## Design Tools

Traditionally, many architects predict the airflow in and around buildings by using "smart arrows," as shown in Figure 3. Drawing the airflow correctly requires a rich knowledge of fluid mechanics. Unfortunately in many cases, the "predicted" airflow pattern can be completely different from that in reality. Furthermore, the smart arrows cannot give the wind speed, or at least the reliable air speed, which is an important parameter for evaluating the benefits of natural ventilation and outdoor comfort. Chandra, Fairey and Houston (1983) developed a simple model for calculating the air exchange rate for natural cross ventilation. However, it is limited in that it can only be applied to buildings with simple geometry and surroundings.

Many empirical and analytical tools have also been developed for manual prediction of natural ventilation in buildings and outdoor thermal comfort, as documented by Allard (1998), Awbi (1996), CIBSE (1997), and Linden (1999). These manual methods are generally very simple and can be expressed by algebraic equations and spreadsheets. Despite being useful, these empirical and analytical tools have great uncertainties when used for complex buildings.

As a result, most traditional studies use wind tunnels to simulate and measure the airflow around buildings for outdoor thermal comfort and a full-scale mock-up room to determine natural ventilation. Figure 4 shows a site model placed in a wind tunnel. By rotating the site model disc and by changing the fan speed, different wind directions and speeds can be simulated.

When the buoyancy effect is not strong, such as during natural cross ventilation, the wind tunnel, together with modeling theory, can also be used to study natural ventilation. For buoyancy-dominant natural ventilation, such as single-sided natural ventilation,

Figure 2 Diurnal and nocturnal air movements near a large body of water (Source: adapted from Moore 1993)

ideally a full-scale mockup is needed in order to satisfy both the Reynolds number that represents inertial force from the wind and the Grashov number that represents the buoyancy force. The experiment usually measures wind speed in the wind tunnel, and wind speed and temperature in the mockup room. Rarely is the wind direction also measured. Although the experimental approaches provide reliable information concerning airflow in and around buildings, the available data is generally limited due to the expensive experimental rigs and processes. Moreover, the approach is not practical for a designer who wishes to optimize his or her designs because the experimental method is very time-consuming. Alternately, another fluid such as heavy refrigerant vapor (Olson, Glicksman and Ferm 1990) or water (Linden 1999) can be used for modeling. These fluids allow the model size to be substantially reduced. Whole buildings can be simulated with the water models. Also, by relaxing some of the modeling criteria, such as matching the Reynolds number, small-scale air models can also be employed.

Numerical simulation has become a new trend for determining natural ventilation and outdoor thermal comfort. Two numerical methods are available for predicting natural ventilation. The first one is the zonal method, which calculates inter-zonal airflow using the Bernoulli equation along with experimental correlations of flow resistance through doorways, windows, and other orifices.

The prediction of the inter-zonal airflow relies on the external pressure distribution caused either by wind or the buoyancy effect. However, the determination of the external pressure is very complex, since the pressure distribution depends on incoming wind speed and direction, building size and shape, and the size and location of the building's interior opening (Vickery and Karakatsanis 1987). Therefore, the accuracy of the zonal method depends on the accuracy of the pressure distribution. Furthermore, the zonal model is incapable of determining thermal comfort around a building, because it does not provide wind velocity information. However, such a method can supply good preliminary estimates of air flow and temperature levels within a building if reasonable estimates of external wind pressure distributions can be made.

The other numerical method, CFD, calculates the airflow distribution for both indoor and outdoor thermal comfort. The CFD technique numerically solves a set of partial differential equations for the conservation of mass, momentum (Navier-Stokes equations), energy, species concentrations, and turbulence quantities. The solution provides the field distribution of pressure, air velocity, temperature, concentrations of water vapor (relative humidity), contaminants, and turbulence. Refer to Chen and Glicksman (2000) for a more detailed description of the CFD technique. Despite having some uncertainties and requiring an engineer with sufficient knowledge of fluid mechanics and a high-capacity computer, the CFD method has been successfully used to predict airflow in and around buildings (Chen 1997, Murakami 1998). With the rapid increase in computer capacity and the development of new CFD program interfaces, the CFD technique is becoming very popular.

The following sections will discuss the applications of CFD to outdoor thermal comfort and natural ventilation design. CFD generally includes large eddy simulation and Reynolds averaged Navier-Stokes equation modeling. Large eddy simulation, as reviewed by Murakami (1998), can give more detailed results, such as an instantaneous airflow field, but it requires more computing time than that of the Reynolds averaged Navier-Stokes equation modeling. Large eddy simulation has started appearing in building environment research, but has yet to be applied as a design tool. Therefore, this

Figure 5 Stata Center: (a) model shown without glass roof and (b) surroundings

Figure 5 Stata Center: (a) model shown without glass roof and (b) surroundings chapter focuses on Reynolds averaged Navier-Stokes equation modeling. Many commercial CFD programs based on the Reynolds averaged Navier-Stokes equation modeling are available on market and are rather similar to each other. This investigation uses the PHOENICS program (CHAM 2000).

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