Diffusion by Continuous Movements
01 Jan 1922-Proceedings of The London Mathematical Society (Oxford University Press (OUP))-Iss: 1, pp 196-212
About: This article is published in Proceedings of The London Mathematical Society.The article was published on 1922-01-01 and is currently open access. It has received 2623 citations till now. The article focuses on the topics: Diffusion (business).
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TL;DR: In this paper, it is shown that the deciding factor for integrable or chaotic particle motion is the nature of the motion of the agitator, which is a very simple model which provides an idealization of a stirred tank.
Abstract: In the Lagrangian representation, the problem of advection of a passive marker particle by a prescribed flow defines a dynamical system. For two-dimensional incompressible flow this system is Hamiltonian and has just one degree of freedom. For unsteady flow the system is non-autonomous and one must in general expect to observe chaotic particle motion. These ideas are developed and subsequently corroborated through the study of a very simple model which provides an idealization of a stirred tank. In the model the fluid is assumed incompressible and inviscid and its motion wholly two-dimensional. The agitator is modelled as a point vortex, which, together with its image(s) in the bounding contour, provides a source of unsteady potential flow. The motion of a particle in this model device is computed numerically. It is shown that the deciding factor for integrable or chaotic particle motion is the nature of the motion of the agitator. With the agitator held at a fixed position, integrable marker motion ensues, and the model device does not stir very efficiently. If, on the other hand, the agitator is moved in such a way that the potential flow is unsteady, chaotic marker motion can be produced. This leads to efficient stirring. A certain case of the general model, for which the differential equations can be integrated for a finite time to produce an explicitly given, invertible, area-preserving mapping, is used for the calculations. The paper contains discussion of several issues that put this regime of chaotic advection in perspective relative to both the subject of turbulent advection and to recent work on critical points in the advection patterns of steady laminar flows. Extensions of the model, and the notion of chaotic advection, to more realistic flow situations are commented upon.
1,730 citations
TL;DR: In this article, a stochastic model of climate variability is considered in which slow changes of climate are explained as the integral response to continuous random excitation by short period "weather" disturbances.
Abstract: A stochastic model of climate variability is considered in which slow changes of climate are explained as the integral response to continuous random excitation by short period “weather” disturbances. The coupled ocean-atmosphere-cryosphere-land system is divided into a rapidly varying “weather” system (essentially the atmosphere) and a slowly responding “climate” system (the ocean, cryosphere, land vegetation, etc.). In the usual Statistical Dynamical Model (SDM) only the average transport effects of the rapidly varying weather components are parameterised in the climate system. The resultant prognostic equations are deterministic, and climate variability can normally arise only through variable external conditions. The essential feature of stochastic climate models is that the non-averaged “weather” components are also retained. They appear formally as random forcing terms. The climate system, acting as an integrator of this short-period excitation, exhibits the same random-walk response characteristics as large particles interacting with an ensemble of much smaller particles in the analogous Brownian motion problem. The model predicts “red” variance spectra, in qualitative agreement with observations. The evolution of the climate probability distribution is described by a Fokker-Planck equation, in which the effect of the random weather excitation is represented by diffusion terms. Without stabilising feedback, the model predicts a continuous increase in climate variability, in analogy with the continuous, unbounded dispersion of particles in Brownian motion (or in a homogeneous turbulent fluid). Stabilising feedback yields a statistically stationary climate probability distribution. Feedback also results in a finite degree of climate predictability, but for a stationary climate the predictability is limited to maximal skill parameters of order 0.5. DOI: 10.1111/j.2153-3490.1976.tb00696.x
1,586 citations
TL;DR: An overview of the challenges and progress associated with the task of numerically predicting particle-laden turbulent flows is provided and suggestions are made for improving closure modelling of some important correlations.
Abstract: The paper provides an overview of the challenges and progress associated with the task of numerically predicting particle-laden turbulent flows The review covers the mathematical methods based on turbulence closure models as well as direct numerical simulation (DNS) In addition, the statistical (pdf) approach in deriving the dispersed-phase transport equations is discussed The review is restricted to incompressible, isothermal flows without phase change or particle-particle collision Suggestions are made for improving closure modelling of some important correlations
1,328 citations
Cites background from "Diffusion by Continuous Movements"
...(4) The theory of Taylor [ 34 ] on the turbulent diffusion of fluid points can be applied directly to solid particles in zero gravity....
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TL;DR: The mathematical theory behind the simple random walk is introduced and how this relates to Brownian motion and diffusive processes in general and a reinforced random walk can be used to model movement where the individual changes its environment.
Abstract: Mathematical modelling of the movement of animals, micro-organisms and cells is of great relevance in the fields of biology, ecology and medicine. Movement models can take many different forms, but the most widely used are based on the extensions of simple random walk processes. In this review paper, our aim is twofold: to introduce the mathematics behind random walks in a straightforward manner and to explain how such models can be used to aid our understanding of biological processes. We introduce the mathematical theory behind the simple random walk and explain how this relates to Brownian motion and diffusive processes in general. We demonstrate how these simple models can be extended to include drift and waiting times or be used to calculate first passage times. We discuss biased random walks and show how hyperbolic models can be used to generate correlated random walks. We cover two main applications of the random walk model. Firstly, we review models and results relating to the movement, dispersal and population redistribution of animals and micro-organisms. This includes direct calculation of mean squared displacement, mean dispersal distance, tortuosity measures, as well as possible limitations of these model approaches. Secondly, oriented movement and chemotaxis models are reviewed. General hyperbolic models based on the linear transport equation are introduced and we show how a reinforced random walk can be used to model movement where the individual changes its environment. We discuss the applications of these models in the context of cell migration leading to blood vessel growth (angiogenesis). Finally, we discuss how the various random walk models and approaches are related and the connections that underpin many of the key processes involved.
1,313 citations