the description of the graded bar and the equilibrium states we imagined for various portions of that and the approach can clearly be used for the salt-diffusion problem, at the 4 m-wide dike. To treat pressure differences in a parallel manner, one notes that materials become less compressible when compressed the pressure-volume curve for a fixed mass is usually concave away from the origin, as a hyperbola is. Then a profile of graded pressure is like a profile of graded concentration it is intrinsically and unquestionably a nonequilibrium state at every point, but the properties of any small portion can be matched with the properties of an imagined equilibrium state. [Pg.33]

Another problem to be discussed is the nonequilibrium state of the powdered polysulfides prepared by solid-gas interactions. In these cases the rate-controlling process is most often the sulfur diffusion in solids. A thick product layer formed on the unreacted starting solid inhibits the sulfur diffusion, resulting in a nonequilibrium state. To describe this state, one can use some indicators which are shared by many sulfide materials (Vaughan and Craig 1978). First, there is a greater number of phases on the surface of the solids than would follow from Gibbs rule the duration of the experiment has a dramatic effect on the number of phases and the compositional identity of the final products. [Pg.574]

There is also another important problem concerning the nature of ki-netically nonequilibrium states of chemical systems. This problem can be formulated in the following way. What are we dealing with, a nonequilibrium mixture of equilibrium molecules or nonequilibrium molecules In any chemical process there appear molecules in nonequilibrium states. For low-molecular compounds, the electronic and vibrational relaxation after the elementary chemical act takes little time (as a rule, less than 10" -10" s). Therefore, there are relaxed atoms, ions, free radicals, and molecules, i.e., the particles in their equilibrium states, that take part in the subsequent chemical acts of chemical transformations. If a system consisting of low-molecular compounds is removed from the state of chemical equilibrium, then, as a rule, we can speak of a nonequilibrium ensemble of equilibrium molecules. [Pg.18]

In this contribution, we describe and illustrate the latest generalizations and developments[1]-[3] of a theory of recent formulation[4]-[6] for the study of chemical reactions in solution. This theory combines the powerful interpretive framework of Valence Bond (VB) theory [7] — so well known to chemists — with a dielectric continuum description of the solvent. The latter includes the quantization of the solvent electronic polarization[5, 6] and also accounts for nonequilibrium solvation effects. Compared to earlier, related efforts[4]-[6], [8]-[10], the theory [l]-[3] includes the boundary conditions on the solute cavity in a fashion related to that of Tomasi[ll] for equilibrium problems, and can be applied to reaction systems which require more than two VB states for their description, namely bimolecular Sjy2 reactions ],[8](b),[12],[13] X + RY XR + Y, acid ionizations[8](a),[14] HA +B —> A + HB+, and Menschutkin reactions[7](b), among other reactions. Compared to the various reaction field theories in use[ll],[15]-[21] (some of which are discussed in the present volume), the theory is distinguished by its quantization of the solvent electronic polarization (which in general leads to deviations from a Self-consistent limiting behavior), the inclusion of nonequilibrium solvation — so important for chemical reactions, and the VB perspective. Further historical perspective and discussion of connections to other work may be found in Ref.[l],... [Pg.259]

In 1977. Professor Ilya Prigogine of the Free University of Brussels. Belgium, was awarded Ihe Nobel Prize in chemistry for his central role in the advances made in irreversible thermodynamics over the last ihrec decades. Prigogine and his associates investigated Ihe properties of systems far from equilibrium where a variety of phenomena exist that are not possible near or al equilibrium. These include chemical systems with multiple stationary states, chemical hysteresis, nucleation processes which give rise to transitions between multiple stationary states, oscillatory systems, the formation of stable and oscillatory macroscopic spatial structures, chemical waves, and Lhe critical behavior of fluctuations. As pointed out by I. Procaccia and J. Ross (Science. 198, 716—717, 1977). the central question concerns Ihe conditions of instability of the thermodynamic branch. The theory of stability of ordinary differential equations is well established. The problem that confronted Prigogine and his collaborators was to develop a thermodynamic theory of stability that spans the whole range of equilibrium and nonequilibrium phenomena. [Pg.349]

A powerful tool for analyzing fluctuations in a nonequilibrium systems is based on the Hamiltonian [57] theory of fluctuations or alternatively on a path-integral approach to the problem [44,58-62]. The analysis requires the solution of two closely interrelated problems. The first is the evaluation of the probability density for a system to occupy a state far from the stable state in the phase space. In the stationary regime, the tails of this probability are determined by the probabilities of large fluctuations. [Pg.473]

Normally the apparatus of equilibrium thermodynamics can be used for the remoteness in the second and third sense and a corresponding choice of space of variables, though in each specific case this calls for additional check. Because for the spaces that do not contain the functions of state (in the descriptions of nonequilibrium systems these are the spaces of work-time or heat-time) the notion of differential loses its sense, and transition to the spaces with differentiable variables requires that the holonomy of the corresponding Pfaffian forms be proved. The principal difficulties in application of the equilibrium models arise in the case of remoteness from equilibrium in the first sense when the need appears to introduce additional variables and increase dimensionality of the problem solved. [Pg.11]

Simplification of the solution or complete exclusion of the problem of dividing the variables into fast and slow is a great computational advantage of MEIS in comparison with the models of kinetics and nonequilibrium thermodynamics. The problem is eliminated, if there are no constraints in the equilibrium models on macroscopic kinetics. Indeed, the searches for the states corresponding to final equilibrium of only fast variables and states including final equilibrium coordinates of both types of variables with the help of these models do not differ from one another algorithmically. With kinetic constraints the division problem is solved by one of the three methods presented in Section 3.4, which are applied in the majority of cases to slow variables limiting the results of the main studied process. [Pg.49]


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See also in sourсe #XX -- [ Pg.574 ]




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