Abstract
The problem of Rayleigh–Taylor (RT) instability is considered. The research technique is based on the statistical treatment of variants of the RT instability numerical simulations. Such treatment can be performed by means of neuron networks. To predict the mixing-zone width and embedded mass at arbitrary instants, a multilayer perceptron model is applied. The initial shape of a heavy–light liquid interface is used to make the prediction.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
A. S. Nuzhny, V. B. Rozanov, R. V. Stepanov, and S. A. Shumsky, Mat. Model., 16, 21 (2004).
A. S. Nuzhny, P. A. Kuchugov, A. G. Korzhov, and V. B. Rozanov, Bull. Lebedev Phys. Inst. (Russ. Acad. Sci.), 36, 23 (2009).
S. Haykin, Neural Networks: A Comprehensive Foundation, Pearson Education, New Jersey (1999).
V. F. Tishkin, V. V. Nikishin, I. V. Popov, and A. P. Favorski, Mat. Model., 7, 15 (1995).
I. Daubechies, Ten Lectures on Wavelets, CBMS-NSF Regional Conf. Series in Appl. Math., Society for Industrial and Applied Mathemedics, Philadelphia (1992), Vol. 61.
A. A. Ezhov and S. A. Shumsky, Neurocomputing and Its Applications in Economics and Business [in Russian], Moscow Engineering Physics Institute (1998).
E. S. Ventzel, Probability Theory [in Russian], Nauka, Moscow (1962).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Korzhov, A.G., Kuchugov, P.A. & Rozanov, V.B. Analysis of Rayleigh–Taylor mixing-zone development by means of a multilayer perceptron. J Russ Laser Res 30, 560–569 (2009). https://doi.org/10.1007/s10946-009-9114-x
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10946-009-9114-x