Manufactured Turbulence with Langevin equations (original) (raw)
Related papers
2005
In a turbulent flow, the number of degree of freedom N can be gigantic, scaling as the 9/4 power of the Reynolds number. In the atmosphere, this number may reach N��� 1016, devastating our hope to implement all scales of the climate system onto a computer. This juggling with numbers illustrates the well known challenge posed by turbulent flows: is there a way to simulate, or describe a turbulent flow, without taking into account all degrees of freedom?
Stochastically generated turbulence with improved kinematic properties
arXiv: Fluid Dynamics, 2017
We present a stochastic turbulence generator based on a vorticity formulation where the generated turbulent field implicitly fulfills the kinematic constraints of an incompressible flow. The generator allows direct access to the turbulent velocity and vorticity field. Enforcing additional constraints such as a divergence-free vorticity field and a specified differentiability of the flow field can also implemented directly within this formulation. The resulting turbulent field contain improved kinematic properties and may be imported into numerical simulations without an excessive loss of energy.
DIRECT NUMERICAL SIMULATION: A Tool in Turbulence Research
Annual Review of Fluid Mechanics, 1998
▪ We review the direct numerical simulation (DNS) of turbulent flows. We stress that DNS is a research tool, and not a brute-force solution to the Navier-Stokes equations for engineering problems. The wide range of scales in turbulent flows requires that care be taken in their numerical solution. We discuss related numerical issues such as boundary conditions and spatial and temporal discretization. Significant insight into turbulence physics has been gained from DNS of certain idealized flows that cannot be easily attained in the laboratory. We discuss some examples. Further, we illustrate the complementary nature of experiments and computations in turbulence research. Examples are provided where DNS data has been used to evaluate measurement accuracy. Finally, we consider how DNS has impacted turbulence modeling and provided further insight into the structure of turbulent boundary layers.