This chapter presents a few more applications of the cellular automata and lattice Boltzmann techniques. We introduce some new ideas and models that have not been discussed in detail previously and which can give useful hints on how to address different problems.

We shall first discuss a lattice BGK model for wave propagation in a heterogeneous media and show how it can be applied to simulate a fracture process or make predictions for radio wave propagation inside a city. Second, we shall present how van der Waals and gravity forces can be included in an FHP cellular automata fluid in order to simulate the spreading of a liquid droplet on a wetting substrate. Then, we shall define a multiparticle fluid with a collision operator inspired by the lattice BGK method in order to avoid numerical instabilities and re-introduce fluctuations in a natural way. Finally, we shall explain how particles in suspension can be transported and eroded by a fluid flow and deposited on the ground. Snowdrift and sand dunes formation is a typical domain where this last model is applicable.

Wave propagation

One-dimensional waves

In this book, we have already encountered one-dimensional wave propagation. The chains of particles, or strings, discussed in section 2.2.9 move because of their internal shrinking and stretching. From a more physical point of view, this internal motion is mediated by longitudinal backward and forward deformation waves.

The cellular automata approach and the related modeling techniques are powerful methods to describe, understand and simulate the behavior of complex systems. The aim of this book is to provide a pedagogical and self-contained introduction to this field and also to introduce recent developments. Our main goal is to present the fundamental theoretical concepts necessary for a researcher to address advanced applications in physics and other scientific areas.

In particular, this book discusses the use of cellular automata in the framework of equilibrium and nonequilibrium statistical physics and in application-oriented problems. The basic ideas and concepts are illustrated on simple examples so as to highlight the method. A selected bibliography is provided in order to guide the reader through this expanding field.

Several relevant domains of application have been mentioned only through references to the bibliography, or are treated superficially. This is not because we feel these topics are less important but, rather, because a somewhat subjective selection was necessary according to the scope of the book. Nevertheless, we think that the topics we have covered are significant enough to give a fair idea of how the cellular automata technique may be applied to other systems.

This book is written for researchers and students working in statistical physics, solid state physics, chemical physics and computer science, and anyone interested in modeling complex systems. A glossary is included to give a definition of several technical terms that are frequently used throughout the text. At the end of the first six chapters, a selection of problems is given.

Scattering and transport

When scattering occurs in systems of large spatial extension like solids, a relationship appears between scattering and transport. This is particularly evident in the process of transport of thermal neutrons in nuclear reactors. On the one hand, neutrons may form beams for the study of matter and, on the other hand, as soon as the target presents spatially distributed scattering centres, the scattering process becomes a diffusion process, i.e., a process of transport as studied in kinetic theory. Clearly, both processes are similar and the difference only appears in the number and distribution of scatterers. Therefore, a fundamental connection exists between scattering theory and nonequilibrium statistical mechanics. The scattering approach to diffusion is also natural since diffusion is studied in finite pieces of material in the laboratory. Diffusion is a property of bulk matter which is extrapolated from experiments on finite samples to a hypothetical infinite sample.

Classically, the scattering on a spatially distributed target may be expected to be chaotic because the collisions on spherical scatterers have a defocusing character. Chaoticity will play an important role in such a connection. In the following, we shall elaborate in this direction with the tools developed in the previous chapters to obtain the so-called escape-rate formulas for the transport coefficients, which precisely express such a relationship (Gaspard and Nicolis 1990, Gaspard and Baras 1995).

We should mention here that Lax and Phillips (1967) proposed in the sixties a scattering theory of transport phenomena based on the properties of classical dynamics.

Today, there is a growing interest in understanding the role of chaos in nonequilibrium statistical mechanics. Although ergodic theory has been one of the seeds of modern dynamical systems theory, it is only recently that new methods have been developed – especially, in periodic-orbit theory – in order to quantitatively characterize the microscopic chaos as well as the intrinsic rates of decay or relaxation of statistical ensembles of trajectories. One of these intrinsic rates is the escape rate associated with the so-called fractal repeller which plays a central role in chaotic scattering. During recent years, chaotic scattering has been discovered in many different fields, from celestial mechanics and hydrodynamics to atomic, molecular, mesoscopic, and nuclear physics. In molecular systems, chaotic scattering provides a classical and statistical understanding of chemical reactions. Chaotic scattering is also closely related to transport processes like diffusion or viscosity. In this way, relationships can be established between the transport coefficients and the characteristic quantities of microscopic chaos, such as the Lyapunov exponents, the Kolmogorov–Sinai entropy, or the fractal dimensions. These results and their developments shed new light on nonequilibrium statistical mechanics and the problem of irreversibility.

The aim of the present book is to describe the theory of chaotic scattering and this new approach to nonequilibrium statistical mechanics starting from the principles of dynamical systems theory and from the hypothesis of microscopic chaos. For lack of space and time, the book only contains results on classical dynamical systems, although many fascinating and closely connected results have also been obtained in the context of quantum dynamics.

Dynamical randomness and the entropy per unit time

If dynamical instability is quantitatively measured by the Lyapunov exponents, on the other hand, dynamical randomness is characterized by the entropy per unit time. The entropy per unit time is a transposition of the concept of thermodynamic entropy per unit volume from space translations to time translations. As Boltzmann showed, the entropy is the logarithm of the number of complexions, i.e., the number of microscopic states which are possible in a certain volume and under certain constraints. In the time domain, the number of complexions becomes the number of possible trajectories in a given time interval. The entropy per unit time is therefore an estimation of the rate at which the number of possible trajectories grows with the length of the time interval.

This scheme is not in contradiction with the famous Cauchy theorem which asserts the uniqueness of the trajectory issued from given initial conditions. Indeed, as in statistical mechanics, the counting proceeds with the constraint that the trajectories belong to cells of phase space. Since each cell is a continuum, the counting becomes nontrivial. Indeed, an initial cell may be stretched into a long and thin cell which will overlap several other cells at the next time step. In this way, the stretching and folding mechanism in phase space implies that the tree of possible trajectories has a number of branches which grows exponentially with a positive branching rate.

The counting may be purely topological, which yields the definition of the topological entropy per unit time of Chapter 2.

The idea that gases are disordered or amorphous states of matter is old. Actually, the word gas was created from the Greek word chaos by Joan-Baptista van Helmont (1577–1644). This Flemish physician and chemist born in Brussels was the first to distinguish different kinds of gases thanks to the experimental method and he also invented an air thermoscope which was the precursor of the modern thermometer. He was contemporary with Bacon (1561–1626), Galileo (1564–1642), Kepler (1571–1630), Descartes (1596–1650), Torricelli (1608–1647), as well as with the famous painter Rubens (1577–1640). His son published his work Ortus medicinae, id est initia phisicare inaudita at Amsterdam in 1648 (Farber 1961).

During the XlXth century, the spatial disorder of gases and of matter in general was quantitatively characterized with the concept of entropy per unit volume. However, the idea of dynamical chaos, i.e., of temporal disorder in physical systems like gases is more recent as it results from a long sequence of observations and works which extends throughout the XXth century with the development of statistical mechanics.

Today, we may say that statistical mechanics and kinetic theory are among the greatest successes of modern science. Since Maxwell and Boltzmann, macroscopic properties of matter can be explained in terms of the motion of atoms and molecules composing matter. In particular, transport properties like diffusion, viscosity, or heat conductivity can be predicted in terms of the parameters of the microscopic Hamiltonians, which are the masses of the atoms and molecules, and the coupling constants of their interaction (Maxwell 1890, Boltzmann 1896).

Hydrodynamics from Liouvillian dynamics

Historical background and motivation

Hydrodynamics describes the macroscopic dynamics of fluids in terms of Navier–Stokes equations, the diffusion equation, and other phenomenological equations for the mass density, the fluid velocity and temperature, or for chemical concentrations. In nonequilibrium statistical mechanics, these phenomenological equations may be derived from a kinetic equation like the famous Boltzmann equation or other master equations describing the time evolution at the level of one-body distribution functions (Balescu 1975, Résibois and De Leener 1977, Boon and Yip 1980). The kinetic equation itself is derived from Liouvillian dynamics using a Markovian approximation such as Boltzmann's Stosszahlansatz. Such approximations may be justified in some scaling limits for dilute fluids or other systems, but the derivation of hydrodynamics is not carried out directly from the Liouvillian dynamics. The only direct link between hydrodynamics and the Liouvillian dynamics – which is used in particular in molecular-dynamics simulations – is established in terms of the Green–Kubo relations.

The recent works in dynamical systems theory have shown that further direct links are possible. In particular, we have observed with the multibaker map in Chapter 6 that the spectrum of the Pollicott–Ruelle resonances actually provides the spectrum of the phenomenological diffusion equation in spatially extended systems (Gaspard 1992a, 1995, 1996). This result suggests that the dispersion relations of hydrodynamics can be obtained in terms of the Pollicott– Ruelle resonances and that the hydrodynamic modes can be constructed as the associated eigenstates.