Neural networks in air turbulence modelling
Correction models
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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 dependi...
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.
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