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3. COMPLEXITY OF NATURAL VENTILATION PROBLEM IN DESIGN A variety of problems arise when teaching students how to design and analyse the natural ventilation design principles. The main problem stems largely from the complexity of the fluid-dynamic behaviour, because of its evident non-linearity and instability which often forces to place one’s trust in computational techniques without critics or possibility of explaining numerical or experimental results. Advances made in methods to predict and measure building airflows have truly revolutionised the fields of building ventilation and air quality research and practice in the past two decades. Tracer gas techniques have been extended and refined to allow more accurate, better-characterised, and more complete multizone measurements of airflows within buildings. Varieties of rigorously defined ventilation effectiveness metrics have grown out of these advances and have placed ventilation system evaluation on a solid objective basis. Macroscopic methods of airflow analysis have been generalised to allow integrated modelling of wind-driven, buoyancy-driven, and mechanically-forced airflow in multizone building systems of arbitrary complexity. The global predictive capability of macroscopic simulation methods have been complimented by a constellation of microscopic methods of analysis, together placed under the more familiar rubric of Computational Fluid Dynamics (CFD), that allow investigation of the details of airflow around buildings and within single and, at this point, simply and well-connected collections of rooms. Consequently, we presently find ourselves armed with a veritable arsenal of tools to evaluate the thermal comfort, air quality and energy conservation efficacy of existing and proposed building ventilation systems. Yet, ironically, we have yet to develop tools to directly answer simple design questions relating to building ventilation: How wide should windows be opened in a given building for wind-driven cross ventilation on a moderate summer day? How should a ventilating monitor and building windows be configured to mitigate internal and solar gains on the same summer day? What size fan is needed to assist stack-driven airflow through the monitor on a more extreme summer day? A typical numerical output of computational fluid-dynamic software is showed in figure
1, where the natural ventilation air velocity field computed for a concert hall design is graphically rendered. As we can readily realise, the sophisticated data that appear to give all needed information of interest, become intractable when for example we ask ourselves what could we do in order to reduce the re-circulation of air due to the vortex that obstruct the extraction of exhaust air above the stage. There’s no tool at moment to support this design problem. Because of this lack only a trial and error approach is available to building, improving or correcting design from a natural ventilation point of view. This fact leads students or unskilled engineers to avoid exploring multiple design alternatives and to avoid carrying out trade-off studies; moreover they tend to get bogged down in carrying out routine calculations often spending time merely in solving data input problems. VENTPad was designed specifically to help students learn natural venti lation by providing an intelligent learning environment that handles qualitative routine calculations, facilitates sensitivity analyses, helps students keep track of modelling assumptions, and detects physically effectiveness of designs. 3.1 FLUID-DYNAMIC MODELS FOR NATURAL VENTILATION IN DESIGN A building system may be considered to be continuum within which the state variables of temperature T, pressure p, air velocity v, and concentration of species ‘ i’ Ci vary in space, x, y, z, and in time, t. The variation of these state variables is governed by fundamental mass, momentum and energy conservation principles, bound by environmental and thermal-mechanical-chemical boundary conditions, that allow prediction of the spatial and temporal variation of these state variables (see Awbi 1991 for an overview). Broadly speaking, two numerical approaches are commonly used for this prediction, namely microscopic and macroscopic analysis. Microscopic analysis, based typically on finite difference or finite element techniques, approximates the continuously defined state variables by a finite set of spatially discrete but temporally continuous state variables defined at or associated with discrete (mesh) points ‘j’ within the continuum. Microscopic methods of analysis provide the means to predict comfort variables and, importantly, their spatial variation within rooms (air dry bulb temperature and velocity are directly predicted while mean radiant temperature and RH distributions may be easily computed at each of the room air mesh points from computed surface temperatures and vapour-phase water concentrations respectively). As a result, microscopic analytical evaluation of comfort in rooms has become one of the primary applications of computational fluid-dynamics (see, for example Awbi and Gan 1994, Gan 1996). In spite of the direct utility of the microscopic approach to comfort prediction, several limitations must be noted, because of its expensiveness (in terms of data input and computation time often longer than a day), the special expertise needed to implement it and to evaluate the results. For this reason it remains a research tool and is very seldom applied in practice. Macroscopic analysis, based on idealising the building system as a collection of one or more control volumes (a space whose behaviour is well known) linked by discrete heat or mass transport paths, also approximates the continuously defined state variables by a finite set of spatially discrete but temporally continuous state variables but now the discrete state variables are associated with either the control volumes or discrete transport paths (windows and doors). Macroscopic methods can provide an economic and accessible means to predict simple measures of thermal comfort within rooms (e.g., spatially averaged room air dry bulb temperature, mean radiant temperature, air velocity, and relative humidity). While they can not provide the spatial detail offered by microscopic analysis (frequently missing local phenomena which may considerably affect comfort as air re-circulation which reduce the diffusion of fresh air in specific zones), macroscopic methods can be readily applied to whole building systems and configured to allow an integrated consideration of interacting building systems (e.g. heat transfer in the building fabric and envelope, HVAC systems, lighting systems, and natural ventilation systems). As in the microscopic case, however, these methods have been formulated to support only a trial and error approach to building design, nevertheless macroscopic analysis allows to link the response of the system directly to key design parameters. For this reason we use a macroscopic model to illustrate our approach of assembling qualitative models; nevertheless microscopic modelling or experimental data could also be used. In this case a more expensive analysis is required in order to extract the causal relationships between relevant state variables and key design parameters. This makes it possible the integration of both approaches peculiarity; on the one hand the ability of detecting local characteristic of air motion within buildings but on the other hand the possibility of linking these characters directly with key design parameters. Thus as an example, consider a building system idealised as a collection of zones linked by discrete airflow paths and conductive heat transfer paths. Macroscopic discrete state variables of pressure and temperature will be associated to each of the zones (i.e. the pressure associated with a specific elevation within the zone identi fied in the figure 2 and the temperature associated with the spatial mean air temperature within the zone). Similarly, an outdoor ambient reference node will be associated with the ambient pressure and temperature. Surface temperature variables will be associated with the surface of each of the several conductive heat transfer paths within the building system and finally, the mass flow rate of air through each of the several discrete airflow paths will be identified. With these variables defined, one may apply mass and energy conservation principles to form systems of equations governing heat transfer and airflow in the building system (see Walton 1989 for details).
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