4.7 Article

A novel numerical method for the time variable fractional order mobile-immobile advection-dispersion model

Journal

COMPUTERS & MATHEMATICS WITH APPLICATIONS
Volume 66, Issue 5, Pages 693-701

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.camwa.2013.01.031

Keywords

Coimbra variable fractional order derivative; Mobile-immobile advection-dispersion equation; Implicit finite difference method; Stability and convergence

Funding

  1. Fujian Provincial Department of Education Fund Class A, China [JA11034]
  2. Talent Foundation of Fuzhou University, China [XRC-0811]
  3. USA NSF [DMS-1025486, DMS-0803360]
  4. NIH [R01-EB012079]
  5. Division Of Earth Sciences
  6. Directorate For Geosciences [1344280] Funding Source: National Science Foundation

Ask authors/readers for more resources

Evolution equations containing fractional derivatives can provide suitable mathematical models for describing anomalous diffusion and transport dynamics in complex systems that cannot be modeled accurately by normal integer order equations. Recently, researchers have found that many physical processes exhibit fractional order behavior that varies with time or space. The continuum of order in the fractional calculus allows the order of the fractional operator to be considered as a variable. In this paper, we consider the mobile-immobile advection-dispersion model with the Coimbra variable time fractional derivative which is preferable for modeling dynamical systems and is more efficient from the numerical standpoint. A novel implicit numerical method for the equation is proposed and the stability of the approximation is investigated. As for the convergence of the numerical method, we only consider a special case, i.e., the time fractional derivative is independent of the time variable t. The case where the time fractional derivative depends on both the time variable t and the space variable x will be considered in a future work. Finally, numerical examples are provided to show that the implicit difference approximation is computationally efficient. (C) 2013 Elsevier Ltd. All rights reserved.

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