Wind Energy

70 C. Wagner
〈u〉
20
15
10
5
——— LES Wagner
–––– DNS Kim et al.
DNS underresolved
LES dyn.Smagorinsky
10−1 100 101 102 0
(y+H/2). Re τ
4
2
0
−2
——— R xx
–––– R yy
R zz
(−R xz −t xz )
.0 .1 .2 .3 .4 .5
y
Fig. 12.3. Mean velocity profiles calculated in LES and DNS (left).Profilesofthe
deviatoric part of the Reynolds stress tensor (right) calculated in LES with the scale
similarity model and compared to DNS data by Kim et al.) (denoted by symbols)
Reynolds numbers he obtained values for CL of the order of 1, but with
a significant variation in wall normal direction. Again CL was statistically
averaged to enforce the necessary smoothness of CL, which is necessary for
conserving the high correlation between τij and Lij
In order to give an impression on the performance of the different described
subgrid scale models, results of LES of a fully developed turbulent channel
flow performed for a Reynolds number Reτ = 360 are presented in Fig. 12.3.
For both, the mean velocity profile and the resulting deviatoric part of the
Reynolds stress tensor Rij = 〈uiuj〉 −〈ui〉〈uj〉 −1/3(〈ukuk〉 −〈uk〉〈uk〉)δij
which are compared to the according data of Kim et al. excellent agreement
is obtained while the mean values of the underresolved DNS and of the LES
with the Smagorinky model shows considerable discrepiances.
12.7 Conclusion
The analysis of the eddy viscosity concept which is applied in most RANS
and traditional LES turbulence models showed that such models are not able
to reliably predict nonisotropic flows. Therefore, if such simulations are performed
to optimize or analyse rotor blades of wind turbines, the obtained
results have to be interpreted with caution.
References
1. Choi H, Moin P, Kim J (1992) Turbulent drag reduction: studies of feeedback
control and flow over riplets, Rep. TF-55, Department of Mechanical Engineering,
Stanford University, Stanford, CA