- Email: [email protected]
Wind driven natural ventilation through multiple windows of a building: A computational approach
Energy and Buildings 45 (2012) 317–325
Contents lists available at SciVerse ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
Wind driven natural ventilation through multiple windows of a building: A computational approach M.Z.I. Bangalee a,∗ , S.Y. Lin a , J.J. Miau a,b a b
Department of Aeronautics and Astronautics, National Cheng Kung University, Taiwan Research Center for Energy Technology and Strategy, National Cheng Kung University, Taiwan
a r t i c l e
i n f o
Article history: Received 30 July 2011 Received in revised form 7 November 2011 Accepted 17 November 2011 Keywords: Natural ventilation Energy saving system CFD RNG k–ε turbulence model
a b s t r a c t Natural ventilation is an energy saving system for the building to ensure occupant’s physical comfort. It has also no contribution to the atmospheric pollution as well as the global warming. In the present study, a building with multiple windows is considered to investigate the wind-driven a ventilation system using computational fluid dynamics (CFD), whose acceptance and accuracy are growing very fast. The Renormalization group (RNG) k–ε turbulence model is chosen to simulate cross and single-sided ventilation with a specified accuracy after validating the methodology through the satisfactory comparison with an experimental result. The CFD model is then applied to investigate the physical mechanism of the air movement. The results are presented in the form of the mean velocity vectors, the magnitude of velocity, the components of velocity, the pressure distribution, the pressure coefficient and the effect of incoming wind velocity inside and outside the building. The necessity of three-dimensional (3D) approach to predict the indoor air movement correctly in studying natural ventilation system is also emphasized in this article. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Natural ventilation is a system in which the internal air of an enclosed space is continuously replaced by the fresh external air through opening for better thermal comfort and good indoor air quality without applying any forced flow. In some situation, a lack of ventilation may cause exorbitant pollution, suffocation, humidity and discomfort of unpleasant odors, smokes, overheats. Ventilation is a part of heating, ventilation and air-conditioning (HVAC) systems which consume an extensive amount of energy. More than half of the world’s annual energy is consumed in building systems, i.e., ventilation, heating, cooling, lighting, cooking, powering to household electronics/appliances. According to Orme [1], HVAC system in service and residential buildings consumes 26.48% of the total annual energy delivered. Recently global warming has attracted huge attention. Fossil fuel based electricity production throws a threat to our near future due to its high green house gas emissions and its upcoming scarcity. International effort is being enhanced to reduce the dependence on fossil fuel based electricity as well as to produce more renewable energy. However, the efficiency of the renewable energy is not satisfactory enough so far to meet the current entire energy demand. Therefore, the reduction
∗ Corresponding author. Tel.: +886 0983496461. E-mail address: [email protected] (M.Z.I. Bangalee). 0378-7788/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.enbuild.2011.11.025
of energy consumption wherever it is possible is desired. An effective natural ventilation system may assist to achieve this goal if the location, the position and the design of the building, the ambient condition, and the environment are satisfactory. There are several approaches to study the physics of natural ventilation system such as experimental investigation, empirical models and numerical solution by computational fluid dynamics (CFD). The most common approach to investigate the flow characteristics of a naturally ventilated building is the experimental investigations, e.g. [2–8]. Cockroft and Robertson [2] proposed a theoretical model to predict the airflow by measuring a wind driven ventilation through a single opening subjected to impinging air stream. Dascalaki et al. [3] studied fifty-two single sided natural ventilation configurations experimentally and then compared the results with the predictions of ventilation models. Finally, a new model to predict air flow rates in single sided natural ventilation configurations was proposed. It was also claimed that for cases where inertia forces are more important than gravitational forces, a systematic difference exists between the theoretical and the experimental values. Dascalaki et al. [4] carried out four, single-sided and wind driven natural ventilation experiments by using tracer gas decay method to derive the average air exchange rate through the opening. Carey and Etheridge [5] studied three cases to investigate the feasibility of natural ventilation design by means of direct wind tunnel modeling of ventilations rates. Jiang et al. [6] also
318
M.Z.I. Bangalee et al. / Energy and Buildings 45 (2012) 317–325
Nomenclature Cp CRNG Cε1RNG Cε2RNG p p0 Pk U, V, W U0 x, y, z k
mean pressure coefficient (–) RNG k–ε turbulence model constant (–) RNG k–ε turbulence model coefficient (–) RNG k–ε turbulence model constant (–) pressure (kg m−1 s−2 ) free stream static pressure (kg m−1 s−2 ) shear production of turbulence (kg m−1 s−3 ) x-, y- and z-component of velocity respectively (m s−1 ) free stream velocity (m s−1 ) Cartesian coordinates (m) turbulence kinetic energy per unit mass (m−2 s−2 )
Greek symbol ε turbulence dissipation rate (m−2 s−3 ) viscosity (kg m−1 s−1 ) density of the fluid (kg m−3 ) RNG k–ε turbulence model constant (–) kRNG εRNG RNG k–ε turbulence model constant (–)
considered the wind tunnel test to study the ventilation phenomena of small building-like models through door-like openings. The velocity, the pressure and the ventilation rate in a wind tunnel to analyze the characteristics of cross ventilation were measured in detail by Murakami et al. [7]. The internal airflow characteristics in a cross-ventilation model were investigated using split-film probes capable of measuring directional velocity components by Ohba et al. [8]. Stavrakakis et al. [9] studied natural cross-ventilation with openings at non-symmetrical locations (i) experimentally in a test chamber and (ii) numerically using computational fluid dynamics techniques. The other approach to investigate the natural ventilation is to apply the computational fluid dynamics (CFD) which provides a cost-effective and accurate alternative to the scale model testing, with variations on the simulation being performed quickly, offering obvious advantages. The development of turbulence modeling and the increase of computer speed make CFD an effective alternative due to its informative result, low labor and low cost. In addition to this, computer costs decrease continuously while labor and materials costs rise. Having these advantages a plenty of research efforts was applied to study the natural ventilation using CFD as reported in [10–20]. A particular CFD approach related to the present study will be discussed shortly after this section. However, it should be noted that natural ventilation can be effective only to certain climates having some limitations as studied by Chen [21]. Furthermore, the CFD approach has also some difficulties as reported by Levermore [22]. Wind and/or buoyancy driven pressure difference is the predominant mechanism for natural ventilation system. The different wind pressure along building faces and/or the different temperature between indoor and outdoor air induces a circulation of flow and establishes natural ventilation in buildings through openings. A review of wind driven ventilation techniques was reported by Khan et al. [23] whereas buoyancy-driven single-sided natural ventilation in buildings was studied by Jiang and Chen [24]. The effects of buoyancy and/or wind on ventilation rates and indoor conditions were also examined by Allocca et al. [16]. In the present study, only wind driven ventilation system is examined in order to analyze the indoor air flow for the cross ventilation as well as the single-sided ventilation. In recent years, most of the studies regarding one story buildings/boxes were carried out to investigate the flow and the
Fig. 1. A schematic view of the computational domain in Case 1.
temperature field through one opening on an individual wall regardless of the ventilation type. However, a full scale building may contain multiple (e.g., at least 2 openings) windows on the front and/or the rear wall to ensure uniform and fresh airflow inside the building for comfort. It is quite difficult to explain exactly how the flows entered from multiple openings interact and how they are mixed up. Nevertheless, it can be stated that the position and the size of the opening, the incoming air velocity, the direction, and the temperature difference play the dominantly important role for the flow through a particular opening to be influenced by the flow through another opening. This article is a part of a series of ongoing investigations where the flow phenomena for the different wind condition, window location, window size, building design, environmental condition, etc. are investigated extensively. RNG k–ε turbulence model [17] is chosen to simulate the present ventilation problem. The commercial software ANSYS ICEM CFD [25] and the solver CFX [25] are used for the discretization of the domain and the simulation respectively. The precision of the numerical methodology has been established by comparing the simulation results with the similar experimental results. Then a full scale, naturally wind-driven ventilated building with two openings on the front and/or the rear wall has been considered to analyze the internal and external flow characteristics. The present study considers two openings on each of the front and/or the rear wall to study the natural air circulation of cross ventilation (4 openings) (Figs. 1 and 2), windward single-sided ventilation (2 openings) and leeward single-sided ventilation (2 openings). 2. The studied model Wind driven cross ventilation and single-sided ventilation have been considered to investigate the flow phenomena inside and around a full scale building of 4.5 m × 4.5 m × 3.25 m (length × width × height) including wall thickness of 0.25 m as shown in Figs. 1 and 2. It should be noted that the floor of the building has been considered as surface (wall), free of thickness since being under the ground it has no effect in the flow field. The square-shaped windows have the dimension of 0.5 m × 0.5 m (length × height) and the center of the windows is located at 2.25 m high from the ground and 0.4 m far from the inner corner edge. A sufficiently large domain around the building is often required to simulate a real case which demands longer computational time. Therefore, there is a challenge to minimize the computational time and to maximize the accuracy simultaneously. The dimension of the computational domain is 40.5 m × 22.5 m × 9.75 m (length × width × height) which is expected to be capable of simulating the actual environment. Three different cases have been considered depending on the windows’ face, position and function: Case 1: Cross ventilation with two openings in windward and two openings in leeward walls.
M.Z.I. Bangalee et al. / Energy and Buildings 45 (2012) 317–325
319
which can be either structured or unstructured, and the second one is the selection of proper turbulence model. Horan and Finn [26] found that the results with the structured mesh were better than that with the unstructured mesh in terms of both solution convergence and predicted airflows in naturally ventilated models. In the present study, a non-uniform structured mesh with stretching factor 1.2 is used to discretize the computational domain. For this huge domain size a perfect numerical resolution is difficult to obtain due to the limitation of the computation facility. However, the finest possible grid has been used which contains approximately 1.82 million elements where particular attention is focused on the areas adjacent to the wall. The smallest spacing of the grid is 1 mm and is set to the near wall to capture the boundary effect on the flow field. ANSYS ICEM CFD 12.0 has been used for the entire grid generation. Grid independence tests have been carried out utilizing meshes with up to 3.67 million elements and the results have exhibited the discrepancy up to 2%. Therefore, the present grid system is not conclusive but adequate enough which is capable to capture the actual flow phenomena with an acceptable accuracy. 3.2. Turbulence model
Fig. 2. The schematic view of the building and the locations of calculation.
Case 2: Single-sided ventilation with two openings in windward wall. Case 3: Single-sided ventilation with two openings in leeward wall. A number of finite points on twelve different vertical lines are chosen to calculate the velocity components. The detail of each line is shown in Fig. 2. The first eight lines start from the ground whereas six lines (from line 1 to line 6) at the center section of the building and other two lines (line 7 and line 8) are close to the left wall and right wall respectively. The rest four lines (from LW1 to LW4) at the center section of the corresponding windows (from window 1 to window 4) start from the elevation of 2 m. Every line comprises of 20–30 points where the velocity components are calculated. Air at 25 ◦ C and 1 atm is chosen as the working fluid. 3. Method of solution The computation of the flow is performed assuming that: a. The flow is steady, three-dimensional, viscous, turbulent, incompressible and isothermal. b. The fluid is Newtonian (air) having constant property. The domain is discretized with the non-uniform structured grid using ICEM CFD [25] and then the proper boundary conditions are imposed. RNG k–ε model is considered for simulating the turbulence with an acceptable accuracy. After establishing the flow characteristics, the numerical solution of the system is carried out by using commercial software ANSYS CFX [25]. The systematic steps corresponding to CFD are described as follows.
The success of CFD approach is the amalgamation of choosing the right flow model and technique. Most indoor and outdoor flow models are turbulent. Among the available turbulent models, large-eddy simulation (LES) and Reynolds averaged Navier–Stokes equations (RANS) are commonly used. Regardless of having advantages and disadvantages, both models were extensively chosen to solve building’s ventilation problems [6,11,16,17,27–30]. LES (Smagorinsky [31]) separates the flow into large and small eddies, computing the large eddies in the three-dimensional time dependent governing equations while approximating the small eddies with subgrid-scale model (SS). RANS model relates the unknown Reynolds-stresses to the mean flow variable through approximations. Jiang et al. [6] showed a good agreement between wind tunnel test data and the LES data. Evola and Popov [17] simulated the same case by Renormalization group (RNG) k–ε and standard k–ε turbulence models and showed that the result obtained by RNG k–ε was in good agreement with that of the experiment by Jiang et al. [6]. According to Evola and Popov [17], the slight disagreement between the result of LES and RNG k–ε was observed in the outside of the box which might not be the major concerned in natural ventilation. At this point, the present authors are fully agreed with Evola and Popov [17]. However, LES is very promising approach and there is no doubt about its excellence. On the other hand, LES models are very time consuming and that’s why it can be used only when extreme precision is required. Many turbulence models are available to be used with RANS. Chen [32,33] investigated eight different turbulence models to study indoor airflows and concluded that RNG k–ε model [34] might be the best. The present study chooses the RANS with RNG k–ε turbulence model (Yakhot and Orszag [34]) to predict the flow field with lower computing cost. 3.3. Governing equations As it was mentioned at the beginning of this section, the flow is steady, incompressible and turbulent, the RANS equations for a Newtonian fluid can be written in the following form:
3.1. Discretization and grid independence
Continuity :
To predict the internal building air movement CFD is used commonly which allows precise resolution of internal space variables to be achieved. In this case there are two important issues to be considered which play a significant role in the precision of the overall computational solution. The first one is the choice of mesh type
Momentum :
∂ (Uj ) = 0; ∂xj Uj
j = 1, 2, 3
∂ ∂p ∂ (Ui ) = − + ∂xj ∂xi ∂xj
i = 1, 2, 3 and j = 1, 2, 3
(1)
( + T )
∂Ui ∂xj
; (2)
320
M.Z.I. Bangalee et al. / Energy and Buildings 45 (2012) 317–325
where T represents an additional viscosity. According to the mixing length argument of the two equations turbulence model, T can be defined as k2 T = CRNG ε
(3)
Now the transport equations for RNG k–ε are
∂ ∂ (Uj k) = ∂xj ∂xj
∂ ∂ (Uj ε) = ∂xj ∂xj
+
T kRNG
+
T εRNG
∂k ∂xj
+ Pk − ε
∂ε ε ∂xj
+ (Cε1RNG Pk −Cε2RNG ε) k
where Cε1RNG = 1.42 −
(4.38 − ) , 4.38(1 + ˇRNG 3 )
=
Pk CRNG ε
(4)
(5)
(6)
In the above equations CRNG , Cε2RNG , kRNG , εRNG and ˇRNG are constants whose values are 0.085, 1.68, 0.7179, 0.7179 and 0.012 respectively. 3.4. Boundary conditions and numerical solution The inlet boundary of the surrounding (Fig. 1) is placed far enough from the building so that the condition inside and around the building could be nearly natural. The surrounding’s inlet air velocity is 1 m s−1 unless otherwise specified. In particular other boundary conditions are as follows: • There is no slip on the wall. • The ground of the surroundings and the outer surface of the building are rough walls. • The inner surfaces of the building are smooth walls. • Inlet boundary condition is imposed on the inlet of the surroundings. • Opening boundary condition is imposed on (a) the windows of the building; (b) all sides of the surroundings other than the ground and the inlet. Considering the above boundary conditions, commercial software ANSYS CFX [25] has been used to solve the governing equations at each grid of the computational domain. Finally, a converged and grid independent solution with residual target 10−4 is obtained. 4. Validation of methodology In order to validate the methodology, a comparison between the simulation result and the available experimental result is performed. A building like model (Fig. 2 in [6]) of dimensions 250 mm × 250 mm × 250 mm and wall thickness 6 mm is simulated and compared with the original experimental results reported by Jiang et al. [6]. The computational domain has 2 m downstream length, 1 m upstream length, 1 m lateral length on both sides and 1 m height. Only single-sided ventilation through an opening (84 mm width × 125 mm height) in windward wall (Case 1 in [6]) is considered for brevity. The simulation results inside the building have been found in good agreement with the experimental results. A non-uniform structured mesh with stretching factor 1.2, 1 mm initial height near the wall and finally 0.92 million control volumes are adopted. Fig. 3 shows the comparison between the simulation velocity components and the experimental velocity components. The values of the velocity components are recorded from some vertical lines (each comprising of 18 points) at the center section of the model. The detailed measuring positions were reported by Jiang
Table 1 Comparison of Cp .
Present Jiang [6], and Evola and Popov [17]
Front face
Rear face
Roof
0.618 0.56–0.7
−0.243 −0.15 to −0.35
−0.347 −0.2 to −0.524
et al. [6]. Table 1 shows the comparison of the mean pressure coefficient Cp on surfaces of the building obtained from the present simulation approach and the investigated results [6,17]. It is clear that the calculated Cp is in the range of the previously reported values. It should be noted here that the comparison model and the measuring points have been chosen exactly the same as reported in [6]. The mean velocity distributions outside the building (above the building height) show some discrepancies where it is larger near the wall and smaller far from the roof. In the present study, the ventilation of a full scale building is concerned where the velocity distribution inside the building is focused. Since the simulation results are in very good agreement with the experimental results inside the building, our methodology to study the wind driven natural ventilation is proved to be valid. 5. Results and discussion The present study is to investigate the natural ventilation of a full scale building to ensure physical comfort to the occupant. Cross ventilation with four windows (on two walls) and single-sided ventilation with two openings (on one wall) are studied to analyze the flow field. Single-sided ventilation case is categorized into two types, i.e., windward ventilation and leeward ventilation. To predict the flow field, 1 m s−1 wind speed is considered for all three cases unless otherwise specified. However, the effect of variable wind speed in the indoor air flow field is also analyzed and discussed. Fig. 4 shows the time averaged velocity distribution inside and outside the building along the YZ plane through window 2 (Fig. 2). It is clear that the velocity inside the building is higher in cross ventilation case than of others. In Case 1 four windows are opened in which the front windows serve as inlet and the rear windows serve as outlet at steady state condition. In addition, the air flow is slightly higher in single-sided windward ventilation case than that of the leeward ventilation case as expected. Some swirls are observed in the vicinity of the wall attributed partially to the bouncing back of the air from the wall. In order to reproduce the swirl region in the vicinity of any wall correctly, it is important to capture the characteristics of the boundary layer adjacent to the surface. So, a sufficient resolution of meshes is required. To check the consistency of the current mesh, the same computation was redone with a fine and huge mesh adjacent to the wall but the result did not change significantly. In single-sided ventilation (Case 2 and Case 3), every window allows inflow and outflow simultaneously and thus they serve the ventilation purpose. Since each window is identical to others, the inflow through windows 1 and 2 should be exactly the same at steady condition in cross ventilation case. But a negligible difference of inflow is observed attributed to the truncation error during discretization and computation which causes the computational domain asymmetric. For all three cases this asymmetric numerical characteristic is observed. In cross and single-sided ventilation case, wake region is also observed clearly behind the building which particularly play a crucial role in the leeward ventilation. Fig. 5 shows the pressure distribution around and inside the building along the YZ plane through the center of the building. A comparatively higher pressure zone is observed in the upwind vertical surface (front wall) of the building whereas low pressure zones are found around the roof and the rear wall. A far distance
M.Z.I. Bangalee et al. / Energy and Buildings 45 (2012) 317–325
321
Fig. 3. Comparison of mean velocity distributions between the values obtained from the present study and the experiment [6] for the single-sided, windward ventilation. Diamond: present; black circles: experiment [6]; other lines: LES [6].
from the roof and the rear wall of the building, the higher pressure zone is recovered which is consistent with the pressure distribution around a cube body. The range of the pressure difference inside the building is very small. However, the pressure near the wall in the building is higher than that at the center. Fig. 6 shows the velocity components V and W on vertical lines ranging from 0 to 6.5 m in Case 1 (cross ventilation). Here the velocity component (U) in the lateral direction (X) of the building is obviously very small (almost zero) hence it is neglected. Three vertical lines (e.g., line 1 in Fig. 2) are selected to show the disturbance on
the velocity components due to the presence of the building. When the distance from the front wall (DFFW) is 1 m, i.e., 1 m upwind far from the front wall the V (downstream direction) and W (vertical direction) components of the velocity change their directions in the lower height due to the disturbance induced by the building’s wall and the ground. At the distance far from the front wall (say, DFFW = 3 m), the velocity is nearly uniform and does not change the direction which establishes no disturbance is felt on the body at this point. As the wind moves closer to the body, it feels the presence of the building and shows random change of magnitude
Fig. 4. Air flow distribution (a) outside and (b) inside the building in Case 1.
322
M.Z.I. Bangalee et al. / Energy and Buildings 45 (2012) 317–325
Fig. 5. Pressure distribution (a) around and (b) inside the building in Case 1.
and direction from point to point. Similarly, the velocity components on the three vertical lines (e.g., line 5 in Fig. 2) behind the building are influenced by the approaching flow. The V component being larger and negative up to the building height (3.25 m) indicates that the flow is in the upstream direction which can be seen in Fig. 4 also. When the distance from the rear wall (DFRW) is 3 m, i.e., 3 m downwind far from the rear wall, the magnitude of the velocity component V in the upstream direction is observed comparatively smaller than that is in 1 m far. It is found that the magnitude of the velocity component W is stronger near the body as well as at higher elevation. In this case, we also see the boundary layer effect on the velocity components clearly. The magnitude of the velocity components decreases with the decrease of height gradually due to the no slip condition at the ground. Above the building height (3.25 m), all velocity components converge to a certain value where the disturbance created by the building dies out. The similar effects are observed in Cases 2 and 3 (single-sided ventilations) as well. The distribution of the velocity components on vertical lines 1 and 5 is shown in Fig. 7. Line 1 is located 1 m upwind far from the front wall where the x-component of the velocity (U) is nearly zero. Everywhere, outside the building the velocity component U is relatively negligible in Case 1. This outcome can be rechecked with Fig. 7b which shows the velocity components on line 5 located 1 m downwind far from the rear wall. The characteristics of the flow in front of the body and behind the body are alike unless the magnitude is not scrutinized. The magnitude of the velocity components and thus the velocity is smaller in the rear area (Fig. 7b) than the velocity in the front area (Fig. 7a). In Case 2 and Case 3, the characteristics of the velocity components on lines 1 and 5 are similar.
a
For the all three cases, the velocity inside the building is shown on vertical lines 2, 3 and 4 in Fig. 8. These lines are equidistant from any two windows on the same wall. Since the velocity distribution differs from line to line, the fluid in central area is not stagnant. So, the purpose of the ventilation is partially served. Unlike the outside phenomenon, U is not negligibly small, compared to the other velocity components inside the building (Fig. 9). This strength of U ensures the mixing of the interior fluid which causes uniform comfort. If the x-velocity component is sufficiently smaller, the flow inside the body will be two dimensional and there will be no lateral movement of the fluid. Therefore, the area which does not face any window directly, is not able to be influenced by the incoming air through the opening. Since line 7 faces the window directly, the y-component of velocity is predominantly strong comparing to the other components. The velocity distribution through the window is shown in Fig. 10 for cross, single-sided windward and single-sided leeward ventilation. This velocity in single-sided ventilation is lower by several times than the velocity in cross ventilation because of the inflow and the outflow through the same window. Natural ventilation of a building entirely depends on the ambient condition around it. The wind velocity, direction and temperature may change frequently from hour to hour. A good design of the ventilation system is expected to be concerned with any kind of atmospheric change. From this point of view the present study predicts the effect of wind velocity in all cases. As we mentioned earlier, the front wall’s windows serve as inlet and the rest windows (on the rear wall) serve as outlet at steady state condition in cross ventilation. The mass flow through the windows increases linearly
b 7
7 DFFW = 1m DFFW = 2m DFFW = 3m
5
5
4
4
3
3
2
2
1
1
0 -0.75
-0.5
-0.25
0
0.25
V [m/s]
DFFW = 1m DFFW = 2m DFFW = 3m
6
Z [m]
Z [m]
6
0.5
0.75
1
0 -0.4
-0.2
0
W [m/s]
Fig. 6. Velocity components (a) V and (b) W in front of the building in Case 1.
0.2
0.4
M.Z.I. Bangalee et al. / Energy and Buildings 45 (2012) 317–325
a
U V W
b
6
5
5
4
4
3
3
2
2
1
1
0 -0.75
-0.5
-0.25
0
0.25
0.5
0.75
U V W
7
6
Z[m]
Z[m]
7
0 -0.5
1
323
-0.25
Vel.component[m/s]
0
0.25
0.5
0.75
1
Vel.component[m/s]
Fig. 7. Velocity components on (a) line 1 and (b) line 5 in Case 1.
a
Case 1
b
Case 2
c
Case 3
3
3
2
2
2
Z [m]
Z [m]
Z [m]
3
1
0
1
0
50
100
150
200
250
0
300
1
0
5
10
Velocity [ mm / s ]
15
0
20
0
5
Velocity [ mm / s ]
10
15
20
25
Velocity [ mm / s ]
Fig. 8. Velocity distributions on lines 2, 3 and 4.
a
Case 1
b
Case 2
c
Case 3
3
3
2
2
2
Z [m]
Z [m]
Z [m]
3
1
1
0 -100
-50
0
50
100
150
200
1
0 -10
250
-5
Velocity distribution [ mm / s ]
0
5
10
15
20
25
0 -5
30
0
Velocity distribution [ mm / s ]
5
10
15
20
Velocity distribution [ mm / s ]
Fig. 9. Velocity distributions on line 7.
Case 1
b
Case 2
c
Case 3
2.5
2.5
2.4
2.4
2.4
2.3
2.2
2.1
Z [m]
2.5
Z [m]
Z [m]
a
2.3
2.2
2.1
2 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Velocity [ m / s ]
0.8
0.9
2
2.3
2.2
2.1
0
0.01
0.02
0.03
0.04
0.05
2
0
0.05
Velocity [ m / s ]
Fig. 10. Velocity distribution with height at the middle of the windows.
0.1
0.15
0.2
Velocity [ m / s ]
0.25
324
M.Z.I. Bangalee et al. / Energy and Buildings 45 (2012) 317–325
a
b 1.4 1.2 1.0 0.8 0.6 0.4
1
2
3
4
2.5
2.0
1.5
1.0
0.5
0.0
5
0.5 Case 1 Case 2 Case 3
Ave. velocity on line3 [m/s]
Area ave. vel. through a window [m/s]
Mass flow rate through windows [kg/s]
Case 1
0.2
c 3.0
1.6
1
Wind velocity [m/s]
2
3
4
5
Wind velocity [m/s]
Case 1 Case 2 Case 3
0.4
0.3
0.2
0.1
0.0
1
2
3
4
5
Wind velocity [m/s]
Fig. 11. Effect of wind velocity on the flow field.
with the increase of incoming wind velocity as shown in Fig. 11a. In single-sided ventilation every window functions as inlet and outlet simultaneously. So, the net mass flow through an opening is obviously zero in Case 2 and Case 3. However, it is easy to predict that the area average velocity through a window increases if the wind velocity increases (Fig. 11b). It is also interesting to observe that the flow at the middle of the building also changes with the same pattern as it changes in the window (Fig. 11c). As it is predicted, the wind speed has significant effect on the interior flow field and thus on the ventilation. Pressure coefficient, Cp is the difference between local static pressure and free stream static pressure, non-dimensionalized by the free stream dynamic pressure. This surface pressure distribution is very important in ventilation system since it is applied to locate the proper positions of opening. Fig. 12 shows the distribution of the mean pressure coefficient, Cp = (p − p0 )/(0.5U02 ) along the building surfaces for the cross ventilation. The pressure and the velocity at a point (at building height) far enough from the building are taken as the reference values. In Fig. 12, Cp is displayed with the height of the building for all external walls except the roof. In case of roof, the pressure coefficient is drawn with the horizontal distance between the front wall and the rear wall. In all three cases Cp exhibits the similar trend. However, in Case 2 the value of Cp on
1.00
Mean Pressure Coeffiecient
0.75
0.50 Front wall Rear wall Side wall Roof
0.25
0.00
the rear wall is found smaller comparing to Case 1 and Case 3. Only in Case 2, the rear wall has no opening on it and eventually acts as an entire solid wall which may induce the differences than the other cases.
6. Conclusions Computational fluid dynamics has been chosen to have a complete understanding of the complexities and characteristics of natural ventilation system for a full scale building through multiple windows. This study is the first step to analyze the flow phenomena inside and around a common shaped single story building with multiple windows under the influence of usual wind speed. Based on the validation of the present methodology, an extensive analysis has been carried out to study the outdoor as well as the indoor airflow distribution. At steady state condition, the windows placed in the front wall work as inlet whereas the other windows placed in the rear wall work as outlet in cross ventilation. On the other hand, every window works as inlet and outlet simultaneously in singlesided ventilation. Everywhere, outside the building the velocity component U is relatively negligible. However, the magnitude of U is not negligible inside the building. Therefore, any two dimensional numerical approach may not be appropriate to capture the real indoor air flow distribution. The mass flow rate through the windows and the indoor air velocity change linearly if the wind velocity changes linearly. The distribution of pressure coefficient suggests that the front wall would be appropriate for inlet openings in cross ventilation while any other wall may serve as exit opening. Among all cases, it has been found that cross ventilation performed better in all respects. If the ambient conditions, the weather and the surroundings are satisfactory, this study may help the building designer to predict the indoor airflow. However, this analysis may not be satisfactory if any change such as variation of incident angle, temperature, design of the building, pollution, etc. take place. Further investigations considering these effects will be accomplished while the present study provides the basic observation.
-0.25
Acknowledgements
-0.50 -0.75
-1.00
0.5
1.0
1.5
2.0
Distance [m] Fig. 12. Surface pressure coefficient in Case 1.
2.5
3.0
The authors wish to appreciate the supports given by the National Science Council, Republic of China under grant no. NSC 99-2221-E-006-055. The first author would like to thank Dr. ShihHsiung Chen, Professor, IAA, NCKU, Taiwan for his invaluable support. The authors also wish to extend their appreciations for the valuable comments and suggestions given by the anonymous reviewers.
M.Z.I. Bangalee et al. / Energy and Buildings 45 (2012) 317–325
References [1] M. Orme, Estimates of the energy impact of ventilation and associated financial expenditures, Energy and Buildings 33 (2001) 199–205. [2] J.P. Cockroft, P. Robertson, Ventilation of an enclosure through a single opening, Building and Environment 11 (1976) 29–35. [3] E. Dascalaki, M. Santamouris, A. Argiriou, C. Helmis, D.N. Asimakopoulos, K. Papadopoulos, A. Soilemes, Predicting single sided natural ventilation rates in buildings, Solar Energy 55 (1995) 327–341. [4] E. Dascalaki, M. Santamouris, A. Argiriou, C. Helmis, D.N. Asimakopoulos, K. Papadopoulos, A. Soilemes, On the combination of air velocity and flow measurements in single sided natural ventilation configurations, Energy and Buildings 24 (1996) 155–165. [5] P.S. Carey, D.W. Etheridge, Direct wind tunnel modelling of natural ventilation for design purpose, Building Services Engineering Research and Technology 20 (1999) 131–140. [6] Y. Jiang, D. Alexander, H. Jenkins, R. Arthur, Q. Chen, Natural ventilation in buildings: measurement in a wind tunnel and numerical simulation with largeeddy simulation, Journal of Wind Engineering and Industrial Aerodynamics 91 (2003) 331–353. [7] S. Murakami, S. Kato, S. Akabayashi, K. Mizutani, Y.D. Kim, Wind tunnel test on velocity–pressure field of cross-ventilation with open windows, ASHRAE Transactions 97 (1991) 525–538. [8] M. Ohba, K. Irie, T. Kurabuchi, Study on airflow characteristics inside and outside a cross-ventilation model, and ventilation flow rates using wind tunnel experiments, Journal of Wind Engineering and Industrial Aerodynamics 89 (2001) 1513–1524. [9] G.M. Stavrakakis, M.K. Koukou, M.G. Vrachopoulos, N.C. Markatos, Natural cross-ventilation in buildings: building-scale experiments, numerical simulation and thermal comfort evaluation, Energy and Buildings 40 (2008) 1666–1681. [10] H.B. Awbi, Air movement in naturally-ventilated buildings, Renewable Energy 8 (1996) 241–247. [11] S.S. Ayad, Computational study of natural ventilation, Journal of Wind Engineering and Industrial Aerodynamics 82 (1999) 49–68. [12] G. Zhang, S. Morsing, B. Bjerg, K. Svidt, J.S. Strøm, Test room for validation of airflow patterns estimated by computational fluid dynamics, Journal of Agricultural Engineering Research 76 (2000) 141–148. [13] K.A. Papakonstantinou, C.T. Kiranoudis, N.C. Markatos, Numerical simulation of air flow field in single-sided ventilated buildings, Energy and Buildings 33 (2000) 41–48. [14] T. Hayashi, Y. Ishizu, S. Kato, S. Murakami, CFD analysis on characteristics of contaminated indoor air ventilation and its application in the evaluation of the effects of contaminant inhalation by a human occupant, Building and Environment 37 (2002) 219–230. [15] M. Bartak, I. Beausoleil-Morrison, J.A. Clarke, J. Denev, F. Drkal, M. Lain, I.A. Macdonald, A. Melikov, Z. Popiolek, P. Stankov, Integrating,
[16] [17] [18]
[19] [20]
[21] [22]
[23] [24]
[25] [26]
[27] [28]
[29]
[30]
[31] [32] [33] [34]
325
CFD and building simulation, Building and Environment 37 (2002) 865– 871. C. Allocca, Q. Chen, L.R. Glicksman, Design analysis of single-sided natural ventilation, Energy and Buildings 35 (2003) 785–795. G. Evola, V. Popov, Computational analysis of wind driven natural ventilation in buildings, Energy and Buildings 38 (2006) 491–501. T. Norton, D.-W. Sun, J. Grant, R. Fallon, V. Dodd, Applications of computational fluid dynamics (CFD) in the modelling and design of ventilation systems in the agricultural industry: a review, Bioresource Technology 98 (2007) 2386–2414. D. Yan, J. Xia, W. Tang, F. Song, X. Zhang, Y. Jiang, DeST – an integrated building simulation toolkit. Part I. Fundamentals, Building Simulation 1 (2008) 95–110. K. Visagavel, P.S.S. Srinivasan, Analysis of single side ventilated and cross ventilated rooms by varying the width of the window opening using CFD, Solar Energy 83 (2009) 2–5. Q. Chen, Computational fluid dynamics for HVAC: successes and failures, ASHRAE Transactions 103 (1997) 178–187. G.J. Levermore, The exponential limit to the cooling of buildings by natural ventilation, Building Services Engineering Research and Technology 23 (2002) 119–125. N. Khan, Y. Su, S.B. Riffat, A review on wind driven ventilation techniques, Energy and Buildings 40 (2008) 1586–1604. Y. Jiang, Q. Chen, Buoyancy-driven single-sided natural ventilation in buildings with large openings, International Journal of Heat and Mass Transfer 46 (2003) 973–988. ANSYS 12.0, Ansys Inc., www.ansys.com. J.M. Horan, D.P. Finn, CFD reliability issues in analysis of naturally ventilated buildings, in: Passive and Low Energy Cooling for the Built Environment, Santorini, Greece, 2005. N.G. Wright, G.J. Easom, Non-linear k–ε turbulence model results for flow over a building at full-scale, Applied Mathematical Modelling 27 (2003) 1013–1033. W. Zhang, Q. Chen, Large eddy simulation of indoor airflow with a filtered dynamic subgrid scale model, International Journal of Heat and Mass Transfer 43 (2000) 3219–3231. Y. Jiang, Q. Chen, Study of natural ventilation in buildings by large eddy simulation, Journal of Wind Engineering and Industrial Aerodynamics 89 (2001) 1155–1178. Y. Jiang, Q. Chen, Effect of fluctuating wind direction on cross natural ventilation in buildings from large eddy simulation, Building and Environment 37 (2002) 379–386. J. Smagorinsky, General circulation experiments with the primitive equations. I. The basic experiment, Monthly Weather Review 91 (1963) 99–164. Q. Chen, Comparison of different k–ε models for indoor air flow computations, Numerical Heat Transfer, Part B: Fundamentals 28 (1995) 353–369. Q. Chen, Prediction of room air motion by Reynolds-stress models, Building and Environment 31 (1996) 233–244. V. Yakhot, S.A. Orszag, Renormalization-group analysis of turbulence, Physical Review Letters 57 (1986) 1722.
Contents lists available at SciVerse ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
Wind driven natural ventilation through multiple windows of a building: A computational approach M.Z.I. Bangalee a,∗ , S.Y. Lin a , J.J. Miau a,b a b
Department of Aeronautics and Astronautics, National Cheng Kung University, Taiwan Research Center for Energy Technology and Strategy, National Cheng Kung University, Taiwan
a r t i c l e
i n f o
Article history: Received 30 July 2011 Received in revised form 7 November 2011 Accepted 17 November 2011 Keywords: Natural ventilation Energy saving system CFD RNG k–ε turbulence model
a b s t r a c t Natural ventilation is an energy saving system for the building to ensure occupant’s physical comfort. It has also no contribution to the atmospheric pollution as well as the global warming. In the present study, a building with multiple windows is considered to investigate the wind-driven a ventilation system using computational fluid dynamics (CFD), whose acceptance and accuracy are growing very fast. The Renormalization group (RNG) k–ε turbulence model is chosen to simulate cross and single-sided ventilation with a specified accuracy after validating the methodology through the satisfactory comparison with an experimental result. The CFD model is then applied to investigate the physical mechanism of the air movement. The results are presented in the form of the mean velocity vectors, the magnitude of velocity, the components of velocity, the pressure distribution, the pressure coefficient and the effect of incoming wind velocity inside and outside the building. The necessity of three-dimensional (3D) approach to predict the indoor air movement correctly in studying natural ventilation system is also emphasized in this article. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Natural ventilation is a system in which the internal air of an enclosed space is continuously replaced by the fresh external air through opening for better thermal comfort and good indoor air quality without applying any forced flow. In some situation, a lack of ventilation may cause exorbitant pollution, suffocation, humidity and discomfort of unpleasant odors, smokes, overheats. Ventilation is a part of heating, ventilation and air-conditioning (HVAC) systems which consume an extensive amount of energy. More than half of the world’s annual energy is consumed in building systems, i.e., ventilation, heating, cooling, lighting, cooking, powering to household electronics/appliances. According to Orme [1], HVAC system in service and residential buildings consumes 26.48% of the total annual energy delivered. Recently global warming has attracted huge attention. Fossil fuel based electricity production throws a threat to our near future due to its high green house gas emissions and its upcoming scarcity. International effort is being enhanced to reduce the dependence on fossil fuel based electricity as well as to produce more renewable energy. However, the efficiency of the renewable energy is not satisfactory enough so far to meet the current entire energy demand. Therefore, the reduction
∗ Corresponding author. Tel.: +886 0983496461. E-mail address: [email protected] (M.Z.I. Bangalee). 0378-7788/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.enbuild.2011.11.025
of energy consumption wherever it is possible is desired. An effective natural ventilation system may assist to achieve this goal if the location, the position and the design of the building, the ambient condition, and the environment are satisfactory. There are several approaches to study the physics of natural ventilation system such as experimental investigation, empirical models and numerical solution by computational fluid dynamics (CFD). The most common approach to investigate the flow characteristics of a naturally ventilated building is the experimental investigations, e.g. [2–8]. Cockroft and Robertson [2] proposed a theoretical model to predict the airflow by measuring a wind driven ventilation through a single opening subjected to impinging air stream. Dascalaki et al. [3] studied fifty-two single sided natural ventilation configurations experimentally and then compared the results with the predictions of ventilation models. Finally, a new model to predict air flow rates in single sided natural ventilation configurations was proposed. It was also claimed that for cases where inertia forces are more important than gravitational forces, a systematic difference exists between the theoretical and the experimental values. Dascalaki et al. [4] carried out four, single-sided and wind driven natural ventilation experiments by using tracer gas decay method to derive the average air exchange rate through the opening. Carey and Etheridge [5] studied three cases to investigate the feasibility of natural ventilation design by means of direct wind tunnel modeling of ventilations rates. Jiang et al. [6] also
318
M.Z.I. Bangalee et al. / Energy and Buildings 45 (2012) 317–325
Nomenclature Cp CRNG Cε1RNG Cε2RNG p p0 Pk U, V, W U0 x, y, z k
mean pressure coefficient (–) RNG k–ε turbulence model constant (–) RNG k–ε turbulence model coefficient (–) RNG k–ε turbulence model constant (–) pressure (kg m−1 s−2 ) free stream static pressure (kg m−1 s−2 ) shear production of turbulence (kg m−1 s−3 ) x-, y- and z-component of velocity respectively (m s−1 ) free stream velocity (m s−1 ) Cartesian coordinates (m) turbulence kinetic energy per unit mass (m−2 s−2 )
Greek symbol ε turbulence dissipation rate (m−2 s−3 ) viscosity (kg m−1 s−1 ) density of the fluid (kg m−3 ) RNG k–ε turbulence model constant (–) kRNG εRNG RNG k–ε turbulence model constant (–)
considered the wind tunnel test to study the ventilation phenomena of small building-like models through door-like openings. The velocity, the pressure and the ventilation rate in a wind tunnel to analyze the characteristics of cross ventilation were measured in detail by Murakami et al. [7]. The internal airflow characteristics in a cross-ventilation model were investigated using split-film probes capable of measuring directional velocity components by Ohba et al. [8]. Stavrakakis et al. [9] studied natural cross-ventilation with openings at non-symmetrical locations (i) experimentally in a test chamber and (ii) numerically using computational fluid dynamics techniques. The other approach to investigate the natural ventilation is to apply the computational fluid dynamics (CFD) which provides a cost-effective and accurate alternative to the scale model testing, with variations on the simulation being performed quickly, offering obvious advantages. The development of turbulence modeling and the increase of computer speed make CFD an effective alternative due to its informative result, low labor and low cost. In addition to this, computer costs decrease continuously while labor and materials costs rise. Having these advantages a plenty of research efforts was applied to study the natural ventilation using CFD as reported in [10–20]. A particular CFD approach related to the present study will be discussed shortly after this section. However, it should be noted that natural ventilation can be effective only to certain climates having some limitations as studied by Chen [21]. Furthermore, the CFD approach has also some difficulties as reported by Levermore [22]. Wind and/or buoyancy driven pressure difference is the predominant mechanism for natural ventilation system. The different wind pressure along building faces and/or the different temperature between indoor and outdoor air induces a circulation of flow and establishes natural ventilation in buildings through openings. A review of wind driven ventilation techniques was reported by Khan et al. [23] whereas buoyancy-driven single-sided natural ventilation in buildings was studied by Jiang and Chen [24]. The effects of buoyancy and/or wind on ventilation rates and indoor conditions were also examined by Allocca et al. [16]. In the present study, only wind driven ventilation system is examined in order to analyze the indoor air flow for the cross ventilation as well as the single-sided ventilation. In recent years, most of the studies regarding one story buildings/boxes were carried out to investigate the flow and the
Fig. 1. A schematic view of the computational domain in Case 1.
temperature field through one opening on an individual wall regardless of the ventilation type. However, a full scale building may contain multiple (e.g., at least 2 openings) windows on the front and/or the rear wall to ensure uniform and fresh airflow inside the building for comfort. It is quite difficult to explain exactly how the flows entered from multiple openings interact and how they are mixed up. Nevertheless, it can be stated that the position and the size of the opening, the incoming air velocity, the direction, and the temperature difference play the dominantly important role for the flow through a particular opening to be influenced by the flow through another opening. This article is a part of a series of ongoing investigations where the flow phenomena for the different wind condition, window location, window size, building design, environmental condition, etc. are investigated extensively. RNG k–ε turbulence model [17] is chosen to simulate the present ventilation problem. The commercial software ANSYS ICEM CFD [25] and the solver CFX [25] are used for the discretization of the domain and the simulation respectively. The precision of the numerical methodology has been established by comparing the simulation results with the similar experimental results. Then a full scale, naturally wind-driven ventilated building with two openings on the front and/or the rear wall has been considered to analyze the internal and external flow characteristics. The present study considers two openings on each of the front and/or the rear wall to study the natural air circulation of cross ventilation (4 openings) (Figs. 1 and 2), windward single-sided ventilation (2 openings) and leeward single-sided ventilation (2 openings). 2. The studied model Wind driven cross ventilation and single-sided ventilation have been considered to investigate the flow phenomena inside and around a full scale building of 4.5 m × 4.5 m × 3.25 m (length × width × height) including wall thickness of 0.25 m as shown in Figs. 1 and 2. It should be noted that the floor of the building has been considered as surface (wall), free of thickness since being under the ground it has no effect in the flow field. The square-shaped windows have the dimension of 0.5 m × 0.5 m (length × height) and the center of the windows is located at 2.25 m high from the ground and 0.4 m far from the inner corner edge. A sufficiently large domain around the building is often required to simulate a real case which demands longer computational time. Therefore, there is a challenge to minimize the computational time and to maximize the accuracy simultaneously. The dimension of the computational domain is 40.5 m × 22.5 m × 9.75 m (length × width × height) which is expected to be capable of simulating the actual environment. Three different cases have been considered depending on the windows’ face, position and function: Case 1: Cross ventilation with two openings in windward and two openings in leeward walls.
M.Z.I. Bangalee et al. / Energy and Buildings 45 (2012) 317–325
319
which can be either structured or unstructured, and the second one is the selection of proper turbulence model. Horan and Finn [26] found that the results with the structured mesh were better than that with the unstructured mesh in terms of both solution convergence and predicted airflows in naturally ventilated models. In the present study, a non-uniform structured mesh with stretching factor 1.2 is used to discretize the computational domain. For this huge domain size a perfect numerical resolution is difficult to obtain due to the limitation of the computation facility. However, the finest possible grid has been used which contains approximately 1.82 million elements where particular attention is focused on the areas adjacent to the wall. The smallest spacing of the grid is 1 mm and is set to the near wall to capture the boundary effect on the flow field. ANSYS ICEM CFD 12.0 has been used for the entire grid generation. Grid independence tests have been carried out utilizing meshes with up to 3.67 million elements and the results have exhibited the discrepancy up to 2%. Therefore, the present grid system is not conclusive but adequate enough which is capable to capture the actual flow phenomena with an acceptable accuracy. 3.2. Turbulence model
Fig. 2. The schematic view of the building and the locations of calculation.
Case 2: Single-sided ventilation with two openings in windward wall. Case 3: Single-sided ventilation with two openings in leeward wall. A number of finite points on twelve different vertical lines are chosen to calculate the velocity components. The detail of each line is shown in Fig. 2. The first eight lines start from the ground whereas six lines (from line 1 to line 6) at the center section of the building and other two lines (line 7 and line 8) are close to the left wall and right wall respectively. The rest four lines (from LW1 to LW4) at the center section of the corresponding windows (from window 1 to window 4) start from the elevation of 2 m. Every line comprises of 20–30 points where the velocity components are calculated. Air at 25 ◦ C and 1 atm is chosen as the working fluid. 3. Method of solution The computation of the flow is performed assuming that: a. The flow is steady, three-dimensional, viscous, turbulent, incompressible and isothermal. b. The fluid is Newtonian (air) having constant property. The domain is discretized with the non-uniform structured grid using ICEM CFD [25] and then the proper boundary conditions are imposed. RNG k–ε model is considered for simulating the turbulence with an acceptable accuracy. After establishing the flow characteristics, the numerical solution of the system is carried out by using commercial software ANSYS CFX [25]. The systematic steps corresponding to CFD are described as follows.
The success of CFD approach is the amalgamation of choosing the right flow model and technique. Most indoor and outdoor flow models are turbulent. Among the available turbulent models, large-eddy simulation (LES) and Reynolds averaged Navier–Stokes equations (RANS) are commonly used. Regardless of having advantages and disadvantages, both models were extensively chosen to solve building’s ventilation problems [6,11,16,17,27–30]. LES (Smagorinsky [31]) separates the flow into large and small eddies, computing the large eddies in the three-dimensional time dependent governing equations while approximating the small eddies with subgrid-scale model (SS). RANS model relates the unknown Reynolds-stresses to the mean flow variable through approximations. Jiang et al. [6] showed a good agreement between wind tunnel test data and the LES data. Evola and Popov [17] simulated the same case by Renormalization group (RNG) k–ε and standard k–ε turbulence models and showed that the result obtained by RNG k–ε was in good agreement with that of the experiment by Jiang et al. [6]. According to Evola and Popov [17], the slight disagreement between the result of LES and RNG k–ε was observed in the outside of the box which might not be the major concerned in natural ventilation. At this point, the present authors are fully agreed with Evola and Popov [17]. However, LES is very promising approach and there is no doubt about its excellence. On the other hand, LES models are very time consuming and that’s why it can be used only when extreme precision is required. Many turbulence models are available to be used with RANS. Chen [32,33] investigated eight different turbulence models to study indoor airflows and concluded that RNG k–ε model [34] might be the best. The present study chooses the RANS with RNG k–ε turbulence model (Yakhot and Orszag [34]) to predict the flow field with lower computing cost. 3.3. Governing equations As it was mentioned at the beginning of this section, the flow is steady, incompressible and turbulent, the RANS equations for a Newtonian fluid can be written in the following form:
3.1. Discretization and grid independence
Continuity :
To predict the internal building air movement CFD is used commonly which allows precise resolution of internal space variables to be achieved. In this case there are two important issues to be considered which play a significant role in the precision of the overall computational solution. The first one is the choice of mesh type
Momentum :
∂ (Uj ) = 0; ∂xj Uj
j = 1, 2, 3
∂ ∂p ∂ (Ui ) = − + ∂xj ∂xi ∂xj
i = 1, 2, 3 and j = 1, 2, 3
(1)
( + T )
∂Ui ∂xj
; (2)
320
M.Z.I. Bangalee et al. / Energy and Buildings 45 (2012) 317–325
where T represents an additional viscosity. According to the mixing length argument of the two equations turbulence model, T can be defined as k2 T = CRNG ε
(3)
Now the transport equations for RNG k–ε are
∂ ∂ (Uj k) = ∂xj ∂xj
∂ ∂ (Uj ε) = ∂xj ∂xj
+
T kRNG
+
T εRNG
∂k ∂xj
+ Pk − ε
∂ε ε ∂xj
+ (Cε1RNG Pk −Cε2RNG ε) k
where Cε1RNG = 1.42 −
(4.38 − ) , 4.38(1 + ˇRNG 3 )
=
Pk CRNG ε
(4)
(5)
(6)
In the above equations CRNG , Cε2RNG , kRNG , εRNG and ˇRNG are constants whose values are 0.085, 1.68, 0.7179, 0.7179 and 0.012 respectively. 3.4. Boundary conditions and numerical solution The inlet boundary of the surrounding (Fig. 1) is placed far enough from the building so that the condition inside and around the building could be nearly natural. The surrounding’s inlet air velocity is 1 m s−1 unless otherwise specified. In particular other boundary conditions are as follows: • There is no slip on the wall. • The ground of the surroundings and the outer surface of the building are rough walls. • The inner surfaces of the building are smooth walls. • Inlet boundary condition is imposed on the inlet of the surroundings. • Opening boundary condition is imposed on (a) the windows of the building; (b) all sides of the surroundings other than the ground and the inlet. Considering the above boundary conditions, commercial software ANSYS CFX [25] has been used to solve the governing equations at each grid of the computational domain. Finally, a converged and grid independent solution with residual target 10−4 is obtained. 4. Validation of methodology In order to validate the methodology, a comparison between the simulation result and the available experimental result is performed. A building like model (Fig. 2 in [6]) of dimensions 250 mm × 250 mm × 250 mm and wall thickness 6 mm is simulated and compared with the original experimental results reported by Jiang et al. [6]. The computational domain has 2 m downstream length, 1 m upstream length, 1 m lateral length on both sides and 1 m height. Only single-sided ventilation through an opening (84 mm width × 125 mm height) in windward wall (Case 1 in [6]) is considered for brevity. The simulation results inside the building have been found in good agreement with the experimental results. A non-uniform structured mesh with stretching factor 1.2, 1 mm initial height near the wall and finally 0.92 million control volumes are adopted. Fig. 3 shows the comparison between the simulation velocity components and the experimental velocity components. The values of the velocity components are recorded from some vertical lines (each comprising of 18 points) at the center section of the model. The detailed measuring positions were reported by Jiang
Table 1 Comparison of Cp .
Present Jiang [6], and Evola and Popov [17]
Front face
Rear face
Roof
0.618 0.56–0.7
−0.243 −0.15 to −0.35
−0.347 −0.2 to −0.524
et al. [6]. Table 1 shows the comparison of the mean pressure coefficient Cp on surfaces of the building obtained from the present simulation approach and the investigated results [6,17]. It is clear that the calculated Cp is in the range of the previously reported values. It should be noted here that the comparison model and the measuring points have been chosen exactly the same as reported in [6]. The mean velocity distributions outside the building (above the building height) show some discrepancies where it is larger near the wall and smaller far from the roof. In the present study, the ventilation of a full scale building is concerned where the velocity distribution inside the building is focused. Since the simulation results are in very good agreement with the experimental results inside the building, our methodology to study the wind driven natural ventilation is proved to be valid. 5. Results and discussion The present study is to investigate the natural ventilation of a full scale building to ensure physical comfort to the occupant. Cross ventilation with four windows (on two walls) and single-sided ventilation with two openings (on one wall) are studied to analyze the flow field. Single-sided ventilation case is categorized into two types, i.e., windward ventilation and leeward ventilation. To predict the flow field, 1 m s−1 wind speed is considered for all three cases unless otherwise specified. However, the effect of variable wind speed in the indoor air flow field is also analyzed and discussed. Fig. 4 shows the time averaged velocity distribution inside and outside the building along the YZ plane through window 2 (Fig. 2). It is clear that the velocity inside the building is higher in cross ventilation case than of others. In Case 1 four windows are opened in which the front windows serve as inlet and the rear windows serve as outlet at steady state condition. In addition, the air flow is slightly higher in single-sided windward ventilation case than that of the leeward ventilation case as expected. Some swirls are observed in the vicinity of the wall attributed partially to the bouncing back of the air from the wall. In order to reproduce the swirl region in the vicinity of any wall correctly, it is important to capture the characteristics of the boundary layer adjacent to the surface. So, a sufficient resolution of meshes is required. To check the consistency of the current mesh, the same computation was redone with a fine and huge mesh adjacent to the wall but the result did not change significantly. In single-sided ventilation (Case 2 and Case 3), every window allows inflow and outflow simultaneously and thus they serve the ventilation purpose. Since each window is identical to others, the inflow through windows 1 and 2 should be exactly the same at steady condition in cross ventilation case. But a negligible difference of inflow is observed attributed to the truncation error during discretization and computation which causes the computational domain asymmetric. For all three cases this asymmetric numerical characteristic is observed. In cross and single-sided ventilation case, wake region is also observed clearly behind the building which particularly play a crucial role in the leeward ventilation. Fig. 5 shows the pressure distribution around and inside the building along the YZ plane through the center of the building. A comparatively higher pressure zone is observed in the upwind vertical surface (front wall) of the building whereas low pressure zones are found around the roof and the rear wall. A far distance
M.Z.I. Bangalee et al. / Energy and Buildings 45 (2012) 317–325
321
Fig. 3. Comparison of mean velocity distributions between the values obtained from the present study and the experiment [6] for the single-sided, windward ventilation. Diamond: present; black circles: experiment [6]; other lines: LES [6].
from the roof and the rear wall of the building, the higher pressure zone is recovered which is consistent with the pressure distribution around a cube body. The range of the pressure difference inside the building is very small. However, the pressure near the wall in the building is higher than that at the center. Fig. 6 shows the velocity components V and W on vertical lines ranging from 0 to 6.5 m in Case 1 (cross ventilation). Here the velocity component (U) in the lateral direction (X) of the building is obviously very small (almost zero) hence it is neglected. Three vertical lines (e.g., line 1 in Fig. 2) are selected to show the disturbance on
the velocity components due to the presence of the building. When the distance from the front wall (DFFW) is 1 m, i.e., 1 m upwind far from the front wall the V (downstream direction) and W (vertical direction) components of the velocity change their directions in the lower height due to the disturbance induced by the building’s wall and the ground. At the distance far from the front wall (say, DFFW = 3 m), the velocity is nearly uniform and does not change the direction which establishes no disturbance is felt on the body at this point. As the wind moves closer to the body, it feels the presence of the building and shows random change of magnitude
Fig. 4. Air flow distribution (a) outside and (b) inside the building in Case 1.
322
M.Z.I. Bangalee et al. / Energy and Buildings 45 (2012) 317–325
Fig. 5. Pressure distribution (a) around and (b) inside the building in Case 1.
and direction from point to point. Similarly, the velocity components on the three vertical lines (e.g., line 5 in Fig. 2) behind the building are influenced by the approaching flow. The V component being larger and negative up to the building height (3.25 m) indicates that the flow is in the upstream direction which can be seen in Fig. 4 also. When the distance from the rear wall (DFRW) is 3 m, i.e., 3 m downwind far from the rear wall, the magnitude of the velocity component V in the upstream direction is observed comparatively smaller than that is in 1 m far. It is found that the magnitude of the velocity component W is stronger near the body as well as at higher elevation. In this case, we also see the boundary layer effect on the velocity components clearly. The magnitude of the velocity components decreases with the decrease of height gradually due to the no slip condition at the ground. Above the building height (3.25 m), all velocity components converge to a certain value where the disturbance created by the building dies out. The similar effects are observed in Cases 2 and 3 (single-sided ventilations) as well. The distribution of the velocity components on vertical lines 1 and 5 is shown in Fig. 7. Line 1 is located 1 m upwind far from the front wall where the x-component of the velocity (U) is nearly zero. Everywhere, outside the building the velocity component U is relatively negligible in Case 1. This outcome can be rechecked with Fig. 7b which shows the velocity components on line 5 located 1 m downwind far from the rear wall. The characteristics of the flow in front of the body and behind the body are alike unless the magnitude is not scrutinized. The magnitude of the velocity components and thus the velocity is smaller in the rear area (Fig. 7b) than the velocity in the front area (Fig. 7a). In Case 2 and Case 3, the characteristics of the velocity components on lines 1 and 5 are similar.
a
For the all three cases, the velocity inside the building is shown on vertical lines 2, 3 and 4 in Fig. 8. These lines are equidistant from any two windows on the same wall. Since the velocity distribution differs from line to line, the fluid in central area is not stagnant. So, the purpose of the ventilation is partially served. Unlike the outside phenomenon, U is not negligibly small, compared to the other velocity components inside the building (Fig. 9). This strength of U ensures the mixing of the interior fluid which causes uniform comfort. If the x-velocity component is sufficiently smaller, the flow inside the body will be two dimensional and there will be no lateral movement of the fluid. Therefore, the area which does not face any window directly, is not able to be influenced by the incoming air through the opening. Since line 7 faces the window directly, the y-component of velocity is predominantly strong comparing to the other components. The velocity distribution through the window is shown in Fig. 10 for cross, single-sided windward and single-sided leeward ventilation. This velocity in single-sided ventilation is lower by several times than the velocity in cross ventilation because of the inflow and the outflow through the same window. Natural ventilation of a building entirely depends on the ambient condition around it. The wind velocity, direction and temperature may change frequently from hour to hour. A good design of the ventilation system is expected to be concerned with any kind of atmospheric change. From this point of view the present study predicts the effect of wind velocity in all cases. As we mentioned earlier, the front wall’s windows serve as inlet and the rest windows (on the rear wall) serve as outlet at steady state condition in cross ventilation. The mass flow through the windows increases linearly
b 7
7 DFFW = 1m DFFW = 2m DFFW = 3m
5
5
4
4
3
3
2
2
1
1
0 -0.75
-0.5
-0.25
0
0.25
V [m/s]
DFFW = 1m DFFW = 2m DFFW = 3m
6
Z [m]
Z [m]
6
0.5
0.75
1
0 -0.4
-0.2
0
W [m/s]
Fig. 6. Velocity components (a) V and (b) W in front of the building in Case 1.
0.2
0.4
M.Z.I. Bangalee et al. / Energy and Buildings 45 (2012) 317–325
a
U V W
b
6
5
5
4
4
3
3
2
2
1
1
0 -0.75
-0.5
-0.25
0
0.25
0.5
0.75
U V W
7
6
Z[m]
Z[m]
7
0 -0.5
1
323
-0.25
Vel.component[m/s]
0
0.25
0.5
0.75
1
Vel.component[m/s]
Fig. 7. Velocity components on (a) line 1 and (b) line 5 in Case 1.
a
Case 1
b
Case 2
c
Case 3
3
3
2
2
2
Z [m]
Z [m]
Z [m]
3
1
0
1
0
50
100
150
200
250
0
300
1
0
5
10
Velocity [ mm / s ]
15
0
20
0
5
Velocity [ mm / s ]
10
15
20
25
Velocity [ mm / s ]
Fig. 8. Velocity distributions on lines 2, 3 and 4.
a
Case 1
b
Case 2
c
Case 3
3
3
2
2
2
Z [m]
Z [m]
Z [m]
3
1
1
0 -100
-50
0
50
100
150
200
1
0 -10
250
-5
Velocity distribution [ mm / s ]
0
5
10
15
20
25
0 -5
30
0
Velocity distribution [ mm / s ]
5
10
15
20
Velocity distribution [ mm / s ]
Fig. 9. Velocity distributions on line 7.
Case 1
b
Case 2
c
Case 3
2.5
2.5
2.4
2.4
2.4
2.3
2.2
2.1
Z [m]
2.5
Z [m]
Z [m]
a
2.3
2.2
2.1
2 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Velocity [ m / s ]
0.8
0.9
2
2.3
2.2
2.1
0
0.01
0.02
0.03
0.04
0.05
2
0
0.05
Velocity [ m / s ]
Fig. 10. Velocity distribution with height at the middle of the windows.
0.1
0.15
0.2
Velocity [ m / s ]
0.25
324
M.Z.I. Bangalee et al. / Energy and Buildings 45 (2012) 317–325
a
b 1.4 1.2 1.0 0.8 0.6 0.4
1
2
3
4
2.5
2.0
1.5
1.0
0.5
0.0
5
0.5 Case 1 Case 2 Case 3
Ave. velocity on line3 [m/s]
Area ave. vel. through a window [m/s]
Mass flow rate through windows [kg/s]
Case 1
0.2
c 3.0
1.6
1
Wind velocity [m/s]
2
3
4
5
Wind velocity [m/s]
Case 1 Case 2 Case 3
0.4
0.3
0.2
0.1
0.0
1
2
3
4
5
Wind velocity [m/s]
Fig. 11. Effect of wind velocity on the flow field.
with the increase of incoming wind velocity as shown in Fig. 11a. In single-sided ventilation every window functions as inlet and outlet simultaneously. So, the net mass flow through an opening is obviously zero in Case 2 and Case 3. However, it is easy to predict that the area average velocity through a window increases if the wind velocity increases (Fig. 11b). It is also interesting to observe that the flow at the middle of the building also changes with the same pattern as it changes in the window (Fig. 11c). As it is predicted, the wind speed has significant effect on the interior flow field and thus on the ventilation. Pressure coefficient, Cp is the difference between local static pressure and free stream static pressure, non-dimensionalized by the free stream dynamic pressure. This surface pressure distribution is very important in ventilation system since it is applied to locate the proper positions of opening. Fig. 12 shows the distribution of the mean pressure coefficient, Cp = (p − p0 )/(0.5U02 ) along the building surfaces for the cross ventilation. The pressure and the velocity at a point (at building height) far enough from the building are taken as the reference values. In Fig. 12, Cp is displayed with the height of the building for all external walls except the roof. In case of roof, the pressure coefficient is drawn with the horizontal distance between the front wall and the rear wall. In all three cases Cp exhibits the similar trend. However, in Case 2 the value of Cp on
1.00
Mean Pressure Coeffiecient
0.75
0.50 Front wall Rear wall Side wall Roof
0.25
0.00
the rear wall is found smaller comparing to Case 1 and Case 3. Only in Case 2, the rear wall has no opening on it and eventually acts as an entire solid wall which may induce the differences than the other cases.
6. Conclusions Computational fluid dynamics has been chosen to have a complete understanding of the complexities and characteristics of natural ventilation system for a full scale building through multiple windows. This study is the first step to analyze the flow phenomena inside and around a common shaped single story building with multiple windows under the influence of usual wind speed. Based on the validation of the present methodology, an extensive analysis has been carried out to study the outdoor as well as the indoor airflow distribution. At steady state condition, the windows placed in the front wall work as inlet whereas the other windows placed in the rear wall work as outlet in cross ventilation. On the other hand, every window works as inlet and outlet simultaneously in singlesided ventilation. Everywhere, outside the building the velocity component U is relatively negligible. However, the magnitude of U is not negligible inside the building. Therefore, any two dimensional numerical approach may not be appropriate to capture the real indoor air flow distribution. The mass flow rate through the windows and the indoor air velocity change linearly if the wind velocity changes linearly. The distribution of pressure coefficient suggests that the front wall would be appropriate for inlet openings in cross ventilation while any other wall may serve as exit opening. Among all cases, it has been found that cross ventilation performed better in all respects. If the ambient conditions, the weather and the surroundings are satisfactory, this study may help the building designer to predict the indoor airflow. However, this analysis may not be satisfactory if any change such as variation of incident angle, temperature, design of the building, pollution, etc. take place. Further investigations considering these effects will be accomplished while the present study provides the basic observation.
-0.25
Acknowledgements
-0.50 -0.75
-1.00
0.5
1.0
1.5
2.0
Distance [m] Fig. 12. Surface pressure coefficient in Case 1.
2.5
3.0
The authors wish to appreciate the supports given by the National Science Council, Republic of China under grant no. NSC 99-2221-E-006-055. The first author would like to thank Dr. ShihHsiung Chen, Professor, IAA, NCKU, Taiwan for his invaluable support. The authors also wish to extend their appreciations for the valuable comments and suggestions given by the anonymous reviewers.
M.Z.I. Bangalee et al. / Energy and Buildings 45 (2012) 317–325
References [1] M. Orme, Estimates of the energy impact of ventilation and associated financial expenditures, Energy and Buildings 33 (2001) 199–205. [2] J.P. Cockroft, P. Robertson, Ventilation of an enclosure through a single opening, Building and Environment 11 (1976) 29–35. [3] E. Dascalaki, M. Santamouris, A. Argiriou, C. Helmis, D.N. Asimakopoulos, K. Papadopoulos, A. Soilemes, Predicting single sided natural ventilation rates in buildings, Solar Energy 55 (1995) 327–341. [4] E. Dascalaki, M. Santamouris, A. Argiriou, C. Helmis, D.N. Asimakopoulos, K. Papadopoulos, A. Soilemes, On the combination of air velocity and flow measurements in single sided natural ventilation configurations, Energy and Buildings 24 (1996) 155–165. [5] P.S. Carey, D.W. Etheridge, Direct wind tunnel modelling of natural ventilation for design purpose, Building Services Engineering Research and Technology 20 (1999) 131–140. [6] Y. Jiang, D. Alexander, H. Jenkins, R. Arthur, Q. Chen, Natural ventilation in buildings: measurement in a wind tunnel and numerical simulation with largeeddy simulation, Journal of Wind Engineering and Industrial Aerodynamics 91 (2003) 331–353. [7] S. Murakami, S. Kato, S. Akabayashi, K. Mizutani, Y.D. Kim, Wind tunnel test on velocity–pressure field of cross-ventilation with open windows, ASHRAE Transactions 97 (1991) 525–538. [8] M. Ohba, K. Irie, T. Kurabuchi, Study on airflow characteristics inside and outside a cross-ventilation model, and ventilation flow rates using wind tunnel experiments, Journal of Wind Engineering and Industrial Aerodynamics 89 (2001) 1513–1524. [9] G.M. Stavrakakis, M.K. Koukou, M.G. Vrachopoulos, N.C. Markatos, Natural cross-ventilation in buildings: building-scale experiments, numerical simulation and thermal comfort evaluation, Energy and Buildings 40 (2008) 1666–1681. [10] H.B. Awbi, Air movement in naturally-ventilated buildings, Renewable Energy 8 (1996) 241–247. [11] S.S. Ayad, Computational study of natural ventilation, Journal of Wind Engineering and Industrial Aerodynamics 82 (1999) 49–68. [12] G. Zhang, S. Morsing, B. Bjerg, K. Svidt, J.S. Strøm, Test room for validation of airflow patterns estimated by computational fluid dynamics, Journal of Agricultural Engineering Research 76 (2000) 141–148. [13] K.A. Papakonstantinou, C.T. Kiranoudis, N.C. Markatos, Numerical simulation of air flow field in single-sided ventilated buildings, Energy and Buildings 33 (2000) 41–48. [14] T. Hayashi, Y. Ishizu, S. Kato, S. Murakami, CFD analysis on characteristics of contaminated indoor air ventilation and its application in the evaluation of the effects of contaminant inhalation by a human occupant, Building and Environment 37 (2002) 219–230. [15] M. Bartak, I. Beausoleil-Morrison, J.A. Clarke, J. Denev, F. Drkal, M. Lain, I.A. Macdonald, A. Melikov, Z. Popiolek, P. Stankov, Integrating,
[16] [17] [18]
[19] [20]
[21] [22]
[23] [24]
[25] [26]
[27] [28]
[29]
[30]
[31] [32] [33] [34]
325
CFD and building simulation, Building and Environment 37 (2002) 865– 871. C. Allocca, Q. Chen, L.R. Glicksman, Design analysis of single-sided natural ventilation, Energy and Buildings 35 (2003) 785–795. G. Evola, V. Popov, Computational analysis of wind driven natural ventilation in buildings, Energy and Buildings 38 (2006) 491–501. T. Norton, D.-W. Sun, J. Grant, R. Fallon, V. Dodd, Applications of computational fluid dynamics (CFD) in the modelling and design of ventilation systems in the agricultural industry: a review, Bioresource Technology 98 (2007) 2386–2414. D. Yan, J. Xia, W. Tang, F. Song, X. Zhang, Y. Jiang, DeST – an integrated building simulation toolkit. Part I. Fundamentals, Building Simulation 1 (2008) 95–110. K. Visagavel, P.S.S. Srinivasan, Analysis of single side ventilated and cross ventilated rooms by varying the width of the window opening using CFD, Solar Energy 83 (2009) 2–5. Q. Chen, Computational fluid dynamics for HVAC: successes and failures, ASHRAE Transactions 103 (1997) 178–187. G.J. Levermore, The exponential limit to the cooling of buildings by natural ventilation, Building Services Engineering Research and Technology 23 (2002) 119–125. N. Khan, Y. Su, S.B. Riffat, A review on wind driven ventilation techniques, Energy and Buildings 40 (2008) 1586–1604. Y. Jiang, Q. Chen, Buoyancy-driven single-sided natural ventilation in buildings with large openings, International Journal of Heat and Mass Transfer 46 (2003) 973–988. ANSYS 12.0, Ansys Inc., www.ansys.com. J.M. Horan, D.P. Finn, CFD reliability issues in analysis of naturally ventilated buildings, in: Passive and Low Energy Cooling for the Built Environment, Santorini, Greece, 2005. N.G. Wright, G.J. Easom, Non-linear k–ε turbulence model results for flow over a building at full-scale, Applied Mathematical Modelling 27 (2003) 1013–1033. W. Zhang, Q. Chen, Large eddy simulation of indoor airflow with a filtered dynamic subgrid scale model, International Journal of Heat and Mass Transfer 43 (2000) 3219–3231. Y. Jiang, Q. Chen, Study of natural ventilation in buildings by large eddy simulation, Journal of Wind Engineering and Industrial Aerodynamics 89 (2001) 1155–1178. Y. Jiang, Q. Chen, Effect of fluctuating wind direction on cross natural ventilation in buildings from large eddy simulation, Building and Environment 37 (2002) 379–386. J. Smagorinsky, General circulation experiments with the primitive equations. I. The basic experiment, Monthly Weather Review 91 (1963) 99–164. Q. Chen, Comparison of different k–ε models for indoor air flow computations, Numerical Heat Transfer, Part B: Fundamentals 28 (1995) 353–369. Q. Chen, Prediction of room air motion by Reynolds-stress models, Building and Environment 31 (1996) 233–244. V. Yakhot, S.A. Orszag, Renormalization-group analysis of turbulence, Physical Review Letters 57 (1986) 1722.