TOWARDS A MORE RELIABLE MODELLING OF NIGHT-TIME VENTILATION WITH BUILDING ENERGY SIMULATION MODELS Sarah Leenknegt 1, Rolf Wagemakers 2, Walter Bosschaerts 2, Dirk Saelens 1 1 Building Physics Section, K.U.Leuven, Leuven, Belgium. 2 Department of Mechanics, Royal Military Academy, Brussels, Belgium. Abstract Night ventilation is a passive method to cool the exposed thermal mass in a room during night. The efficiency depends heavily on surface convection. Traditionally in Building Energy Simulation (BES) models, this flux is represented by one surface and reference temperature and one convective heat transfer coefficient (CHTC). In reality, the flux will vary both locally and in time, which can only be modelled with CFD. The research presented here focuses on two elements: (1) the convection regimes during the increased ventilation period, i.e. local and transient behaviour, and (2) the capability to model this behaviour in BES. To investigate this, a simple room is analyzed through a transient CFD-simulation in 2D. The CFD model includes the thermal mass in ceiling and floor, with both vertical walls considered as adiabatic and an ACH of 7 h -1 at 16 C. It is shown that within the period of free cooling, the transient behaviour is limited and buoyancy remains dominant in this case. This causes the incoming air to fall down, rather than adhere to the ceiling, which results in a four times higher CHTC at the floor compared to the ceiling. A comparison is made between the local CHTC-values and CHTC-correlations from literature. Appropriate correlations are available, but the selection algorithm in BES poses problems: each surface should be evaluated individually, rather than be assigned based on a global room convection classification. TRNSYS is capable of modelling the surface and air temperature evolution fairly well, given that the corrected CHTC s are used, rather than the internal calculation. Keywords: CFD, BES, night ventilation, conjugate heat transfer, convection correlations 1 Introduction Night ventilation is a passive method to cool a building. During day, the thermal inertia inside is used to buffer the heat. During night time, high air change rates (4-10 h -1 ) are used to cool down the exposed surfaces. Driving forces can be natural or mechanical. Though different researchers show that night ventilation can be an efficient cooling technique, its cooling potential is difficult to predict accurately (Breesch, 2006) and is often predicted using Building Energy Simulation (BES) models, such as TRNSYS, Energy+, etc. However, such models make a simplified treatment of the convective surface heat flux density q c, which is an important parameter for the evaluation of night ventilation. The convective heat flux is expressed by Eq. (1), where h c is the convective heat transfer coefficient (CHTC), T s is the temperature of the surface and T ref is a chosen reference temperature. =h (1) In literature, the convective heat transfer has already been addressed extensively. Numerous researchers have provided CHTC-correlations, which give an equation to calculated the surfaceaveraged CHTC for natural, forced or mixed convection, horizontal or vertical surfaces, and in function of reference temperature, characteristic dimension of the surface/room, jet momentum or air change rate. T ref is usually taken at 100 mm from the surface, i.e. at the free stream outside the viscous and thermal boundary layer. When Eq. (1) is applied in BES, it is solved with the assumption of a uniform surface heat flux, isothermal surfaces. T ref is taken as the temperature in the air node, which is equivalent with the room-averaged air temperature. Therefore, perfectly mixed zone air is assumed.
4.75 m thermal mass 200 mm 100 mm 100 mm velocity inlet 2.4 m coupled 100 mm Figure 1. Overview of mesh construction (2D), with detail of inlet, near wall mesh and thermal mass There are several ways to deal with surface convection in BES. A first approach is to apply a correlation to a given surface. With this equation, a CHTC-value for this surface is determined for every time-step. This approach is commonly applied in BES-software, though often only natural convection correlations are used (Goldstein & Novoselac (2010)). However, the convection regime can change over time, so it can be inaccurate to assume a fixed correlation per surface over the full simulation period. Therefore, a second and more detailed approach is to implement a selection algorithm that selects a CHTC-correlation per time-step, in function of the current convection regime in the room. This has been done by Beausoleil-Morrison (2000) for ESP-r, based on existing and newly developed correlations. Finally, the most complex approach consists of solving BES parallel to a CFD-simulation of the intra-zonal flow of the room, which has been researched by Beausoleil- Morrison (2000) and Zhai & Chen (2004). This is a promising approach, though such a simulation requires a long calculation time. This limits its practical use at the current time. This paper will focus on night ventilation, and more particularly, on the conjugate heat transfer during the free cooling period. There are two main questions to answer: (1) Which convection regimes are seen during the increased ventilation period and how do they vary locally and in time? (2) Is BES capable to capture this behaviour, both with regard to the model of the thermal mass as to the surface convection? These research questions are addressed through a series of transient 2D CFD-simulations. They are confronted with a simulation in TRNSYS. In order to monitor the transient behaviour, the thermal mass is included in the CFD-model. 2 Modelling and methodology A CFD-study is made of the transient conjugate heat transfer between air and thermal mass of the ceiling and floor in a room with high cool ventilation flow. This investigation is made with Fluent 6.3 and Fluent 12. The influence of air density variations is taken into account with the incompressible ideal gas law. The simulations are made of a 2D-section of a room, which is shown in Figure 1. As we want to simulate the transient conjugate heat transfer, the internal surfaces cannot be considered as adiabatic, nor can a fixed surface flux be used. Also by imposing a fixed surface temperature, the flux from surface to air is influenced by this boundary condition. Therefore, the choice was made to include solid regions in the CFD-model to formally include the thermal mass. In office buildings, the thermal capacity is mainly located in floor and ceiling, as opposed to the light-weight partition walls. Therefore, only the mass in floor and ceiling are modelled and both vertical walls are considered as adiabatic. This is also visible on Figure 1. The thermal mass consists of 100 mm of heavy reinforced concrete with a density of 2400 kg/m³, capacity of 840 J/(kg.K) and a conductivity of 2.2 W/(m.K). The transient cooling of the air-mass system is simulated for 8 hours. First a steady state simulation is made isothermally at 22 C, which is a typical temperature at the end of the working day in summer. The air change rate is 0.9 h -1, representing the hygienic air change rate during office hours. With this as initial condition, a transient simulation is started with an air change rate of 7 h -1, representing the increased air change rate during night ventilation. The inlet is
modelled as a velocity-inlet, while the outlet is chosen as a pressure-outlet (0 Pa) on the opposite side of the room. Both are located at 20 cm from the ceiling with a height of 10 cm. The inlet velocity is 0.253 m/s with an inlet temperature of 16 C, representing cool outside air. The two outer surfaces of the concrete slabs are thermally coupled, equivalent with a 20 cm concrete slab, cooled on both sides. As the main focus of this research is on the convective surface heat flux, the CFD-model must be able to capture boundary layer flow. It is known that there can be a large error in surface heat flux for flows with low turbulence when log-law wall functions are used, see Awbi (1998), Novoselac (2005). Enhanced wall treatment was therefore used for the near wall treatment. As the location of the first grid point y p is considered crucial for the determination of surface convection (Awbi & Hatton (1998), Zhai & Chen (2004)), the mesh sensitivity study was done with y p varying between 0.1, 0.2, 0.3, 0.4, 0.5, 1, 2, 3, 4 mm. Also the maximum cell length was varied from 1, 5, 10 and 20 cm, resulting in a total of 36 meshes ranging from 2457 to 191 178 cells. The mesh sensitivity study was run for a flow time of 6 minutes. When plotting the surface-averaged floor temperature at six minutes, there is clear convergence for a first grid point distance y p 0.5 mm and for a maximum cell length 5 cm. A Richardson extrapolation (Franke et al., 2007) was done on the finest meshes and it was seen that the solution of the y p =0.1mm-5cm mesh varies only with 0.2% from the exact solution resulting from this extrapolation. The selected mesh therefore has a first boundary cell thickness of 0.2 mm and maximum cell length of 5 cm, resulting in a mesh with 22 000 cells. The simulation time step is started at a low value of 0.1 s for stability, and then increased after 120 seconds to a fixed value of 1 s. Since the flow is at low Reynoldsnumber, the sensitivity of the turbulence model is considered crucial and is therefore investigated. Five models are compared: standard, realizable and RNG k-ε and standard and SST k-ω. The influence on the thermal parameters, like mass, surface and air temperature and surface heat flux is limited, though the influence of the turbulence models is much more noticeable for eddy viscosity, turbulent kinetic energy and air velocities. As the focus here is on the thermal parameters, the differences are not discussed. According to the review by Zhai et al. (2007) and Zhang et al. (2007), the RANS model RNG k-ε is stable and performs well in varying situations (natural and forced convection flows). Susin et al (2009) also describes good results for this model. Further results are discussed only for the RNG k-ε model with enhanced wall treatment. Additionally, turbulence intensity T i and turbulence length scale l must be estimated. As both inlet and outlet are assumed to be covered with a ventilation grille with 2 cm spacing between the horizontal lamellae, it is assumed that the size of the eddies at the inlet and outlet will be approximately 0.02 m. T i is estimated at 5%. An isothermal sensitivity study for l and T i, with values respectively between 0.02-0.1 m and 2-15 %, showed negligible influence of these assumptions. 3 Discussion of results 3.1 Flow pattern When considering night ventilation, the ceiling is considered as the main source of thermal capacity. In order to realize an efficient cooling of the exposed ceiling, supply openings are usually located high in the facade, close to the ceiling. It is then implicitly expected that the incoming cool air will adhere to the ceiling (Coanda effect) and realize an increased surface convection, resulting in a fast cooling of the ceiling. The flow pattern that was observed in the CFD-study however, presents a different picture. Figure 2 shows the stabilized flow in the room. Contrary to what is expected, the supply air falls down after entering, sweeps over the floor, and is pulled up on the opposite side towards the outlet. The air outside this boundary layer flow is nearly still (room-averaged air velocity around 0.05 m/s). The air at the ceiling is strongly stratified, with a temperature gradient of approximately 4 K over 20 cm. At the floor, the higher air velocities reduce the boundary layer to less than 10 cm, but with an equally large temperature gradient.
Figure 2. (left) Pathlines coloured by velocity [0 to 0.3 m/s]; (right) Schematic presentation of flow 3.2 Surface convection In BES, a surface is simplified to a single node with one convection regime. In Figure 2, a decreasing air velocity is noted over the length of the floor, and a large vortex hitting the left side of the ceiling can be observed. These will, together with small detaching vortices at left and right ends of floor and ceiling, cause local variations in the CHTC. This can also be seen in Figure 3, which displays the actual CHTC at floor (left) and ceiling (right). These values are calculated with reference temperature taken at 100 mm from the surface (CFD_100mm). A series of correlations from literature are also calculated based on the local reference temperature taken from CFD. Sensitivity of the reference temperature on the calculated CHTC is shown, with the average room air temperature as alternative reference temperature (CFD_Tavg). In the following paragraphs, a discussion is made of the local convection gradients at ceiling and floor. (1) At the ceiling, the CHTC is generally very low. Over most of the surface, there is a good fit with the natural convection correlation for a stratified ceiling by Awbi & Hatton (1999). On a small area close to the outlet, a local increase is seen. This corresponds with the main vortex seen in Figure 2. The air that does not leave through the outlet is pulled further upwards and hits the ceiling. This process injects momentum in the boundary layer and results in a higher heat flux, though the CHTC here is still lower than predicted by the mixed convection correlation by Beausoleil-Morrison (2000). The influence of reference temperature is negligible for the ceiling. (2) The influence of the reference temperature is much more important at the floor, especially close to the left wall. It is here that the cold supply air falls down. The temperature at 100 mm of the surface is locally about 1 K colder than the room-averaged air temperature. This larger temperature difference T s-100 results in a smaller effective CHTC 100. Nevertheless, the reattachment of the cold air causes a local increase in the CHTC. This local increase is captured best by the mixed convection correlation by Awbi & Hatton (2000), which was developed through measurements of a cold jet over a heated room surface. However, the CHTC decreases rapidly over the length of the room. CHTC (W/(m².K)) 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0 0.75 1.5 2.25 3 3.75 4.5 m 0 0.75 1.5 2.25 3 3.75 4.5 m CFD_100mm CFD_Tavg CFD_100mm CFD_Tavg 'BM-mix-bu-fl' 'AwH-mix-fl' 'AwH-nat-str-cl' 'AlH-nat-str-cl' 'AlH-nat-bu-fl' 'Fish-for-wdiff-fl' 'BM-mix-str-cl' 'AwH-for-ht-cl' 'AwH-for-ht-fl' 'Fish-for-wdiff-cl' Figure 3. Comparison of CHTC-correlations with local CHTC-values from CFD (left: floor; right: ceiling) Legend: BM = Beausoleil-Morrison (2000), AwH-nat = Awbi & Hatton (1999), AwH-mix = Awbi & Hatton (2000), AwH-for = Awbi & Hatton (2000), AlH = Alamdari & Hammond (1983), Fish = Fisher (1995). CHTC (W/(m².K)) 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
As the floor is warmer than the air, the floor is buoyant and combined with the increased air velocities, mixed convection is expected here. The CHTC s are however overestimated by the Beausoleil-Morrison correlation. They were developed for radial ceiling diffusers, which create a more symmetrical forced flow than in the presented case. Finally, at the right side of the floor, a local increase is seen where the boundary flow detaches from the floor and flows upwards. Around the detachment point, the velocity very close to the surface is very low. Just after this point, two small vortices arise, resulting in higher velocities close to the surface and a higher convective surface heat flux. A typical configuration for night ventilation is a natural ventilation wall-sided air supply. In literature, no correlations for floors were found that explicitly deal with the resulting mixed convection. The Awbi & Hatton (2000) correlations are only valid for the surface over which the cold jet is flowing directly and the Fisher (1995) correlation for the floor is independent of temperature. 3.3 Application in BES With regard to the application in BES, the selection of the applicable CHTC-correlation per time step and per surface is crucial. We can review this for both discussed surfaces, using the correlation selection algorithm of Beausoleil-Morrison (2000). The convection regime according to his classification scheme should be described as Mixed flow caused by air handling system with wall diffusers and surface-air temperature differences, though mixed flow is only covered by Beausoleil- Morrison (2000) in case of ceiling diffusers (regime E). Three alternatives are therefore investigated and compared at flow time 8h in Table 1 and were also included in Figure 3. As BES uses only surface-averaged CHTC, and the correlations are also developed for this, the surface-averaged CHTC are added in Table 1 and compared to CFD. All correlations are rescaled to the same reference temperature (at 100mm from surface). The correlations for mixed convection from Beausoleil- Morrison (2000) are much higher than occur in the CFD-model, both for floor and ceiling. They are developed for ceiling diffusers and are clearly not applicable for wall-sided supply. Therefore, the correlations from Fisher (1995) for wall diffusers would seem more applicable. The floor correlation depends only on air change rate, while the ceiling correlation also takes into account the temperature difference between supply and exhaust air. Nevertheless, the convection at the ceiling is again much higher than in the CFD-case. However, the surface averaged convection at the floor by Fisher (1995) is very close to the CFD-results, even though no thermal effects are taken into account. Finally, when comparing to the last correlations by Alamdari & Hammond (1983), there is very good correspondence for the ceiling, which is indeed largely stratified. Also for the floor, there is reasonable correspondence, with a small underestimation. These findings can be verified with the dimensionless Richardson number Ri (Gr/Re²), which gives the buoyant forces relative to the kinetic forces. Ri shows that most of the floor surface is dominated by forced convection, which confirms that validity of the Fisher (1995) correlation. The ceiling on the other hand is dominated by natural convection. Table 1. Comparison of CFD with three convection regimes according to Beausoleil-Morrison (2000) Floor Ceiling CHTC SurfAvgCFD 1.93 W/(m²K) 0.57 W/(m²K) Classification correlation W/(m²K) correlation W/(m²K) Mixed flow caused by air handling system with ceiling diffusers and surface-air temperature differences mixed convection for buoyant floor, by Beausoleil-Morrison (2000) (BM-mix-bu-fl) 2.62 135 % mixed convection for stratified ceiling, by Beausoleil-Morrison (2000) (BM-mix-str-cl) 3.67 646 % Forced flow caused by air handling system with wall diffusers Buoyancy caused by surface to air temperature differences, no heating elements, no mechanical air supply free horizontal jet in isothermal rooms, floor, by Fisher (1995) (Fish-for-wdiff-fl) natural convection on buoyant floor, by Alamdari & Hammond (1983) (AlH-nat-bu-fl) 1.98 102 % 1.77 91 % free horizontal jet in isothermal rooms, ceiling, by Fisher (1995) (Fish-for-wdiff-cl) natural convection, stratified ceiling, by Alamdari & Hammond (1983) (AlH-nat-str-cl) 1.46 257 % 0.55 97 %
3.4 Transient behaviour A discussion is made of the transient behaviour of surface and air temperatures, as well as the CHTC. All are plotted in Figure 4 and compared to the results of an identical case solved in TRNSYS 16. First transient behaviour observed in CFD is discussed, and secondly a comparison is made between CFD and the BES-model TRNSYS. (1) For perfectly mixed air and with the imposed air change rate of 7 h -1, a full change of the zone-air is realized after 9 minutes. In reality, the mixing will not be perfect and three phases can be distinguished. A first phase takes 20 to 25 minutes (2.5 air changes) and is characterized by a strong temperature decrease of 4 to 5 K in the bulk air as well as close to the floor, and about 2-3 K close to the ceiling. The surface averaged temperatures decrease by 0.42 K/h and 0.05 K/h, respectively for floor and ceiling. Notice the much faster initial cooling of the floor, which is due to initially higher local air velocities and turbulent intensity close to the floor surface. After the initial transition phase of the flow, the CHTC at the floor stabilizes too at a lower value, see also Figure 4. At the ceiling, the opposite occurs: it takes about half an hour until the main vortex, visible in Figure 2, is pulled high enough to hit the ceiling, resulting in a rapid increase of the CHTC until stabilization. The second phase shows very well mixed air in most of the room and a very small vertical temperature gradient (0.2-0.3 K/m). The continuing supply of cool air results in a further reduction of the average bulk air temperature of only ± 0.05 K/h, for the next 7-8 hours. The surface averaged temperatures decrease by 0.1 K/h and 0.08 K/h, respectively for floor and ceiling. Even though the CHTC at the floor is about four times higher than at the ceiling, the cooling rate of both surfaces is nearly the same. This can be explained by the heat flow within the slab. The heat conduction between the surfaces is equivalent with the temperature difference between surface and core, seen on Figure 4 (left). It results in a much more efficient cooling of the floor slab than the ceiling slab: DT fl >> DT cl. These two phases are also visible in Figure 4. Finally, the third phase is not reached, but will occur when the bulk air temperature has decreased to a low enough value. This transition can be estimated with Ri. When a linear extrapolation is made on the bulk air temperature, Ri = 1 is realized after ± 27 hours of total flow time and with a temperature difference of only 0.8 K between supply and room averaged air. At this point, the buoyant and kinetic forces are equal and the incoming air will no longer be pulled down by the weight of the cold air and could adhere to the ceiling. The flow field will change significantly, as well as the local convection regimes. When and if the Coanda effect will take place, requires further simulation. This depends also on the specific configuration (i.e. distance from inlet to ceiling). However, in a real office room, it is very unlikely that this will occur, as the period of night-time ventilation is practically limited to 12 hours. (2) The same room is simulated with TRNSYS 16. The dynamical behaviour of thermal mass in TRNSYS is modelled with the transfer function method (Seem, 1987). TRNSYS has an internal calculation of convection coefficients, though it is also possible to implement user-defined correlations or values. The internal calculation however is often used, and assumes purely natural convection (stratified or buoyant). The equations have the general form as given in Eq. (2), with constants A and B depending on slope, direction of the heat flux and temperature difference between surface and air node, resulting in a total of five equations. h = (2) Two TRNSYS-simulations of a 20 cm concrete slab, cooled on both sides, are compared on Figure 4: (1) with time-variable CHTC from the CFD-simulation (TFM_x) and (2) with the internal CHTC-calculation (TFM_x_int). The CHTC used for TFM_x, are also included in Figure 4 (right). The other boundary conditions (ACH, initial temperature, supply temperature) are identical to the CFD-case. The results from TRNSYS were averaged over the simulation time-base.
Surface-avg'd temperature (K) TFM CFD 295.0 294.8 294.6 294.4 294.2 294.0 293.8 0 60 120 180 240 300 360 420 480 0.3 0.0-0.3 = transfer function method, in TRNSYS (_fl = floor, _cl = ceiling) = results from the periodic CFD-case (_fl = floor, _cl = ceiling, _mid = centre of slab) Figure 4. Comparison of transfer function method with CFD for free cooling of a concrete slab Tsurf - Tmid (K) min TFM_fl TFM_cl CFD_fl CFD_cl CFD_mid TFM_fl_int TFM_cl_int DT_fl DT_cl Note: the average air temperature was calculated based on 1421 equally spaced points (each point represents 0.01 m²). When the CHTC-values are taken from CFD, we see that the surface temperature evolution in TRNSYS corresponds well to CFD-results, and so does the average air temperature. Notice that the TFM-results include the influence of radiation, though as there are only two surfaces included in the TFM-case, with a very small temperature difference, the impact of radiation is very low. Room avg'd temperature (K) 295 294 293 292 291 290 0 60 120 180 240 300 360 420 480 2.50 2.25 2.00 1.75 1.50 1.25 1.00 0.75 0.50 0.25 0.00 min CHTC (W/(m²K)) CFD_air TFM_air TFM_air_int CHTC_fl CHTC_cl 4 Conclusions The investigated room shows a combination of natural, mixed and forced convection. This can be expected during intensive (night) ventilation, as there is some source of forced convection due to wind or mechanical ventilation, but the air velocities have to compete with the influence of buoyancy. This is caused by the large temperature gradients that occur during night ventilation. By including thermal mass in the model, it is possible to follow up the transient behaviour of the air-mass system and its influence on the room convection regime. A first phase shows a fast initial cooling of the floor, due to the falling air. After a stable flow pattern is realized (± 25 min), a slowly but continuously decreasing surface heat flux is noted. This is the result of the decrease in surface temperature and therefore temperature difference T s-100mm. Although this will influence the flow pattern slightly, the CHTC decreases with only 4 to 6 % over the following 7.5 hours. After stabilization, the average cooling rate is quite slow. Through extrapolation, it is expected to take at least a full day before the air has cooled enough to overcome the dominance of the buoyancy effect, which will cause the flow field to change significantly. It is important to note that it is the floorslab that is cooled most efficiently, with the CHTC at the floor nearly four times higher than at the ceiling. This could have a large influence on the design guidelines of natural night ventilation. A comparison was made between the transfer function method in TRNSYS and CFD, and both surface and air temperature evolution corresponds fairly well when CHTC-values are used from the CFD-simulation. This indicates that the use of a selection-algorithm to choose appropriate CHTCcorrelations is a relatively cheap way to improve the modelling of surface convection in BES-models. However, it is illustrated that the selection of an appropriate convection correlation is difficult. A calculation was made of the Richardson number close to ceiling and floor surface, which indicate that the floor is dominated by forced convection and the ceiling by natural convection. This is confirmed when comparing the surface averaged CHTC from CFD with possible correlations from literature. This contradicts with what is expected. It can be concluded that the selection of an appropriate convection regime cannot solely rely on an overall room classification. A separate classification per surface is recommended.
Finally, though there are correlations available to describe a wide range of the commonly occurring convection regimes, there are still gaps. For example, all correlations for forced or mixed convection are based on mechanical air supply, assuming that the supply air is continuous and homogenous (symmetrical or controlled direction) and at relatively high air velocities. In this case, the Fisher-correlation (1995) was found suitable. However, in case of an open window, the air supply could be much more variable, both in temperature, direction and velocity. It must be investigated if the Fisher-correlation (1995) should be extended for wall-sided natural supply. Acknowledgements This research was funded by the Research Foundation Flanders (FWO Vlaanderen). Their financial contribution is greatly appreciated. References Alamdari, F., Hammond, G.P., (1983) Improved data correlations for buoyancy-driven convection in rooms, Building Services Engineering Research & Technology, 106-112 Awbi, H.B., (1998) Calculation of convective heat transfer coefficients of room surfaces for natural convection, Energy and Buildings, vol. 28, 219-227 Awbi, H.B., Hatton, A., (1999) Natural convection from heated room surfaces, Energy and Buildings, vol. 30, nr. 3, 233-244 Awbi, H.B., Hatton, A., (2000) Mixed convection from heated room surfaces, Energy and Buildings 32, 153-166 Beausoleil-Morrison, I., (2000) PhD. The adaptive coupling of heat and air flow modeling within dynamic whole-building simulation, University of Strathclyde, Glasgow, UK Breesch, H., (2006) PhD. Natural night ventilation in office buildings. Performance evaluation based on simulation, uncertainty and sensitivity analysis, UGent, Belgium Fisher, D.E., (1995) PhD. An experimental investigation of mixed convection heat transfer in a rectangular enclosure, University of Illinois, Urbana, USA Franke et al, (2007) Best practice guideline for CFD simulation of flows in the urban environment, COST Action 732 Goldstein, K., Novoselac, A., (2010) Convective heat transfer in rooms with ceiling slot diffusers, HVAC&R Research 16(5), 629-655 Novoselac, A., (2005) PhD. Combined air flow and energy simulation program for building mechanical system design, The Pennsylvania State University, US Seem, J.E. (1987) PhD. Modeling of heat transfer in buildings, University of Wisconsin-Madison Susin et al., (2009) Evaluating the influence of the width of inlet slot on the prediction of indoor air flow: Comparison with experimental data, Building and Environment 44, 971-986 Zhai, Z., Chen, Q., (2004) Numerical determination and treatment of convective heat transfer coefficient in the coupled building energy and CFD simulation, Building and Environment 39, 1001-1009 Zhai et al., (2007) Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD - Part-1: Summary of Prevalent Turbulence Models, HVAC&R Research 13(6), 853-870 Zhang et al., (2007) Evaluation of various turbulence models in predicting airflow and turbulence in enclosed environments by CFD: Part 2 comparison with experimental data from literature, HVAC&R Research, 13(6), ASHRAE, 871-886