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Computational evaluation of wind pressures on tall buildings

Agerneh K. Dagnew 1, Girma T. Bitsuamalk2, Ryan Merrick3

1Research Assistant, Civil and Environmental Engineering department (CEE), International

Hurricane Research Center (IHRC), Florida International University (FIU), Miami, Florida,

USA, adagn001@fiu.edu

2Assistant Professor of Wind/Structural Engineering, CEE, IHRC, FIU, Miami, Florida, USA

bitsuamg@fiu.edu

3Senior Technical Coordinator, RWDI USA LLC, Miramar, Florida, USA,

Ryan.Merrick@rwdi.com

ABSTRACT

At present, the wind engineering toolbox consists of wind-tunnel testing of scaled models, limited

full-scale testing, field measurements, and mechanical load/pressure testing. The evolution of

computational wind engineering (CWE) based on computational fluid dynamics (CFD)

principles are making the numerical evaluation of wind loads a potentially attractive

proposition. This is particularly true in light of the positive development trends in hardware and

software technology, as well as numerical modeling. The present study focuses on numerical

evaluation of wind pressures on tall buildings by using the Commonwealth Advisory

Aeronautical Council (CAARC) building model (Melbourne, 1980). The CARRC model has been

used extensively to study wind loading on tall buildings in wind tunnel studies and is usually

adopted for calibration of experimental techniques. Numerically obtained pressure coefficients

on the surface of CAARC building under different configurations of adjacent building are

compared with wind tunnel data collected at the RWDI USA LLC laboratory for the present

study and from literature. The present numerical simulation uses mostly Reynolds Averaged

Navier- Stokes equations (RANS) and Large Eddy Simulation (LES) for selected few cases.

KEY WORDS: Computational fluid dynamics, boundary layer wind tunnel, wind pressure, tall

building, RANS, LES

INTRODUCTION

Buildings, bridges, large span roof structures and other civil structures must be able to withstand

the external loads imposed by nature, at least to the extent that the disastrous damage of natural

force reduced to the acceptable limit (Irwin, 2007). Wind is one of the major forces responsible

for the catastrophic failure and loss of life. Therefore, accurate evaluation and prediction of wind

loads and proper mitigations are very important in reducing the adverse effects of wind in the

built environment. In addition to the mean and the background (due to turbulent fluctuations)

loads on rigid buildings, flexible structures will be subjected to resonant wind loads as well. The

primary source of the along-wind motion is the pressure fluctuations in the windward and

leeward faces due to the fluctuation in the approach flow and its interaction with the building.

Building codes and Standards usually provide loads along the wind direction for common shapes

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in open and suburban terrain category perhaps with the exception of the AS-NZ 2002 code which

attempts to provide provisions in for the cross-wind direction as well. The cross-wind motion is

mainly caused by fluctuations in the separating shear layers. Torsional motion can be caused due

to imbalance in the instantaneous pressure distribution on each face of the building either due to

oblique wind directions, unsteadiness in the approaching flow, partial sheltering and interference

from surrounding buildings or due to own shape and dynamic structural properties. Studies

shows that for many high-rise buildings, the crosswind and torsional response may exceeds the

along wind response in terms of both limit state and serviceability requirements (Kareem, 1985).

Nevertheless, most existing standards, including ASCE07-05, only provides procedure for

evaluation of along-wind effects. For complex cases, the standards refer to physical model

testing in boundary layer wind tunnel (BLWT) facility.

The success of application of computational fluid dynamics (CFD) in aeronautical engineering is

very encouraging. Acknowledging the difference between streamlined and bluff body flows, the

use of computational fluid dynamics for predicting wind effects in the atmospheric boundary

layer appears very promising. This is particularly so considering the recent advances in hardware

and software technology, development of reliable sub-grid turbulence models and numerical

reproduction of inflow turbulence (Tamura 2008). At this stage of CWE application, however, a

systematic validation of CWE models through comparison with wind tunnel experiments shall

continue to enhance the confidence and warrant its use for practical applications.

Significant progress has been made in the application of CWE to evaluate wind loads on

buildings (e.g. Murakami and Mochida 1988; Selvam 1997; Stathopoulos 1997; Wright et al.

2003; Camarri et al. 2005; Tamura 2006; Tutar and Celik 2007; El-Okda et al. 2008; Tominaga

et al. 2008a; Cóstola et al. 2009 and others). Significant progress has also been made on the

evaluation of wind load modifications due to topographic elements (Bitsuamlak et al. 2004,

2006; Poggi and Katul 2007). Some countries have already established working groups to

investigate the practical applicability of CWE and develop recommendations for their use for

wind resistant design of actual buildings and for assessing pedestrian level winds, within the

framework of the Architectural Institute of Japan (AIJ) (Tamura et al. 2008, Tominaga et al.

2008b`) and the European cooperation in the field of scientific and technical (COST) research

(Franke et al. 2004). Further, AIJ provides methods for predicting wind loading on buildings by

the Reynolds Averaged Navier Stokes equations (RANS) and LES. Practical applications of

CWE are widespread in areas such as pedestrian level wind evaluation Chang (2006), Lam and

To (2006), Blocken and Carmeliet (2008), Yoshie et al. (2007), and Tablada et al. (2009), where

only the mean wind speeds are required for evaluating pedestrian comfort (Stathopoulos and Hu

2004). CWE applications for wind driven rain are reported by researchers such as Choi (2000),

and Blocken and Cameliet (2004). Some CFD wind flow studies for urban neighborhood

include Zhang et al. (2006), Huang et al. (2006), and Jiang et al. (2008). While most of studies

mentioned above focus on straight winds, studies by Lin and Savory (2006), and Hangan and

Kim (2008) focused on simulation of downburst. Other common uses of CWE, include

augmentation of experimental wind engineering research: Sengupta and Sarkar (2008)

augmented their microburst and tornado wind simulator facility with numerical simulation;

Merrick and Bitsuamlak (2008) used numerical simulation to facilitate selection of an artificial

surface roughness length to be applied on curved surfaced buildings during wind tunnel testing

as a means to compensate for High Reynolds number effects that is usually missing from low

wind speed tunnels.

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The majority of numerical prediction of wind pressure loads on buildings devoted on the basic

cube shape, because of its geometrical simplicity yet representing the complex features of

building aerodynamics and availability of experimental data (Stathopoulos 2002; Nozawa and

Tamura 2002; Lim et al. 2009). These include full-scale low-rise building structures such as

Silsoe Cube (Wright and Easom 1999, 2003), Texas Tech University (Senthooran et al. 2004)

and Wall of Wind test building (Bitsuamlak et al. 2008). Some of the computational studies for

Tall Buildings include studies by Nozawa and Tamura (2002), Huang et al., (2007), Tominaga et

al., (2008), Tamura (2008) and Braun et al. (2009). Huang et al. (2007) and Braun et al. (2009)

focused on the aerodynamics of Commonwealth Advisory Aeronautical Council (CAARC)

building model and investigate the flow pattern and mean and rms pressure coefficient. CAARC

tall building is considered as one of the most extensively studied building model and popular in

wind tunnel researcher community as well (Wardlaw and Moss 1970 as referenced by Braun et

al. 2009; Melbourne 1980; Obasaju 1992). As a result, the present study also uses the CAARC

building model both for the numerical as well as boundary layer wind tunnel investigation thus

allowing a comparative study among different researcher results which is required perhaps at this

stage of CWE application for building aerodynamics. Different from previous CAARC studies in

literature which focuses on isolated building, the present study include experimental and

numerical aerodynamic analysis of CAAR building model under different orientation of adjacent

building conditions. While the numerical simulation uses Fluent 6.3 commercial software and

employs mostly RANS and LES (for limited case) turbulence models, the experimental part

include boundary layer wind tunnel experiments conducted at RWDI USA LLC, Miramar FL for

the present study.

Unlike flow around streamed line object, analysis of flow around sharp edged bluff-body

involves many difficulties as pointed out by Murakami (1998) and Stathopoulos (1997).

Intensive studies have been done on the suitability of various turbulence models (Murakami and

Mochida 1989; Murakami 1998; Castro and Graham 1999; Saha and Ferziger 1996, 1997).

RANS has been used in wind engineering application due to their simplicity in modeling and

reduced computational cost. Despite the computational cost, LES (large eddy simulation) is

believed to have reached the stage where analysis can be carried out within a reasonable

computational time for complicated turbulent flow around actual buildings and should be

considered as a complementary tool for wind loads evaluation (Tamura, 2008).

NUMERICAL MODEL

Based on a preliminary exploratory study on surface mounted cube, different turbulence models

from literature have been examined for their relative suitability for the present bluff body

application. Figure 1 shows comparison between numerically obtained pressure coefficients on

surface mounted cube by several researches with experimental results. As expected LES

provides superior results that agree well the experimental data obtained from wind tunnel and

full-scale tests. From RANS family of models the RNG k- provides relatively better result

compared to the standard k- model. In the present study RNG k- has been therefore opted due

to its relatively good agreement with BLWT compared to Standard k- and relatively lower

computational resource demand compared to LES. Only for comparison purpose LES has been

applied for Case 1A and Case 1B. Due to the chaotic nature of flow around bluff bodies, the

turbulence flows significantly affected by the presence of walls. The near-wall treatment has a

great impact on the result of the numerical simulation. Murakami et al. (1999) discussed the

difficulty in using no-slip boundary condition at solid walls for flows with high Reynolds

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number, such as the present case. For such flow environment, Werner and Wengle (1991) wall

functions in the viscous sub layer (as provided by Fluent 6.3), which will reduce the use of

excessive fine meshes around the solid walls can be used. While for Case 1A, very fine meshes

are used in the near-wall region in attempt to resolve the low Reynolds number flow up to the

wall surface and Werner and Wengle (1991) wall functions are applied for Case 1B. Case 2 and

3 uses non-equilibrium wall functions as described in FLUENT user manual (2006).

Figure 1 Comparison of mean pressure coefficients by several researchers using several turbulence models.

NUMERICAL AND WIND TUNNEL MODEL SETUPS

The geometrical modeling for the numerical simulation of CAARC building model mimics the

1:400 scale rigid model of the wind tunnel testing. The CAARC model has a rectangular

prismatic shape with dimensions 100 ft (x) by 150 ft (y) by 600 ft (z) height. The flow is

described in a Cartesian coordinate system (x, y, z), in which the x-axis is aligned with the

stream-wise direction, the z-axis is in the perpendicular direction and the y-axis is in the vertical

direction. The computational domain dimensions, boundary conditions and the wind tunnel

configurations and are given in Fig. 2. The driver (inlet), lateral and downstream boundaries are

extended laterally to minimize blockage issue on the aerodynamics of the test building. The

Reynolds number based on building height H and inflow velocity UHat

Hz

is

5

108.3 x

. Open

exposure velocity profile with power low exponent of 0.16 is used during the present BLWT test.

-2

-1.5

-1

-0.5

0

0.5

1

1.5

Cp

Bitsuamlak et al., 2008 (K-e)

Wright and Easom, 2003 (K-e)

Wright and Easom, 2003 (RNG)

Wright and Easom, 2003 (NL)

Lim et al., 2009 (LES)

Wind tunnel (Holscher et al., 1998)

Wind tunnel (Richards et al., 2007)

Full Scale (Richards et al., 2007)

A

B

D

C

5

Table 1: Study cases

Case Configuration Velocity profile Scale

Case 1A Isolated BLWT (Huang 2007) 1:250

Case 1B Isolated BLWT (present)

Case 1C Isolated ASCE (exposure B)

Case 1D Isolated ASCE (exposure C)

Case 2A full height adj. bldg upwind of CAARC ASCE (exposure B)

Case 2B full height adj. bldg upwind of CAARC ASCE (exposure C) 1:400

Case 2C full height adj. bldg downwind of CAARC ASCE (exposure B)

Case 2D full height adj. bldg downwind of CAARC ASCE (exposure C)

Case 3A half height adj. bldg upwind of CAARC ASCE (exposure B)

In the present study, three building configurations under four different flow fields have been

investigated. Table 1 describes all cases considered for the numerical simulation of CAARC

building model. Case 1 simulates wind effects on the isolated building without considering the

interference action of the surroundings. Case 2 simulates CAARC building with an adjacent

surrounding building having similar height with the test building placed upwind and downwind

of the study building. Case 3 simulates similar to Case 2 but the adjacent building has only half

height of the CAARC model.

The computational domain contains non uniform grid points and the grid stretching ratio for LES

simulation is less than 1.1 to avoid cut-off wave number between neighboring grids, see Fig. 3.

High mesh resolution is used in the low Reynolds viscous sub-layer zone. The inlet velocity

boundary condition comprising four different mean profiles as shown in Fig. 4 has been

simulated. For transient flow simulation generation of inflow turbulence for LES analysis (Case

1A), the velocity fluctuation is generated using spectral synthesizer available in Fluent. Free out

flow boundary conditions are imposed as the downstream of the computational domain. Wall

treatments as described in Numerical Model section are used at the building wall and the ground

surface. At the top and side boundaries are assumed to have zero velocity gradient and

symmetric boundary conditions are applied.

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(a) Computational domain and boundary conditions: Case 1.

(b) Wind tunnel configurations.

Isolated test

building

7

(c) Computational domain: Case 3. (d) Computational domain: Case 2.

Figure 2 Computational and wind tunnel set up.

Figure 3 Typical grid used in the present study: Case 1A.

Horizontal section

Vertical section

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Figure 4 Inflow boundary condition (velocity profiles) used in the present study.

RESULT AND DISCUSSIONS

For validation and comparison purposes, both standard and LES simulations have been used

for Case 1A and Case 1B. Measurements of mean pressure coefficients normalized by the

dynamic pressure at the building height are compared with the present BLWT data as well as

those obtained from literature. The mean pressure coefficients, Cp, on the upper windward,

sidewall and leeward faces are extracted for the isolated building case at 2/3H of the building as

shown in Fig. 5 for various inflow boundary conditions. As shown in Fig. 5, there is a good

agreement between the present LES as well as Braun’s (2009) and Huang’s (2007) numerical

simulation results of the current study with the experimental results on the windward face. The

agreement deteriorates slightly at the sidewalls and improves at the lee-ward wall. The standard

method over-predicts the pressure coefficient at the stagnation point and at flow separation

point.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

8 9 10 11 12 13 14 15 16

U(m/s)

Z(m) - Model scale

BLWT (Huang et aL., 2007)

BLWT (Present study)

ASCE (Exposure - C)

ASCE (Exposure - B)

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Figure 2. Comparison of mean pressure coefficient.

Figure 3. Mean velocity contour at mid-vertical and 2/3 H horizontal section.

Figure 5 Measurements of mean pressure coefficients over the perimeter at Z/H =2/3: Velocity pressure

normalized by UH (12.7m/s)

Windward wall Leeward wall

Figure 6 Contour plot of mean pressure distribution over the building walls (Case 1A)

-2

-1.5

-1

-0.5

0

0.5

1

1.5

0 0.5 1 1.5 2 2.5 3 3.5 4

X'/Dx

Cp

Pre sent study (ke - C a se 1A )

Pre sent study (LES - C ase 1A)

Pre sent study (LES - C ase 1B)

Bra un et a l., (2009)-numerical

Huang et al., (2007)-numerical

Wind tunnel (B LW T- P resent st udy)

Wind tunnel (Tong Ji Unive rsity)

10

-2

-1.5

-1

-0.5

0

0.5

1

1.5

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

X'/Dx

Mean Cp

Case 1B (RNG )

Case 1C (Expo sure-B)(RNG)

Case 1D (Expo sure-C)(RNG)

Wind Tunnel (Prese nt study)

Figure 6 shows mean-pressure contour pots of the windward and leeward face of configuration

Case 1A. The horseshoe vortex shape contour generated on the front wall agrees well with

Huang et al. (2007) and Braun et al. (2009) flow field investigation. To asses the effects of

inflow boundary conditions, the present work studies four different inflow conditions as outlined

in table 1. Where each of the categories varies in their velocity profile, turbulence intensity and

turbulence integral length scale. Figure 7 exhibits the comparison of mean pressure coefficients

obtained by numerical simulation with the wind tunnel data. Among all, there is reasonable

agreement between Case 1C and the experiment on the windward face. As expected there is

substantial increase in prediction of mean Cp by the numerical method.

Figure 7 Measurements of mean pressure coefficients over the perimeter at Z/H =2/3 (Case 1): Velocity

pressure normalized by UG (14.63 m/s)

Effect of adjacent building plays an important role on the aerodynamic response of buildings.

Each building site is unique and needs to be examined case by case. In the present study a

limited attempt was made to assess the effect of different adjacent building with different height,

Case 2 with same height as with the study building and Case 3 with half height. The numerical

as well as the wind tunnel test results, reveals the sheltering effect due to the presence of tall

structures upwind of the study building as shown in Fig. 8. Approximately a 150% reduction in

mean pressure coefficient has been observed relative to the isolated case. Figure 9 presents the

same case but when the adjacent building is placed in the leeward direction of the flow. In this

case the interference has no significant on the front wall pressure distribution but there is

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Figure 8 Comparison of mean pressure coefficients over the perimeter at Z/H =2/3 (Case 2A & 2B): Velocity

pressure normalized by UG(14.63 m/s).

Figure 9 Measurements of mean pressure coefficients over the perimeter at Z/H =2/3 (Case 2C & 2D):

Velocity pressure normalized by UG (14.63 m/s)

-2

-1 . 8

-1 . 6

-1 . 4

-1 . 2

-1

-0 . 8

-0 . 6

-0 . 4

-0 . 2

0

012345

X ' /D x

M e an C p

W ind Tu nne l (P r e se nt stud y)

C ase 2 A (E xp o sur e -B )(R NG )

C ase 2 B (E xp o sur e -C)(RN G)

-2

-1. 5

-1

-0. 5

0

0. 5

1

1. 5

0 0. 5 1 1 .5 2 2 .5 3 3 .5 4 4. 5 5

X '/D x

Me an C p

W i nd tunn e l (Pr e se nt s tud y)

C a se 2 C (E x po s ure -B )

C a se 2 D (E x po s ure -C )

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reduction in suction pressure coefficient. The presence of the half height adjacent building have

less significant impact on the mean pressure load compared to the effect of full height adjacent

building as can be expected.

Figure 10 Comparison of mean pressure coefficients over the perimeter at Z/H =2/3: Surrounding

interference effects

Mean pressure contours on the building surface are shown in Figs 11a and 11b for Cases 2 and 3

respectively. Flow modification due to complex fluid structure interactions including the

adjacent buildings are also shown in Figs. 11c and 11d for Cases 2 and 3 respectively. Flow

separation points, down washes on windward walls, recirculation behind buildings that are

signature of tall buildings are clearly depicted in these figures. These capabilities of CFD

simulations are very useful in explaining aerodynamics phenomena among wind engineers and to

structural engineers and architects.

-2

-1.5

-1

-0.5

0

0.5

1

1.5

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

X'/Dx

Mean C p

Case 1B (BLWT - Present study)

Case 1B (Exposure - B)(RNG)

Case 2 (BLWT - Present study)

Case 2A (Exposure - B)(RNG)

Case 3 (BLWT - Present study)

Case 3A (Exposure - B)(RNG)

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(a) Case 2 (b) Case 3

Mean pressure coefficients

(c) Velocity path lines: Case 2

(d) Velocity path lines: Case 3

Figure 11 Mean pressure coefficient and velocity path lines: Case 2& Case 3

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CONCLUSIONS AND FUTURE WORKS

Several comparisons between CFD and experimental analysis have been discussed in order to

better understand the current state-of-the-art and assess the potential use of numerical wind load

predictions approaches for practical use. In the past, consistent efforts have been made to make

CFD a better tool for evaluation of wind loads. The application of CFD techniques to predict

wind load, on CAARC model and with different surrounding conditions reveals the suitability of

CFD tools for preliminary assessments and detail explanation of complex building aerodynamic

characteristics. Consistent to reports in literature, numerical data comparison with

measurements in boundary wind tunnel carried out in the present study suggests that an accurate

time-dependent analysis, such as LES analysis is very vital. Planned future studies include detail

LES analysis of buildings incorporating various orientation of surrounding effects.

ACKNOWLEDGMENT

The support from National Science Foundation (through NSF Career award to the second author)

is gratefully acknowledged.

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