Comparison of three different calculation models for wind-driven rain on building facades

Comparison of three different calculation models for

wind-driven rain on building facades

Citation for published version (APA):

Blocken, B. J. E., & Carmeliet, J. E. (2008). Comparison of three different calculation models for wind-driven rain on building facades. In S. Roels, G. Vermeir, & D. Saelens (Eds.), Proceedings of the Building Physics

Symposium in honour of Professor Hugo L.S.C. Hens, 29-31 October 2008, Leuven, Belgium (pp. 207-210). Katholieke Universiteit Leuven.

Document status and date: Published: 01/01/2008

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Comparison of three different calculation models for wind-driven

rain on building facades

B. Blocken

1

and J. Carmeliet

2'3

<1> Building Physics and Systems, Eindhoven University of Technology, The Nether lands

<2> Chair of Building Physics, ETHZ, Zurich, Switzerland

<3) Empa, Swiss Federal Laboratories for Materials Testing and Research, Switzerland

Keywords: driving rain, wind flow; building facade, HAM transfer analysis, durability

1.

Introduction

This paper briefly presents some of the headlines of an extensive comparison study of three different calculation models for wind-driven rain (WDR) on building facades. The three models are the empirical model in the European Standard Draft for WDR assessment (ESD; [1]), the semi-empirical model by Straube and Burnett (SB; [2]) and the CFD model by Choi [3] extended by Blocken and Carmeliet [4]. These three models are applied to determine the WDR coefficient on the windward facade of a low-rise cubic building, a wide low-rise building, a wide high-rise building and a tower building, under steady-state conditions of wind and rain. In each case, the reference wind direction is perpendicular to the windward facade. CFD validation was performed earlier based on wind tunnel measurements and full-scale on-site WDR measurements. The CFD simulations of the WDR coefficient are considered as the reference case, and the performance of the two semi-empirical models is evaluated by comparison with these CFD results. The full study is reported in [5,6].

2. Description of buildings and surroundings

The four building configurations are shown in Fig. 1. The buildings are isolated and are placed on a large grass-covered, uniformly rough and flat terrain with Zo = 0.03 m.

3. CFD validation

Two types of CFD validation were performed: validation of the CFD wind-flow pattern by comparison with PN wind tunnel measurements [6] and validation of the calculated WDR intensities by comparison with on-site WDR measurements [4,7-9]. It was shown that the Reynolds Stress Model (RSM) is superior in reproducing the mean wind-flow pattern upstream of the building configurations, and that the CFD model for WDR can provide accurate results of the WDR distribution and amount on the windward building facade, at least for wind direction perpendicular to this facade.

(c) / (a) (b) LJJt1o ( ffi12.5 +--i> 10

~=====

=

=~

12.5 ----➔ 10 50 50 50 1 2.5 (d)

-

20 80

4. Wind-driven rain model application

The three WDR models are applied to calculate the WDR coefficient a at the windward facade of

the four building models, for reference wind speed U10 = 10 mis, wind direction perpendicular to the

facade (0

=

0°) and three different horizontal rainfall intensities:

Rii

=

1, 10 and 30 mm/h. The WDR

coefficient in the CFD model is obtained by dividing the catch ratio 11 by U 10:

(1)

Earlier research has shown that 11 is in good approximation a linear function of U10, except at facade

positions that are sheltered by horizontal projections such as roof overhangs or balconies [7], which is

not the case in this study. Therefore, a can be considered independent of U10 . It is however a function

of Rb. The WDR coefficient in the ESD model is given by:

a

=

~-CR -CT -O-W (2)

9

where CR is the roughness coefficient, Cr the topography coefficient, 0 the obstruction factor and W

the wall factor [1]. Note that a in this model is independent ofU10 and Rh. The WDR coefficient in the

SB model is given by:

a

=

DRF

.

RAF

.c~r

p

(3)

where DRF is the driving rain function, RAF the rain admittance function, z the height above ground

and ap the power-law exponent [2,5]. In this model, a is independent ofU10, but it is a function of Rb

by the DRF. For the CFD model, 3D steady RANS simulations with the RSM are performed for the four building configurations. The calculation of the raindrop trajectories, the specific catch ratio and the catch ratio are performed with author-written program codes [7]. For more details on the application of the CFD, ESD and SB model to these four buildings, the reader is referred to [5,6)

5. Results

5.1 Comparison for high-rise wide building

Fig. 2 compares the WDR coefficients by the three models along two vertical lines: one in the middle of the facade and one at the edge. The CFD results show that the WDR coefficients and their

vertical gradients are highest near the top of the facade. At the lower part of the facade, a is rather

low. The ESD model overestimates a in the middle of the facade, but provides quite good results for

the vertical edge. The SB model provides very large overestimations for Rb

=

1 mm/h, and significant

overestimations at the top of the facade, for both lines and for both Rb values. The SB model also

overestimates a at the edge for both Rb values.

5 .2 Comparison in terms of reproduction of the wind-blocking effect

For isolated buildings, the term "blocking effect" (7) refers to the disturbance of the wind-flow pattern by the presence of the building and the associated decrease of the upstream streamwise wind-velocity component near the building (wind-speed slow-down). This leads to a reduced driving force for WDR and hence a lower WDR exposure of the facade. The higher and the wider the building, the stronger this effect will be. This physical phenomenon, which can be observed with the CFD model, should - ideally - also be reproduced by the two semi-empirical models. Since the wind-blocking effect is related to both the width and the height of the windward facade of the building, it can be related to the building scaling length BSL:

where BL is the larger and Bs is the smaller dimension of the windward facade. This parameter was defined by Wilson (1989) for estimating dimensions of flow recirculation regions on building roofs.

..c:

E

E II

rt

..c:

E

E 0 ... II 40 1

fr

I ~ 30 J ( . )

.s

I lo N 20 I~} I 10 _I 0 0.1 0.2 0.3 40 ~ 30

.s

N 20 ; • l

d

i

10 (I)/ ' I

u.

r

·

0 ' "" 0 0.1 a (s/m) CFD 0.2 a (s/m) 0.3 0.4 0.4 (b) ₅₀ 40 ~ 30

.s

N 20 10 0 ,:.:i (f) ' UJ .I T 0 0.1 0.2 0.3 40 ~ 30

.s

N 20 a (s/m) 1 Q I ,,. .. ,'..._ ~-· -. 1 1 i "r::St.J i I 0 '· . 0 0.1 0.2 a (s/m) 0.3 0.4 0.4

Fig. 2. Comparison of wind-driven rain coefficient

a

(s/m) obtained by three models: CFD, European

Standard Draft (ESD) and Straube and Burnett (SB), along two vertical lines: (a,c) middle and (b,d)

edge, for Rh

=

1 and 10 mm/h. Note that the SB model provides a minimum and maximum value

(SBmin and SBmax).

Fig. 3 shows the average WDR coefficient Uavg over the facade as a function of the BSL, for Rh= 1, 10

and 30 mm/h, the four buildings and the three calculation models. It is clear that the CFD model predicts the wind-blocking effect: Uavg decreases with increasing BSL. The BSD model however only

shows a significant decrease of Uavg with increasing BSL from the two low-rise buildings to the two

high-rise buildings. This indicates that Uavg in the BSD model is primarily governed by the height of

the building model, rather than by the combination of building width and height: the BSD model does not reproduce the wind-blocking effect. The SB model shows the opposite trend as the CFD model:

Uavg is significantly larger for the tower building than for the low-rise wide building. Again, Uavg in this

model is clearly related to building height, rather than to building width: the SB model does not reproduce the wind-blocking effect.

6. Discussion and conclusions

The CFD model results have been used as the reference solution, based on earlier validation studies. Comparison with the results of the BSD and the SB model allows determining the capabilities and deficiencies of these models. While both models correctly include several important features of WDR, there are also some deficiencies. In the SB model, the RAF values at the top edge and vertical edge of the facade are too large, and they are responsible for significant overestimations in WDR exposure. The BSD model does not include lateral gradients in WDR coefficient. The wind-blocking effect is not reproduced by the BSD and the SB model. This effect implies that both the building

height and the building width strongly influence the WDR exposure of the facade. In the ESD and SB model, only the building height is considered as an important parameter, while the influence of building width is neglected (in ESD) or negligible (in SB). The detailed analysis ofWDR distributions on buildings by CFD and the comparison of these results with the results of the other models allow determining the capabilities and deficiencies of these models, and, as such, guide the future improvement of these models.

(a) Rh= 1 mm/h (b) Rh = 10 mm/h 0.25 ~ - - - ~ 0.25 ~ - - - , 0.2 E 0.15 ~ [ 1:l 0.1 0.05 0.2

I

0.15

l

0.1 0.05 o ~ - - ~ - - ~ - - ~ o ~ - - ~ - - ~ - - ~ 0 20 40 60 0 20 40 60 BSL(m) BSL (m) (c) Rh= 30 mm/h 0.25 0.2 DI ~

u

□

E

0.15

l

_l

i

_9P.

_l

~ g> - •. "'_fl?._a:,: lj" 0.1

~~

ESD 0.05 _{""'r.•~ . . .} . . _{. ....}

C;

....

.•

.:)1...! , .. , 0 0 20 40 60 BSL (m)

Fig. 3. Wind-driven rain coefficient averaged over the windward facade (

aavr)

versus building scaling

length (BSL), for the three different Rh, the four buildings and the three calculation models.

REFERENCES

[1] CEN. 2006. Hygrothermal performance of buildings - Calculation and presentation of climatic data - Part 3: Calculation of a driving rain index for vertical surfaces from hourly wind and rain data (ISO/DIS 15927-3:2006). DRAFT prEN ISO 15927-3.

[2] J.F.Straube, E.F.P.Burnett. 2000. Simplified prediction of driving rain on buildings. Proc. Int. Building Physics Conf., Eindhoven, The Netherlands, 18-21 September 2000, 375-382.

[3] E.C.C.Choi. 1993. Simulation of wind-driven rain around a building. J. Wind Eng. Ind. Aerodyn. 46&47, 721-729.

[4] B.Blocken, J.Carmeliet. 2002. Spatial and temporal distribution of driving rain on a low-rise building. Wind Struct. 5( 5), 441-462.

[5] B.Blocken, J.Carmeliet. 2008. Comparison of different calculation models for wind-driven rain on buildings. Part I. Model theory. Wind Struct. Submitted.

[6] B Blocken, G.Dezso, J.van Beeck, J.Carmeliet. 2008. Comparison of different calculations models for wind-driven rain on buildings. Part II. Application to idealised buildings. Wind Struct. Submitted.

[7] B.Blocken, J.Carmeliet. 2006. The influence of the wind-blocking effect by a building on its wind-driven rain exposure. J. Wind Eng. Ind. Aerodyn., 94(2), 101-127.

[8] M.Abuku, B.Blocken, K.Nore, J.V.Thue, J.Carmeliet, S.Roels. 2008. On the validity of numerical wind-driven rain simulation on a rectangular low-rise building under various oblique winds. Build. Environ., In Press. (doi: 10.1016/j.buildenv.2008.05.003).

[9] P.M.Briggen, B.Blocken, H.L. Schellen. 2008. Wind-driven rain on the facade of a monumental tower: numerical simulation, full-scale validation and sensitivity analysis. Build. Environ., Submitted.