Truncation effects in superdiffusive front propagation with Lévy flights.

By Diego del-Castillo-Negrete. 2009

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A numerical and analytical study of the role of exponentially truncated Lévy flights in the superdiffusive propagation of fronts in reaction-diffusion systems is presented. The study is based on a variation of the Fisher-Kolmogorov equation where the diffusion operator is replaced by a lambda -truncated fractional derivative of order alpha , where 1lambda is the characteristic truncation length scale. For lambda=0 there is no truncation, and fronts exhibit exponential acceleration and algebraically decaying tails. It is shown that for lambda not equal0 this phenomenology prevails in the intermediate asymptotic regime (chit);{1alpha}x1lambda where chi is the diffusion constant. Outside the intermediate asymptotic regime, i.e., for x>1lambda , the tail of the front exhibits the tempered decay varphi approximately e;{-lambdax}x;{(1+alpha)} , the acceleration is transient, and the front velocity v_{L} approaches the terminal speed v_{*}=(gamma-lambda;{alpha}chi)lambda as t-->infinity , where it is assumed that gamma>lambda;{alpha}chi with gamma denoting the growth rate of the reaction kinetics. However, the convergence of this process is algebraic, v_{L} approximately v_{*}-alpha(lambdat) , which is very slow compared to the exponential convergence observed in the diffusive (Gaussian) case. An overtruncated regime in which the characteristic truncation length scale is shorter than the length scale of the decay of the initial condition, 1nu , is also identified. In this extreme regime, fronts exhibit exponential tails, varphi approximately e;{-nux} , and move at the constant velocity v=(gamma-lambda;{alpha}chi)nu .

Subfields: Computer Networks and Communications, Modeling and Simulation, Statistical and Nonlinear Physics, AI for Physical Sciences

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  4. Jean-Michel Roquejoffre

In Modeling and Simulation

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In Statistical and Nonlinear Physics

  1. Tenreiro Machado
  2. X. Cabré
  3. Changfeng Gui
  4. Mingfeng Zhao
  5. Jean-Michel Roquejoffre
  6. Virginia S. Kiryakova
  7. Francesco Mainardi
  8. Guy Barles

In AI for Physical Sciences

  1. X. Cabré
  2. Changfeng Gui
  3. Mingfeng Zhao
  4. Jean-Michel Roquejoffre

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