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New Computer Efficient Approximations of Random Functions for Solving Stochastic Transport Problems

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Abstract

A new grid approximation of a homogeneous isotropic random field with a given average correlation length is developed. The approximation is constructed by partitioning the coordinate space into an ensemble of cubes whose size reproduces the average correlation length in the case of a field value chosen independently from a given one-dimensional distribution in each partition element. The correlative randomized algorithm recently proposed by the authors for modeling particle transport through a random medium is formulated. The accuracy and computational cost of corresponding Monte Carlo algorithms intended to compute gamma radiation transfer through a random medium of Voronoi diagram type are compared. To test the hypothesis that the one-dimensional distribution and the correlation length of the optical density of the medium have a large effect on radiation transfer, additional computations are performed for a random Poisson “field of air balls” in water. The grid approximation is generalized to anisotropic random fields.

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REFERENCES

  1. B. Davison, Neutron Transport Theory (Clarendon, Oxford, 1957).

    Google Scholar 

  2. G. I. Marchuk, G. A. Mikhailov, M. A. Nazaraliev, et al., The Monte Carlo Methods in Atmospheric Optics (Nauka, Novosibirsk, 1976; Springer-Verlag, Berlin, 1980).

  3. J. Spanier and E. M. Gelbard, Monte Carlo Principles and Neutron Transport Problems (Addison Wesley, Reading, Mass., 1969).

    Google Scholar 

  4. W. A. Coleman, “Mathematical verification of a certain Monte Carlo sampling technique and applications of the techniques to radiation transport problems,” J. Nucl. Sci. Eng. 32 (1), 76–81 (1968).

    Article  Google Scholar 

  5. E. Woodcock, T. Murphy, P. Hemmings, and S. Longworth, “Techniques used in the GEM code for Monte Carlo neutronics calculations in reactors and other systems of complex geometry,” Proceedings of the Conference on Applications of Computing Methods to Reactor Problems (1965), Vol. 557, p. 2.

  6. G. A. Mikhailov and T. A. Averina, “The maximal section algorithm in the Monte Carlo method,” Dokl. Math. 80 (2), 671–673 (2009).

    Article  MathSciNet  Google Scholar 

  7. G. A. Mikhailov, “Randomized Monte Carlo algorithms for problems with random parameters ('double randomization' method),” Numer. Anal. Appl. 12, 155–165 (2019).

    Article  MathSciNet  Google Scholar 

  8. A. Y. Ambos and G. A. Mikhailov, “Numerically statistical simulation of the intensity field of the radiation transmitted through a random medium,” Russ. J. Numer. Anal. Math. Model. 33 (3), 161–171 (2018).

    Article  MathSciNet  Google Scholar 

  9. C. Larmier, A. Zoia, F. Malvagi, E. Dumonteil, and A. Mazzolo, “Monte Carlo particle transport in random media: The effects of mixing statistics,” J. Quant. Spectrosc. Radiat. Transfer 196, 270–286 (2017).

    Article  Google Scholar 

  10. G. N. Glazov and G. A. Titov, “Statistical characteristics of the attenuation coefficient in broken cloudiness. I: Model with balls of identical radius,” Issues of Laser Sounding of the Atmosphere (Novosibirsk, 1976), pp. 126–139 [in Russian].

  11. G. A. Mikhailov and I. N. Medvedev, “New correlative randomized algorithm for estimating the influence of the medium stochasticity on particle transport,” Dokl. Math. 103 (3), 143–145 (2021).

    Article  MathSciNet  Google Scholar 

  12. I. N. Medvedev and G. A. Mikhailov, “New correlative randomized algorithms for statistical modelling of radiation transfer in stochastic medium,” Russ. J. Numer. Anal. Math. Model. 36 (4), 219–225 (2021).

    Article  MathSciNet  Google Scholar 

  13. A. Yu. Ambos, “Numerical models of mosaic homogeneous isotropic random fields and problems of radiative transfer,” Numer. Anal. Appl. 9 (1), 12–23 (2016).

    Article  MathSciNet  Google Scholar 

  14. E. Storm and H. I. Israel, “Photon cross sections from 1 keV to 100 MeV for elements Z =1 to Z =100,” At. Data Nuclear Data Tables 7 (6), 565–681 (1970).

    Article  Google Scholar 

  15. S. M. Ermakov and G. A. Mikhailov, Statistical Modeling (Nauka, Moscow, 1982) [in Russian].

    Google Scholar 

  16. U. Fano, L. V. Spencer, and M. J. Berger, “Penetration and diffusion of X rays,” in Neutrons and Related Gamma Ray Problems: Encyclopedia of Physics (Springer-Verlag, Berlin, 1959).

    Google Scholar 

  17. S. A. Brednikhin, I. N. Medvedev, and G. A. Mikhailov, “Estimation of the criticality parameters of branching processes by the Monte Carlo method,” Comput. Math. Math. Phys. 50 (2), 345–356 (2010).

    Article  MathSciNet  Google Scholar 

  18. I. N. Medvedev and G. A. Mikhailov, “Randomized exponential transformation algorithm for solving the stochastic problems of gamma-ray transport theory,” Russ. J. Numer. Anal. Math. Model. 35 (3), 153–162 (2020).

    Article  MathSciNet  Google Scholar 

  19. I. N. Medvedev, “On the efficiency of using correlative randomized algorithms for solving problems of gamma radiation transfer in stochastic medium,” Russ. J. Numer. Anal. Math. Model. 37 (4), 231–240 (2022).

    Article  MathSciNet  Google Scholar 

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Funding

This work was carried out under the state project of ICMMG SB RAS no. 0251-2022-0002.

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Correspondence to G. A. Mikhailov or I. N. Medvedev.

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Translated by I. Ruzanova

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Mikhailov, G.A., Medvedev, I.N. New Computer Efficient Approximations of Random Functions for Solving Stochastic Transport Problems. Comput. Math. and Math. Phys. 64, 314–325 (2024). https://doi.org/10.1134/S0965542524020088

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  • DOI: https://doi.org/10.1134/S0965542524020088

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