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THE EFFECT OF TEMPERATURE ON INSULATION PERFORMANCE: CONSIDERATIONS FOR OPTIMIZING WALL AND ROOF DESIGNS

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Recent field and laboratory research into the real world performance of insulation materials in roofs and walls has shown that the industry's reliance on R-value at a standard temperature does not always provide the whole picture. Insulation properties vary significantly with cold to hot temperatures, meaning that heat loss or gain into a building is not always as predicted using standard calculation techniques. This is a consideration for all insulation types, in particular those used in roofing or continuous exterior insulation applications where they are exposed to more extreme cold or hot temperatures. This paper will present measurements from field monitoring studies, which identify and demonstrate how insulated roofs and walls exhibit thermal performance that is different than assumed by designers. This is important because of peak energy demand and annual heating and cooling costs as well as comfort and durability considerations. Laboratory testing results are also presented to demonstrate and explain this phenomena. New testing methods have been developed to quantify this temperature dependency. Temperature dependent R-value curves will be presented for all common building insulation materials. Finally, computer simulations were prepared using the updated insulation properties. These were calibrated with the field data and extended to demonstrate the impact that these insulation properties have on the actual energy use, temperature profiles, moisture risk, and thermal comfort implications in buildings. The computer simulations allow us to explore possible solutions for the building industry including optimizing the design of roof and wall assemblies in different climate zones.
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THE EFFECT OF TEMPERATURE ON INSULATION PERFORMANCE:
CONSIDERATIONS FOR OPTIMIZING WALL AND ROOF DESIGNS
by
Chris Schumacher, Lorne Ricketts, Graham Finch, and John Straube
31st RCI International Convention and Trade Show, Orlando
ABSTRACT
Recent field and laboratory research into the real world performance of insulation
materials in roofs and walls has shown that the industry’s reliance on R-value at a
standard temperature does not always provide the whole picture. Insulation properties
vary significantly with cold to hot temperatures, meaning that heat loss or gain into a
building is not always as predicted using standard calculation techniques. This is a
consideration for all insulation types, in particular those used in roofing or continuous
exterior insulation applications where they are exposed to more extreme cold or hot
temperatures.
This paper will present measurements from field monitoring studies, which identify
and demonstrate how insulated roofs and walls exhibit thermal performance that is
different than assumed by designers. This is important because of peak energy
demand and annual heating and cooling costs as well as comfort and durability
considerations.
Laboratory testing results are also presented to demonstrate and explain this
phenomena. New testing methods have been developed to quantify this temperature
dependency. Temperature dependent R-value curves will be presented for all
common building insulation materials.
Finally, computer simulations were prepared using the updated insulation properties.
These were calibrated with the field data and extended to demonstrate the impact that
these insulation properties have on the actual energy use, temperature profiles,
moisture risk, and thermal comfort implications in buildings. The computer
simulations allow us to explore possible solutions for the building industry including
optimizing the design of roof and wall assemblies in different climate zones. !
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INTRODUCTION
In North America the thermal performance of building materials is most commonly
reported in terms of R-value and most insulation materials have ‘label R-values’
stamped on them (or at least displayed in large print on the packaging). R-value is a
measure of the thermal resistance of a material it tells how effectively a layer of
material limits heat flow (for a given thickness).
Many credit Everett Shuman with proposing R-value as an easy-to-compare,
repeatable measure of insulation performance. Shuman was the director of Penn
State’s Institute for Building Research through the 1960s. He may not have been the
first to introduce the concept of thermal resistance but he actively promoted the
concept on the basis of its simplicity (Moe, 2014). Prior to the adoption of R-value,
thermal performance was expressed in terms of conductance or the ability for
materials to conduct heat. Materials provide better performance when they have lower
thermal conductance, but industry decision-makers felt that consumers would be
confused by the concept that “smaller is better.” When thermal performance is
expressed in terms of R-value or thermal resistance, higher numbers represent better
performance.
The R-value went on to become the de facto metric across North America, familiar to
both consumers and professionals. It has helped many designers and consumers make
more energy-efficient choices, but its importance in influencing purchase decisions
has also led to some unscrupulous marketing claims. In the aftermath of the 1970s
energy crisis1 in the United States, fraudulent R-value claims became so widespread
the United States Congress passed a consumer-protection law in response, the
“Federal R-Value Rule” (16 Code of Federal Regulations [CFR] Part 460, “Trade
Regulation Rule Concerning the Labeling and Advertising of Home Insulation”).
MEASUREMENT OF LABEL R-VALUES
Under this rule, claims about residential insulation must be based on specific ASTM
procedures. The most commonly used are ASTM C177, Standard Test Method for
Steady-state Heat Flux Measurements and Thermal Transmission Properties by
Means of the Guarded-hot-plate Apparatus, and ASTM C518, Standard Test Method
for Steady-state Thermal Transmission Properties by Means of the Heat Flow Meter
Apparatus. Tests can be quickly completed using commercially available machines
and small easy-to-handle samples—typically between 12 x 12 in (305 x 305 mm) and
24 x 24 in (609 x 609 mm). Samples are placed in direct contact with a pair of air-
impermeable hot and cold plates in the machine. The rule requires R-value tests be
conducted at a mean temperature of 24 °C (75 °F) and a temperature differential of
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!
1!For!more!information!about!the!1970s!energy!crisis,!its!causes!and!effects,!the!reader!is!directed!to!
en.wikipedia.org/wiki/1970s_energy_crisis!
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27.8 °C (50 °F). For reasons of technical ease, this means insulation is usually tested
with the cold side at approximately 10 °C (50 °F), and the warm side at around 38 °C
(100 °F).2 In other words, the label R-value typically only provides a metric of a
material’s thermal performance under one standard test condition.
INDUSTRY USE OF LABEL R-VALUES
Label R-values are used by designers, contractors, code officials, etc. to:
1. Verify code compliance
2. Assess energy performance
3. Assess durability / moisture performance3
Some codes simply require insulation materials meet a specific label R-value;
however, codes are moving towards requiring assemblies with specific effective R-
values that account for thermal bridging through penetrating slabs, roof and wall
framing; primary, secondary, and cladding-related structural elements; and, in some
cases, even through fasteners. Label R-values are used in all code compliance
applications but this does not accurately reflect in-service performance.
Label R-values might provide a good starting point for assessing energy performance
and durability / moisture performance; however, as this paper illustrates, they may not
result in accurate predictions of performance. Thermal bridging is only one factor
that influences in-service performance of building assemblies. Aging, thermal mass,
moisture impacts, and temperature dependence are but some of the other factors that
explain why label R-values do not adequately reflect in-service performance of
building assemblies and materials. Where appropriate, aging, or long term thermal
resistance (LTTR), can be accounted for using methods described in ASTM C1303
and CAN/ULC S770-09. Codes and practices are established to prevent insulation
materials from accumulating moisture at levels that have a significant impact on
thermal performance. Researchers at Oak Ridge National Lab evaluated the benefit
of thermal mass across a range of different climates and demonstrated opportunity for
energy savings (Kosny et. al. 2001). This paper focuses on the role of temperature
dependence: that is the change in an insulating material’s apparent thermal resistance
(or conductivity) with change in temperature (i.e. the mean temperature which is
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2!The!actual!language!of!the!rule!permits!test!temperature!differentials!of!27.8!C!±!5.6!C!(50!F!±!10!F)!
for!cold-side!temperatures!of!7.2!to!12.7!C!(45!to!55!F)!and!hot!side!temperatures!of!35!to!40!C!(95!to!
105!F).!
3!Designers!use!the!label! R-values!of!insulation! installed!between!framing! members! (i.e.!in!the! stud!
spaces)!and!as! continuous! insulation!on!the! outside! of!framing!(e.g.!exterior! insulation)!to!estimate!
condensing! plane! temperatures! and! evaluate! the! potential! for! moisture! accumulation! (due! to! air!
leakage!and!vapour!diffusion)!and!problems!in!building!enclosure!assemblies!!
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defined as the average of the temperatures on hot and cold sides of the layer of
insulation material).
The potential issues are demonstrated through comparisons between predicted
performance and field-measured performance of roof and wall assemblies.
PREDICTED VS MEASURED FIELD PERFORMANCE OF LOW-SLOPE ROOFS
A recent study of conventional roof assemblies in the Lower Mainland of British
Columbia, a Zone 4 climate, assessed the in-service thermal performance different
assemblies installed on the same building (Rickets et. al. 2014). For comparison, two
different insulation arrangements (polyisocyanurate [PIC] only, and stone wool [SW]
only) and three different roof membrane colours (white, grey, and black) were
investigated, for a total of 6 different roof assemblies as shown in Figure 1. The two
insulation combinations were designed so as to have similar label R-values (R-21.0
and R-21.9 for the PIC and SW arrangements respectively) to allow for direct
comparison of their in-service performance. An image of the test roof area is provided
in Figure 2 (Finch et. al. 2014).
Figure 1 PIC only roof assembly (left) and stone wool only roof assembly (right)
included in the study
!
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Figure 2 Photo of test roof area showing three different roof membrane colours:
black, white and grey
To date, this field study has been running for approximately 3 years with hourly
monitoring of performance parameters including heat flux, temperatures, and relative
humidity levels within the assemblies. Figure 3 and Figure 4 show the theoretical heat
flux through the roof assemblies calculated using ambient air temperature, interior
temperature, and the label R-values as compared to the measured heat flux.
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Figure 3 Chart comparing theoretical calculated heat flux and measured heat flux
through the average of the PIC and SW roof assemblies in the study for the year of
2014
Figure 4 Chart comparing theoretical calculated heat flux and measured heat flux
through the average of the black, grey, and white roof assemblies in the study for
the year of 2014
0
100
200
300
400
500
600
-200
-150
-100
-50
0
50
100
150
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
Degree%Days%[°C·days]
Daily%Energy%Transfer%[W·hr/m²%per%day]
PIC SW Theoretical HeatingGDegreeGDaysG(18°C)
Outward HeatG
Flow
Inward HeatG
Flow
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Degree%Days%[°C·days]
Daily%Energy%Transfer%[W·hr/m²%per%day]
?@A8- B0-3 C5*:D E@-<0-8A:*5 F-*8A+6G>-60--G>*3HGI"JKLM
948N*0O F-*8G
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P+N*0O F-*8G
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Figure 3 and Figure 4 clearly indicate that theoretically calculated heat flux through
the roof assemblies is substantially different than that measured in-service. This
difference is a clear example of how label R-values do not account for all aspects of
heat flow through an assembly even at locations where there are no thermal bridges or
other discontinuities in the insulation (i.e. clearwall locations). Incorrect accounting
of assembly thermal performance in design calculations has real world implications
for building energy consumption, thermal comfort, and moisture risk. Energy
modelling has shown that the heating and cooling energy consumption for a
commercial retail building can be under predicted by up to 15% when not accounting
for temperature dependent thermal conductivities and roof colour (Finch et al, 2014).
PREDICTED VS MEASURE FIELD PERFORMANCE OF EXTEIOR-INSULATED
WALL ASSEMBLIES
Another recent study assessed the thermal and moisture performance of exterior-
insulated wall assemblies on the north- and south-facing orientations of a test hut in
Waterloo Ontario, a Zone 5 / 6 climate (Straube, 2015). On each orientation four base
wall assemblies (each 4 x 8 ft.) were constructed using 1/2 in. GWB (gypsum wall
board) on a 2 x 6 wood-frame with fiber glass batt insulation (label R-value of R-22),
7/16 in. OSB sheathing, a spun-bonded polyolefin WRB (water resistive barrier), a ¾
in. drained and ventilated air space, and clad with fiber cement clapboard siding.
North and South datum walls were designated and completed without any exterior
insulation. A 6 mil polyethylene vapour retarder was installed, in accordance with
Canadian Building Code requirements, on the inside of the stud frame, as shown in
Figure 5. The remaining six walls (three North and three South) were completed
without interior vapour retarders, but with exterior insulation installed between the
WRB and the air space. Three types of exterior insulation were investigated (3 in
Stone Wool, 2.5 in XPS (extruded polystyrene), and 2 in PIC). In each case the
thickness of the exterior insulation was specified to achieve a label R-value of R-12.
Figure 6 shows the exterior-insulated and datum test wall assemblies prior to
installation of the fiber cement clapboard siding.
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Figure 5 Datum test wall assembly
Figure 6 Exterior-insulated (3 on left) and Datum (1 at middle) test wall
assemblies before siding
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The test wall assemblies were monitored for more than 2 years. Temperature, wood
moisture content, and relative humidity were measured at key points. The monitoring
facilitated an assessment of the moisture sensitivity of the different systems, under
normal operating conditions, and their resilience when subjected to simulated rain
leaks (via injection of water at the sheathing layer) or imposed air leakage (via a
controlled flow rate from the interior).
In cold climates continuous exterior insulation may be applied over structural
sheathing (e.g. OSB) to increase sheathing temperatures, reducing the potential for air
leakage condensation and moisture accumulation in the sheathing. Figure 7 plots the
temperature measured at the indoor side of the OSB sheathing (i.e. the condensing
plane) of the four north-facing test walls over the first 10 days of 2014. As expected,
the sheathing temperatures track the outdoor temperature and the datum wall (without
exterior insulation) exhibits the lowest temperatures. The other three test walls exhibit
higher sheathing temperatures, owing to the exterior insulation.
Figure 7 Temperature measured at inside of OSB sheathing over first 10 days of
2014
Four snapshots (indicated by the dashed rectangular regions) were identified for
further analysis. Figure 7 summarizes the calculated sheathing surface temperatures
(based on Label R-value) and compares these to the measured temperatures. It is
reasonable to expect small differences between the calculated and measured sheathing
temperatures for the Datum wall because there is little insulation outside of the OSB
so changes in insulation or sheathing R-value have little impact on the predicted
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surface temperature. However, the other three wall assemblies have roughly 1/3 of the
total insulation on the exterior of the OSB sheathing and, for these assemblies, there
is more significant difference between the calculated (based on label R-values) and
measured temperatures.!
Table 1 Comparison between Predicted vs Measured Sheathing Temperature (using
Label R-values)
Datum
3"
SW
2.5"
XPS
2"
PIC
10
9
5
3
3
3
3
68
68
68
68
68
68
68
35.6
11.3
25.7
-9.4
-9.4
-9.4
-9.4
32.4
56.7
42.3
77.4
77.4
77.4
77.4
23.2
23.2
23.2
23.2
23.2
23.2
23.2
2.1
2.1
2.1
2.1
14.1
14.6
15.1
25.3
25.3
25.3
25.3
37.3
37.8
38.3
0.08
0.08
0.08
0.08
0.38
0.39
0.39
38.3
16.1
29.3
-2.9
19.9
20.6
21.2
37.8
16.0
29.3
-2.6
23.4
21.6
16.7
-0.6
-0.1
0.0
0.3
3.4
1.0
-4.5
BETTER R-VALUE MEASUREMENT AND DOCUMENTATION
The predicted durability and energy performance of insulations might be improved by
moving from a single label R-value (determined at mean temperature 24 C or 75 F) to
a table of R-values determined over a range of mean temperatures. NRCA
recommends the use of two R-values for PIC roof insulation: R-5 / in. for heating
conditions and R-5.6 / in. for cooing conditions (Graham 2015). However, even
further breakdown (i.e. R-values at more mean temperatures) may be justified. ASTM
C1058, Standard Practice for Selecting Temperatures for Evaluating and Reporting
Thermal Properties of Thermal Insulation, suggests six mean temperatures for
measuring and documenting the thermal performance of insulation materials intended
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for building enclosure applications. The suggested mean temperatures and associated
hot and cold side temperatures are summarized in Table 2.4 In all cases the
temperature difference is 50°F or approximately 28°C. Table 3 presents measured R-
value / in. for the roof and wall insulation materials employed in the two field studies.
Here PIR referes to polyisocyanurate wall insulation with reflective (foil) facers.
Table 2 ASTM C1058 suggested mean temperatures for
testing building envelope insulations
Mean
Temperature
“Hot Side”
“Cold Side”
(°F)
(°C)
(°F)
(°C)
(°F)
(°C)
25
-4
50
10
0
-18
40
4
65
18
15
-10
50
10
75
24
25
-4
75
24
100
38
50
10
100
38
125
52
75
24
110
43
135
57
85
29
Table 3 Measured R-value / in. at standard mean temperatures
Mean
Temperature
Roof Insulation
Exterior Insulation for
Walls
(°F)
SW
PIC
SW
XPS
PIR
25
4.2
4.6
4.7
5.5
4.9
40
4.1
5.1
4.5
5.3
5.2
75
3.8
5.3
4.2
4.9
5.4
110
3.7
4.9
3.9
4.6
4.9
The standard temperature measurements confirm that all of the tested insulation
materials exhibit some temperature dependency. Where the R-value exhibits a near
linear temperature dependency it should be possible to use the data in Table 3 to
predict the material R-value over the full range of temperatures that buildings
typically experience. However, in those cases where the temperature dependence does
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4!Some!materials!exhibit!very!linear!temperature!dependence!and!can!be!characterized!using!only!2!
or!3! setpoints;!other! materials!exhibit!much!more!dramatic! temperature!dependence! (as!illustrated!
in!this!paper)!and!may!require!testing!at!more!than!the!6!setpoints!identified!in!ASTM!C1058!
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not exhibit a near straight line relationship, it is necessary to conduct further material
testing and analysis.
The authors have developed a measurement and analysis method5 to produce
temperature dependent R-value curves that can be employed to predict the thermal
performance of any insulation material, under any temperature conditions.6 The
method uses regression to determine a convergent R-value curve from numerous
measurements made while the temperature difference decreases towards zero.
Figure 8 presents the temperature dependent R-value curves for the three wall exterior
insulation materials and two roof insulation materials used in the field studies.
Figure 8 Temperature Dependent R-value curves for Roof and Wall Insulations
Studied
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!
5!This! measurement! and! analysis! method! is! the! subject! of! a! draft! paper! proposed! for! ASTM! C16!
Symposium!on!Advances!in!Hygrothermal!Performance!of!Building!Envelopes:!Materials,!Systems!and!
Simulations,!Oct!2016.!
6!The!method!specifically!addresses!the!insulation!material.!!It!does!not!address!the!assembly!with!all!
thermal!bridges!due!to!framing,!fasteners,!etc.!!However,!the!method!does!produce!data!that!can!be!
used!to! evaluate!the! performance!of! insulation!layers!in!hybrid!insulated!assemblies!(e.g.! walls!with!
some!insulation!between!the!framing!members!and!more!installed!as!continuous!exterior!insulation)!!
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COMPARISON OF “IMPROVED” PREDITIONS VS MEASUREMENTS FOR
ROOF
Using the same roof assemblies as previously discussed, it is possible to calculate an
improved theoretical estimate of the heat flow through the roof assembly. This
improved calculation accounts for actual in-service roof temperatures which are
primarily impacted by roof membrane colour, but are also influenced by the
insulation type and arrangement. The calculation is also improved by accounting for
temperature dependent thermal conductivity for both the PIC and SW insulations. The
non-linear conductivity of the PIC was measured using the converging delta T
method described above. The result of this improved theoretical calculation are
compared to the measured results and the original theoretical calculation in Figure 9
and Figure 10 for the PIC roofs and the grey roofs respectively.
Figure 9 Chart comparing calculated heat flux using the improved method with
that calculated using the original method and the measured heat flux through the
average of the PIC roof assemblies in the study for the year of 2014
0
100
200
300
400
500
600
-200
-150
-100
-50
0
50
100
150
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
Degree%Days%[°C·days]
Daily%Energy%Transfer%[W·hr/m²%per%day]
PIC Theoretical ImprovedGTheoreticalG-GPIC HeatingGDegreeGDaysG(18°C)
Outward HeatG
Flow
Inward HeatG
Flow
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Figure 10 Chart comparing calculated heat flux using the improved method with
that calculated using the original method and the measured heat flux through the
average of the grey roof assemblies in the study for the year of 2014
Figure 9 and Figure 10 clearly indicate that when actual in-service roof temperatures
and temperature dependent conductivity effects are accounted for, theoretical
calculations more closely match measured results. That said, room for improvement
exists, and this may in part be due to movement of moisture within the roof
assemblies and differences in insulation thermal mass.
COMPARISON OF “IMPROVED” PREDICTED VS MEASURED PERFORMANCE
OF WALL ASSEMBLIES
The temperature-dependent R-value curves were used to improve the surface
temperature predictions made for the OSB sheathings in the wall field study.
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Degree%Days%[°C·days]
Daily%Energy%Transfer%[W·hr/m²%per%day]
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Table 4 presents a comparison of the improved predictions and the measured surface
temperatures for the day 3 snapshot. Use of the temperature dependent R-values
results in much better agreement between predicted and measured surface
temperatures.
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Table 4 Comparison Between Predicted vs Measured
Sheathing Temperature (using R-value curves)
3"
SW
2.5"
XPS
2"
PIC
Snapshot (day)
3
3
3
Interior T (°F)
68
68
68
Exterior T (°F)
-9.4
-9.4
-9.4
Delta T (°F)
77.4
77.4
77.4
R-value In
(ft2·°F·hr/Btu)
23.2
23.2
23.2
R-value Out
(ft2·°F·hr/Btu)
17.0
15.5
11.3
R-value Tot
(ft2·°F·hr/Btu)
40.2
38.7
34.5
Ratio (-)
0.42
0.40
0.33
Calculated OSB
T (°F)
23.4
21.7
16.0
Measured OSB
T (°F)
23.4
21.6
16.7
Difference (°F)
0.0
-0.1
0.7
CONCLUSIONS AND RECOMMENDATIONS
In North America building insulation materials are typically tested and labeled in
accordance with the “Federal R Value Rule” (16 Code of Federal Regulations [CFR]
Part 460, “Trade Regulation Rule Concerning the Labeling and Advertising of Home
Insulation”). Thermal performance, specifically R-value, is assessed under a single
set of conditions: at a mean temperature of 74°F (24°C) and under a temperature
difference of approximately 50°F (28°C). Laboratory measurements made at other
standard mean temperatures (suggested by ASTM C1058) indicate that, for most
insulation materials, R-value is temperature dependent. Many insulation materials
exhibit nearly linear temperature dependency while others exhibit unique temperature
dependent R-value curves. The latter can be characterized and quantified using
special measurement techniques.
Field monitoring studies on roof and exterior insulated wall assemblies suggest that
more complex thermal and durability considerations may not be adequately
represented using conventional label R-values. The use of temperature dependent R-
values has been demonstrated to improve predictions of the energy performance and
moisture durability of building enclosure assemblies.
Page 17
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References
Finch,! G.,! Dell,! M.,! Hanam,! B.,! and! Ricketts,! L.! (2014)! Conventional* Roofing* Assemblies:*
Measuring*the*Thermal*Benefits*of* Light*to* Dark*Roof*Membranes*and* Alternate*Insulation*
Strategies.!Proceedings!of!the!28th!RCI!International!Convention!and!Trade!Show!
Graham,!M.! (2015),!Testing* R-values:*Polyisocyanurate’s*R-values*are* found*to*be*less* than*
their*LTTR*values.!!Professional!Roofing,!March!2015!
Kosny,!J.,!Petrie,!T.,!Gawin,!D.,!Childs,!P.,!Desjarlais,!A.,!&!Christian,!J.!(2001).!Thermal!mass-
energy!savings!potential!in!residential!buildings.!Retrieved*Oct,!28,!2015.!
Moe,! K.! (2014).! Insulating* modernism:* Isolated* and* Non-isolated* thermodynamics* in*
architecture.!Birkhäuser.!
Ricketts,!L.,! Finch,!G.,!Dell,!M.!(2014)! Study* of*Conventional*Roof*Performance.!Vancouver,!
BC:!RDH!Building!Engineering!Ltd.!!
Straube,! J.! (2015),! Field* hygrothermal* performance* of* highly* insulated* wood-framed* wall*
systems.* Research! Report! for! NRCan,! Building! Engineering!Group,! University! of! Waterloo,!
Waterloo,!ON,!Canada!!
... In particular, insulating materials resist heat flow as a result of microscopic dead air-volumes or a closed cell structure across which the temperature difference is not large, and the radiation heat transfer mode reduces [19]. In general, the heat transfers through conduction and convection are linearly proportional to the temperature across a layer of insulation, while radiation has an exponential behaviour with respect to the temperature difference [5,20]. ...
Article
The thermal conductivity, a fundamental property for insulating materials, is often advertised using a single value implied to be constant. However, research shows that the effective thermal conductivity changes as a result of material aging and the environmental parameters, including temperature and moisture content levels. In recent years, linear temperature-dependent laws have been occasionally proposed for inorganic fibrous materials, although there is increasing awareness of the fact that foam insulating materials have less regular temperature-dependent behaviours. This depends on the fact that the equilibrium among the different gasses in the material changes over time. In this paper, several polyurethane and polyisocyanurate foams are analysed in order to determine how the effective thermal conductivity is altered after accelerated aging obtained by exposing them to high temperature, high relative humidity levels, and freeze-thaw cycling. For each material, and after each aging exposure condition, measurements of the thermal conductivity were collected over a large temperature range from -20°C to +40°C using a Heat Flux Meter. These experimental results allow to build 3-D plots showing the effective thermal conductivity as a function of temperature and moisture content for both pristine and aged materials. Then, the measured results are used in hygrothermal simulations performed inputting the measured temperature- and moisture- dependent thermal conductivity in order to determine the effective performance of the considered insulating materials in both pristine and aged conditions. Results show that the aging of the foams and the operating temperatures have higher impacts on the insulating performance of polyisocyanurates than on polyurethanes. Additionally, high moisture levels contribute to lower performance in all foam materials, with open cell foams experiencing the greatest thermal resistance reduction. The increase in the energy fluxes across the insulating layer with respect to the constant thermal conductivity assumption was significantly higher once the effective thermal conductivity of aged materials was considered, especially when polyisocyanurate foams were modelled in cold and humid conditions.
Article
At present, thermal conductivity is usually taken as a constant value in the calculation of building energy consumption and load. However, in the actual use of building materials, they are exposed to the environment with continuously changing temperature and relative humidity. The thermal conductivity of materials will inevitably change with temperature and humidity, leading to deviations in the estimation of energy consumption in the building. Therefore, in this study, variations in the thermal conductivity of eight common building insulation materials (glass wool, rock wool, silica aerogel blanket, expanded polystyrene, extruded polystyrene, phenolic foam, foam ceramic and foam glass) with temperature (in the range of 20–60 °C) and relative humidity (in the range of 0–100%) were studied by experimental methods. The results show that the thermal conductivity of these common building insulation materials increased approximately linearly with increasing temperature with maximum growth rates from 3.9% to 22.7% in the examined temperature range. Due to the structural characteristics of materials, the increasing thermal conductivity of different materials varies depending on the relative humidity. The maximum growth rates of thermal conductivity with humidity ranged from 8.2% to 186.7%. In addition, the principles of selection of building insulation materials in different humidity regions were given. The research results of this paper aim to provide basic data for the accurate value of thermal conductivity of building insulation materials and for the calculation of energy consumption.
Article
The thermal properties of closed-cell foam insulation display a more complex behaviour than other construction materials due to the properties of the blowing agent captured in their cellular structure. Over time, blowing agent diffuses out from and air into the cellular structure resulting in an increase in thermal conductivity, a process that is temperature dependent. Some blowing agents also condense at temperatures within the in-service range of the insulation, resulting in non-linear temperature dependent relationships. Moreover, diffusion of moisture into the cellular structure increases thermal conductivity. Standards exist to quantify the effect of gas diffusion on thermal conductivity, however only at standard laboratory conditions. In this paper a new test procedure is described that includes calculation methods to determine Temperature Dependent Long-Term Thermal Conductivity (LTTC (T) ) functions for closed-cell foam insulation using as a test material, a Medium-Density Spray Polyurethane Foam (MDSPF). Tests results are provided to show the validity of the method and to investigate the effects of both conditioning and mean test temperature on change in thermal conductivity. In addition, testing was conducted to produce a moisture dependent thermal conductivity function. The resulting functions were used in hygrothermal simulations to assess the effect of foam aging, in-service temperature and moisture content on the performance of a typical wall assembly incorporating MDSPF located in four Canadian climate zones. Results show that after 1 year, mean thermal conductivity increased 15%–16% and after 5 years 23%–24%, depending on climate zone. Furthermore, the use of the LTTC (T) function to calculate the wall assembly U-value improved accuracy between 3% and 5%.
Article
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In certain climates, massive building envelopes-such as masonry, concrete, earth, and insulating concrete forms (ICFs)-can be utilized as one of the simplest ways of reducing building heating and cooling loads. Very often such savings can be achieved in the design stage of the building and on a relatively low-cost basis. Such reductions in building envelope heat losses combined with optimized material configuration and the proper amount of thermal insulation in the building envelope help to reduce the building cooling and heating energy demands and building related CO 2 emission into the atmosphere. Thermal mass effects occur in buildings containing walls, floors, and ceilings made of logs, heavy masonry, and concrete This paper presents a comparative study of the energy performance of light-weight and massive wall systems. An overview of historic and current U.S. field experiments is discussed herein and a theoretical energy performance analysis of a series of wall assemblies for residential buildings is also presented. Potential energy savings are calculated for ten U. S. climates. Presented research work demonstrate that in some U. S. locations, heating and cooling energy demands for buildings containing massive walls of relatively high R-values can be lower than those in similar buildings constructed using lightweight wall technologies.
Study of Conventional Roof Performance
  • L Ricketts
  • G Finch
  • M Dell
Ricketts, L., Finch, G., Dell, M. (2014) Study of Conventional Roof Performance. Vancouver, BC: RDH Building Engineering Ltd.
Conventional Roofing Assemblies: Measuring the Thermal Benefits of Light to Dark Roof Membranes and Alternate Insulation Strategies
  • G Finch
  • M Dell
  • B Hanam
  • L Ricketts
Finch, G., Dell, M., Hanam, B., and Ricketts, L. (2014) Conventional Roofing Assemblies: Measuring the Thermal Benefits of Light to Dark Roof Membranes and Alternate Insulation Strategies. Proceedings of the 28 th RCI International Convention and Trade Show
Field hygrothermal performance of highly insulated wood-framed wall systems
  • J Straube
Straube, J. (2015), Field hygrothermal performance of highly insulated wood-framed wall systems. Research Report for NRCan, Building Engineering Group, University of Waterloo, Waterloo, ON, Canada
Testing R-values: Polyisocyanurate's R-values are found to be less than their LTTR values
  • M Graham
Graham, M. (2015), Testing R-values: Polyisocyanurate's R-values are found to be less than their LTTR values. Professional Roofing, March 2015
Testing R-values: Polyisocyanurate's R-values are found to be less than their LTTR values. Professional Roofing
  • M Graham
Graham, M. (2015), Testing R-values: Polyisocyanurate's R-values are found to be less than their LTTR values. Professional Roofing, March 2015