Particles dispersion and deposition in inhomogeneous turbulent flows using continuous random walk models

2019 ◽  
Vol 31 (8) ◽  
pp. 083301 ◽  
Author(s):  
Amir A. Mofakham ◽  
Goodarz Ahmadi
Keyword(s):  
Random Walk ◽  
Turbulent Flows ◽  
Continuous Random Walk

On the age distribution of a Markov chain

1978 ◽  
Vol 15 (1) ◽  
pp. 65-77 ◽  
Author(s):  
Anthony G. Pakes
Keyword(s):  
Markov Chain ◽  
Random Walk ◽  
Limit Theorems ◽  
Age Distribution ◽  
Weak Limit ◽  
Absorbing State ◽  
Absorbing Markov Chain ◽  
Continuous Random Walk ◽  
A Chain ◽  
Watson Process

This paper develops the notion of the limiting age of an absorbing Markov chain, conditional on the present state. Chains with a single absorbing state {0} are considered and with such a chain can be associated a return chain, obtained by restarting the original chain at a fixed state after each absorption. The limiting age, A(j), is the weak limit of the time given Xn = j (n → ∞).A criterion for the existence of this limit is given and this is shown to be fulfilled in the case of the return chains constructed from the Galton–Watson process and the left-continuous random walk. Limit theorems for A (J) (J → ∞) are given for these examples.

On the age distribution of a Markov chain

1978 ◽  
Vol 15 (01) ◽  
pp. 65-77 ◽  
Author(s):  
Anthony G. Pakes
Keyword(s):  
Markov Chain ◽  
Random Walk ◽  
Limit Theorems ◽  
Age Distribution ◽  
Weak Limit ◽  
Absorbing State ◽  
Absorbing Markov Chain ◽  
Continuous Random Walk ◽  
A Chain ◽  
Watson Process

This paper develops the notion of the limiting age of an absorbing Markov chain, conditional on the present state. Chains with a single absorbing state {0} are considered and with such a chain can be associated a return chain,obtained by restarting the original chain at a fixed state after each absorption. The limiting age,A(j), is the weak limit of the timegivenXn=j(n → ∞).A criterion for the existence of this limit is given and this is shown to be fulfilled in the case of the return chains constructed from the Galton–Watson process and the left-continuous random walk. Limit theorems forA(J) (J →∞) are given for these examples.

Conditional limit theorems for a left-continuous random walk

1973 ◽  
Vol 10 (1) ◽  
pp. 39-53 ◽  
Author(s):  
A. G. Pakes
Keyword(s):  
Random Walk ◽  
Limit Theorems ◽  
Absorbing State ◽  
Conditional Limit Theorems ◽  
Recurrent Markov Chain ◽  
Continuous Random Walk ◽  
Condition C ◽  
Positive Recurrent ◽  
Positive Integers ◽  
Conditional Limit

The present work considers a left-continuous random walk moving on the positive integers and having an absorbing state at the origin. Limit theorems are derived for the position of the walk at time n given: (a) absorption does not occur until after n, or (b) absorption does not occur until after m + n where m is very large, or (c) absorption occurs at m + n. A limit theorem is given for an R-positive recurrent Markov chain on the non-negative integers with an absorbing origin and subject to condition (c) above.

The maximum and time to absorption of a left-continuous random walk

1976 ◽  
Vol 13 (3) ◽  
pp. 444-454 ◽  
Author(s):  
P. J. Green
Keyword(s):  
Random Walk ◽  
Limit Theorem ◽  
Joint Distribution ◽  
Distribution Of The Maximum ◽  
Conditional Limit Theorem ◽  
Continuous Random Walk ◽  
Conditional Limit

For a left-continuous random walk, absorbing at 0, the joint distribution of the maximum and time to absorption is derived. A description of the tails of the distributions and a conditional limit theorem are obtained for the cases where absorption is certain.

Conditional limit theorems for a left-continuous random walk

1973 ◽  
Vol 10 (01) ◽  
pp. 39-53 ◽  
Author(s):  
A. G. Pakes
Keyword(s):  
Random Walk ◽  
Limit Theorems ◽  
Absorbing State ◽  
Conditional Limit Theorems ◽  
Recurrent Markov Chain ◽  
Continuous Random Walk ◽  
Condition C ◽  
Positive Recurrent ◽  
Positive Integers ◽  
Conditional Limit

The present work considers a left-continuous random walk moving on the positive integers and having an absorbing state at the origin. Limit theorems are derived for the position of the walk at time n given: (a) absorption does not occur until after n, or (b) absorption does not occur until after m + n where m is very large, or (c) absorption occurs at m + n. A limit theorem is given for an R-positive recurrent Markov chain on the non-negative integers with an absorbing origin and subject to condition (c) above.

On the maximum and absorption time of left-continuous random walk

1978 ◽  
Vol 15 (2) ◽  
pp. 292-299 ◽  
Author(s):  
Anthony G. Pakes
Keyword(s):  
Random Walk ◽  
Limit Theorems ◽  
Initial Position ◽  
Absorption Time ◽  
Conditional Limit Theorems ◽  
Negative Drift ◽  
Continuous Random Walk ◽  
Conditional Limit ◽  
Transformed Distribution

In a recent paper Green (1976) obtained some conditional limit theorems for the absorption time of left-continuous random walk. His methods require that in the driftless case the increment distribution has exponentially decreasing tails and that the same is true for a transformed distribution in the case of negative drift.Here we take a different approach which will produce Green's results under minimal conditions. Limit theorems are given for the maximum as the initial position of the random walk tends to infinity.

scholarly journals The backward Îto method for the Lagrangian simulation of transport processes with large space variations of the diffusivity

2007 ◽  
Vol 4 (4) ◽  
pp. 623-652 ◽  
Author(s):  
D. Spivakovskaya ◽  
A. W. Heemink ◽  
E. Deleersnijder
Keyword(s):  
Random Walk ◽  
Turbulent Flows ◽  
Transport Processes ◽  
Test Cases ◽  
Continuous Change ◽  
Smooth Functions ◽  
Large Space ◽  
Lagrangian Simulation ◽  
Itô Calculus ◽  
Ito Method

Abstract. Random walk models are a powerful tool for the investigation of transport processes in turbulent flows. However, standard random walk methods are applicable only when the flow velocities and diffusivity are sufficiently smooth functions. In practice there are some regions where the rapid but continuous change in diffusivity may be represented by a discontinuity. The random walk model based on backward Îto calculus can be used for these problems. This model was proposed by LaBolle et al. (2000). The latter is best suited to the problems under consideration. It is then applied for two test cases with discontinuous diffusivity, highlighting the advantages of this method.

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