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The influence of wind direction on natural ventilation: Application to a large semi-enclosed stadium T. van Hooff1,2, B. Blocken3. 1 PhD student, Building Physics and Systems, Eindhoven University of Technology, Eindhoven, The Netherlands, [email protected] 2 PhD student, Laboratory of Building Physics, Katholieke Universiteit Leuven, Leuven, Belgium 3 Associate Professor, Eindhoven University of Technology, Eindhoven, The Netherlands, [email protected] ABSTRACT Natural ventilation is a commonly applied way in building engineering to ensure a healthy and comfortable indoor climate. In this paper CFD simulations of the natural ventilation of a large semi-enclosed stadium in the Netherlands are described. Simulations are performed to assess the air exchange rate for a total of eight wind directions. The CFD model consists of both the complex stadium geometry and the urban environment in which the stadium is located. A grid sensitivity analysis is conducted, furthermore, validation of the CFD model is performed using full-scale 3D wind velocity measurements. Comparison of the calculated air exchange rates showed that the wind direction has a significant effect on the air exchange rate; differences up to 100% were found for the air exchange rate, which can be explained by the position and size of the buildings upstream of the stadium. INTRODUCTION Natural ventilation is nowadays more and more applied in buildings to maintain a healthy and comfortable indoor climate. The driving force for natural ventilation can be wind or buoyancy, but usually a combination of these forces is present. Wind speed and wind direction can influence the amount of natural ventilation to a large extent, but numerical studies of natural ventilation in which the wind conditions are varied are limited. A previous study concerning the influence of wind direction on the air exchange rate (ACH) was conducted by Horan and Finn [1] who examined the air exchange rate for four wind directions and found significant differences in ACH. Jiang and Chen [2] performed Large Eddy Simulations of fluctuating wind directions, but their study emphasized on short term small fluctuations of wind direction. In this paper the influence of wind direction on the air exchange rate of a large multifunctional sports stadium during summer conditions is investigated using Computational Fluid Dynamics (CFD). Sports stadia are increasingly being used for other purposes, such as concerts, conferences and other activities. One of these multifunctional sports stadia, and subject of this study, is the Amsterdam ‘ArenA’ stadium in the Netherlands. The ArenA is equipped with a retractable roof construction. When the roof is closed, ventilation can only occur through relatively small openings in the building envelope. No HVAC systems are present in the stadium, and therefore the air exchange rate depends on natural ventilation through the roof and openings in the building envelope, being the only means to ensure indoor air quality and thermal comfort. A specific feature of this research is the combined and simultaneous simulation of the wind flow in the complex urban environment around the stadium with the air flow inside the stadium. DESCRIPTION OF STADIUM AND SURROUNDINGS SURROUNDINGS The urban area considered in this study is part of the city of Amsterdam, which is located in the north-west of the Netherlands. The city of Amsterdam and its surroundings are located on very flat terrain; height differences are limited to less than 6 m. The stadium is surrounded by medium and high rise office buildings, and buildings with an entertainment function, such as a cinema and a concert hall. The height of the current surrounding buildings varies from 12 m to a maximum of 95 m for the “ABN-AMRO” office building (Fig. 1a) located on the southwest side of the ArenA. The aerodynamic roughness length y0 of the surroundings, which is needed for the CFD simulations, is determined from the updated Davenport roughness classification [3]. The area on the north side of the ArenA can be classified as “closed terrain” due to the urban character that is present in a radius of 10 km upwind. The estimated y0 for this area is 1.0 m (Fig. 1b). The south side area of the ArenA is not as rough as the north side due to the presence of agricultural and natural areas and can be characterised with an y0 of 0.5 m (Fig. 1b). Figure 1: (a) Amsterdam ArenA and its surroundings. The two arrows indicate the ArenA stadium and the highest building in its proximity (ABN-AMRO: 95 m). (b) Terrain surrounding the stadium with a radius of 10 km and estimated aerodynamic roughness length y0. The white square represents the computational domain used in this study. STADIUM The Amsterdam ArenA is a so-called oval stadium (Fig. 2a). The roof is dome shaped and can be closed by moving two large panels with a projected horizontal area of 110 x 40 m2 (L x W). The roof consists of a steel frame, largely covered with semi-transparent polycarbonate sheets, while steel sheets are applied at the edge of the roof until a distance of 18 m from the gutter. The stand consists of two separate tiers and runs along the entire perimeter. Figures 2a-c show a detailed plan view and the two cross-sections αα’ and ββ’. The exterior stadium dimensions are 226 x 190 x 72 m3 (L x W x H). The stadium has a capacity of 51,628 seated spectators and an interior volume of about 1.2 x 106 m3. Figure 2: (a) Horizontal cross-section of stadium. The arrows indicate the four large openings (gates) in the corners of the stadium. (b) Cross-section αα’; (c) cross-section ββ’. The four measurement positions () for air temperature and CO2 concentration inside the stadium are indicated Dimensions in m. The ArenA is one of the many multifunctional stadiums that have been built in Europe during the last two decades. Apart from sports events, they also host a wide variety of other activities, such as concerts, conferences and festivities. For this purpose, many of these stadia are equipped with a roof construction that can be opened and closed depending on the weather conditions and the type of event. However, they are generally not equipped with HVAC systems to control the conditions of the very large indoor air volume (up to 106 m3), which is also the case for the ArenA. Indoor air quality problems can occur for the configuration with closed roof because of the large number of spectators and insufficient natural ventilation. During the summer, overheating can be an additional problem. In absence of HVAC systems, natural ventilation is the only means to ensure indoor air quality. Natural ventilation can occur through the openings that are present in the envelope of the stadium. The ArenA has several of such openings. The semi-transparant roof is the largest potential opening. During concerts and other festivities however, which are usually held in the summer period, the roof is closed most of the time to provide shelter for the spectators and the technical equipment. When the roof is completely open, it is the largest opening (4,400 m2) in the stadium envelope. When it is closed, natural ventilation of the stadium can only occur through a few smaller openings. The four gates in the corners of the stadium (Fig. 2a) together form the second largest (potential) opening (4 x 41.5 m2). They are open most of the time, but are sometimes closed during concerts to limit noise nuisance for the surroundings. Additionally, two relatively narrow openings are present in the upper part of the stadium. The first opening is situated between the stand and the steel roof construction, and runs along the entire perimeter of the roof (Fig. 3a). The total surface area of this opening is 130 m2. The other opening is situated between the fixed and movable part of the roof (Fig. 3b). This opening is only present along the two longest edges of the stadium. The total surface area of this opening is about 85 m2. Of these openings, only the roof and gates can be opened/closed. In the basic current configuration analysed in this study, the roof is closed, and all other openings are open. Figure 3: (a) Ventilation opening between the stand and the roof construction and (b) between the fixed and the movable part of the roof. FULL-SCALE MEASUREMENTS AIR EXCHANGE RATE To assess the natural ventilation in the stadium, CO2 measurements were performed at four different locations (Fig. 2a-c), and converted to air exchange rates using the concentration decay method: ACH ln C 0 ln C 1 1 0 (1) With ACH = air exchange rate in h-1, C(τ0) is the concentration at time 0 in ppm, C(τ1) the concentration at τ1 in ppm and (τ1- τ0) the time between the two measurements. The CO2 concentration at the four positions was measured during three consecutive evenings on which concerts took place: June 1st until June 3rd, and were made after each concert, when CO2 concentrations had reached a maximum level caused by the attendants. During the measurements, the potential (y0 = 0.03 m) daily averaged wind speed U10 measured by the KNMI at Schiphol airport on these three evenings was about 3.5 m/s and the wind direction on all three days was about 40° from north. The outdoor temperature during the concerts on all evenings was about 19˚C on average, and the indoor air temperature was about 26˚C. Because of the similar conditions, the calculated air exchange rate on these three nights was averaged. Table 1 shows that the average air exchange rate for all four measurement positions is about 0.7 h-1 on these evenings, whereas the minimum air exchange rate according to ASHRAE Standard 62-1 [4] should be at least 1.5 h-1. Table 1: Average air exchange rate at four positions measured after three concerts. Position North, first tier (NE1) North, second tier (NE2) Southeast, first tier (SE1) Air exchange rate (h-1) 0.65 0.74 0.69 Southeast, second tier (SE2) 0.61 WIND VELOCITY For CFD validation purposes, the 3D wind velocity in and around the stadium was measured in the period September-November 2007, on days with strong winds (reference wind speed above 8 m/s). Measurements were made with ultrasonic anemometers, positioned on mobile posts, at a height of 2 m above the ground. The measurement positions included the four openings in the corners of the stadium. Reference wind speed (Uref; meas) was measured on top of a 10 m mast on the roof of the 95 m high ABN-AMRO office building, which is the highest building in the proximity of the stadium (Fig. 1). The data were sampled at 5 Hz, averaged into 10-minute values and analysed. The measurement results will be reported together with the simulation results in the validation section. INDOOR THERMAL CONDITIONS To analyse the indoor conditions, full-scale measurements were also made of indoor and outdoor air temperature and relative humidity. Furthermore, the irradiance of the sky was measured to investigate the influence of solar radiation on the indoor air temperature. These measurements showed that the indoor temperature depends strongly on the solar radiation, and can exceed the outdoor temperature by up to 6°C (Fig. 4), which indicates that the natural ventilation is not capable of removing enough warm air during the day. The CO2 measurements already showed that the air exchange rate during and after the three concert evenings does not meet the ASHRAE requirements. Figure 4: The measured air temperature a inside and outside the stadium and the measured irradiance E, on a sunny day (July 18, 2007) and a cloudy day (July 20, 2007) CFD SIMULATIONS: COMPUTATIONAL MODEL AND PARAMETERS MODEL GEOMETRY AND COMPUTATIONAL DOMAIN The computational model of the stadium reproduces its geometrical complexity with high resolution, down to details of 0.02 m. This is required to accurately model the flow through the narrow ventilation openings (Fig. 3a,b). Because data with such high resolution are not available from GIS and/or city databases, the construction drawings of the stadium were used. The buildings that are situated in a radius of 500 m from the stadium are modelled explicitly, but only by their main shape. Buildings that are located at a greater distance are modelled implicitly, by imposing an increased equivalent sand-grain roughness height kS and roughness constant CS at the bottom of the computational domain. These values are based on the aerodynamic roughness length y0 of the terrain in and beyond the computational domain and on the relationship between kS, CS and y0 for the specific CFD code [5]. The computational domain has dimensions L x W x H = 2,900 x 2,900 x 908.5 m³. The maximum blockage ratio is 1.6%, which is below the recommended maximum of 3% [6-7]. Franke et al. [6] also state that the distance from the building to the side, to the inlet and to the top of the domain should be at least five times the height of the building and the distance from the building to the outlet should be fifteen times the height. Since the stadium is 72 m high and the smallest distance to the inlet of the domain is 1,130 m, these requirements are also fulfilled. GRID The computational grid consists of 5.6 million prismatic and hexahedral cells. The grid is a hybrid grid; it is partially structured and partially unstructured. Special attention was paid to the precise modelling and high grid resolution of the ventilation openings of the stadium. A high grid resolution is used in the proximity of these openings in order to accurately model the flow. A grid sensitivity analysis was performed with grids containing 3.0 million, 5.6 million and 9.2 million cells. The 5.6 million grid was found to provide fairly grid-independent results. Some parts of the computational grid are displayed in Figure 5a,b. Figure 5: (a) View from north showing part of the computational grid on the surfaces of the stadium and its surroundings. (b) Bird-eye view of the geometry and grid on the southeast side of the stadium, illustrating details such as the roof gutter which is modelled in detail for the air flow through the ventilation opening shown in Fig. 3a. BOUNDARY CONDITIONS At the inlet of the domain a logarithmic wind speed profile is imposed with an aerodynamic roughness length y0 of 0.5 m and 1.0 m, depending on the wind direction, and a reference wind speed U10 of 10 m/s. The corresponding turbulent kinetic energy and the turbulent dissipation rate profiles are also imposed at the inlet. The roughness of the bottom of the domain is taken into account by imposing appropriate values for the sand-grain roughness ks and the roughness constant Cs which are calculated using Equation 2 [5]: kS 9.793 y0 CS (2) To avoid the use of excessively large cells near the ground when using the default values for Cs, the sand-grain roughness ks in the Fluent 6.3.26 code has been taken equal to 0.7 m and in order to achieve horizontal homogeneity of the approach-flow mean wind speed profile in this situation, the value for Cs is set equal to 7 with a user defined function (UDF). More information on this matter is provided in [5,8]. For the ground surface in the direct vicinity around the explicitly modelled buildings and the stadium, y0 = 0.03 m is taken, which is imposed by setting kS = 0.59 m and CS = 0.5. The temperature of the inlet air is set to 20ºC. Zero static pressure is set at the outlet of the domain and the top is modelled as a slip wall (zero normal velocity and zero normal gradients of all variables). To roughly take into account the increasing air temperature inside the stadium because of solar irradiation, estimated surface temperatures are imposed on several surfaces inside the stadium. These surface temperatures vary from 22ºC to 50ºC. Note that the intention of these simulations was only to compare the performance of different ventilation configurations. It was not intended to model the exact transient thermal behaviour of the stadium under transient meteorological conditions. OTHER COMPUTATIONAL PARAMETERS The 3D steady RANS equations are solved in combination with the realizable k-ε turbulence model using the commercial CFD code Fluent 6.3.26 [9]. The realizable k-ε turbulence model is chosen because of its general good performance for wind flow around buildings [10]. Pressurevelocity coupling is taken care of by the SIMPLE algorithm, pressure interpolation is standard and second order discretisation schemes are used for both the convection terms and the viscous terms of the governing equations. The Boussinesq approximation is used for thermal modelling. CFD SIMULATIONS: VALIDATION AND RESULTS VALIDATION CFD validation for the stadium is based on the wind speed measurements mentioned previously. The measured wind speed at the locations in the four gates is divided by the reference wind speed measured on top of the ABN-AMRO office building. This wind speed ratio is also calculated with CFD and both ratios are compared. The validation is performed for two wind directions, and for a closed and opened roof, depending on the configuration of the roof during the measurements. For brevity, results are only shown for a wind direction of 228˚ and a closed roof, since the configuration with the closed roof is the most interesting one from a ventilation point of view. Figure 6 compares simulated and measured mean wind speed, indicating a good agreement for the wind speed ratios (Fig. 6a) and for the wind direction in the gates (Fig. 6b), except for gate D. The CFD simulation predicts a flow parallel to the opening of gate D, whereas the measurements showed flow directed into the stadium. Overall, a fair to good agreement is obtained for the simulations, and the stadium model is used to evaluate different ventilation configurations. Figure 6: Comparison between numerical and experimental results in the four gates A, B, C and D, for closed roof and reference wind direction φ of 228º. (a) wind speed ratio U/Uref; (b) local wind direction φ. RESULTS Simulations are performed with eight wind directions: = 16°, 61°, 106°, 151°, 196°, 241°, 286° and 331°, all perpendicular or under an angle of 45° to the symmetry axis of the stadium . For wind directions between 286° and 16°, no immediate buildings are present upstream of the stadium, as opposed to between 61° and 241°. The most, and also the largest, buildings are situated upstream of the stadium for a wind direction of 196° (Fig. 1a). The reference wind speed U10 at 10 m height at the inlet is 10 m/s for all simulations. For each ventilation configuration, the simulated mass flow rates through each opening are used to determine the ACH with Eq. (3) [11]. ACH Q 3600 V (3) where Q is the volumetric air flow rate into the enclosure (m3/s) and V the volume of the enclosure (m3). The results of the calculations are shown in Figure 7 and indicate that the ACH indeed strongly depends on the wind direction.The air exchange rate for a wind direction of 16° for example is twice as high as for a wind direction of 196°, which is the wind direction with the lowest air exchange rate. This low air exchange rate for = 196º can be explained by the presence of a group of large buildings upstream of the stadium. These buildings provide some shelter from wind. Figure 8 shows the contours of the wind speed ratio U/U10 around the ArenA for = 196º (SSW) in horizontal planes at heights of 10, 20, 40 and 60 m above the ArenA deck, at which the lowest openings (gates) are situated. The lower values around the ArenA indicate that the stadium is indeed situated in the wake of the office buildings, causing the lower ACH for this wind direction, which is the prevailing wind direction in the Netherlands. Note that these values are much higher than the measured ACH because of the much higher wind speed and different meteorological conditions as during the measurements. Figure 7: Air exchange rates obtained with CFD for eight wind directions (U10 = 10 m/s). Large differences are present for the various wind directions. DISCUSSSION In this study the air exchange range of a large multifunctional stadium has been calculated using CFD simulations. Further research is needed on several aspects of the study that has been performed. First of all, the CFD simulations in this study were performed steady-state, concerning both the flow and the heat transfer. Transient thermal simulations can be performed with CFD in the future for a more detailed thermal analysis. Another possibility is to use a Building Energy Simulation tool to simulate the transient thermal behaviour in a coupled approach with CFD simulations for the air flow pattern. This coupling will be subject of future research by the authors. Transient CFD simulations will also be performed to study the influence of pulsating flow and large eddies on the air exchange between the building interior and the external flow. Although the validation study showed a good agreement between the measurements and the RANS simulations, it would be interesting to compare the air exchange rates obtained with RANS simulations with results of transient simulations that do take into account time-dependent flow properties [12,13]. Transient simulations will be performed using Large Eddy Simulation (LES) and/or Detached Eddy Simulation (DES). Secondly, the balance between wind and buoyancy as driving forces for natural ventilation will be subject of future research. CFD simulations will be performed to gain more knowledge on the interaction of both driving forces. Figure 8: Contours of wind speed ratio U/U10 in four horizontal planes, for φ = 196° (SSW) and U10 = 10 m/s; at (a) 10 m; (b) 20 m; (c) 40 m and (d) 60 m above the ArenA deck. The lower wind speed ratios around the stadium indicate that it is situated in the wake of the surrounding buildings, which explains the lower air exchange rates for this wind direction. CONCLUSIONS Coupled CFD simulations are performed to assess natural ventilation in a large semi-enclosed stadium for a range of wind directions. The following conclusions are made: Measurements have shown that the air exchange rate of the stadium, with its roof closed, was insufficient to avoid overheating during summer. Furthermore, the air exchange rate measured after three concerts was about 0.7 h-1 during the measurement period, this is only half of the recommended air exchange rate by ASHRAE. Wind speed measurements have been used to validate the CFD model of the stadium and its surroundings and a good agreement has been found. A grid sensitivity analysis has been performed that has shown that a grid with 5.6 million cells is adequate for this study. The results demonstrate the importance of modelling the surrounding urban environment for natural ventilation analysis. Furthermore, this study shows the need to perform simulations with a range of wind directions, especially if one wants to take into account the effect of the urban environment and building asymmetry on natural ventilation. ACKNOWLEDGEMENTS The first author is currently a PhD student funded by both Eindhoven University of Technology in the Netherlands and the Katholieke Universiteit Leuven in Flanders, Belgium. The work in this paper is a result of his master thesis project at Eindhoven University of Technology. The measurements reported in this paper were supported by the Laboratory of the Unit Building Physics and Systems (BPS). Special thanks go to Ing. Jan Diepens, head of LBPS, and Wout van Bommel, Ing. Harrie Smulders and GeertJan Maas, members of the Laboratory of the Unit BPS, for their important contribution. The authors also want to thank Martin Wielaart, manager at the Amsterdam ArenA, for his assistance during the measurements. REFERENCES [1] J.M. Horan, D.P. Finn, Sensitivity of air change rates in a naturally ventilated atrium space subject to variations in external wind speed and direction, Energy Build. 40(8) (2008) 1577-1585. [2] Y. Jiang, Q. Chen, Effect of fluctuating wind direction on cross natural ventilation in buildings from large eddy simulation, Build. Environ. 37(4) (2002) 379-386. [3] J. Wieringa, Updating the Davenport roughness classification, J. Wind Eng. Ind. Aerodyn. 41(1) (1992) 357-368. [4] ASHRAE, ASHRAE Standard 62.1-2004, Ventilation for acceptable indoor air quality, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Atlanta, GA, USA 2004. [5] B. Blocken, T. Stathopoulos, J. Carmeliet, CFD simulation of the atmospheric boundary layer: wall function problems, Atmos. Environ. 41(2) (2007a) 238-252. [6] J. Franke, A. Hellsten, H. Schlünzen, B. Carissimo, (Eds.), Best practice guideline for the CFD simulation of flows in the urban environment. COST Office Brussels, ISBN 3-00-018312-4, 2007. [7] Y. Tominaga, A. Mochida, R. Yoshie, H. Kataoka, T. Nozu, M. Yoshikawa, T. Shirasawa, AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings, J. Wind Eng. Ind. Aerodyn. 96(10-11) (2008) 1749-1761. [8] B. Blocken, J. Carmeliet, T. Stathopoulos, CFD evaluation of wind speed conditions in passages between parallel buildings—effect of wall-function roughness modifications for the atmospheric boundary layer flow, J. Wind Eng. Ind. Aerodyn. 95(9-11) (2007b) 941-962. [9] Fluent Inc., Fluent 6.3 User’s Guide. Fluent Inc., Lebanon, 2006. [10] J. Franke, C. Hirsch, A.G. Jensen, H.W. Krüs, M. Schatzmann, P.S. Westbury, S.D. Miles, J.A. Wisse, N.G. Wright, N.G., Recommendations on the use of CFD in wind engineering. In: J.P.A.J. van Beeck (Eds.), Proceedings of the International Conference on Urban Wind Engineering and Building Aerodynamics. COST Action C14, Impact of Wind and Storm on City Life Built Environment. Von Karman Institute, Sint-Genesius-Rode, Belgium, 2004. [11] ASHRAE, Handbook of Fundamentals. American Society of Heating, Refrigerating and AirConditioning Engineers, Inc., Atlanta, USA, 2005. [12] Y. Jiang, Q. Chen, Study of natural ventilation in buildings by large eddy simulation. J. Wind Eng. Ind. Aerodyn. 89(13) (2001) 1155–1178. [13] N.G. Wright, D.M. Hargreaves, Unsteady CFD simulations for natural ventilation, Int. J. Vent. 5(1) (2006) 13-20.