Buoyancy Effects On Building Pressurization In Extreme Cold Climates

Abstract

This research investigates building pressurization due to buoyancy effect. The American Society of Heating, Refrigeration, and Air Conditioning Engineers (ASHRAE) presents an idealized equation to calculate the buoyancy effect. This dissertation compares differential pressure measurements from an actual building exposed to extremely cold temperatures to this idealized model. It also presents new statistical models based on the collected data. These new models should provide engineers with improved tools to properly account for building pressurization for designs in extreme cold climates. Building pressurization, the differential pressure between the interior of a building and its exterior surroundings, is an important design consideration. Pressurization is the driving force in building infiltration/exfiltration. It also affects air flow within building zones. Improper calculation of pressurization can result in under-sizing the building's heating and cooling systems, improper operation of air distribution systems, improper operation of elevators, and freezing and failure of water distribution and circulation systems. Building pressurization is affected by: wind (speed and direction), exterior-to-interior temperature difference, and mechanical equipment operation. In extreme cold climates, the predominant effect is air buoyancy due to temperature differences across the building envelope. The larger the temperature difference, the larger the buoyancy effect. In extreme cold climates, the largest temperature differences often occur at times when wind speed is negligible. This dissertation also demonstrates the use of existing data sources such as building automation systems to collect data for basic research. Modern systems automation provides a tremendous amount of data that, in the past, had to be collected through separate instrumentation and data acquisition systems. Taking advantage of existing automation systems can provide the required data at greatly reduced costs when compared to previous industry practices. The statistical analysis approach taken in this research expands the tools for engineering design. Actual interactions of real world variables are analyzed and used to produce prediction models. These techniques allow the model to incorporate relationships which may not be fully understood at the underlying principle level but are evidenced in the data collected from actual installations.* *This dissertation includes a CD that is compound (contains both a paper copy and CD as part of the dissertation). The CD requires the following applications: Internet Browser; Adobe Acrobat; Microsoft Office; Image Viewer.