CFD Ventilation Simulationmeroney/PapersPDF/CEP09-10-1ppt.pdf · 1 CFD Prediction of Airflow in...
Transcript of CFD Ventilation Simulationmeroney/PapersPDF/CEP09-10-1ppt.pdf · 1 CFD Prediction of Airflow in...
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CFD Prediction of Airflow in Buildings for Natural Ventilation
Robert N. Meroney, P.E.Wind Engineering Software
Colorado State University
Prepared for
11th Americas Conference on Wind Engineering
June 22-26, 2009
San Juan, Puerto Rico
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Natural Ventilation Multistory Buildings
Daytime stack effect Nighttime stack effect
Double facade Central atrium
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Natural Ventilation in Commercial Buildings
Genzyme Center,
Cambridge MA
Corporate headquarters
biotechnology company
Designed from “inside – out”
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Natural Ventilation in Skyscrapers
City of Bristol Education Building - Skills Academy
Crystal Island Tower –
Norman Foster, Moscow
Gazprom Tower, St
Petersburg
Russia Tower – Norman
Foster, St. Petersburg
Encana Tower, Calgary,
Canada
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Project Goals
• Replicate natural ventilation measurements performed by Dr. Panagiota Karava (2008).– Velocity field
– Peak pressure field
– Opening flow rates
• Examine numerical modeling alternatives and their relative effectiveness– Turbulence models – Std k-ε, RNG k-ε, Realizable k-ε, k-ω, RMS, LES, & DES
– Grid resolution, boundary conditions
• Examine domain decomposition as a numerical modeling alternative
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Karava Natural Ventilation Study
• Dr. Panagiota Karava, University of Western Ontario, – specialist in natural ventilation design.
• PhD Thesis: Airflow Prediction in Buildings for Natural Ventilation Design: Wind Tunnel Measurements and Simulation, Concordia University, 2008– Building Model 20 x 20 x 16 m high (1:200
scale ration), openings placed on different walls
– Simulated atmospheric boundary, p = 0.11
– Measurements with PIV & hot-film anemometry and fast-response pressure transducers
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Numerical Model
• Domain size and flow conditions set to meet COST harmonization recommendations.
• Computational domain prepared with a combination of hexagonal and tetrahedral shapes totaling ~1 to 2 million cells. Cells adapted near walls to sizes less than 0.03 cm for the wind-tunnel scale model (8 x 10 x 10 cm)
• Wall boundaries had zero or 2 mm thickness.• Only configurations with wind normal to
openings considered.• All solutions obtained with the FLUENT 6.3 CFD
code.
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1.00
-
-
0.75
-
-
0.50
-
-
0.25
-
-
0.0
z/zg
1.00
-
-
0.75
-
-
0.50
-
-
0.25
-
-
0.0
z/zg
p = 0.11
Approach Flow Conditions
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Flow field – Realizable k-ε
Pressure Coefficient, Cp
Cp = -1.1 ok
Separation & reattach-
ment on roof
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Flow field – LES
Velocity Magnitude z = 50 cm RMS Velocity Magnitude z = 50 cmMean Velocity Magnitude z = 50 cm
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Flow field - DES Realizable k-ε at wall
RMS Velocity Magnitude z = 50 cmVelocity Magnitude z = 50 cmMean Velocity Magnitude z = 50 cm
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Flow field - DES Realizable k-ε at wall
Cp = -0.93 ok
Separation & reattach-
ment on roof
Pressure Coefficient, Cp
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Domain Decomposition
• Kurabuchi et al. (2009) proposed one perform a full domain calculation around a sealed building, then calculate internal flows separately (fast)– Predict external flow over sealed building with CFD
– Note external pressures,
– Note adjusted internal pressure– Note tangential dynamic pressure at surface or θ
• Calculate Q and Cpinternal– Q = CDinAVH (Cpin – Cpinternal)
1/2…………...…Eq (1)
– Cpinternal = (Cpin + Cpout)/2…………………….Eq (2)
• Calculate internal flow using Q and Vtangential
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External Cp Variation, (rke model)
-0.168-0.025-0.3130.6040.5070.893E-walls
-0.168-0.025-0.3010.5920.5450.873E
-0.168-0.241-0.0540.4140.8170.867D
-0.031-0.269-0.2690.3970.8280.873C
+0.013-0.250-0.2500.6440.8020.540B
-0.203-0.245+0.0500.6400.8090.514A
-0.186-0.226-0.3060.6950.8560.856Box - sealed
246246Case y(cm) =
Downwind FaceUpwind Face
Denotes opening locations
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Boundary Conditions for Domain Decomposition
-203.380.00280.670.3150.856E
-453.010.00250.670.2500.675C
203.500.00290.670.2750.856A
Θ
(degrees)
Vin
(m/sec)
Q
(m3/sec)
CDinCpinternalCpinCase
θV
Cpin
Cpout
Q
Cpinternal
Sealed Building Ventilated Building
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Vector Section – Case A: rkePIV Measurements Full domain CFD calculation
Domain Decomposition
Vena Contracta
No Vena Contracta
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Velocity Ratio – VX/VH
CASE A CASE C
CASE E
PIV A, C & E
CFD full domain
CFD decomposition,
various angles
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External Flow Characteristics
-1.220-0.2350.535RkeD
-1.090-0.2180.545RkeC
-1.280-0.2380.587RkeB
-0.930-0.2270.568RkeA
-0.940-0.1600.720KaravaBox
-0.825-0.2540.595RkeBox
-0.931-0.2520.627DESE
-0.931-0.2870.611LESE
-0.868-0.3320.581RMSE
-1.400-0.2410.829k-ωE
-0.530-0.2570.635RNGE
-1.180-0.2430.556RkeE
-1.600-0.2660.570SkeE
Cprooftop
Minimum
Cpdownwind
Average
Cp upwind
Average
Turbulence
Model
Case
Discounting Ske, RNG & k-ω, then average upwind Cp = -0.594 ±
0.027, and average downstream Cp = -0.273 ± 0.037. Deviations are
less than AIJ Working Group study in multiple wind tunnels.
Cdrag = (Cpupwind –Cpdownwind)Average. Drag coefficient for sealed
building is 0.85, whereas drag coefficient for paired openings sets is
0.80 ± 0.03. Drag coefficients for Case E over various turbulence
models ranged from 0.80 to 1.07.
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Cross ventilation flow rate & Cpinternal
0.3150.2690.4000.516RkeE-walls
0.1850.2000.2260.424RkeD
0.2500.1000.1300.1800.479RkeC
0.3350.3800.3580.478RkeB
0.2750.2300.2690.4300.545RkeA
0.3150.3010.4000.481DESE
0.3150.3050.4000.520LESE
0.3150.3050.4000.478RMSE
0.3150.2690.4000.483k-ωE
0.3150.2510.4000.463RNGE
0.3150.3150.4000.488RkeE
0.3150.3050.4000.484SkeE
Cpinternal
Eq 2
Cpinternal
WT
Cpinternal
CFD
DFRWTDFRCFDTurb.
model
Case
CFD = Computational Fluid Dynamics, WT = Wind Tunnel measurements of Karava
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Internal Pressure Coefficients Case E: Various Turbulence Models
RMS model
Cp = 0.297 avg
LES model
Cp = 0.305 avg
DES model
Cp = 0.301 avg
Realizable k-ε model
Cp = 0.315 avg
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Internal Pressure Coefficients Case A-D: Realizable k-ε Model
Case A
Cp = 0.269 avg
CpKARAVA = 0.230
Case B
Cp = 0.358 avg
CpKARAVA = 0.380
Case C
Cp = 0.130 avg
CpKARAVA = 0.100
Case D
Cp = 0.226 avg
CpKARAVA = 0.200
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Static Pressure Coefficient, CpCASE A CASE C
CASE E
Karava pressure
measurement
CFD full domain
CFD decomposition
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• Data are vertex maximum peaks over building interior
surface for Case A using the LES model.
• Average of all peaks = 0.725
• Average of segment peaks = 0.870 taken over
thirty 100 second sampling intervals
• Cppeak = Cpmean + g * CpRMS where g ≈ 5………Eq (3)
Internal Pressure FluctuationsCASE A: LES model
Segment
Peak
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Peak & mean internal pressure coefficients
____0.882 (0.713)
0.7490.0860.319E
0.400.1000.978
(0.288)
0.2390.0260.109C
0.750.2300.870 (0.339)
0.4580.0610.153A
Cppeak
WT
Cpmean
WT
Cppeak
CFD TS#
Cppeak
Eq 3
Cprms
CFD
Cpmean
CFD
Case
CFD = Computational Fluid Dynamics,
WT = Wind Tunnel measurements of Karava, and
TS = Time Series
# Numbers are vertex maximum peaks over building interior surface
# Numbers in (italics) are peaks over five selected interior points
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Conclusions
• CFD model replicated wind tunnel data– Internal flow field, magnitude and directions
– Internal Ux/UH profiles between openings
– Internal Cp profiles between openings
– Internal character of peak pressure variations
– External front, rear & roof pressure values
• Domain decomposition also replicated wind tunnel data within experimental and numerical uncertainty.