IDEAS home Printed from https://ideas.repec.org/a/bpj/mcmeap/v21y2015i1p33-48n6.html
   My bibliography  Save this article

Stochastic simulation of fluctuation-induced reaction-diffusion kinetics governed by Smoluchowski equations

Author

Listed:
  • Sabelfeld Karl K.

    (Institute of Computational Mathematics and Mathematical Geophysics, Russian Academy of Sciences, Lavrentiev Prosp. 6, 630090 Novosibirsk, Russia)

  • Levykin Alexander I.

    (Institute of Computational Mathematics and Mathematical Geophysics, Russian Academy of Sciences, Lavrentiev Prosp. 6, 630090 Novosibirsk, Russia)

  • Kireeva Anastasiya E.

    (Institute of Computational Mathematics and Mathematical Geophysics, Russian Academy of Sciences, Lavrentiev Prosp. 6, 630090 Novosibirsk, Russia)

Abstract

A stochastic algorithm for simulation of fluctuation-induced reaction-diffusion kinetics is presented and further developed following our previous study [J. Math. Chem. (2015), DOI 10.1007/s10910-014-0446-6] where this method was used to describe the annihilation of spatially separate electrons and holes in a disordered semiconductor. This model is based on the spatially inhomogeneous, nonlinear Smoluchowski equations with random initial distribution density. Here we focus on the spatial distribution of the reactants, and study the segregation effect which we have found under certain reaction conditions. In addition, to extend simulations on large samples we implemented the method in the cellular-automata framework interpreted as a stochastic interacting particles system in discrete but randomly progressed time instances. We have suggested a first passage time technique to characterize the clustering of electrons and holes, which seems to be quite convenient and informative instrument also in more general processes when there is a need to analyze the segregation phenomena.

Suggested Citation

  • Sabelfeld Karl K. & Levykin Alexander I. & Kireeva Anastasiya E., 2015. "Stochastic simulation of fluctuation-induced reaction-diffusion kinetics governed by Smoluchowski equations," Monte Carlo Methods and Applications, De Gruyter, vol. 21(1), pages 33-48, March.
  • Handle: RePEc:bpj:mcmeap:v:21:y:2015:i:1:p:33-48:n:6
    DOI: 10.1515/mcma-2014-0012
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/mcma-2014-0012
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.1515/mcma-2014-0012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sabelfeld K.K. & Rogasinsky S.V. & Kolodko A.A. & Levykin A.I., 1996. "Stochastic algorithms for solving Smolouchovsky coagulation equation and applications to aerosol growth simulation," Monte Carlo Methods and Applications, De Gruyter, vol. 2(1), pages 41-88, December.
    2. Kolodko, A. & Sabelfeld, K. & Wagner, W., 1999. "A stochastic method for solving Smoluchowski's coagulation equation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 49(1), pages 57-79.
    3. Sabelfeld, Karl & Kolodko, Anastasia, 2003. "Stochastic Lagrangian models and algorithms for spatially inhomogeneous Smoluchowski equation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 61(2), pages 115-137.
    4. Y. L. Zheng & S. L. Nie & H. Ji & Z. Hu, 2013. "Application of a Fuzzy Programming Through Stochastic Particle Swarm Optimization to Assessment of Filter Management Strategies in Fluid Power System Under Uncertainty," Journal of Optimization Theory and Applications, Springer, vol. 157(1), pages 276-286, April.
    5. Sabelfeld, K.K., 1998. "Stochastic models for coagulation of aerosol particles in intermittent turbulent flows," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 47(2), pages 85-101.
    6. Kolodko A. A. & Sabelfeld K. K., 2001. "Stochastic Lagrangian model for spatially inhomogeneous Smoluchowski equation governing coagulating and diffusing particles," Monte Carlo Methods and Applications, De Gruyter, vol. 7(3-4), pages 223-228, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eibeck Andreas & Wagner Wolfgang, 2001. "Stochastic algorithms for studying coagulation dynamics and gelation phenomena," Monte Carlo Methods and Applications, De Gruyter, vol. 7(1-2), pages 157-166, December.
    2. Wagner, Wolfgang, 2003. "Stochastic, analytic and numerical aspects of coagulation processes," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 62(3), pages 265-275.
    3. Sabelfeld, Karl K., 2018. "A random walk on spheres based kinetic Monte Carlo method for simulation of the fluctuation-limited bimolecular reactions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 143(C), pages 46-56.
    4. Kolodko A. A. & Sabelfeld K. K., 2001. "Stochastic Lagrangian model for spatially inhomogeneous Smoluchowski equation governing coagulating and diffusing particles," Monte Carlo Methods and Applications, De Gruyter, vol. 7(3-4), pages 223-228, December.
    5. Sabelfeld Karl K. & Eremeev Georgy, 2018. "A hybrid kinetic-thermodynamic Monte Carlo model for simulation of homogeneous burst nucleation," Monte Carlo Methods and Applications, De Gruyter, vol. 24(3), pages 193-202, September.
    6. Kolodko, A. & Sabelfeld, K. & Wagner, W., 1999. "A stochastic method for solving Smoluchowski's coagulation equation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 49(1), pages 57-79.
    7. Sabelfeld K. & Levykin A. & Privalova T., 2007. "A Fast Stratified Sampling Simulation of Coagulation Processes," Monte Carlo Methods and Applications, De Gruyter, vol. 13(1), pages 71-88, April.
    8. Sabelfeld Karl K., 2016. "Splitting and survival probabilities in stochastic random walk methods and applications," Monte Carlo Methods and Applications, De Gruyter, vol. 22(1), pages 55-72, March.
    9. Sabelfeld, Karl & Kolodko, Anastasia, 2003. "Stochastic Lagrangian models and algorithms for spatially inhomogeneous Smoluchowski equation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 61(2), pages 115-137.
    10. Albanese Claudio & Armenti Yannick & Crépey Stéphane, 2020. "XVA metrics for CCP optimization," Statistics & Risk Modeling, De Gruyter, vol. 37(1-2), pages 25-53, January.
    11. Kolodko Anastasya A. & Wagner Wolfgang, 1997. "Convergence of a Nanbu type method for the Smoluchowski equation," Monte Carlo Methods and Applications, De Gruyter, vol. 3(4), pages 255-274, December.
    12. Sabelfeld K. & Kurbanmuradov O., 2000. "Coagulation of aerosol particles in intermittent turbulent flows," Monte Carlo Methods and Applications, De Gruyter, vol. 6(3), pages 211-254, December.
    13. Lécot C. & Tarhini A., 2008. "A quasi-stochastic simulation of the general dynamics equation for aerosols," Monte Carlo Methods and Applications, De Gruyter, vol. 13(5-6), pages 369-388, January.
    14. Guiaş, Flavius, 2010. "Direct simulation of the infinitesimal dynamics of semi-discrete approximations for convection–diffusion–reaction problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(4), pages 820-836.
    15. Sabelfeld, K.K., 1998. "Stochastic models for coagulation of aerosol particles in intermittent turbulent flows," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 47(2), pages 85-101.
    16. Vladimir Stojanovic & Novak Nedic, 2016. "A Nature Inspired Parameter Tuning Approach to Cascade Control for Hydraulically Driven Parallel Robot Platform," Journal of Optimization Theory and Applications, Springer, vol. 168(1), pages 332-347, January.
    17. Sabelfeld K.K. & Kolodko A.A., 1997. "Monte Carlo simulation of the coagulation processes governed by Smoluchowski equation with random coefficients," Monte Carlo Methods and Applications, De Gruyter, vol. 3(4), pages 275-312, December.
    18. Kolodko A. & Sabelfeld K., 2003. "Stochastic particle methods for Smoluchowski coagulation equation: variance reduction and error estimations," Monte Carlo Methods and Applications, De Gruyter, vol. 9(4), pages 315-339, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:mcmeap:v:21:y:2015:i:1:p:33-48:n:6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.