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Effects of Air Leakage on Buildings' Overall Thermal Resistances Based on U.S. Climate Zones.

ABSTRACT

Air leakage causes significant energy losses in buildings and depends on local pressure gradients caused by temperature differences and wind. Airtightness requirements of the 2012 International Energy Conservation Code change by location. However, airtightness codes do not consider resistance caused by air leakage from wind-driven pressure gradients, only temperature-difference-driven pressure gradients. Codes for thermal resistance also don't reflect reductions in thermal performance caused by air leakage, excluding air leakage completely in thermal resistance calculations. Air leakage needs to be included in thermal resistance codes and wind factors need to be included in airtightness standards to better understand how air leakage affects overall thermal performance. We simulated a 40 x 25 ft (12 x 7.6 m), two-story home with three different levels of airtightness to study effective thermal resistance. We also modeled the home with code-compliant airtightness to study energy loss through the building envelope as a function of localized weather. With the results, we generated contour maps to visualize trends of increased energy loss and decreased thermal resistance of code-insulated homes across the United States. These results indicate that present code requirements do not effectively account for the impact of air leakage on thermal performance or reveal where in the United States airtightness of buildings most affects energy performance.

INTRODUCTION

Air leakage greatly impacts the amount of energy needed to heat or cool a building and is responsible for about 13% of energy used in homes. About 28% of heating loads and 16% of cooling loads in homes are because of air leakage (Huang et al. 1999). In 2010, approximately 2.26 and 0.59 quadrillion Btu (quads or 1.055 x J) were consumed by heating and cooling systems respectively because of air leakage (EIA 2013). Air leakage can strain HVAC systems and decrease indoor air quality and energy efficiency because one third to half of the air in a home comes directly from the external environment (Sherman 2009). In a study performed by Logue et al., improving airtightness in residential homes can decrease the national energy use by 0.7 quads (Logue et al. 2013). Codes are set to decrease the amount of airflow through the building's envelope thereby making the building more airtight.

To quantify how airtight a building is, a unit of measurement was established by researchers at Princeton University (Holladay 2010). This unit measures the number of times air is exchanged in a building under a 50 Pa (0.116 oz/i[n.sup.2]) pressure difference (ACH50). In 2012, the International Energy Conservation Code (IECC) changed generalized airtightness standards from 2009 to be based on climate zone (IECC 2012). According to the IECC, ACH50 must be less than or equal to five in Climate Zones 1 and 2 and less than or equal to three in Climate Zones 3 through 8. However, most homes have an ACH50 higher than the current standards (Pallin et al. 2016). Figure 1 shows a map of the climate zones in the United States.

A primary factor that influences air leakage is pressure gradients between the exterior and interior of the building. Pressure gradients that result in air leakage are a result of indoor and outdoor temperature differences and wind (LBNL 2015). Current airtightness codes use only temperature differences as the primary driving force in air leakage and do not take wind forces into consideration. Therefore, it is important to account for both a climates' wind and temperature conditions to study how air leakage affects thermal resistance, energy loss through the building envelope, and evaluate the relative importance airtightness has in different climates.

There are codes for the thermal resistance of the building envelope as well, which are described as R-values ([m.sup.2]*K/W, [f[t.sup.2]*[degrees]F/Btu]). Table 1 shows the IECC codes and standards for residential energy efficiency describing R-values (IECC 2015). Insulation used in building envelopes have code requirements dependent on climate zone and vary for different building features (e.g., walls, floors, etc.). These insulation codes exclude air leakage (convection) completely and only consider pressure gradients caused by temperature differences in determining insulation values (heat transfer by conduction).

Current code R-values only consider thermal resistance for conductive heat transfer and exclude air leakage resistance (convective heat transfer) and energy losses associated with it. However, when looking at heat transfer caused by air leakage through a building wall, convective and conductive heat transfer can be combined (Pallin et al. 2016). The thermal resistance representing heat transfer resistance through a wall caused by both conduction and convection can be referred to as an effective R-value, [R.sub.eff]. Unlike current R-values [R.sub.eff] is dependent on air pressure gradients over the wall surfaces caused by both temperature differences between the interior and exterior environments and wind conditions (both heat conduction and convection). Because [R.sub.eff] includes resistance to air leakage considering conduction and convection, the values become climate dependent. The mathematical definition of [R.sub.eff] is given in the following methods section.

In this study, the effects of air leakage on overall thermal resistance, importance of airtightness in various climates, energy losses through the building envelope, and energy pricing for electricity and natural gas are examined. Theoretical simulations used 103 locations evenly distributed across the United States.

METHODS

Home Size

The home simulated in this study was a two-story home with a volume of 450 [m.sup.3] (16,000 f[t.sup.3]), three different ACH50 values (1, 3, and 5), a square footage of 186 [m.sup.2] (2000 f[t.sup.2]), and a surface area of 410 [m.sup.2] (4400 f[t.sup.2]). We accounted for ceiling, walls, and windows when calculating the overall surface area.

Climates

Weather files from 103 different cities across the United States were used because of their varying temperature, relative humidity, and wind conditions. Figure 2 displays the chosen locations.

Weather files were collected from White Box (2016), the National Oceanic and Atmospheric Administration (NOAA 2016), and typical meteorological year (TMY3) files provided by the Energy Plus website (DOE 2016). The White Box and NOAA weather files were based on measurements performed in 2010 and contained hourly readings, of which temperatures and wind speeds were used for this study. The TMY3 weather file contains actual weather data from each month of the year representing the typical month within the past 30 years.

Simulations

Overview. To analyze the effects of air leakage on Rvalues and resulting total energy loss, simulations were conducted in MATLAB (Pallin 2016). A new effective Rvalue [R.sub.eff] was calculated, accounting for air leakage by conduction and convection, because current code recommended envelope R-values are not affected by convective air leakage. [R.sub.eff] includes resistance to convective air leakage by considering two primary climate conditions that create pressure gradients: temperature differences and wind speeds. The simulation codes took the weather file information and produced [R.sub.eff]. Using [R.sub.eff], losses in thermal resistance, increase in energy loss per unit area, and energy prices were studied. The result of investigating how [R.sub.eff] varies with climate conditions and the impacts on cost are presented though contour maps. These maps were generated to examine the importance of airtightness in various climates across the United States.

Inputs and Initial Values. R-values used in the simulation for Climate Zones 1 through 7 complied with the latest IECC 2015 code requirements (IECC 2015), which do not consider air leakage. The total R-value over the building envelope [R.sub.tot] used in the simulation considered the thermal resistances of fenestrations (windows), ceilings/roofs, and exterior walls. Equation 1 calculated [R.sub.tot] for each location.

[mathematical expression not reproducible] (1)

where A represents surface areas and U represents the fenestration U-factor. The [R.sub.tot] was then converted to thermal conductivity using Equation 2:

[K.sub.cond] = [[1]/[[R.sub.tot]]] (2)

where [K.sub.cond] (W/[m.sup.2]*K [f[t.sup.2]*h*[degrees]F/Btu]) represents overall building enclosure thermal conductivity.

In this study, two major weather conditions impacting air pressure differences over the building envelope are investigated: temperature gradients (stack/buoyancy effect) and wind loads. To estimate the contribution of stack effect on the amount of air leakage, the height to the neutral plane must be known (the height at which the internal and external pressures are the same, i.e., no pressure gradient exists). Because the simulated building is a two-story construction, a neutral plane between the first and second floor is assumed at 2.7 m (9 ft). In addition, to estimate the stack effect, indoor temperature conditions must be known. For this study, the thermostat set points were 23[degrees]C (73.4[degrees]F) during the cooling season and 20[degrees]C (68[degrees]F) during the heating season.

The relationship between volumetric airflow (CFM50) and ACH50 is shown in Equation 3. ACH50 provides the number of times the building's air is replaced per hour and CFM50 quantifies this information by including actual house volume.

CFM50 = [[ACH50 * House volume]/[60]] (3)

To determine pressure gradients induced by temperature and wind forces, the airtightness of the building envelope must be known. The airtightness of a building can be quantified using ACH50, as presented in the introduction section. A pressure exponent n and flow coefficient C also need to be determined because they characterize volumetric airflow (CFM50) at different pressures shown in Equation 4:

CFM50 = C * [p.sup.n] (4)

Knowing flow characteristics (C and n) allows one to determine airflow at different pressure differences.

ACH50 values of 1, 3, and 5 were assumed for three trials examining the impact of varying levels of air leakage on Rvalues. An ACH50 value of 5 was assumed for all other trials. The pressure exponent n of 0.65 was used for all trials, as is common in calculations considering both laminar and turbulent air flows (Urquhart and Richman 2014). The CFM50 value was divided by the surface area of the house to calculate the CFM50 per square foot (square metre) ([R.sub.a]) (f[t.sup.3]/s/f[t.sup.2] [[m.sup.3]/s/[m.sup.2]]). Equation 4 determined our flow coefficient C according to ASTM Standard E779-10 (ASTM 2010). Calculated values for CFM50, C, and [R.sub.a] can be found in Table 2.

Heating and Cooling. The outputs of the model were [R.sub.eff], energy loss through the building envelope, and energy costs associated with these energy losses. Weather file information and the previously calculated [R.sub.tot] values were used to determine [R.sub.eff] and thus total energy flow through the building envelope.

It is intuitive that heating and cooling demands vary from climate to climate. To differentiate between heating and cooling, thermostat set points were established. If the external environment was above the upper set point, the HVAC system was thought to be in cooling mode. If the external environment was below the lower set point, the HVAC system was thought to be in heating mode. For any temperature between the two set points (20[degrees]C to 23[degrees]C [68[degrees]F to 73.4[degrees]F]), the building was assumed to be unconditioned. To understand how heating and cooling affected energy loss, heating and cooling degree hours were used to quantify heating and cooling needs of the home in each location. Degree hours measure the number of degrees per hour the external temperature was above or below the set-point (Layberry 2008). HDH represents heating degree hours and CDH represents cooling degree hours. Equations 5 and 6 calculate degree hours:

HDH = [summation] [T.sub.lower] - |[T.sub.outdoor]| if [T.sub.lower] [less than or equal to] [T.sub.outdoor] (5)

CDH = [summation] [T.sub.outdoor] - |[T.sub.upper]| if [T.sub.upper] [less than or equal to] [T.sub.outdoor] (6)

where [T.sub.lower] and [T.sub.upper] represent the lower and upper thermostat set points ([degrees]C [[degrees]F]) and [T.sub.outdoor] represents the external temperature ([degrees]C [[degrees]F]).

Calculations. To calculate [R.sub.eff] and heating and cooling energy losses, pressure caused by wind speeds [P.sub.w] and temperature differences [P.sub.t] had to be determined. Temperature and wind speed pressures (Pa [psi]) were calculated using Equations 7 and 8:

[P.sub.t] = z * 3456 * [([[1]/[[T.sub.outdoor]]]) - ([[1]/[[T.sub.indoor]]])] (7)

[P.sub.w] = 0.52 * [v.sup.2] (8)

Equation 7 calculates pressure caused by temperature differences between the interior of the building and the exterior environment, where z is the neutral plane height (m [ft]) and the constant has units of kg/([m.sup.2]*[s.sup.2]) (l[b.sub.m]/[f[t.sup.2]*[s.sup.2]]) (Hagentoft 2001). Equation 8 is taken from ASCE standards for minimum design loads. The coefficient is the product of multiple correction factors for a general building in ASCE standards (ASCE 2010) represents wind velocity. The total pressure was calculated by summing [P.sub.t] and [P.sub.w].

[R.sub.eff] was calculated to consider convective air leakage effects in R-values. To calculate [R.sub.eff], heat flow because of convection was defined as follows:

[Q.sub.conv] = [R.sub.a] * [[rho].sub.a] * [C.sub.a] * ([T.sub.e] - [T.sub.i]) (9)

where

[R.sub.a] = [[C * [P.sup.n]]/[A]] (10)

where
[R.sub.a]  = ([m.sup.3]/s)/*[m.sup.2] ([f[t.sup.3]/min]/f[t.sup.2])
           hourly airflow per unit area (values shown in Table 2)
C          = [m.sup.3]/s/[P.sup.n] (f[t.sup.3]/min/ps[i.sup.n])
           airflow coefficient
P          = Pa (psi)
           air pressure
n          = unitless pressure exponent
A          = [m.sup.2] (f[t.sup.2])
           surface area of the building envelope


C, n, and [R.sub.a] values are shown in Table 2. The convective conductance for air leakage is defined as follows:

[mathematical expression not reproducible] (11)

where [[rho].sub.a] * [c.sub.a] (J/(m3*K) [Btu/(ft3*[degrees]F)]) represents the volumetric heat capacity. [R.sub.eff] can then be calculated as follows:

[R.sub.eff] = [[1]/[([K.sub.cond] + [K.sub.conv])]] (12)

[R.sub.eff] (K*[m.sup.2]/W [[degrees]F*f[t.sup.2]/Btu]) was calculated separately for heating and cooling after this initial calculation. [mathematical expression not reproducible] describes the effective R-value only for when the building was in heating mode, and [mathematical expression not reproducible] describes the effective R-value for when the building was in cooling mode.

Energy losses during heating and cooling conditions were also calculated separately. To obtain the energy loss, the difference between the initial R-values and the new [R.sub.eff] for heating and cooling in thermal resistance was found. The inverse of the loss was then multiplied by the heating and cooling degree hours ([degrees]C*h [[degrees]F*h]) to obtain the increase in energy loss Q (W*h/[m.sup.2], [0.33 Btu/f[t.sup.2]]) through the building envelope.

Examining Correlations between Climates. To understand the importance of airtightness in different climates, contour maps were used to show the effects of air leakage on the code level R-values and energy losses and prices. The percent decrease in thermal resistance was calculated by using Equation 13. Equations 14 and 15 calculate the percent increase heating and cooling load demands.

Percent decrease = 1 - [[[R.sub.eff]]/[[R.sub.tot]]] (13)

[mathematical expression not reproducible](14)

[mathematical expression not reproducible] (15)

These values are shown in the appendix. To analyze results, a comparison study was done. The study used ratios to compare the increase in energy costs and reduction in thermal performance during heating and cooling in various climates. These results help in understanding the relative importance of airtightness in climates across the United States.

RESULTS

Contour maps were generated to examine the relative importance of the following in different climates: airtightness during heating and cooling seasons, heating and cooling energy costs, and percent decrease in code level R-value with assumed ACH50 values of 1, 3, and 5. Comparative studies using ratios to signify where airtightness had high importance are used for the first four maps; a value of 1 signifies the highest values (energy loss, reduction in thermal performance, etc.) and thus greater relative importance of airtightness or impact on cost. The final four maps graph actual percentages and overall prices. All airtightness and energy pricing trials assumed an ACH50 of 5. Percent increase in energy loss was also examined during heating and cooling seasons to further study the relative importance of airtightness.

Airtightness: Cooling

A comparison study was completed to study the relative importance of airtightness during cooling by studying the energy losses for different climates. Results can be seen in Figure 3.

A comparison of the reduction in thermal performance caused by air leakage is seen in Figure 3. A value of 1 represents a higher reduction in overall thermal performance caused by air leakage and where airtightness is most important. Locations with the highest reductions were generally located in Climate Zones 2 or 3 and included all of Climate Zone 1, meaning these areas had the most energy loss. Some other areas of high airtightness importance (0.4 to 0.5 on the relative importance scale) were located in Climate Zone 5B. The smaller reductions in overall thermal performance were generally located in Climate Zones 4, 5, 6, 7, and 8. Most locations with lower reductions were in the Mid- to Northwest or New England areas.

Percent Increase in Energy Loads During Cooling Caused by Air Leakage

The percent increase in energy loads caused by air leakage can be seen for each climate in the appendix. The percentage of increase increased as the ACH50 value became greater. For ACH50 = 5, the reduction in thermal performance from air leakage varied between 40% to 70% across the United States, ACH50 = 3 reduction varied between 29% to 58%, and ACH50 = 1 reduction varied between 12% to 32%. All of these reductions in thermal performance drove the energy loads to increase.

Cooling Energy Costs

Cooling prices were based off the average state electricity prices (cents/kWh [cents/Btu]) (U.S. EIA 2016a). The results of comparing different climates' energy costs are shown in Figure 4.

Figure 4 depicts the impact on energy cost during cooling caused by air leakage. A higher scale value represents higher energy costs and greater impact of air leakage on cost. The areas with the highest cooling energy costs because of air leakage are along the eastern coast and parts of California and Texas. Most of the Midwest tends to have lower costs.

Airtightness: Heating

A comparison was done to study the relative importance of airtightness during heating in different climates by considering the energy losses in individual climates. Results can be seen in Figure 5.

Higher values on the relative importance scale represent higher importance of airtightness and reduction in overall thermal performance (or greater energy loss). Areas with higher reductions (having a relative importance value of 0.35 or higher) are generally located in Climate Zones 5 and higher, with the exception of a few locations in Climate Zone 4C in the Pacific Northwest. Areas with smaller reductions in overall thermal performance are generally located in Climate Zone 3 or lower. Climate Zone 4 still has a considerable reduction in overall thermal performance; however, it is not as significant as those in Climate Zones 5 and higher.

Percent Increase in Energy Loads During Heating Caused by Air Leakage

The percent increase in energy loads caused by air leakage can be seen for each climate in the appendix. It was found that the percentage of increase, increased as the ACH50 value became greater. Locations located in the northern and Midwest regions of the country had the largest percentages. For ACH50 = 5, the reduction in thermal performance from air leakage varied between 44% to 71% across the United States, ACH50 = 3 reduction varied between 32% to 60%, and ACH50 = 1 reductionvariedbetween14%to34%. All of these reductions in thermal performance drove the energy loads to increase.

Heating Energy Costs

Prices for heating were based off state average prices for natural gas ($/1000f[t.sup.3] [$/28.3[m.sup.3]]) (EIA2016b). The results of the comparison study of different climates' energy costs are shown in Figure 6.

The Pacific Northwest and New England have the highest overall heating energy costs. The Midwest also has higher heating energy costs. Most of the Southeast and Southwest have low heating energy costs. This map has similar trends of higher values in the same areas as the airtightness heating map.

Percent Reduction in Thermal Resistance (ACH50 = 5)

Initial R-values only took into consideration thermal conductivity, while the new included effects of air leakage caused by thermal convection along with thermal conduction. Figure 7 shows the percent reduction in R-value when convective air leakage effects are taken into account at ACH50 = 5. The darker the shading, the higher the percent decrease in overall thermal resistance. White in the maps does not signify zero reduction, but rather very low reduction in values.

According to IECC 2012 code R402.4, Climate Zones 1 and 2 must have an ACH50 less than or equal to 5 and Climate Zones 3 through 8 must have an ACH50 less than or equal to 3 (IECC 2012). Because the ACH50 requirements change between Climate Zones 2 and 3, a black line was drawn on the map to separate these two climate zones. The black line is shown on the following four maps, Figures 7 through 10.

Percent decreases of at least 30% are shown through many parts of the country. Coastal areas tend to have larger decreases in thermal resistance, and areas that previously have been shown to have significantly larger reductions in overall thermal performance in heating and cooling airtightness maps also tend to have larger decreases in resistance values. Climates around the Rocky Mountains and down into the Southwest had smaller decreases in thermal resistance than most areas in the United States.

Percent Reduction in Thermal Resistance (ACH50 = 3)

Figure 8 shows the percent reduction in thermal resistance when convective air leakage effects are taken into account for ACH50 = 3. The black line shows the separation between Climate Zones 2 and 3.

It is shown that with fewer air changes per hour, the amount of decrease in thermal resistance becomes smaller with most of the country falling in a range of 20% to 25% decrease, a 5% to 10% change from the previous 30% decrease. Areas with larger decreases (35% to 40%), again, are located primarily along coastlines and in the Midwest region. Areas that previously had larger reductions in overall thermal performance also have a larger reduction in thermal resistance.

Percent Reduction in Thermal Resistance (ACH50 = 1)

Figure 9 shows the percent reduction in thermal resistance when convective air leakage effects are taken into account for ACH50 = 1. The black line shows the separation between Climate Zones 2 and 3.

It is apparent that the decrease in thermal resistance, in general, is relatively small, staying somewhere in the range of a 5% to 10% decrease. Areas with larger decreases in thermal resistance (25% to 35%) seem to be predominantly located, again, on coastlines or desert regions.

Overall Price Differences because of Air Leakage

We lastly examined the total impact of air leakage on energy costs. An airtightness of ACH50 = 5 was assumed. Figure 10 presents the results.

The darker shading represents higher increase in price from the code-level R-value to [R.sub.eff]. Prices increased across the country ($100 to $700 a year) for energy when air leakage effects are included in energy considerations. It is apparent that most of the country shows large increases in energy price, but the two areas with the highest increases are the Pacific Northwest and New England. Areas along the coastline show the most significant increases in energy prices.

CONCLUSION

Pressure gradients drive air leakage through the building envelope, but current airtightness codes only consider temperature differences in driving these gradients. Wind conditions also cause pressure gradients, but the effects of wind are not considered in airtightness codes. Also, thermal resistance codes (R-values) do not consider air leakage and the energy losses associated with it, but only consider conductive heat transfer. To determine the effect of convective and conductive energy losses together through the building envelope, an effective thermal resistance [R.sup.eff] can be presented.

[R.sup.eff] was calculated for each climate to account for air pressure differences over the building envelope caused by temperature gradients and wind. It should be noted that our weather files were from 2010 with the exception of 14 climates that are TMY3 files. We also only simulated one building type for consistency of results. Results demonstrate that air leakage has great effect on thermal performance, decreasing all climates' thermal resistance values by at least 35% when assuming a common ACH50 value of 5 and increasing energy losses through buildings. Results also suggest that there is a directly proportional relationship between the decrease in thermal resistance (or decrease in overall thermal performance) and an increase in ACH50. There was also a trend of larger decreases in thermal resistance being located primarily along the coastlines, demonstrating the varying levels of importance of airtightness.

Heating and cooling prices are location dependent as well, not only because of individual state prices but also because of the location-dependent percentages of overall thermal performance reduction. Overall energy prices considered the difference between using current code-level R-values versus using [R.sup.eff]. By using [R.sup.eff] we could see the increases in price because of thermal resistance degradation from taking into account air leakage effects. Maps showed that overall energy prices are also location-dependent, demonstrating higher increases in energy price along coastlines and locations further north. Thus,itcanbeconcludedthatclimatedoeshave an effect on overall thermal resistance and the relative importance of airtightness in buildings.

Current codes for airtightness do not necessarily reflect where in the United States airtightness matters most, and codes for thermal resistance do not account for resistance against air leakage. Considering air leakage in codes ([R.sup.eff], wind factors, etc.) could possibly improve them and save energy. The new values that include resistance to air leakage give a better indication of thermal performance of a home in various climates and how the importance of airtightness can be localized.

ACKNOWLEDGMENTS

This research was performed at Oak Ridge National Laboratory, managed by UT-Battelle for the U.S. Department of Energy.

REFERENCES

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APPENDIX
Percent Increases in Heating, Cooling, and Overall Load Demands

Location             CZ   CH50     Overall         Heating
                                 5    3    1    5    3    1

Miami, FL            1          48%  36%  16%  44%  32%  14%
Houston, TX          2          52%  40%  18%  54%  41%  19%
Phoenix, AZ          2          49%  36%  16%  45%  33%  14%
Tallahassee, FL      2          47%  35%  15%  48%  35%  15%
Orlando, FL          2          49%  36%  16%  49%  36%  16%
Savannah, GA         2          47%  35%  15%  49%  37%  16%
New Orleans, LA      2          50%  37%  17%  53%  40%  18%
Mobile, AL           2          50%  37%  16%  52%  40%  18%
Fort Meyers, FL      2          46%  34%  15%  45%  33%  14%
Daytona Beach, FL    2          47%  35%  15%  47%  35%  15%
Gainesville, FL      2          44%  32%  14%  45%  33%  14%
West Palm Beach, FL  2          54%  41%  19%  52%  40%  18%
Tampa, FL            2          46%  34%  14%  46%  34%  15%
Lake Charles, LA     2          50%  37%  17%  53%  40%  18%
Pensacola, FL        2          50%  38%  17%  52%  40%  18%
Corpus Christi, TX   2          60%  48%  23%  61%  48%  24%
Brunswick, GA        2          50%  37%  17%  52%  39%  18%
Dallas, TX           3          63%  51%  26%  64%  51%  26%
Las Vegas, NV        3          61%  48%  24%  59%  46%  22%
San Francisco, CA    3          63%  50%  25%  63%  50%  25%
Elizabeth City, NC   3          62%  50%  25%  63%  50%  25%
Myrtle Beach, SC     3          59%  47%  22%  60%  47%  23%
Jackson, MS          3          49%  37%  16%  52%  40%  18%
Charleston, SC       3          54%  41%  19%  56%  43%  20%
Birmingham, AL       3          50%  37%  17%  53%  40%  18%
Little Rock, AR      3          52%  40%  18%  55%  43%  20%
Atlanta, GA          3          56%  43%  20%  59%  46%  22%
Los Angeles, CA      3          52%  39%  18%  51%  39%  17%
Cape Hatteras, NC    3          60%  47%  23%  61%  49%  24%
Oklahoma City, OK    3          64%  51%  26%  66%  54%  28%
El Paso, TX          3          55%  42%  20%  56%  43%  20%
Memphis, TN          3          56%  43%  20%  58%  46%  22%
Bakersfield, CA      3          50%  37%  16%  48%  36%  16%
Sacramento, CA       3          52%  39%  18%  51%  38%  17%
Wilmington, NC       3          54%  41%  19%  56%  43%  20%
Camarillo, CA        3          52%  39%  18%  51%  38%  17%
Camp Pendleton, CA   3          48%  35%  15%  46%  34%  15%
Monterey, CA         3          52%  40%  18%  52%  40%  18%
San Luis, CA         3          56%  43%  20%  55%  42%  20%
New York, NY         4          70%  59%  32%  72%  60%  34%
Albuquerque, NM      4          63%  51%  26%  63%  51%  26%
Seattle, WA          4          61%  49%  24%  62%  49%  24%
Astoria, OR          4          64%  52%  27%  64%  52%  27%
Bellingham, WA       4          63%  51%  25%  63%  51%  26%
Bremerton, WA        4          59%  46%  22%  59%  47%  22%
Hoquiam, WA          4          65%  53%  27%  65%  53%  27%
Philadelphia, PA     4          64%  51%  26%  65%  53%  27%
Columbia, MO         4          65%  52%  27%  67%  55%  29%
Knoxville, TN        4          54%  41%  19%  57%  44%  21%
Wilmington, DE       4          62%  50%  25%  64%  52%  26%
Roanoke, VA          4          57%  44%  21%  59%  46%  22%
Eugene, OR           4          59%  47%  23%  59%  46%  22%
Norfolk, VA          4          64%  51%  26%  66%  54%  28%
Wichita, KS          4          69%  57%  30%  70%  58%  32%
Charleston, WV       4          52%  39%  18%  54%  41%  19%
Lexington, KY        4          60%  48%  23%  62%  50%  25%
Amarillo, TX         4          71%  59%  33%  71%  60%  33%
Portland, OR         4          60%  47%  23%  60%  47%  23%
Richmond, VA         4          59%  47%  23%  61%  49%  24%
Washington, D.C.     4          63%  50%  25%  65%  52%  27%
Atlantic City, NJ    4          63%  51%  25%  64%  52%  27%
Olympia, WA          4          57%  45%  21%  57%  45%  21%
Arcata, CA           4          59%  47%  23%  59%  47%  23%
Crescent City, CA    4          65%  52%  27%  65%  52%  27%
Santa Rosa, CA       4          55%  42%  20%  54%  42%  19%
Salisbury, MD        4          61%  49%  24%  62%  50%  25%
Chicago, IL          5          66%  54%  28%  67%  55%  29%
Salt Lake City, UT   5          64%  52%  27%  64%  52%  26%
Reno, NV             5          59%  47%  23%  59%  46%  22%
Lincoln, NE          5          66%  54%  28%  67%  55%  29%
Aurora, CO           5          61%  49%  24%  62%  49%  25%
Indianapolis, IN     5          65%  52%  27%  66%  54%  28%
Des Moines, IA       5          66%  54%  28%  68%  56%  30%
Boise, ID            5          62%  49%  24%  62%  49%  25%
Syracuse, NY         5          63%  51%  26%  64%  52%  26%
Springfield, IL      5          65%  53%  27%  67%  55%  29%
Columbus, OH         5          60%  48%  23%  62%  49%  25%
Goodland, KS         5          70%  59%  32%  71%  59%  33%
Boston, MA           5          69%  57%  31%  70%  58%  32%
Scottsbluff, NE      5          67%  55%  29%  67%  55%  29%
Lewiston, ID         5          55%  43%  20%  56%  43%  20%
Erie, PA             5          66%  54%  28%  67%  55%  29%
Providence, RI       5          65%  53%  27%  66%  53%  28%
Fort Wayne, IN       5          65%  52%  27%  66%  53%  28%
Hartford, CT         5          62%  49%  24%  62%  50%  25%
Pendleton, OR        5          61%  48%  24%  61%  49%  24%
Yakima, WA           5          59%  46%  22%  60%  47%  23%
Flagstaff, AZ        5          60%  47%  23%  60%  47%  23%
Minneapolis, MN      6          67%  55%  29%  68%  56%  30%
Helena, MT           6          65%  53%  27%  65%  53%  27%
Bismarck, ND         6          69%  57%  31%  69%  57%  31%
Burlington, VT       6          66%  54%  28%  66%  54%  28%
Alpena, MI           6          65%  53%  27%  66%  53%  28%
Lander Hunt, WY      6          62%  50%  25%  63%  50%  25%
Madison, WI          6          66%  54%  28%  67%  54%  28%
Rapid City, SD       6          70%  59%  32%  70%  59%  32%
Portland, ME         6          65%  53%  27%  65%  53%  27%
Glasgow, MT          6          70%  59%  32%  71%  59%  33%
Bar Harbor, ME       6          66%  54%  28%  66%  54%  28%
Anchorage, AK        7          66%  53%  28%  66%  53%  28%
Grand Forks, ND      7          71%  60%  33%  71%  60%  33%
Duluth, MN           7          70%  58%  32%  70%  59%  32%
Fairbanks, AK        8          65%  53%  27%  65%  53%  27%

Location                Cooling
                      5    3    1

Miami, FL            49%  37%  16%
Houston, TX          51%  38%  17%
Phoenix, AZ          51%  39%  17%
Tallahassee, FL      47%  34%  15%
Orlando, FL          49%  36%  16%
Savannah, GA         45%  33%  14%
New Orleans, LA      46%  34%  15%
Mobile, AL           46%  33%  14%
Fort Meyers, FL      47%  35%  15%
Daytona Beach, FL    47%  34%  15%
Gainesville, FL      43%  31%  13%
West Palm Beach, FL  54%  41%  19%
Tampa, FL            46%  33%  14%
Lake Charles, LA     46%  34%  14%
Pensacola, FL        47%  35%  15%
Corpus Christi, TX   60%  47%  23%
Brunswick, GA        47%  35%  15%
Dallas, TX           63%  50%  25%
Las Vegas, NV        63%  51%  26%
San Francisco, CA    65%  52%  27%
Elizabeth City, NC   61%  49%  24%
Myrtle Beach, SC     59%  46%  22%
Jackson, MS          43%  32%  13%
Charleston, SC       52%  39%  18%
Birmingham, AL       44%  32%  14%
Little Rock, AR      47%  34%  15%
Atlanta, GA          51%  38%  17%
Los Angeles, CA      63%  50%  25%
Cape Hatteras, NC    56%  43%  20%
Oklahoma City, OK    59%  47%  23%
El Paso, TX          54%  41%  19%
Memphis, TN          50%  38%  17%
Bakersfield, CA      52%  39%  18%
Sacramento, CA       56%  43%  20%
Wilmington, NC       51%  38%  17%
Camarillo, CA        59%  46%  22%
Camp Pendleton, CA   55%  42%  20%
Monterey, CA         48%  35%  15%
San Luis, CA         65%  52%  27%
New York, NY         66%  53%  28%
Albuquerque, NM      63%  51%  25%
Seattle, WA          59%  46%  22%
Astoria, OR          64%  52%  27%
Bellingham, WA       53%  41%  19%
Bremerton, WA        58%  46%  22%
Hoquiam, WA          61%  48%  24%
Philadelphia, PA     57%  45%  21%
Columbia, MO         56%  43%  20%
Knoxville, TN        47%  34%  15%
Wilmington, DE       55%  43%  20%
Roanoke, VA          49%  36%  16%
Eugene, OR           63%  50%  25%
Norfolk, VA          59%  46%  22%
Wichita, KS          66%  54%  28%
Charleston, WV       41%  29%  12%
Lexington, KY        51%  38%  17%
Amarillo, TX         70%  58%  32%
Portland, OR         59%  47%  23%
Richmond, VA         54%  41%  19%
Washington, D.C.     57%  44%  21%
Atlantic City, NJ    56%  44%  20%
Olympia, WA          54%  41%  19%
Arcata, CA           55%  43%  20%
Crescent City, CA     0%   0%   0%
Santa Rosa, CA       61%  48%  24%
Salisbury, MD        58%  45%  22%
Chicago, IL          62%  49%  24%
Salt Lake City, UT   65%  53%  27%
Reno, NV             63%  50%  25%
Lincoln, NE          62%  50%  25%
Aurora, CO           55%  42%  19%
Indianapolis, IN     57%  44%  21%
Des Moines, IA       59%  46%  22%
Boise, ID            59%  47%  23%
Syracuse, NY         56%  44%  21%
Springfield, IL      55%  43%  20%
Columbus, OH         53%  40%  18%
Goodland, KS         68%  56%  29%
Boston, MA           63%  51%  26%
Scottsbluff, NE      63%  51%  26%
Lewiston, ID         51%  38%  17%
Erie, PA             58%  45%  21%
Providence, RI       61%  49%  24%
Fort Wayne, IN       57%  44%  21%
Hartford, CT         55%  42%  20%
Pendleton, OR        60%  47%  23%
Yakima, WA           56%  43%  20%
Flagstaff, AZ        57%  44%  21%
Minneapolis, MN      62%  49%  25%
Helena, MT           61%  49%  24%
Bismarck, ND         66%  54%  28%
Burlington, VT       59%  46%  22%
Alpena, MI           60%  48%  23%
Lander Hunt, WY      60%  47%  23%
Madison, WI          59%  46%  22%
Rapid City, SD       68%  56%  30%
Portland, ME         61%  48%  24%
Glasgow, MT          67%  55%  29%
Bar Harbor, ME       62%  50%  25%
Anchorage, AK        66%  54%  28%
Grand Forks, ND      65%  53%  27%
Duluth, MN           63%  51%  25%
Fairbanks, AK        53%  40%  18%


Simon Pallin, PhD

Associate Member ASHRAE

Phillip Boudereaux

Michaela Stockdale

Elizabeth Beuchler

Simon Pallin is a building envelope researcher and Phillip Boudreaux is a whole building and community integration researcher on the Research and Development Staff at the Energy Science and Transportation Division of Oak Ridge National Laboratory, Oak Ridge, TN. Michaela Stockdale is a student in mechanical engineering at Tennessee Technological University, Cookeville, TN. Elizabeth Buechler is a student in mechanical engineering at Tufts University, Medford, MA.
Table 1. R-Values for Different Building Envelope Types and Climate
Zones (IECC 2015)

Climate Zone     Fenestration U-Factor,  Ceiling R-Value,
                 W/K*[m.sup.2]           K*[m.sup.2]/W
                 (Btu/h*f[t.sup.2]*      (h*f[t.sup.2]*
                 [degrees]F)             [degrees]F/Btu)

1                    NR                  5.3 (30)
2                2.3 (0.4)               6.7 (38)
3                2.0 (0.35)              6.7 (38)
4 except Marine  2.0 (0.35)              8.6 (49)
5 and Marine 4   1.8 (0.32)              8.6 (49)
6                1.8 (0.32)              8.6 (49)
7 and 8          1.8 (0.32)              8.6 (49)

Climate Zone     Frame Wall R-Value, K*[m.sup.2]/W
                 (h*f[t.sup.2]*[degrees]F/Btu)



1                2.3 (13)
2                2.3 (13)
3                3.5 or 2.3 + 0.9 h (20 or 13 + 5 h)
4 except Marine  3.5 or 2.3 + 0.9 h (20 or 13 + 5 h)
5 and Marine 4   3.5 or 2.3 + 0.9 h (20 or 13 + 5 h)
6                3.5 + 0.9 or 2.3 + 1.8 h (20 + 5 or 13 + 10 h)
7 and 8          3.5 + 0.9 or 2.3 + 1.8 h (20 + 5 or 13 + 10 h)

Table 2. Calculated Values for CFM50, C, and [R.sub.a]

ACH50,               CFM50,            C,
ach (air changes/s)  [m.sup.3]/s       [m.sup.3]/s/P[a.sup.n]
                     (f[t.sup.3]/min)  (f[t.sup.3]/min/P[a.sup.n])

1                    0.13 (260)        2.3x10-6 (0.0048)
3                    0.38 (800)        6.8x10-6 (0.014)
5                    0.63 (1300)       1.1x10-5 (0.024)

ACH50,               [-R.sub.]
ach (air changes/s)  ([m.sup.3]/s)/[m.sup.2]
                     ([f[t.sup.3]/min]/[f[t.sup.2]])

1                    0.0010 (0.061)
3                    0.0031 (0.18)
5                    0.0052 (0.30)
COPYRIGHT 2017 American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc. (ASHRAE)
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2017 Gale, Cengage Learning. All rights reserved.

Article Details
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Author:Pallin, Simon; Boudereaux, Phillip; Stockdale, Michaela; Beuchler, Elizabeth
Publication:ASHRAE Transactions
Article Type:Report
Geographic Code:1USA
Date:Jul 1, 2017
Words:6771
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