Neural networks in air turbulence modelling - Pekunov, Vladimir
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Local and integral (corrective, correcting Abramovich-Sekundov and Spalart-Allmaras models) neural network models of turbulent viscosity are proposed, showing intermediate accuracy results between one-parameter and two-parameter models. A new algorithm for controlling the numerical calculation error in some typical problems of aerodynamics has been proposed. Its effectiveness on a number of problems is shown. The algorithm is based on the application of particle indicators and uses the particle-in-cell method. The principles of data compression, representing physical quantities varying…mehr

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Produktbeschreibung
Local and integral (corrective, correcting Abramovich-Sekundov and Spalart-Allmaras models) neural network models of turbulent viscosity are proposed, showing intermediate accuracy results between one-parameter and two-parameter models. A new algorithm for controlling the numerical calculation error in some typical problems of aerodynamics has been proposed. Its effectiveness on a number of problems is shown. The algorithm is based on the application of particle indicators and uses the particle-in-cell method. The principles of data compression, representing physical quantities varying depending on some parameter, by constructing cluster-neural network descriptions, are proposed.
Autorenporträt
Vladimir Viktorovich Pekunov nasceu a 21 de Janeiro de 1977 em Zapolyarny. Licenciado pela Universidade Estatal de Energia de Ivanovo. D. em Ciências Técnicas, autor de mais de 85 trabalhos de investigação. Os seus principais interesses de investigação são programação teórica, modelação de turbulência, geração de programas, redes neurais.