Continuous Dynamical Systems

Chapter 4 covers much of the same ground as chapter 3 but from a more formal dynamical systems theory approach. The discrete CA world is examined in the context of what is known about the behavior of continuous dynamical systems, and a number of important methodological tools developed by dynamical systems theory (i.e. Lyapunov exponents, invariant measures, and various measures of entropy and... 

Without immersing ourselves in mathematical rigor, we define a dynamical system - from a physicist s perspective - as being any physical system that evolves in time according to some well-defined rule. To be somewhat more specific, a general continuous dynamical system satisfies the following two properties ... 

A partial analogy between the dynamics of CA and the behaviors of continuous dynamical systems may be obtained by exploiting a fundamental property of CA systems namely, continuity in the Cantor-set Topology. We recall from section 2.2.1 that the collection of all one-dimensional configurations, or the CA phase space, r = where E = 0,1,..., fc 9 cr and Z is the set of integers by which each site of the lattice is indexed, is a compact metric space homeomorphic to the Cantor set under the metric... 

The formalism for computing Lyajmnov exponents for continuous dynamical systems that was introduced in the last section can also be used, with only minor modifications, for determining exponents for CA as well. The major modification involves replacing the Euclidean norm, V t) - used for measuring the divergence of two nearby trajectories (see equation 4.60) - by the Cantor-set metric, d t) ... 

Second, observe that the methodology need not be constrained to CA systems alone. With a suitable coarse-graining, the same methodology can easily be extended to provide the spectra of continuous dynamical systems, such as onedimensional mappings of the unit interval to itself. 

Recently, Wiggins et al. [15] provided a firm mathematical foundation of the robust persistence of the invariant of motion associated with the phase-space reaction coordinate in a sea of chaos. The central component in RIT that is, unstable periodic orbits, are naturally generalized in many DOFs systems in terms of so-called normally hyperbolic invariant manifold (NHIM). The fundamental theorem on NHIMs, denoted here by M, ensures [21,53] that NHIMs, if they exist, survive under arbitrary perturbation with the property that the stretching and contraction rates under the linearized dynamics transverse to jM dominate those tangent to M. Note that NHIM only requires that instability in either a forward or backward direction in time transverse to M is much stronger than those tangential directions of M, and hence the concept of NHIM can be applied to any class of continuous dynamical systems. In the case of the vicinity of saddles for Hamiltonian problems with many DOFs, the NHIM is expressed by a set of all (p, q) satisfying both q = p = Q and o(Jb) + En=i (Jb, b) = E, that is. 

M. Alvarez-Ramirez and J. Delagado, Discrete and Continuous Dynamical Systems. 9, 1149 (2003). 

The most basic concept is that of a dynamical (or a semidynamical) system. Let 7T M X R- M be a function of two variables, where M is R" and R denotes the real numbers. (We use M for the first variable or state space to suggest that the results are true in greater generality.) The function 7T is said to be a continuous dynamical system if tt is continuous and has the following properties ... 

Guzzo, M. and Benettin, G. (2001) A spectral formulation of the Nekhoroshev theorem and its relevance for numerical and experimental data analysis. Discrete and Continuous Dynamical Systems-Series B, Volume 1, n. 1. 

There are various definitions of stability which describe how a dynamic system being at an equilibrium reacts on a small disturbance. In machining science the Lyapunov stability and the asymptotic stability are of high interest. Consider the continuous dynamic system ... 

In contrast with the above, chaotic dynamics evolves an asymptotic trajectory in phase space, tracing out a strange attractor. For continuous dynamical system, there are an infinite number of unstable limit cycles embedded in such an attractor, each characterized by a distinct number of oscillations per period. We may note that there are two types of plots which provide information about the attractor. 

To obtain the unconditional stability of the midpoint method in local coordinates, one would have to consider the decoupling transformation from cartesian to local coordinates for the discrete variables etc. But this transformation, which for the continuous variables is not constant, necessarily is in error which depends on k, not e. The stability properties of the discrete dynamical systems obtained by the midpoint discretization in the different sets of coordinatc.s may therefore be significantly different when it 3> e [3]. 

Continuous—steady state Continuous—dynamic Eutectic systems 1,4 4,8,9... 

Let s continue with system curves. Up to this point, all elevations, temperatures, pressures and resistances in the drawings and graphs of systems and tanks have been static. This is not reality. Let s now consider the dynamic system curve and how it coordinates with the pump curve. 

Apart from their pedagogical value, reversible rules may be used to explore possible relationships between discrete dynamical systems and the dynamics of real mechanical systems, for which the microscopic laws are known to be time-reversal invariant. What sets such systems apart from continuous idealizations is their exact reversibility, discreteness assures us that computer simulations run for arbitrarily long times will never suffer from roundoff or truncation errors. As Toffoli points out, ...the results that one obtains have thus the force of theorems [toff84a]. ... 

As should have been made plausible by the above discussion, symbolic dynamics provides an intuitive conceptual bridge between continuous and discrete dynamical systems. On the one hand, except for the fact that the coarse-grained discrete dynamics of symbolic systems is typically nonlocal in character (see the following section), symbolic dynamical systems are essentially CA-in-disguise. On the other hand, by exploiting the fundamental CA property of continuity in the Cantor-set topology under the metric defined by equation 4.49, real-valued representations of CA dynamics may be readily obtained. We discuss these two alternative points of... 

We recall from our earlier discussion of chaos in one-dimensional continuous systems (see section 4.1) that period-doubling is not the only mechanism by which chaos can be generated. Another frequently occurring route to chaos is intermittency. But while intermittency in low dimensional dynamical systems appears to be constrained to purely temporal behavior [pomeau80], CMLs exhibit a spatio-temporal intermittency in which laminar eddies are intermixed with turbulent regions in a complex pattern in space-time. 

The time that a molecule spends in a reactive system will affect its probability of reacting and the measurement, interpretation, and modeling of residence time distributions are important aspects of chemical reaction engineering. Part of the inspiration for residence time theory came from the black box analysis techniques used by electrical engineers to study circuits. These are stimulus-response or input-output methods where a system is disturbed and its response to the disturbance is measured. The measured response, when properly interpreted, is used to predict the response of the system to other inputs. For residence time measurements, an inert tracer is injected at the inlet to the reactor, and the tracer concentration is measured at the outlet. The injection is carried out in a standardized way to allow easy interpretation of the results, which can then be used to make predictions. Predictions include the dynamic response of the system to arbitrary tracer inputs. More important, however, are the predictions of the steady-state yield of reactions in continuous-flow systems. All this can be done without opening the black box. 

This condition of balanced motion is called dynamic equilibrium. Although a dynamic system contains objects that move continuously, a system at equilibrium shows no change in its observable properties. Our example of ink in water is dynamic because the water and ink molecules continually move about. The mixture is at equilibrium when the color is uniform and unchanging. In any part of the solution, ink molecules continue to move, but the number of ink molecules in each region does not change. 

The main process variables in differential contacting devices vary continuously with respect to distance. Dynamic simulations therefore involve variations with respect to both time and position. Thus two independent variables, time and position, are now involved. Although the basic principles remain the same, the mathematical formulation, for the dynamic system, now results in the form of partial differential equations. As most digital simulation languages permit the use of only one independent variable, the second independent variable, either time or distance is normally eliminated by the use of a finite-differencing procedure. In this chapter, the approach is based very largely on that of Franks (1967), and the distance coordinate is treated by finite differencing. 

Supercritical fluid extraction can be performed in a static system with the attainment of a steady-state equilibrium or in a continuous leaching mode (dynamic mode) for which equilibrium is unlikely to be obtained (257,260). In most instances the dynamic approach has been preferred, although the selection of the method probably depends just as much on the properties of the matrix as those of the analyte. The potential for saturation of a component with limited solubility in a static solvent pool may hinder complete recovery of the analyte. In a dynamic system, the analyte is continuously exposed to a fresh stream of solvent, increasing the rate of extraction from the matrix. In a static systea... 

As a third example let us consider the growth kinetics in a chemostat used by Kalogerakis (1984) to evaluate sequential design procedures for model discrimination in dynamic systems. We consider the following four kinetic models for biomass growth and substrate utilization in the continuous baker s yeast fermentation. 

This sequence of states is a discrete representation of the continuous dynamical trajectory starting from zo at time t = 0 and ending at z at time t = . Such a discrete trajectory may, for instance, result from a molecular dynamics simulation, in which the equations of motion of the system are integrated in small time steps. A trajectory can also be viewed as a high-dimensional object whose description includes time as an additional variable. Accordingly, the discrete states on a trajectory are also called time slices. 

---