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Transcript of Craig Smith Fernando Porte-Agel WIRE, EPFL, Switzerland [email protected] Craig Smith WIRE, EPFL...
Craig Smith
Fernando Porte-Agel
WIRE, EPFL, Switzerland
Craig SmithWIRE, EPFL
Subgrid models in LES of drainage flows
Craig SmithWIRE, EPFL
Why study drainage flows?
Source: Whiteman 2008
zzjetjet = 5-10m – requires very fine = 5-10m – requires very fine resolution, or highly anisotropic resolution, or highly anisotropic gridsgrids
Craig SmithWIRE, EPFL
Science questions
• Predictability of drainage flowsPredictability of drainage flows
• Subgrid model performanceSubgrid model performance
• Monin Obukov similarity (MOS)Monin Obukov similarity (MOS)
Rattlesnake Ridge – SE WA, USAHoran and Dorst, 1983
Craig SmithWIRE, EPFL
Observations
Rattlesnake Ridge – SE WA, USAHoran and Dorst, 1983
x = 7m, y = 10m
Craig SmithWIRE, EPFL
A B
A
B CNumerical setup
x = 20m, z = 2.5-3.8m
500x20x400
x = 13.3m, z = 1.6-2.4m
900x30x620
WIRE LES code – spectral difference in horizontal, center difference in vertical, 2nd order Adams Bashforth in time, z0 = 0.1 m, qsfc = -15-40 Wm-2 (not at peak)Subgrid models – Smagorinsky (wall corrected), Dynamic, Lagrangian scale dependent dynamic
Craig SmithWIRE, EPFL
SGS models
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• Smagorinsky (wall corrected) – model Smagorinsky (wall corrected) – model coefficients are constant (isotropic coefficients are constant (isotropic homogenous TKE)homogenous TKE)
• Dynamic – uses a 2Dynamic – uses a 2ndnd test filter to dynamically test filter to dynamically optimize model coefficientsoptimize model coefficients
• Scale dependent dynamic – allows model Scale dependent dynamic – allows model coefficients to vary with filter size coefficients to vary with filter size
Oregon State UniversityCollege of Oceanic and Atmospheric Sciences
log(Elog(Ekk))
log(k)log(k)kkc c ==
Inertial Inertial subrangesubrange
resolved resolved scalesscales
DissipationDissipation subrangesubrange
unresolved unresolved scalesscales
SGS models
1.0sC 4.0
1.0Pr 12 sgssC
ss CC
ss CC
Craig SmithWIRE, EPFL
Scale dependent Lagrangian dynamic after 45 minutes of integration
Velocity
Results
Potential temperature
Craig SmithWIRE, EPFL
U TKE
Scale dependent Lagrangian dynamic after 45 minutes of integration
Results
Craig SmithWIRE, EPFL
Rattlesnake ridge observations comparison
Results
Craig SmithWIRE, EPFL
U TKE
Smagorinsky ->low near surface temperature -> 10-20% more cold air flux
Craig SmithWIRE, EPFL
dzumhsfc
sfc
T
0
Smagorinsky ->low near surface temperature -> 10-20% more cold air flux
U TKE
Craig SmithWIRE, EPFL
Results
Smagorinksy1.0sC
Dynamic
Scale dependent dynamic
ss CC
ss CC
Assumption of constant model coefficients in Smagorinsky
Craig SmithWIRE, EPFL
Results
Dynamic
Scale dependent dynamic
12
2
2
s
s
C
C
2
422
2
2
2
s
s
s
s
C
C
C
C
Assumption of scale invariance in dynamic model
Craig SmithWIRE, EPFL
In contextObservations suggest zObservations suggest zjj = 5- = 5-
10m10m
Author bc
Burkholder et al 2010 - very small periodic many
Axelsen 2009 glacial 2.6x2.6x0.4 periodic ???
Skyllingstad 2003 SE WA 0.75x0.75x0.75 slope FSF
Smith and Skyllingstad 2005 VTMX 3x3x3 slope FSF100x100x5 valley 1.5 TKE (ARPS)
Chow et al 2006 MAP 150x150x20 valley 1.5 TKE and DRM (ARPS)
Catalano and Cenedese 2010 - 50x50x2 valley WRF in LES mode
Zhong and Whiteman 2008 VTMX 250x250x2.1 valley 2.5 TKE (RAMS)
location sgs
Craig SmithWIRE, EPFL
Future work• Numerical setupNumerical setup
• Grid aspect ratioGrid aspect ratio
• Spanwise heterogeneitySpanwise heterogeneity
Smagorinsky model for katabatic winds – constant Smagorinsky model for katabatic winds – constant PrPrsgssgs
Craig Smith
Fernando Porte-Agel
WIRE, EPFL, Switzerland
Craig SmithWIRE, EPFL
Subgrid models in LES of drainage flows
Craig SmithWIRE, EPFL
Smagorinsky – original vs wall corrected
Results