In many countries, the fan pressurization method is the most frequently chosen approach for

measuring the air leakage of houses. The measurements are usually performed at pressures that

far exceed pressures to which buildings are exposed to under normal conditions. A fit of these

tests to the power-law formulation allows an extrapolation to data points outside the measured

pressure range. With the Ordinary Least Square (OLS) fitting method, the pressure exponent and

flow coefficient can be determined. However, the measurement results are highly sensitive to

uncertainties induced by external factors like changing wind conditions during the tests, which is

neglected by OLS. This may lead to errors in the prediction of flows at lower pressures. The

Weighted Line of Organic Correlation (WLOC) is an alternative approach and takes

measurement uncertainty into account. In this paper, a statistical analysis of an extensive data

set of pressurization measurements has been performed. Both regression techniques have been

compared for almost 7500 fan pressurization measurements of six houses in 109 different house

leak configurations. The variability in predicting pressure exponent and flow coefficient for both

WLOC and OLS regression was analyzed using probability density functions. It was found that

the Weighted Line of Organic Correlation significantly decreases the uncertainty in predicting

pressure exponent, flow coefficient, and other low-pressure air leakage metrics compared to the

Ordinary Least Square fitting. The authors highly recommend the implementation of WLOC in

current measurement standards and test equipment.

Air tightness is an important property of building envelopes. It is a key factor in determining infiltration and related wall-performance properties such as indoor air quality, maintainability and moisture balance. Air leakage in U.S. houses consumes roughly 1/3 of the HVAC energy but provides most of the ventilation used to control IAQ. The Lawrence Berkeley National Laboratory has been gathering residential air leakage data from many sources and now has a database of more than 100,000 raw measurements. This paper uses that database to develop a model for estimating air leakage as a function of climate, building age, floor area, building height, floor type, energy-efficiency and low-income designations. The model developed can be used to estimate the leakage distribution of populations of houses.

10aair leakage10aair tightness10afan pressurization10aleakage area1 aSherman, Max, H. uhttps://buildings.lbl.gov/publications/air-tightness-us-homes-model02323nas a2200253 4500008004100000245011700041210006900158300001200227490000600239520138700245653005601632653002301688653001701711653003701728653002801765653005601793100002401849700001901873700002501892700002101917700002401938700002601962856008101988 1985 eng d00aTemperature- and wind-induced air flow patterns in a staircase. Computer modelling and experimental verification0 aTemperature and windinduced air flow patterns in a staircase Com a105-1220 v83 aThe typical infiltration load for a residential building has been found to range from one-third to one-half of the total space conditioning load. However, most infiltration measurements have been made on single-family houses. Information about the role of infiltration in the energy consumption of large buildings is limited. Furthermore, the prediction of infiltration rates in high-rise buildings is a complex problem. The forces that drive this flow result from the superposition of wind pressure on the faces of the building and the stack effect across the height of the building. Infiltration models have shown the latter effect to be significant in single-family residences, particular in colder climates and, consequently, the stack effect is even greater in high-rise buildings. For this work, we performed tracer gas and fan pressurization measurements on a 30 m tall University of California dormitory in order to determine the importance of both wind and stack effect upon infiltration. Measured pressure and tracer gas distributions were compared with those from a predictive infiltration computer model for high-rise buildings. To study the influence of the air flow pattern around the building, this model uses various wind velocity profiles characteristic of urban areas and different sets of surface pressure coefficients derived from wind tunnel experiments.

10aair-infiltration ‘multi-cell’ calculation model10afan pressurization10aleakage area10athermal buoyancy and wind effect10atracer gas measurements10awind pressure data and air infiltration calculation1 aFeustel, Helmut, E.1 aZuercher, C.H.1 aDiamond, Richard, C.1 aDickinson, Bruce1 aGrimsrud, David, T.1 aLipschutz, Ronnie, D. uhttps://buildings.lbl.gov/publications/temperature-and-wind-induced-air-flow