Transcript STURM4_Howard_Addad_presentation_oct2009.ppt

STURM4-progress meeting, Clamart 13 Oct
2009
EDF – U o Man collaborations on Unstructured meshes
for DNS and LES
* 2 conf. paper papers presented by Y. Addad (UoM) and R. Howard
(EDF)
* Int. Symp on Convective Heat and Mass Transfer in Sustainable Energy, April 26 –
1 May, 2009 Hammamet, Tunisia
* France-Japan joint Seminar on Thermal fatigue, 5th to 6th October 2009, Tokyo,
Japan
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International Symposium on
Convective Heat and Mass
Transfer in Sustainable Energy
April 26 – 1 May, 2009 Yasmine Hammamet, Tunisia
THE EFFECT OF
DIFFERENTS GRIDS AND
DIFFERENT LES MODELS
ON TURBULENCE
STATISTICS IN CHANNEL
FLOWS
Richard Howard and Yacine Addad
DIFFERENTS GRIDS AND DIFFERENT LES MODELS
IN CHANNEL FLOWS
Stretched
Box
64  64  64
uniform
72  24  36
hybrid
72  24  36
6 23
Reynolds (u tau) 180
Code Saturne
http///www.code-saturne.org
Collocalised, Cell centered, 2nd order central differences
3
DIFFERENTS GRIDS AND DIFFERENT LES MODELS
IN CHANNEL FLOWS
Production
Dissipation
Viscous
Pressure
Turbulent
 Ui
 u i  u i   2
Dk
1 dp  1 


uiuiu j 


 u iu j 


u
u

u i

i i
2
Dt
 dxi 2  x j
 xj
 xj  xj 2  xj
Subgrid model contributions

 xk
4

  ui  u k
 u i t 

 x

 xi
 k


  ui  u k
  t 


 x
 xi

 k
  ui

 x
 k
DIFFERENTS GRIDS AND DIFFERENT LES MODELS
IN CHANNEL FLOWS
Second derivatives

TermI  u i t
 xk
  u i  u k

 x   x
i
 k
  t u i
Gradients of
turbulent viscosity
 
TermII  u i t
 xk
Products of
velocity gradients
   u i  u k

 x k   x k
 xi
  u i  u k

 x   x
i
 k


 
  ui t

 xk

  t   u i  u k
u i

 x k   x k
 xi
  u i  u k
TermIII   t 

 xi
  xk
  u i  u k

 x   x
i
 k

  u i t 

 xk





  ui  uk


 x
 xi
 k
  u i  u k

 x   x
i
 k





 t   u i  u k
  u i


 x

x
 xi
k 
k

  u i
  u i  u k


  x  t   x   x
i
 k
 k
  ui  u k


 x
 xi
 k
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
  u i  t 

 xk





  u i

 x
 k
  u i
  u i  u k
 t
  t 

 x
 x
 xi
k

 k
 ui

 x
 k




DIFFERENTS GRIDS AND DIFFERENT LES MODELS
IN CHANNEL FLOWS
Stretched
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DIFFERENTS GRIDS AND DIFFERENT LES MODELS
IN CHANNEL FLOWS
Stretched
WALE
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Term I (second derivatives)
Term II (gradients of turbulent viscosity)
Term III (products of gradients)
Smagorinsky
DIFFERENTS GRIDS AND DIFFERENT LES MODELS
IN CHANNEL FLOWS
Uniform
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DIFFERENTS GRIDS AND DIFFERENT LES MODELS
IN CHANNEL FLOWS
Hybrid
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DIFFERENTS GRIDS AND DIFFERENT LES MODELS
IN CHANNEL FLOWS
• The dominant term is TermIII (products of
velocity gradients which includes a kind subgrid
dissipation term)
• The WALE model dissipates the resolved
fluctuations in v and w more than the other models.
• The hybrid grid show numerical noise particularly
at the channel centreline. The sloping faces of the
non structured grid act to transfer the local mean
velocity field between each velocity component.
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Thermal mixing in T-Junction: Hexa Grid, STAR V4 LES
3.1Dh
Grid Cells= 2.56 M.
3Dc
LES RUNS:
13Dc
SGS Model: Smago.
PrSGS=0.9 (Default).
URANS RUNS:
Non-linear k- model
Suga et al. 2006.
Numerical schemes:
2nd order in space.
2nd order in time.
Conclusions and future work
- URANS model:
- Fails to capture the complex features of the flow.
- Most information needed for thermal fatigue studies is lost.
- Not able to capture high frequency events.
- Can this be improved or other approaches tested (example DES) ??
- LES with Unstructured grids and Professional Software:
- Second order accuracy seems OK.
- Mesh is extremely important, adapted to (1/10) large eddy scale ?
=> need unstructured mesh
- Work in progress: How about tetrahedral and polyhedral cells, different SGS models??
Acknowledgements:
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UK research council project “Keeping the Nuclear Option Open” (KNOO)
FR ANR-CIS STURM4