KnE Engineering

ISSN: 2518-6841

The latest conference proceedings on all fields of engineering.

Modeling of Gravitational Separation By the Method of Smoothed Particles Hydrodynamics (SPH)

Published date:Jul 17 2018

Journal Title: KnE Engineering

Issue title: VII All-Russian Scientific and Practical Conference of Students, Graduate Students and Young Scientists (TIM'2018)

Pages:199–204

DOI: 10.18502/keg.v3i5.2670

Authors:
Abstract:

The article deals with the peculiarities of solving the problem of numerical simulation of gravity separation of dispersed particles. A simulation model is created by using the Monte Carlo method, in which the ‘first principles’ (elementary particles) are particles of the charge and reaction products. The object-oriented language ActionScript 3.0 was chosen as the programming language. At the same time, the most difficult
(computational) task was to find neighbors (complexity N2 ). In this article, the comparison analysis of the improved algorithm of neighbors search of complexity (2⋅N⋅k) with standard neighbors search is given; the object of comparison is the quantity of the displayed particles moving in real time.


Keywords: modeling of flows, gravity separator, the Monte Carlo method, smoothed particles, complexity of the algorithm, neighbors search

References:

[1] Tsymbal, V. P., Pavlov, V. V., Sechenov, P. A., et al. (2016). Simulation modeling of interaction of dispersed particles in the jet-emulsion unit and gravitational separation. Chernye Metally, vol. 6, pp. 54–60.


[2] Valger, S. A., Fedorov, A. V., and Fedorova, N. N. (2013). Simulation of incompressible turbulent flows in the vicinity of poorly flowing bodies using a PC ANSYS Fluent. J. Computing Technologies, vol. 18, no. 5, pp. 27–40.


[3] Lazareva, G. G. (2010). Modern numerical methods of gravitational gas dynamics. J. Vestnik Novosibirsk State University. Series: Mathematics, vol. 10, no. 1, pp. 40–64.


[4] Metropolis, N. and Ulam, S. (1949). The Monte Carlo method. Journal of American Statistical Association, vol. 44, no. 247, pp. 335–341.


[5] Kroese, D. P., Brereton, T., Taimre, T., et al. (2014). Why the Monte Carlo method is so important today. WIREs Computational Statistics, vol. 6, pp. 386–392.


[6] Dunn, W. L. and Shultis, J. K. (2011). Exploring Monte Carlo Methods. Elsevier.


[7] Kroese, D. P., Taimre, T., and Botev, Z. I. (2011). Handbook of Monte Carlo Methods. Wiley.


[8] Lavrov, V. V., Spirin, N. A., Burikin, A. A., et al. (2012). Technology and software development tools used in the creation of the computer decision support system to solve technological problems in the blast-furnace process. J. Athematic and Software Systems in the Industrial and Social Spheres, vol. 2, pp. 147–152.


[9] http://help.adobe.com/en_US/FlashPlatform/reference/actionscript/3/index. html


[10] Gusev, D. I. (2012). Nearest neighbor search algorithm, J. Software Products and Systems, vol. 3, pp. 231–234.


[11] Sechenov, P. A., Tsymbal, V. P., and Olennikov A. A. (2016). Simulation model for separation of dust constituents of manganese production. J. Cybernetics and Programming, vol. 2, pp. 34–41.

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