Uncertainty in building fan pressurization tests: Review and gaps in research. (15th July 2022) - Mélois, A.; Carrié, F.R.; El Mankibi, M.; Moujalled, B.
Abstract: Building fan pressurization tests are based on a measurement method developed in the 1970s to estimate the airflow rate that penetrates the building envelope through unintentional openings. These tests are increasingly used for compliance checks with building energy performance requirements and may result in severe penalties. The uncertainty of the measurement results has therefore become a key concern in several countries. To address this concern, this paper gives an overview of the current knowledge regarding the sources of error that impact measurement uncertainty. We first analyse the sources of error due to the underlying model of fan pressurization methods, described in commonly used standards such as ISO 9972 and ASTM 779–19. We then examine the sources of error due to measuring equipment, protocol, and analysis. The papers reviewed are classified according to the major findings relevant to the uncertainty analysed, to provide a holistic approach to uncertainty sources which is missing from the literature. Overall, this review shows that several sources are well known and can be quantified or contained within existing methods; for instance, the uncertainty of airflow and pressure measurement devices. It also brings to light the fact that there remain many unanswered questions and gaps in research. One specific concern is the assumed homogeneity in space and time of the differential pressures across leakage paths. Although the literature shows that this can dominate the uncertainty in the result, it provides little guidance on how to deal with the role played by this problem in uncertainty analyses. Highlights: Proposes a holistic approach to characterize building airtightness test uncertainty. Reviews the characterization and quantification of the various sources of error. Gives limits of the actual knowledge regarding fan pressurization test uncertainty. Suggests future research to better understand and quantify sources of error.