ФИЛЬТРАЦИЯ НАТУРНЫХ ДАННЫХ ДЛЯ ЧИСЛЕННОГО МОДЕЛИРОВАНИЯ ТРЕХМЕРНЫХ ТУРБУЛЕНТНЫХ ТЕЧЕНИЙ С ПРИМЕНЕНИЕМ ПОДХОДА LES

Abstract

Turbulence and subsequent mixing are important mechanisms which determine the dynamics of the coastal zone, as well as the transfer of momentum, mass and heat. In this paper, smallscale motion is excluded from the Navier–Stokes equations by applying the filtering operation. It is then modeled using subgrid models. In two-dimensional and three-dimensional cases, various types of filters are used: a box filter, a Gaussian filter and a Fourier filter, with a gradual decrease in the filter width. This enables the reproduction of a wider frequency range of fluctuations of the solution. The natural data obtained during the expedition in the Central-Eastern part of the Sea of Azov and in the Taganrog Bay at the research vessel Deneb of the Southern Scientific Center of the Russian Academy of Sciences were subjected to the filtration procedure. The hydrophysical ADCP probe Workhorse Sentinel 600 was used to measure the three-dimensional velocity vector of the water medium. In this way more than 3,000,000 initial measurements were obtained, at each of the 17 stations more than 150,000 were taken. The data thus obtained will be used for the digital simulation of three-dimensional turbulent flows using the LES approach and comparison with the results of averaging by RANS.