Surface-sampled simulations of turbulent flow at high Reynolds number

04/26/2017
by   Neil D. Sandham, et al.
0

A new approach to turbulence simulation, based on a combination of large-eddy simulation (LES) for the whole flow and an array of non-space-filling quasi-direct numerical simulations (QDNS), which sample the response of near-wall turbulence to large-scale forcing, is proposed and evaluated. The technique overcomes some of the cost limitations of turbulence simulation, since the main flow is treated with a coarse-grid LES, with the equivalent of wall functions supplied by the near-wall sampled QDNS. Two cases are tested, at friction Reynolds number Re_τ=4200 and 20,000. The total grid node count for the first case is less than half a million and less than two million for the second case, with the calculations only requiring a desktop computer. A good agreement with published DNS is found at Re_τ=4200, both in terms of the mean velocity profile and the streamwise velocity fluctuation statistics, which correctly show a substantial increase in near-wall turbulence levels due to a modulation of near-wall streaks by large-scale structures. The trend continues at Re_τ=20,000, in agreement with experiment, which represents one of the major achievements of the new approach. A number of detailed aspects of the model, including numerical resolution, LES-QDNS coupling strategy and sub-grid model are explored. A low level of grid sensitivity is demonstrated for both the QDNS and LES aspects. Since the method does not assume a law of the wall, it can in principle be applied to flows that are out of equilibrium.

READ FULL TEXT

page 10

page 18

research
07/15/2021

Predicting the near-wall region of turbulence through convolutional neural networks

Modelling the near-wall region of wall-bounded turbulent flows is a wide...
research
12/08/2019

Post-processing techniques of 4D flow MRI: velocity and wall shear stress

As the original velocity field obtained from four-dimensional (4D) flow ...
research
08/06/2022

Deep Learning Closure Models for Large-Eddy Simulation of Flows around Bluff Bodies

A deep learning (DL) closure model for large-eddy simulation (LES) is de...
research
10/29/2021

Numerical implementation of efficient grid-free integral wall models in unstructured-grid LES solvers

Two zonal wall-models based on integral form of the boundary layer diffe...
research
05/31/2019

A large eddy simulation method for DGSEM using non-linearly optimized relaxation filters

In this paper, we apply a specifically designed dissipative spatial filt...
research
05/31/2019

A highly efficient large eddy simulation method for DGSEM using non-linearly optimized relaxation filters

In this paper, we apply a specifically designed dissipative spatial filt...
research
09/24/2021

Clipping over dissipation in turbulence models

Clipping refers to adding 1 line of code A=minA,B to force the variable ...

Please sign up or login with your details

Forgot password? Click here to reset