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This article was originally published in the January/February 1994 issue of Home Energy Magazine. Some formatting inconsistencies may be evident in older archive content.
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Home Energy Magazine Online January/February 1994BLOWER DOORS
Infiltration: Just ACH50 Divided by 20?
by Alan Meier Alan Meier is executive editor of Home Energy.
This Home Energy classic, originally printed in 1986, explains a simple way to take one air infiltration measurement and determine a home's average air infiltration rate.
Many researchers have sought to develop a correlation between a onetime pressurization test and an annual infiltration rate. Translating blower door measurements into an average infiltration rate has bedeviled the retrofitter and researcher alike. The rate of air infiltration constantly varies, yet the pressurization test is typically a single measurement. Nevertheless, many researchers have sought to develop a correlation between a onetime pressurization test and an annual infiltration rate. ACH Divided by 20 In the late 1970s, a simple relation between a onetime pressurization test and an average infiltration rate grew out of experimentation at Princeton University. For a few years, the correlation remained Princeton folklore because no real research supported the relationship. In 1982, J. Kronvall and Andrew Persily compared pressurization tests to infiltration rates measured with tracergas for groups of houses in New Jersey and Sweden. They focused on pressurization tests at 50 Pascals because this pressure was already used by the Swedes and Canadians in their building standards. (This measurement is typically called ACH50.) Other countries and groups within the United States have also adopted ACH as a measure of house tightness. Persily (now at the National Institute of Science and Technology) obtained a reasonably good estimate of average infiltration rates by dividing the air change rates at 50 Pascals by 20, that is: average infiltration rate (ACH) = ACH50(1)  20 In this formula, ACH50 denotes the hourly air change rate at a pressure difference of 50 Pascals between inside and outside. Thus, for a house with 15 ACH at 50 Pascals (ACH50 = 15), one would predict an average air change rate of (15/20 = ) 0.75 ACH. This simple formula yields surprisingly reasonable average infiltration estimates, even though it ignores many details of the infiltration process. These details are described below:
Ideally, this additional information should be applied to the formula in order to get a correlation factor more accurate for that house. Unfortunately, the formula was developed from data in just a few houses in New Jersey and Sweden, and it cannot be easily adjusted to other locations and circumstances. Should a retrofitter in Texas also use ACH50/20, or is dividing by 15 more appropriate for the Texas climate and house construction types? The LBL Infiltration Model Researchers at Lawrence Berkeley Laboratory developed a model to convert a series of fan pressurization measurements into an equivalent leakage area. (See HE, Blower Doors: Infiltration Is Where the Action Is, Mar/Apr. '86, p.6. and the ASHRAE Book of Fundamentals chapter on ventilation and infiltration.) The equivalent leakage area roughly corresponds to the combined area of all the house's leaks. A second formula converts the equivalent leakage area into an average infiltration rate in air changes per hour. This formula combines the physical principles causing infiltration with a few subjective estimates of building characteristics, to create relatively robust estimates of infiltration. ASHRAE has approved the technique and describes the formulae in ASHRAE Fundamentals. The LBL infiltration model is now the most commonly accepted procedure for estimating infiltration rates. Max Sherman at LBL used this model to derive the theoretical correlation between pressure tests at 50 Pascals and annual average infiltration rates.1 His major contribution was to create a climate factor to reflect the influence of outside temperature (which determines the stack effect) and windiness. Sherman estimated the climate factor using climate data for North America and plotted it (see Figure 1). Since the factor reflects both temperature and seasonal windiness, a cold, calm location could have the same climate factor as a warm, windy location. The map also reflects summer infiltration characteristics. Note how Texas and Vermont have the same climate factors. Sherman found that the correlation factor in the revised formula could be expressed as the product of several factors: correlation factor, N = C * H * S * L C = climate factor, a function of annual temperatures and wind (see Figure 1 H = height correction factor (see Table 1) S = wind shielding correction factor (see Table 2) L = leakiness correction factor (see Table 3)
average air changes per hour = ACH50  N This formula provides a more customized ruleofthumb than the original ACH50/20 , when additional information about the house is available. An Example The application of the climate correction is best shown in an example. Suppose you are pressure testing a new, lowenergy house in Rapid City, South Dakota. It is a twostory house, on an exposed site, with no surrounding vegetation or nearby houses to protect it from the wind. 1. At 50 Pascals, you determine that the ACH50 is 14. 2. You consult Figure 1, and determine that the house has a climate factor, C, of 1417. Since Rapid City is near a higher contour line, select 17. 3. The house is two stories tall, so the appropriate height correction factor, H (from Table 1), is 0.8. 4. The house is very exposed to wind, and there are no neighboring houses or nearby trees and shrubs. The appropriate wind shielding correction factor, S (from Table 2), is 0.9. 5. The house is new, and presumably wellbuilt. The appropriate leakiness factor, L (from Table 3), is 1.4. 6. Calculate N: N = 17 * 0.8 * 0.9 * 1.4 = 17 Calculate the average annual infiltration rate: ACH = ACH50  17 = 14  17 = 0.82 The difference in this case (between dividing by 20 and 17) is not greatonly 17%but it demonstrates how the building conditions and location can affect the interpretation of pressurization tests. Sherman compared his results to those reported by Persily. Sherman noted that he obtained a correlation factor (N) of about 20 for a typical house in the New Jersey area. Thus, Sherman's theoretically derived correlation factor yields results similar to Persily's empirically derived correlation factor. The range of adjustment can be quite large. In extreme cases, the correlation factor, N, can be as small as 6, and as large as 40. In other words, the ACH50/20 rule of thumb could overestimate infiltration by a factor of two, or underestimate it by a factor of about three. This formula is still only a theory; it has not been validated with field measurements. Moreover, there is considerable controversy regarding the physical interpretation of the climate factor. For example, the formula yields a yearround average infiltration rate, rather than just for the heating season. Such a result is useful for houses with both space heating and cooling, but it may be misleading for some areas. Recommendations There is no simple way to accurately convert a single pressuretest of a building to an average infiltration rate, because many building and climatedependent factors affect true infiltration. Longterm tracer gas measurements are the only reliable way to obtain average infiltration rates. However, tracer gas measurements are impractical for retrofitters, and even most conservation researchers. A simplified rule of thumb to let the retrofitter quickly translate a pressuretest to an infiltration rate is clearly attractive. Persily and Kronvall developed a crude conversion technique, ACH50/20, that provides reasonable results. On the other hand, it was impossible to customize the relationship of ACH50/20 to local conditions. What are the components of the magic number, 20? Now Sherman has created a similar conversion factor that can be modified to reflect local building and climate conditions. This correlation factor accounts for windiness, climate, stack effect, and construction quality. Some judgement is needed to select the appropriate correction factors, but the blowerdoor user can now understand the quantitative impact of local conditions on infiltration. For example, a threestory house will have significantly more infiltration than a ranch houseeven though the pressure tests are identicaldue to a greater stack effect. (Clearly an infiltration standard should take these factors into account.) Of course, Sherman's correlation factor still cannot account for occupant behavior or perversities in the building's construction. Nor is it a substitute for tracergas measurements. Field measurements must also be conducted to validate the formula. Still, it puts a scientific foundation behind what was previously an empirically derived relationship. It is a modest step forward in the efficient and accurate use of the blower door. n Note 1. See Max Sherman, Estimation of Infiltration for Leakage and Climate Indicators, in Energy and Buildings, 10, 1987, p.81.
BLOWER DOORS
Table 1. Height Correction Factor Select the most appropriate value and insert in Equation 2. Number of stories 1 1.5 2 3 Correction factor H 1.0 0.9 0.8 0.7Table 2. Wind Shielding Correction Factor Select the most appropriate value and insert in Equation 2. Extent of shielding wellshielded normal exposed Correction factor S 1.2 1.0 0.9Table 3. Leakiness Correction Factor
Select the most appropriate value and insert in Equation 2. small cracks large holes Type of holes (tight) normal (loose) Correction factor L 1.4 1.0 0.7
LEAKAGE/INFILTRATION RATIO
Figure 1. Climate correction factor, C, for calculating average infiltration rates in North America. Note that the climate correction factor depends on both average temperatures and windiness. It also includes possible air infiltration during the cooling season. For these reasons, locations in greatly dissimilar climates, such as Texas and Vermont, can have equal factors. Select the value nearest to the house's location and insert it in Equation 2. This map is based on data from 250 weather stations.
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