Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Моделирование отрывных течений (DES)× | Уравнения Рейнольдса для осреднённых течений (RANS)× | |
|---|---|---|
| Область | Гидродинамика | Гидродинамика |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1997 | 1895 |
| Автор метода≠ | Philippe Spalart | Osborne Reynolds |
| Тип≠ | Hybrid turbulence modeling approach | Computational turbulence modeling approach |
| Основополагающий источник≠ | Spalart, P. R., Jou, W. H., Strelets, M., & Allmaras, S. R. (1997). Comments on the feasibility of LES for wings, and on a hybrid RANS/LES approach. Advances in DNS/LES, 1, 4-8. link ↗ | Reynolds, O. (1895). On the dynamical theory of incompressible viscous fluids and the determination of the criterion. Philosophical Transactions of the Royal Society A, 186, 123-164. DOI ↗ |
| Другие названия | DES, hybrid RANS-LES | RANS, Reynolds-averaged flow simulation |
| Связанные | 5 | 5 |
| Сводка≠ | Detached Eddy Simulation (DES) is a hybrid turbulence modeling approach introduced by Spalart in 1997 that combines the computational efficiency of RANS in attached boundary layers with the accuracy of LES in separated wake regions. By automatically switching between RANS and LES based on local grid spacing and turbulence length scales, DES provides superior predictions for flows with large separations, shear layers, and vortex shedding at a cost between pure RANS and pure LES. DES has become the standard method for complex aerospace applications involving separation and transient phenomena. | The Reynolds-Averaged Navier-Stokes (RANS) equations represent a time-averaged form of the Navier-Stokes equations developed by Osborne Reynolds in 1895. This approach decomposes turbulent flow into mean and fluctuating components, enabling practical simulation of turbulent flows by modeling turbulent stresses rather than resolving all scales. RANS remains the most widely used computational fluid dynamics method in engineering applications due to its computational efficiency. |
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