Kolmogorov

Davies (Turbulence Phenomena, Academic, New York, 1972) presents a good discussion of the spectrum of eddy lengths for well-developed isotropic turbulence. The smallest eddies, usually called Kolmogorov eddies (Kolmogorov, Compt. Rend. Acad. Sci. URSS, 30, 301 32, 16 [1941]), have a characteristic velocity fluctuation given by... 

Most of the energy dissipation occurs on a length scale about 5 times the Kolmogorov eddy size. The characteristic fluctuating velocity for these energy-dissipating eddies is about 1.7 times the Kolmogorov velocity. 

The algorithm for estimating the LDC and LDM for teehniques of test analysis with visual indieation is suggested. It ineludes the steps to eheek the suffieieney of experimental material [1]. The hypothesis ehoiee about the type of frequeney distribution in unreliable reaetion (UR) region is based on the ealeulation of eriteria eomplex Kolmogorov-Smirnov eriterion,... 

Typical quoted values for the Kolmogorov microscale of turbulence for agitated vessels are normally in the range 25 —50 pm. 

For turbulent fluid-indueed stresses aeting on partieles it is neeessary to eon-sider the strueture and seale of turbulenee in relation to partiele motion in the flow field. There is as yet, however, no eompletely satisfaetory theory of turbulent flow, but a great deal has been aehieved based on the theory of isotropie turbulenee (Kolmogorov, 1941). 

Middleman, S., 1965. Mass transfer from particles in agitated systems Application of the Kolmogorov theory. American Institute of Chemical Engineers Journal, 11, 750-752. 

L. E. Levine, K. Lakshmi Narayan, K. F. Kelton. Finite size corrections for the Johnson-Mehl-Avrami-Kolmogorov equation. J Mater Res 72 124, 1997. 

ISee A. Kolmogorov, Dokl. Akad. Naiik SSSR 124 (1959) 754, Ya.G.Sinai, Dokl. Akad. Naiik SSSR 124 (1959) 768. 

SbS = Smea.s B) (compare to the Kolmogorov-Sinai entropy, defined in equation 4.101). 

The Kolmogorov consistency theorem [gnto88] asserts that any set of self- and mutually- consistent probability functions Pj, j = 1,2,..., jV may be extended to a unique shift-invariant measure on F,... 

Below, we will define a canonical procedure for constructing probabilities of blocks of arbitrary lengths consistent with a given block probability function P. The Kolmogorov consistency theorem will then allow us to use this set of finite block probabilities to define a measure on the set of infinite configurations, F. 

It is an easy exercise to show that if Pn satisfies the Kolmogorov consistency conditions (equations 5.68) for all blocks Bj of size j < N, then T[N- N+LPN) satisfies the Kolmogorov consistency conditions for blocks Bj of size j < N + 1. Given a block probability function P, therefore, we can generate a set of block probability functions Pj for arbitrary j > N hy successive applications of the operator TTN-tN+i, this set is called the Bayesian extension of Pn-... 

The Local Structure Operator By the Kolmogorov consistency theorem, we can use the Bayesian extension of Pn to define a measure on F. This measure -called the finite-block measure, /i f, where N denotes the order of the block probability function from which it is derived by Bayesian extension - is defined by assigning t.o each cylinder c Bj) = 5 G F cti = 6i, 0 2 = 62, , ( j — bj a value equal to the probability of its associated block ... 

Probabilities for blocks Bj with length j < N may be calculated by appealing to the Kolmogorov consistency conditions (equation 5.68). We now examine some low order LST approximations in more detail. 

Hhe other 2-block probabilities are obtained by appealing to the Kolmogorov consistency conditions, defined in equation 5.68 Pio = Pol = Pi — Pll-... 

A basis set of probabilities, B = p(i),P(2), >P(s) is selected for parameterizing arbitrary iV-block probabilities. It is a simple exercise to show that, because of the constraints imposed by the the Kolmogorov consistency conditions (equation 5.68, s -= 2 basis elements are necessary. 

The essence of the LST for one-dimensional lattices resides in the fact that an operator TtN->N+i could be constructed (equation 5.71), mapping iV-block probability functions to [N -f l)-block probabilities in a manner which satisfies the Kolmogorov consistency conditions (equation 5.68). A sequence of repeated applications of this operator allows us to define a set of Bayesian extended probability functions Pm, M > N, and thus a shift-invariant measure on the set of all one-dimensional configurations, F. Unfortunately, a simple generalization of this procedure to lattices with more than one dimension, does not, in general, produce a set of consistent block probability functions. Extensions must instead be made by using some other, approximate, method. We briefly sketch a heuristic outline of one approach below (details are worked out in [guto87b]). 

Kolmogorov s Theorem Any real-valued continuous function f defined on an N-dimensional cube can be represented as... 

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