Parametric analysis of moisture sorption isotherms for wood sheathing using hygrothermal modelling

Journal Pre-proof Parametric analysis of moisture sorption isotherms for wood sheathing using hygrothermal modelling Kevin Zhang, Dr Russell Richman PII:

S2352-7102(18)31067-2

DOI:

https://doi.org/10.1016/j.jobe.2019.101047

Reference:

JOBE 101047

To appear in:

Journal of Building Engineering

Received Date: 10 September 2018 Revised Date:

31 October 2019

Accepted Date: 1 November 2019

Please cite this article as: K. Zhang, D.R. Richman, Parametric analysis of moisture sorption isotherms for wood sheathing using hygrothermal modelling, Journal of Building Engineering (2019), doi: https:// doi.org/10.1016/j.jobe.2019.101047. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

The Impact of Variability in Moisture Storage Properties of Wood Based Sheathing on Enclosure Durability Due to Temperature and Weather-based Ageing

Part 1

Parametric Analysis of Moisture Sorption Isotherms For Wood Sheathing Using Hygrothermal Modelling

Kevin Zhang Updated: October, 2019

Contents 1.0

Introduction .......................................................................................................................... 1

2.0

Related Literature................................................................................................................. 2

3.0

Methodology ........................................................................................................................ 4

3.1

Hygrothermal Modelling Performance Metrics ............................................................... 5

3.1.1

RHT Index ................................................................................................................ 5

3.1.2

Mold Index ................................................................................................................ 6

3.2

Hygrothermal Modeling ................................................................................................... 7

3.2.1

Sorption Isotherm Curve Variation ............................................................................. 8

3.2.2

Additional Inputs ........................................................................................................ 10

3.2.3

Wall Types .................................................................................................................. 11

4.0

Results ................................................................................................................................ 12

4.1 Phase 1 Modelling............................................................................................................... 12 4.1.1

RHT Index Comparison .......................................................................................... 12

4.1.2

Mold Index Comparison ......................................................................................... 15

4.2

Phase 2 Modelling .......................................................................................................... 18

4.2.1

Mold Index Map ..................................................................................................... 18

5.0

Discussion .......................................................................................................................... 26

6.0

Conclusions ........................................................................................................................ 28

7.0

References .......................................................................................................................... 29

Abstract A key component of building performance with respect to durability and energy efficiency is at the meso-level, i.e., the wall assembly and components. Performance of these assemblies can be determined through in-situ experimental work (such as building and monitoring a test hut), or by hygrothermal numerical modelling. Modelling is highly dependent on inputs including material data, thermodynamic equations, and weather data; however, numerical modelling, if well executed, can be highly resource efficient compared to experimental work. This study examines material moisture storage through sorption isotherms, which is not well measured in comparison to thermal and vapour resistance. In particular, the study is a parametric assessment of an existing numerical model to determine the effects of modified moisture sorption isotherms compounded with other factors such as wall type, climate, driving rain calculation method, air cavity air change rate, insulation thickness, and presence of an air barrier. Mold index was used as the durability performance indicator. It has been demonstrated that with changes of sorption isotherms up to 25%, the critical threshold for mold index (MI = 3) could be surpassed. This suggests the importance of having accurate sorption isotherm data in hygrothermal modelling. The study forms the first part of a three-phase project to quantify the sensitivity of an existing numerical model to sorption isotherms, to measure isotherms at varying temperature and age, and

to

calibrate

the

model

to

these

measured

isotherms.

1.0 Introduction Building durability is an important factor in infrastructure resiliency. To better predict durability and performance of buildings, numerical modelling can be used. Many different types of hygrothermal analysis software utilizing numerical modelling exist which output varied simulation results due to variation among inputs, boundary conditions, material properties and solution algorithms [1]. It has been shown for various types of numerical modelling software that variations to inputs such as material properties can give a range of results, and that model validation is a key component of a strong, repeatable study [2]–[5]. In particular, moisture storage is an important material property that can affect HVAC loads, occupant comfort, and durability [6]–[8] Sorption isotherm data within hygrothermal analysis program databases and other technical organization publications, however, is not well developed. For example, ASHRAE authors a database of thermal conductivity and vapour transmission data for common construction materials in North America that is maintained well, but without a sorption isotherm companion database. Reasons for the lack of data include the time and difficulty of measuring sorption isotherms [9]. This parametric study forms part of a multi-part project to develop, test, and validate moisture sorption measurements in wood sheathing boards at varying temperatures and quantify the importance of accurate sorption isotherms in assessing building enclosure durability; this paper focuses on the first two phases. In the first phase of the project, the effect of altering sorption isotherms from a standard database in a variety of common wall types is analyzed. The second phase of the project develops a durability matrix based on high-risk walls from the first phase considering wall type, insulation thickness, climate, and orientation. 1

2.0 Related Literature Various studies have been completed with respect to the accuracy of numerical modelling in building science, especially as the emergence of rudimentary numerical models alongside the widespread adoption of computers presented a revolution in engineering design. Contemporary numerical modelling has allowed for more advanced and potentially more accurate predictions of performance of building materials and systems as computers have become more powerful, including the modelling of novel materials or systems, coupling of models for higher order complexity, and indoor environment [10]–[16]. A study involving the building of a series of test walls and comparing to simulation data was completed in the 1990’s in Canada. Results were compared to WALLDRY, a proprietary modelling software, and the authors found that the model moisture content results were highly inaccurate [2]. After some input changes, they were able to replicate the results in approximately half of the models. The author concluded that the equilibrium moisture content equation (or the sorption isotherm) for wood was a highly sensitive portion of the model [2]. Sensitive model components require a high degree of accuracy with inputs, as it has been demonstrated that results can be poor otherwise. The International Energy Agency (IEA) conducted a multi-year study involving 10 countries to analyze and compare modelling techniques and accuracy. Hens in 2002 published a summary as part of the IEA, detailing Annex 24 of the study relating to heat, air, and moisture transfer in highly insulated building envelopes. This study contained a very broad scope but touched on multiple gaps from numerical modelling of heat, air, and moisture transfer in walls. Comparison of different models demonstrated variation of results, which was attributed to simplified material

2

properties, different boundary condition formulae, and different methods to simplify geometry of components. This demonstrates that model inputs can effect results significantly, and that there is a possible decrease in accuracy with simplifications [1]. Straube and Burnett mentioned the difficulty of accurate and complete material property data in a review paper, comparing both commercially available and internally utilized modelling software and outlining how each software package approached heat and moisture transport processes [3]. Delgado expanded on Straube, Burnett, and Hens’ review by applying two models to local cities and comparing the results, showing the large discrepancies between the models. He concluded that model validation is critical to achieving accurate results [4]. Work in the late 90’s examined the effects of moisture sorption and storage on moderating relative humidity, demonstrating through modelling and testing that moisture buffering by absorbent walls can (in some cases) eliminate the need for mechanical ventilation entirely [17]. Further work examined strategies for studying humidity buffering materials in interior spaces including modelling and experimental testing, concluding that moisture buffering can have significant effects on indoor air and material durability, and that there is a lack of research in this field [18]. With respect to wood and its moisture buffering ability, particularly in a non-sheathing capacity, some studies have demonstrated a non-negligible effect. Experimental data from four multi-unit residential buildings in Sweden that were built with modular Scotch pine floors and walls indicated that large amounts of exposed wood contributed to buffering indoor temperature fluctuations, but not so much with relative humidity fluctuations [19]. Some numerical modelling work was also developed by the previous author to model moisture buffering capacity of heavy

3

timber, with inputs of air exchange and effective wood area [20]. A study in Turkey, where the climate is mixed hot-dry and temperate-humid, examined the downfalls of building code criteria that only accounted for degree-days, neglecting humidity requirements for comfort. A case study using climate data in Istanbul concluded that steady state thermal design was not sufficient because it did not capture the thermal mass of the envelope, further showing the important for energy storage (either in the form of heat or moisture) as a consideration in design [21]. Literature review and experimental results from exposing plywood to different relative humidities and some numerical modelling indicated that moisture buffering capacity has the potential to reduce heating and cooling energy consumption by up to 5% and 30%, respectively. This study also developed a model to predict moisture buffering [6]. The literature shows that numerical modelling can produce varied results based on the modelling parameters and type of model, and that these results can be highly sensitive to changes in material inputs. In particular, moisture sorption is not measured and published comprehensively. Experimental work has demonstrated that wood sheathing such as plywood can have a significant moisture buffering capability, highlighting the importance of wood sheathing in overall building performance. It is important to have accurate moisture sorption measurement data to decrease model error and better predict building performance.

3.0 Methodology The study comprises two phases. The first phase is an evaluation of common wall construction styles in North America and is compared using two performance indicators: RHT Index and Mold Index. The RHT Index is used to evaluate the effects of changing the moisture sorption on

4

the models and is a relative indicator. The Mold Index is used to evaluate the risk for mold growth for model and is an objective indicator. The second phase captures the high-risk wall assemblies as determined by the first phase and further evaluates them through modifying additional parameters, including façade orientation and insulation thickness. A mold index durability map is produced with the high-risk walls. A 2D model is also completed to observe the potential effects of cavity studs on results of the 1D models.

3.1

Hygrothermal Modelling Performance Metrics

3.1.1 RHT Index The RHT index was used as a durability indicator for hygrothermal analysis. Outputs from modelling includes temperature and relative humidity of a wall component, but without context, these values do not yield useful information. The RHT index was used as a metric for the localized hygrothermal response in the critical zone of a wall, such as within the most hygrothermal component, and was therefore used to quantify deterioration potential [22]–[24]. The RHT index is defined as the long-term combined moisture and temperature response, calculated by multiplying the difference of the measured relative humidity and a threshold relative humidity by the difference of the measured temperature and a threshold temperature: (

−

)∗(

−

)

(1)

Where the timestep i in this study was 1 hour. The threshold value for RH was 80%, and for temperature was 0°C. These values represent a conservative estimate for biological growth and

5

corrosion potential [22]. Any values that result in a negative term were ignored and instead 0 was used for the summation. Although there has been some work using the RHT index as a metric, it is better as a relative comparison value as there is no definition for “good” or “bad” values. Some recent work correlating climatic index data to RHT index values has been completed, which allows for good climate design selection criteria [25].

3.1.2 Mold Index The Mold Index was also used as a durability indicator for hygrothermal analysis. Temperature and relative humidity of wall components were outputted from the model, but without context, these values do not yield useful information. The Mold Index utilizes these outputs to calculate mold growth risk. Mold Index has been adopted by ASHRAE in their Standard 160, Criteria for Moisture-Control Design Analysis in Buildings. Originally conceived as a numerical model relating the effects of temperature, relative humidity, and exposure time on mold growth [26]– [28], this numerical model was based on wood species from northern regions [29]. The primary formulas for Mold Index are as follows [30]: =

Δ

+

(2)

= Mold index at the current hour = Mold index for the previous hour = Change in mold index

=

168 "#$(−0.68'(

− 13.9'(

+ 0.14, + 66.02)

(3)

6

W

= = = = =

Mold growth intensity factor Mold index attenuation factor Temperature Relative humidity Material sensitivity parameter

Mold growth, mold attenuation, and material sensitivity parameters were calculated for each time step. MATLAB was utilized to perform the calculations. Any time-step values that resulted in a negative term were ignored and instead 0 was used for the summation. A MI value less than 3 is considered low risk, whereas values greater than 3 are considered at risk for mold growth.

3.2

Hygrothermal Modeling

WUFI (Fraunhofer Institute in Building Physics) is a commercially available hygrothermal modelling program that offers both one- and two-dimensional abilities. Results of WUFI simulations have been validated in the past through both laboratory and field tests in various scenarios including traditional wall assemblies and alternative building systems [31]– [39][36][35]. WUFI contains databases for material properties including liquid transport coefficients, vapour diffusion resistance, thermal conductivity, and moisture storage. Phase 1 simulations comprised 6 Canadian cities and 9 wall types. A summary of other parameters is presented in Table 1. Table 1 - Parameters used in numerical models

Category City climate (Köppen Classification and description)

Sorption

Parameters Vancouver Calgary Winnipeg Toronto Montreal St. John’s +10%

Csb Dfb Dfb Dfa Dfb Dfb

Temperate oceanic Warm summer humid continental Warm summer humid continental Hot summer humid continental Warm summer humid continental Warm summer humid continental

7

isotherm

Wall type

Driving rain Air Leakage

+25% -10% -25% Residential/masonry/wood frame Residential/exterior insulation Residential/EIFS, face sealed Residential/EIFS, drained Residential/EIFS, drained/cement board Residential/commercial vapour barrier Residential/super-insulated 1% (ASHRAE 160) WUFI Driving rain coefficient method 8 ACH 30 ACH

The cities were chosen based on the variety of climates across Canada. All climates are heating season dominated.

3.2.1

Sorption Isotherm Curve Variation

Sorption isotherm curves were modified by +/-10% and +/- 25% based on previous moisture sorption studies showing variation of 10% to 50% [40] [41]. If the equations for multilayer absorption are examined, it can also be shown via sorption theory that there can be variability in curves up to 40%. The equation is as follows [42]:

, = 2.6289 /: ( 1 2

= = = = =

.

/0

12 1 − (( + 1)2 + (2 5 34 67 1 − 2 1 + (1 − 1)2 − 12 5

(4)

Moisture content, in kg/m3 Specific pore surface (m2/m) Layers of water molecules Energy exchange between water molecule and pore wall (J/kg) Relative humidity

8

From Eq. (1), as relative humidity approaches zero, the effects of specific pore surface are significant on the moisture content of the material (i.e. moisture content becomes proportional to specific pore surface). Porosity, however, does not accurately describe a material, as two materials may have similar porosity as a percentage of total volume, but have significantly different specific pore diameters and/or connectivity (which result in different specific pore surface values and transport). For example, brick and sand limestone both have a porosity of approximately 30% - however, brick has 8µm average pore diameter, whereas the sand limestone has 0.1µm average pore diameter. The sand limestone’s specific pore surface is 6000 times greater than that of the brick and is far more hygroscopic at low relative humidity. In the field of bio-aggregates, porosity has been studied extensively. Due to the heterogeneity of the material, it is difficult to accurately quantify pore structure – indeed, it can be highly dependent on the measurement methodology [43]–[48]. Wood as an organic material can have a variable pore structure throughout due to the growth of the tree [49]. In softwoods, the growth of the tree in the earlier part of the season differs from growth in later portions of the year (earlywood vs. latewood) The thickness of cell walls differs between earlywood and latewood, and this results in a variable density and porosity [50]–[52]. Even within the same growth rings, variability in pore structure can be observed [53]. Figure 1 depicts the variation of pore size in relation to normalized ring position, as captured by Derome et. al [52].

9

Figure 1 – Porosity as a function of normalized ring position. Measurements with an x-ray were adjusted by normalized the position of the cell within the growth ring.

From the graph, it can be observed that pores in latewood are smaller than earlywood but form a higher fraction of the total porosity. The porosity variation between earlywood, intermediate wood, and latewood can be upwards of 40%. Based on Eq. (1), at low relative humidities, this can result in a moisture content variability of up to 40%.

3.2.2

Additional Inputs

Driving rain was modelled with two methods: as per ASHRAE Standard 160, and with the program’s own driving rain coefficient method. Finally, the vented cavity was modelled with 8 ACH, and with 30 ACH, representing a standard range based on related literature [54]. Conservatively, a lower bound of 8 ACH was chosen based on a previous vented brick cavity study [55] and an upper bound of 30 ACH was chosen based on previously unpublished work in determining a critical ACH related to drying potential.

10

3.2.3 Wall Types The wall construction styles were chosen to generally capture some combinations of low-rise residential light timber construction common in North America. The breakdown of each wall type is as follows: Table 2 - Summary of major wall component. Critical materials are italicized and in red.

Wall

Ext. finish

Ext. Insulation None

Sheathing

Frame

Insulation

Int. finish

Brick

Cavity Insulation Yes

1

Fibreboard

Wood stud

Gypsum

2

Brick

Yes

XPS

OSB

Wood stud

3

EIFS

No

EPS

OSB

Wood stud

4

EIFS

Yes

EPS

OSB

Wood stud

5

EIFS

Yes

EPS

Cement board

Wood stud

6

Brick

Yes

None

Gypsum

Wood stud

7

Wood

No

XPS

OSB

Wood stud

Mineral wool Mineral wool Mineral wool Mineral wool Mineral wool Mineral wool Mineral wool

Gypsum Gypsum Gypsum Gypsum Gypsum Gypsum

For all simulations, two points were inserted at the exterior and interior of the controlling hygroscopic material (primarily the sheathing board). The sheathing board will be the critical material in these walls, as it provides the ideal substrate for mold growth. A summary of each wall and the monitor points can be found in Table 3. Table 3 - Summary of measurement points for each wall in WUFI. Critical materials are italicized.

Wall 1

Monitor Points (mm from exterior surface) Fibreboard Outer (0.03 mm from surface)

Fibreboard Inner (10.6 mm from surface)

2

OSB Outer (0.03 mm from surface)

OSB Inner (11.1 mm from surface)

3

OSB Outer (0.03 mm from surface)

OSB Inner (10.7 mm from surface)

4

OSB Outer (0.04 mm from surface)

OSB Inner (10.7 mm from surface)

Mineral Wool Outer (2.2 mm from surface) Mineral Wool Outer (1.4 mm from surface) Mineral Wool Outer (2.2 mm from surface) Mineral Wool Outer (2.2 mm from

Mineral Wool Inner (88.9 mm from surface) Mineral Wool Inner (88.5 mm from surface) Mineral Wool Inner (88.9 mm from surface) Mineral Wool Inner (88.9 mm from

11

5

Cement Board Outer (0.04 mm from surface)

Cement Board Inner (10.6 mm from surface)

6

Gypsum Outer (0.14 mm from surface)

Gypsum Inner (11.4 mm from surface)

7

OSB Outer (0.03 mm from surface)

OSB Inner (10.6 mm from surface)

surface)

surface)

Mineral Wool Outer (2.2 mm from surface) Mineral Wool Outer (1.2 mm from surface) Cellulose Outer (2.1 mm from surface)

Mineral Wool Inner (88.9 mm from surface) Mineral Wool Inner (87.9 mm from surface) Cellulose Inner (298.8 mm from surface)

4.0 Results 4.1 Phase 1 Modelling 4.1.1 RHT Index Comparison To organize the results, data was grouped into cases and cohorts. Cases organized the models based on the sorption isotherm values. Cohorts organized the models based on the combination of the wind driven rain calculation method and the cavity air circulation. Cases were as follows: • • • • •

Case A: Base case (default WUFI values) Case B: 10% increase in sorption isotherm of critical material Case C: 25% increase in sorption isotherm of critical material Case D: 10% decrease in sorption isotherm of critical material Case E: 25% decrease in sorption isotherm of critical material

The critical material was the most hygroscopic material in the wall assembly of interest. Generally, these are porous materials such as wood. Wood is critical as it is a strong candidate for organic deterioration related to mold growth. The porous structure of wood is also complex due to the heterogeneity, making it an ideal candidate for study. Cohorts were as follows: • • • •

Cohort 1: Driving rain coefficient method, 8 ACH Cohort 2: Driving rain coefficient method, 30 ACH Cohort 3: ASHRAE 160 method, 8 ACH Cohort 4: ASHRAE 160 method, 30 ACH

Each city and wall type was organized into cases and cohorts. 12

Overall, a cavity volumetric air flow of 8 ACH resulted in higher RHT Index values than 30 ACH. Walls 1 and 2 (masonry clad light timber frame residential walls) generally experienced significant changes to RHT Index values when parameters were modified. Walls 3, 4, and 5 (EIFS clad light timber residential walls) experienced moderate changes to RHT Index values. Wall 9 (super insulated passive house style light timber frame residential wall) also experienced minimal changes to RHT Index values. Figure 2 below summarizes the changes to RHT value for each wall, as the average of the sheathing measurement points for the cohorts with the highest RHT value.

13

Figure 2 shows that Wall 1 and Wall 5 had some significant changes to RHT value from Figure 2 - Average RHT Percentage Changes for All Walls, for Most Significant Cohort

14

modifying the moisture sorption values. Walls 2-4 displayed some moderate RHT value changes, while Wall 6 and 7 had minimal changes. A further comparison that takes the average of all walls is presented in Figure 3.

Average Percentage Changes from Base Case, All Walls (RHT) 120% 100% 80%

Case B

60%

Case C Case D

40% Case E 20% 0% Vancouver

Calgary

Winnipeg

Toronto

Montreal

St John

Figure 3 - Percentage Change from Base Case, Averaged Across All Walls

This graph shows that generally, a greater change to sorption (i.e. Case C and E, where sorption was modified by 25%) resulted in greater RHT value changes. Vancouver was an outlier. As well, the more humid and cold climates (Vancouver, Montreal, and St. John’s) resulted in more significant changes to RHT compared to the base case.

4.1.2 Mold Index Comparison The models were also compared with the Mold Index (MI) as a durability metric. Since MI is also an objective durability metric, both a summary of the calculated values and the percentage differences from the base case are presented here. Walls 1-5 resulted in negligible changes to MI when compared to the base case and no tables are presented. Walls 6 and 7 are summarized in Figure 4, as the average of the sheathing measurement points.

15

Figure 4 – Average MI Percentage Changes for Walls 6 and 7, for Most Significant Cohort

The percentage change shown in Figure 4 are more significant for Wall 7, particularly in the more humid and cold climates. Due to the negligible changes to MI between cases, the actual calculated MI values were also reviewed. Walls 1, 4, 5, and 6 experienced MI values greater than 3 (i.e. the threshold for mold risk). Wall 7 experienced MI values greater than 2. All other walls had MI values of 0 (i.e. no risk for mold growth). Out of a total of 864 scenarios modelled, 140 resulted in a MI value above 3. The distribution for critical scenarios is summarized by various categories in Figure 5.

16

Figure 5 - Critical MI Scenarios, Arranged by Various Categories

Figure 5 above shows that cities with both low temperatures and higher relative humidities (Montreal and St. John’s) comprise a greater proportion of the critical scenarios. The driving rain calculation method did not appear to have a great influence on the criticality, as each method resulted in a similar number of critical scenarios. The cases (recall that cases B and C were increased sorption values and cases D and E were decreased sorption values) resulted in similar numbers of critical scenarios, with a slight increase with cases C and D. Wall 6 resulted in a 17

large proportion of critical scenarios. Finally, a lower amount of air changes in the wall cavity resulted in more critical scenarios.

4.2

Phase 2 Modelling

4.2.1 Mold Index Map This phase further analyzed the type of low-rise wall construction that was deemed at-risk for mold growth and explored the anatomy further. Particularly, this round examined the effects of wood sheathing type, orientation, insulation thickness, and vapour resistance (either with or without a polyethylene vapour retarder). Two types of walls were modelled; the first one was based on Walls 1-6 (a light timber framed residential wall), and the second based on Wall 7 (cellulose insulated double-stud passive house style wall). The results for the first wall with OSB sheathing is shown in Figure 6.

18

Insul Thick 25 50 100 150 200 250 300 350 400 450 500 550 600

Vancouver S W N E 0.6 0 0 2 0.6 0 0 2 0.5 0 0 2.1 0.5 0 0 2.2 0.4 0 0 2.3 0.3 0 0 2.4 0.3 0 0.1 2.5 0.2 0 0.1 2.6 0.1 0 0.1 2.6 0.1 0 0.2 2.7 0 0 0.2 2.8 0 0 0.2 2.9 0 0 0.3 3

Insul Thick 25 50 100 150 200 250 300 350 400 450 500 550 600

Vancouver S W N E 1.9 2.1 2.8 4 1.9 2.1 2.8 4 1.8 2.1 2.8 4 1.8 2 2.7 4 1.8 2 2.7 4 1.7 1.9 2.7 4 1.7 1.9 2.6 4 1.6 1.8 2.6 4 1.6 1.8 2.5 4 1.5 1.7 2.5 4 1.5 1.7 2.5 4 1.5 1.7 2.4 3.9 1.4 1.6 2.4 3.9

S 0 0 0 0 0 0 0 0 0 0 0 0 0

S 0 0 0 0 0 0 0 0 0 0 0 0 0

Calgary W N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

E 0 0 0 0 0 0 0 0 0 0 0 0 0

S 0 0 0 0 0 0 0 0 0 0 0 0 0

Vapour Closed Winnipeg W N E S 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Toronto W N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Montreal St. Johns S W N E S W N 0 0 0 0 0 0 0.1 0 0 0 0 0 0 0.1 0 0 0 0 0 0 0.1 0 0 0 0 0 0 0.1 0 0 0.1 0 0.1 0 0.1 0 0 0.1 0 0.1 0 0.1 0 0 0.1 0 0.2 0 0.1 0.1 0 0.2 0 0.3 0 0 0.1 0 0.2 0.1 0.3 0 0 0.1 0 0.2 0.1 0.4 0 0 0.1 0 0.3 0.1 0.5 0 0 0.2 0 0.3 0.1 0.5 0 0 0.2 0 0.3 0.1 0.6 0 0

E 0 0 0 0 0 0 0 0 0 0 0 0 0

Vapour Open Calgary Winnipeg Toronto W N E S W N E S W N E S 0 0.1 0 1.1 1.8 2.3 1.8 0.2 1.4 2.1 1.4 0.8 0 0.1 0 1 1.7 2.2 1.8 0.2 1.4 2 1.4 0.8 0 0.1 0 1 1.6 2.1 1.7 0.2 1.3 1.9 1.3 0.8 0 0.1 0 0.9 1.5 2 1.6 0.2 1.2 1.8 1.2 0.8 0 0.1 0 0.8 1.4 1.9 1.5 0.2 1.1 1.7 1.1 0.8 0 0.1 0 0.7 1.3 1.8 1.3 0.1 1 1.7 1 0.8 0 0.1 0 0.6 1.2 1.7 1.2 0.1 0.9 1.6 0.9 0.8 0 0 0 0.5 1.1 1.6 1.1 0.1 0.8 1.5 0.9 0.7 0 0 0 0.4 1 1.5 1 0.1 0.7 1.4 0.8 0.7 0 0 0 0.3 0.9 1.4 0.9 0 0.7 1.3 0.7 0.7 0 0 0 0.3 0.8 1.3 0.8 0 0.6 1.2 0.6 0.7 0 0 0 0.2 0.7 1.2 0.7 0 0.5 1.1 0.5 0.7 0 0 0 0.1 0.6 1 0.6 0 0.4 1 0.4 0.7 Figure 6- MI Map for Wall 1 with OSB Sheathing, by City

Montreal W N 2.1 3.1 2.1 3.1 2 3 2 2.9 1.9 2.8 1.9 2.7 1.8 2.6 1.8 2.5 1.7 2.4 1.7 2.3 1.6 2.2 1.5 2.1 1.5 2

E 2.8 2.8 2.6 2.5 2.4 2.2 2.1 2 1.9 1.7 1.6 1.5 1.3

S 1.9 2 2.1 2.3 2.4 2.6 2.7 2.9 3 3.1 3.3 3.4 3.6

St. Johns W N 2.5 3.2 2.4 3.2 2.4 3.2 2.3 3.1 2.3 3 2.2 3 2.2 2.9 2.1 2.9 2.1 2.8 2 2.8 1.9 2.7 1.9 2.7 1.8 2.6

E 0 0 0 0 0 0 0 0 0 0 0 0 0

E 3.2 3.1 3.1 3.1 3 3 3 3 2.9 2.9 2.9 2.8 2.8

Legend MI Colour

0

0.5

1

1.5

2

2.5

3

4

5

From Figure 6, it is apparent that a vapour barrier has a significant effect on mold risk. In the vapour closed models, only Vancouver demonstrated any mold growth risk (but only in the East elevation). The threshold value of MI = 3 was reached with an insulation thickness of 600 mm, which is on the extreme end. The vapour open results show that the colder and more humid climates (Vancouver, Montreal, and St. Johns) are at high risk for mold growth.

19

Insul Thick 25 50 100 150 200 250 300 350 400 450 500 550 600

Insul Thick 25 50 100 150 200 250 300 350 400 450 500 550 600

S 0 0 0 0 0 0 0 0 0 0 0 0 0

Vancouver W N E 0 0 2.1 0 0 2.1 0 0 2.2 0 0 2.3 0 0 2.4 0 0.1 2.4 0 0.1 2.5 0 0.2 2.6 0 0.2 2.7 0 0.3 2.8 0 0.3 2.9 0 0.4 2.9 0 0.4 3

Vancouver S W N E 1.7 1.9 2.5 3.8 1.7 1.8 2.5 3.8 1.7 1.8 2.4 3.8 1.6 1.8 2.4 3.7 1.6 1.7 2.4 3.7 1.5 1.7 2.4 3.7 1.5 1.7 2.4 3.7 1.5 1.6 2.4 3.7 1.4 1.6 2.4 3.7 1.4 1.5 2.3 3.7 1.3 1.5 2.3 3.7 1.3 1.5 2.3 3.7 1.3 1.4 2.3 3.7

S 0 0 0 0 0 0 0 0 0 0 0 0 0

Calgary W N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

E 0 0 0 0 0 0 0 0 0 0 0 0 0

S 0 0 0 0 0 0 0 0 0 0 0 0 0

Vapour Closed Winnipeg W N E S 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Toronto W N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

E 0 0 0 0 0 0 0 0 0 0 0 0 0

S 0 0 0 0 0 0 0 0 0 0 0 0 0

Montreal W N 0 0.5 0 0.5 0 0.5 0 0.4 0 0.4 0 0.3 0 0.3 0 0.2 0 0.2 0 0.1 0 0.1 0 0.1 0 0

Vapour Open Calgary Winnipeg Toronto Montreal S W N E S W N E S W N E S W N 0 0 0 0 0.7 1.1 1.6 1.1 0 1 1.6 1 1.7 1.3 2.8 0 0 0 0 0.6 1 1.6 1.1 0 0.9 1.6 1 1.7 1.3 2.7 0 0 0 0 0.6 1 1.5 1 0 0.9 1.5 0.9 1.6 1.2 2.6 0 0 0 0 0.5 0.9 1.4 0.9 0 0.8 1.4 0.8 1.5 1.2 2.4 0 0 0 0 0.5 0.8 1.3 0.9 0 0.7 1.3 0.7 1.4 1.1 2.3 0 0 0 0 0.4 0.8 1.2 0.8 0 0.6 1.2 0.7 1.3 1 2.2 0 0 0 0 0.3 0.7 1.1 0.7 0 0.5 1.2 0.6 1.2 1 2 0 0 0 0 0.3 0.6 1 0.7 0 0.5 1.1 0.5 1.1 0.9 1.9 0 0 0 0 0.2 0.6 0.9 0.6 0 0.4 1 0.4 1 0.8 1.8 0 0 0 0 0.1 0.5 0.9 0.5 0 0.3 0.9 0.4 0.9 0.8 1.6 0 0 0 0 0.1 0.4 0.8 0.5 0 0.2 0.8 0.3 0.8 0.7 1.5 0 0 0 0 0 0.4 0.7 0.4 0 0.1 0.7 0.2 0.7 0.6 1.3 0 0 0 0 0 0.3 0.6 0.3 0 0.1 0.6 0.1 0.6 0.6 1.2 Figure 7 - Mold Risk Map for Wall 1 with Plywood Sheathing, by City

E 0 0 0 0 0 0.1 0.2 0.2 0.3 0.4 0.4 0.5 0.6

St. John's S W N 0.1 0 0 0.1 0 0 0.1 0 0 0.1 0 0 0.1 0 0 0.1 0 0 0.1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

E 1.1 1.1 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.2 1.2 1.2 1.2

S 2.3 2.3 2.2 2.2 2.1 2.1 2 2 1.9 1.9 1.8 1.8 1.7

St. John's W N 2 2.7 2 2.7 2 2.7 1.9 2.6 1.9 2.6 1.8 2.5 1.8 2.5 1.8 2.5 1.7 2.4 1.7 2.4 1.6 2.4 1.6 2.3 1.5 2.3

E 0 0 0 0 0 0 0 0 0 0 0 0 0

E 2.3 2.3 2.3 2.2 2.2 2.2 2.1 2.1 2.1 2 2 2 1.9

Legend MI Colour

0

0.5

1

1.5

2

2.5

3

4

5

Figure 7 shows that the vapour closed plywood system had less mold growth risk overall compared to the OSB, although the vapour closed walls were highly similar. The plywood has a slightly greater moisture capacity at high RH values in comparison to the OSB, which gives the wall an overall greater moisture storage ability. Without a vapour barrier, there is greater diffusion allowed between the components of the wall. This may allow for a greater drying rate

20

compared to the vapour closed case, which may be why the plywood outperforms the OSB in vapour open models. In general, for all of Wall 1, the vapour open systems benefitted from greater insulation thicknesses as the MI values decreased as insulation thickness increased. The opposite was true for the vapour closed systems. A summary comparison map is presented in Figure 8. Wall 1 Summary Mold Index Map - Cities Vapour Closed

Vapour Open

OSB Insul Thick

Vancouver Calgary Winnipeg Toronto Montreal St. Johns S WN E S WN E S WN E S WN E S WN E S WN E

Vancouver Calgary Winnipeg Toronto Montreal St. John's S WN E S WN E S WN E S WN E S WN E S WN E

25 50 100 150 200 250 300 350 400 450 500 550 600

Plywood 25 50 100 150 200 250 300 350 400 450 500 550 600

Figure 8 - All Wall 1 Mold Index Maps, by City Legend MI Colour

0

0.5

1

1.5

2

2.5

3

4

5

Wall 2 is presented next, with OSB sheathing.

21

Insul Thick 25 50 100 150 200 250 300 350 400 450 500 550 600

S 0 0 0 0 0 0 0 0 0 0 0 0 0

Vancouver W N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

E 0 0 0 0 0 0 0 0 0 0 0 0 0

S 0 0 0 0 0 0 0 0 0 0 0 0 0

Calgary W N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

E 0 0 0 0 0 0 0 0 0 0 0 0 0

S 0 0 0 0 0 0 0 0 0 0 0 0 0

Vapour Closed Winnipeg Toronto W N E S W N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Vapour Open Insul Vancouver Calgary Winnipeg Toronto Thick S W N E S W N E S W N E S W N 25 1.3 1.4 1.7 2.4 0 0 0 0 0 0 0.2 0 0 0 0.5 50 1.2 1.3 1.6 2.2 0 0 0 0 0 0 0.2 0 0 0 0.5 100 1.1 1.2 1.4 2 0 0 0 0 0 0 0.2 0 0 0 0.4 150 0.9 1 1.2 1.7 0 0 0 0 0 0 0.2 0 0 0 0.4 200 0.8 0.9 1.1 1.5 0 0 0 0 0 0 0.1 0 0 0 0.3 250 0.6 0.7 0.9 1.2 0 0 0 0 0 0 0.1 0 0 0 0.3 300 0.5 0.6 0.7 0.9 0 0 0 0 0 0 0.1 0 0 0 0.2 350 0.4 0.4 0.5 0.7 0 0 0 0 0 0 0.1 0 0 0 0.2 400 0.2 0.3 0.3 0.4 0 0 0 0 0 0 0 0 0 0 0.1 450 0.1 0.1 0.1 0.2 0 0 0 0 0 0 0 0 0 0 0 500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 550 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 600 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Figure 9- MI Map for Wall 2 with OSB Sheathing, by City

E 0 0 0 0 0 0 0 0 0 0 0 0 0

S 0 0 0 0 0 0 0 0 0 0 0 0 0

Montreal W N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

E 0 0 0 0 0 0 0 0 0 0 0 0 0

S 0 0 0 0 0 0 0 0 0 0 0 0 0

St. John's W N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

E 0 0 0 0 0 0 0 0 0 0 0 0 0

E 0 0 0 0 0 0 0 0 0 0 0 0 0

S 0 0 0 0 0 0 0 0 0 0 0 0 0

Montreal W N 0 0.5 0 0.4 0 0.4 0 0.3 0 0.3 0 0.2 0 0.2 0 0.1 0 0.1 0 0 0 0 0 0 0 0

E 0.1 0.1 0 0 0 0 0 0 0 0 0 0 0

S 1.7 1.6 1.4 1.3 1.1 0.9 0.7 0.5 0.3 0.1 0 0 0

St. John's W N 1.6 1.9 1.5 1.8 1.3 1.6 1.2 1.4 1 1.2 0.8 1 0.6 0.8 0.5 0.5 0.3 0.3 0.1 0.1 0 0 0 0 0 0

E 1.7 1.6 1.4 1.2 1 0.9 0.7 0.5 0.3 0.1 0 0 0

Legend MI Colour

0

0.5

1

1.5

2

2.5

3

4

5

Figure 9 shows that the vapour closed wall was at no risk for mold growth regardless of insulation thickness, orientation, or climate. The vapour open wall was mildly at risk for mold growth with low insulation thickness in Vancouver and St. John’s. This is commensurate with previous work reviewing a sub-arctic application of this style of wall, whereas a vapour open building was constructed and did not displayed any signs of deterioration. Similar to Wall 1, the east orientation in Vancouver and the north orientation in St. John’s were at higher risk. 22

Insul Thick 25 50 100 150 200 250 300 350 400 450 500 550 600

S 0 0 0 0 0 0 0 0 0 0 0 0 0

Vancouver W N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

E 0 0 0 0 0 0 0 0 0 0 0 0 0

S 0 0 0 0 0 0 0 0 0 0 0 0 0

Calgary W N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

E 0 0 0 0 0 0 0 0 0 0 0 0 0

S 0 0 0 0 0 0 0 0 0 0 0 0 0

Vapour Closed Winnipeg Toronto W N E S W N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Vapour Open Insul Vancouver Calgary Winnipeg Toronto Thick S W N E S W N E S W N E S W N 25 1 1.2 1.6 2.4 0 0 0 0 0 0 0 0 0 0 0.1 50 0.9 1.1 1.5 2.2 0 0 0 0 0 0 0 0 0 0 0 100 0.8 1 1.3 2 0 0 0 0 0 0 0 0 0 0 0 150 0.7 0.9 1.1 1.7 0 0 0 0 0 0 0 0 0 0 0 200 0.6 0.7 1 1.5 0 0 0 0 0 0 0 0 0 0 0 250 0.5 0.6 0.8 1.2 0 0 0 0 0 0 0 0 0 0 0 300 0.4 0.5 0.6 0.9 0 0 0 0 0 0 0 0 0 0 0 350 0.3 0.3 0.5 0.7 0 0 0 0 0 0 0 0 0 0 0 400 0.2 0.2 0.3 0.4 0 0 0 0 0 0 0 0 0 0 0 450 0.1 0.1 0.1 0.2 0 0 0 0 0 0 0 0 0 0 0 500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 550 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 600 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Figure 10 - MI Map for Wall 2 with Plywood Sheathing, by City

E 0 0 0 0 0 0 0 0 0 0 0 0 0

S 0 0 0 0 0 0 0 0 0 0 0 0 0

Montreal W N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

E 0 0 0 0 0 0 0 0 0 0 0 0 0

S 0 0 0 0 0 0 0 0 0 0 0 0 0

St. John's W N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

E 0 0 0 0 0 0 0 0 0 0 0 0 0

E 0 0 0 0 0 0 0 0 0 0 0 0 0

S 0 0 0 0 0 0 0 0 0 0 0 0 0

Montreal W N 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

E 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 0 0

S 1.4 1.3 1.2 1 0.9 0.7 0.6 0.4 0.3 0.1 0 0 0

St. John's W N 1.4 1.7 1.3 1.6 1.1 1.4 1 1.3 0.8 1.1 0.7 0.9 0.5 0.7 0.4 0.5 0.2 0.3 0.1 0.1 0 0 0 0 0 0

E 1.4 1.4 1.2 1 0.9 0.7 0.6 0.4 0.3 0.1 0 0 0

Legend MI Colour

0

0.5

1

1.5

2

2.5

3

4

5

The plywood wall was similar to the OSB wall; however, the vapour open MI values were slightly less. This is commensurate with Wall 1, whereas the plywood vapour open MI values were slightly less than the OSB values. As discussed prior, plywood has a greater moisture capacity at high RH compared to OSB. Overall, Vancouver had the highest mold growth risk. The next set of mold index maps are based on only Vancouver’s climate and will compare the changes to sorption isotherm. 23

Insul Thick 25 50 100 150 200 250 300 350 400 450 500 550 600

Insul Thick 25 50 100 150 200 250 300 350 400 450 500 550 600

S 0.62 0.59 0.52 0.46 0.39 0.33 0.26 0.2 0.13 0.07 0 0 0

Default W N 0 0 0 0 0 0 0 0 0 0.01 0 0.04 0 0.07 0 0.1 0 0.13 0 0.16 0 0.19 0 0.22 0 0.26

S 1.91 1.89 1.85 1.8 1.76 1.72 1.68 1.63 1.59 1.55 1.5 1.46 1.42

Default W N 2.15 2.83 2.12 2.81 2.08 2.77 2.03 2.73 1.98 2.69 1.94 2.65 1.89 2.61 1.84 2.57 1.8 2.53 1.75 2.49 1.7 2.45 1.66 2.41 1.61 2.37

E 1.98 2.02 2.11 2.2 2.29 2.37 2.46 2.55 2.64 2.73 2.82 2.9 2.99

E 4.03 4.03 4.02 4.01 4 4 3.99 3.98 3.97 3.96 3.95 3.95 3.94

S 0 0 0 0 0 0 0 0 0 0 0 0 0

-10% W N 0 0 0 0 0 0 0 0 0 0.01 0 0.05 0 0.09 0 0.13 0 0.17 0 0.21 0 0.25 0 0.28 0 0.32

Vapour Closed -25% E S W N 2.16 0 0 0 2.2 0 0 0 2.29 0 0 0 2.39 0 0 0.01 2.48 0 0 0.05 2.57 0 0 0.1 2.67 0 0 0.15 2.76 0 0 0.19 2.85 0 0 0.24 2.94 0 0 0.29 3.04 0 0 0.33 3.13 0 0 0.38 3.22 0 0 0.42

E 2.67 2.68 2.71 2.74 2.77 2.8 2.83 2.86 2.89 2.92 2.95 2.98 3.01

S 0 0 0 0 0 0 0 0 0 0 0 0 0

+10% W N 0 0 0 0 0 0 0 0 0 0.02 0 0.04 0 0.07 0 0.09 0 0.11 0 0.13 0 0.15 0 0.17 0 0.19

E 1.95 1.99 2.07 2.14 2.22 2.29 2.37 2.44 2.52 2.6 2.67 2.75 2.82

S 0 0 0 0 0 0 0 0 0 0 0 0 0

+25% W N 0 0 0 0 0 0 0 0 0 0 0 0.01 0 0.03 0 0.04 0 0.06 0 0.07 0 0.09 0 0.1 0 0.12

E 2.11 2.13 2.18 2.23 2.27 2.32 2.37 2.42 2.46 2.51 2.56 2.61 2.65

S 2 2 2 2 2 2 2 2 2 2 2 2 2

-10% W N 2.22 2.74 2.19 2.73 2.14 2.71 2.1 2.68 2.05 2.66 2 2.64 1.95 2.62 1.9 2.59 1.86 2.57 1.81 2.55 1.76 2.53 1.71 2.5 1.66 2.48

Vapour Open -25% E S W N 4.21 2.07 2.34 3.01 4.2 2.05 2.31 2.99 4.19 2.01 2.26 2.96 4.17 1.96 2.21 2.92 4.15 1.92 2.16 2.88 4.13 1.87 2.11 2.85 4.11 1.83 2.06 2.81 4.1 1.78 2.01 2.77 4.08 1.74 1.96 2.73 4.06 1.69 1.91 2.7 4.04 1.65 1.86 2.66 4.02 1.6 1.81 2.62 4 1.55 1.76 2.59

E 4.42 4.41 4.39 4.36 4.34 4.32 4.3 4.27 4.25 4.23 4.21 4.18 4.16

S 1.85 1.83 1.79 1.75 1.71 1.67 1.62 1.58 1.54 1.5 1.46 1.42 1.38

+10% W N 2.08 2.72 2.06 2.7 2.01 2.66 1.97 2.63 1.92 2.59 1.88 2.56 1.83 2.52 1.79 2.49 1.74 2.45 1.7 2.42 1.65 2.38 1.61 2.35 1.56 2.31

E 3.84 3.84 3.84 3.84 3.84 3.84 3.84 3.83 3.83 3.83 3.83 3.83 3.83

S 1.78 1.76 1.72 1.68 1.64 1.6 1.56 1.52 1.48 1.44 1.41 1.37 1.33

+25% W N 1.99 2.55 1.96 2.54 1.92 2.51 1.88 2.48 1.84 2.46 1.8 2.43 1.76 2.4 1.71 2.38 1.67 2.35 1.63 2.32 1.59 2.3 1.55 2.27 1.51 2.24

E 3.56 3.56 3.57 3.58 3.58 3.59 3.6 3.61 3.62 3.62 3.63 3.64 3.65

Figure 11 - MI Map for Wall 1 with OSB Sheathing, by Sorption Change Legend MI Colour

0

0.5

1

1.5

2

2.5

3

4

5

Figure 11 shows that, in the vapour closed system, changes to sorption isotherm in the sheathing can result in varying criticality for mold growth. At a 10% or 25% decrease, with very high amounts of insulation, MI > 3. At increased sorption curves though, the critical value of MI = 3 is not reached. The same pattern exists for the vapour open systems, albeit at a far higher MI value. The next map is for plywood.

24

Insul Thick 25 50 100 150 200 250 300 350 400 450 500 550 600

Insul Thick 25 50 100 150 200 250 300 350 400 450 500 550 600

S 0 0 0 0 0 0 0 0 0 0 0 0 0

S 1.71 1.69 1.65 1.61 1.57 1.53 1.49 1.45 1.42 1.38 1.34 1.3 1.26

Default W N 0 0 0 0 0 0 0 0 0 0.01 0 0.06 0 0.11 0 0.16 0 0.21 0 0.26 0 0.31 0 0.36 0 0.41

Default W N 1.87 2.46 1.85 2.46 1.81 2.44 1.77 2.43 1.74 2.41 1.7 2.4 1.66 2.38 1.62 2.37 1.59 2.35 1.55 2.34 1.51 2.32 1.47 2.31 1.44 2.29

E 2.07 2.11 2.19 2.27 2.36 2.44 2.52 2.61 2.69 2.77 2.86 2.94 3.02

E 3.76 3.76 3.75 3.74 3.73 3.73 3.72 3.71 3.7 3.69 3.68 3.67 3.67

S 0 0 0 0 0 0 0 0 0 0 0 0 0

S 1.77 1.77 1.77 1.77 1.77 1.77 1.77 1.77 1.77 1.77 1.77 1.77 1.77

-10% W N 0 0 0 0 0 0 0 0.05 0 0.09 0 0.14 0 0.19 0 0.23 0 0.28 0 0.33 0 0.37 0 0.42 0 0.47

Vapour Closed -25% E S W N 2.6 0 0 0 2.61 0 0 0 2.63 0 0 0 2.65 0 0 0.06 2.67 0 0 0.12 2.69 0 0 0.18 2.71 0 0 0.24 2.73 0 0 0.3 2.74 0 0 0.36 2.76 0 0 0.42 2.78 0 0 0.48 2.8 0 0 0.54 2.82 0 0 0.6

E 2.79 2.8 2.83 2.85 2.88 2.9 2.93 2.95 2.98 3 3.03 3.06 3.08

S 0 0 0 0 0 0 0 0 0 0 0 0 0

+10% W N 0 0 0 0 0 0 0 0 0 0.01 0 0.06 0 0.11 0 0.16 0 0.21 0 0.26 0 0.31 0 0.36 0 0.41

E 2.07 2.11 2.19 2.27 2.36 2.44 2.52 2.61 2.69 2.77 2.86 2.94 3.02

S 0 0 0 0 0 0 0 0 0 0 0 0 0

+25% W N 0 0 0 0 0 0 0 0 0 0 0 0.03 0 0.06 0 0.09 0 0.12 0 0.15 0 0.18 0 0.22 0 0.25

E 2.13 2.15 2.19 2.22 2.26 2.3 2.33 2.37 2.41 2.44 2.48 2.52 2.55

-10% W N 1.9 2.68 1.9 2.66 1.9 2.63 1.8 2.6 1.8 2.57 1.7 2.54 1.7 2.51 1.7 2.48 1.6 2.45 1.6 2.42 1.6 2.39 1.5 2.36 1.5 2.32

Vapour Open -25% E S W N 4.01 1.82 2.01 2.65 3.99 1.8 1.99 2.64 3.95 1.76 1.95 2.63 3.92 1.72 1.91 2.61 3.89 1.68 1.87 2.59 3.85 1.64 1.83 2.58 3.82 1.6 1.79 2.56 3.78 1.56 1.74 2.55 3.75 1.52 1.7 2.53 3.71 1.48 1.66 2.51 3.68 1.44 1.62 2.5 3.65 1.4 1.58 2.48 3.61 1.36 1.54 2.46

E 4.11 4.1 4.07 4.05 4.02 4 3.97 3.94 3.92 3.89 3.87 3.84 3.81

S 1.67 1.66 1.62 1.58 1.54 1.5 1.46 1.42 1.38 1.34 1.31 1.27 1.23

+10% W N 1.82 2.37 1.8 2.37 1.77 2.35 1.73 2.34 1.69 2.33 1.66 2.32 1.62 2.31 1.59 2.3 1.55 2.29 1.51 2.28 1.48 2.27 1.44 2.26 1.4 2.25

E 3.62 3.61 3.6 3.59 3.57 3.56 3.55 3.53 3.52 3.51 3.49 3.48 3.47

S 1.63 1.61 1.57 1.53 1.49 1.45 1.41 1.38 1.34 1.3 1.26 1.22 1.18

+25% W N 1.76 2.29 1.74 2.29 1.71 2.28 1.67 2.27 1.64 2.26 1.61 2.25 1.57 2.24 1.54 2.23 1.5 2.22 1.47 2.22 1.44 2.21 1.4 2.2 1.37 2.19

E 3.71 3.7 3.66 3.63 3.6 3.56 3.53 3.5 3.46 3.43 3.39 3.36 3.33

Figure 12 - MI Map for Wall 1 with Plywood Sheathing, by Sorption Change Legend MI Colour

0

0.5

1

1.5

2

2.5

3

4

5

Figure 12 shows a similar pattern to the OSB map, whereas in the vapour closed system, changes to the sorption isotherm can affect the criticality of the mold index.

25

5.0 Discussion The type of cladding (EIFS or masonry) did not appear to have a major effect on the sensitivity, however, the presence of a drainage plane appeared to increase the sensitivity. In some climates (Vancouver and Montreal), the changes to RHT from modifying sorption isotherms was significant, reaching as high as 500% from a 10% change to the sorption isotherm. This demonstrates that results can be highly sensitive to the sorption isotherm material data. The presence of a vapour barrier and outboard insulation generally resulted in lower RHT sensitivity. The increased thermal and moisture resistance results in the sheathing maintaining consistent conditions to the interior. The MI results exhibited similar trends, with the wood framed vented cavity walls having the greatest MI values. Climates with more precipitation, lower temperatures, and fewer cavity air changes were at greater risk for mold growth. This information was used in the second round of MI modelling, as only Vancouver and a wood-framed stud wall were examined. The MI mold risk maps demonstrated that in a traditional light timber stud wall system (Wall 1) in most Canadian climates, a vapour barrier is necessary. Only in Calgary, which has the lowest yearly average precipitation amongst the climates modelled, a vapour barrier is not necessary. The model, however, does not account for convective moisture transfer. Practically, a vapour barrier would not be needed in Calgary only if the wall was completely airtight. Any penetrations would result in potential condensation issues in the winter as the temperature in Calgary is relatively low. Comparing the OSB and the plywood, it is shown that the OSB results in lower mold growth risk in a vapour closed system. The plywood, however, results in lower mold growth risk in the

26

vapour open systems. As discussed, this is a result of the slightly higher moisture storage capacity of plywood. Both OSB and plywood are superior to fibreboard sheathing with respect to mold growth risk. In the super insulated passive house style wall (Wall 7), with sufficient insulation (greater than 150mm), a vapour open system would not surpass the critical MI value of 3.0. The OSB and plywood sheathing performed very similarly. The modelling results demonstrated the mold growth resistance of this wall; however, the construction material and thickness of the wall was excessive. Practically, this would be an expensive wall and given that the addition of a vapour barrier in the traditional light timber stud wall allows for comparable performance, it is not viable unless the user was trying to reach specific energy usage targets. From the broader set of modelling and RHT analysis, it was confirmed that Vancouver was the most high-risk climate. The second round of modelling and the use of the mold index as a durability metric then focused on the effects of changes to the sorption isotherm on mold growth risk. From the durability maps constructed, it was apparent that with either plywood or OSB sheathing and insulation thicknesses greater than 450 mm, variability in the sorption isotherm could result in the MI crossing the allowable threshold of MI = 3. For example, examining the OSB durability map at 450 mm of insulation, east elevation, it is shown that MI = 2.73 with the default sorption isotherm curve used. MI = 2.94 if the sorption isotherm is reduced by 10%. This demonstrates that the mold growth risk can approach the threshold of MI = 3 with a 10% change in sorption isotherm. Literature demonstrates that variability in wood due to its growth across various seasons can results in variable porosity. Combined with manufacturing variability for OSB and plywood sheathing, it can be stated that the default values for sorption isotherms may not be exact, and 27

this could result in model results that may not accurately predict actual situations. The interactions between the metrics used in this study (elevation, insulation thickness, climate, sorption isotherm) have not previously been examined in a modelling context. In particular, sorption isotherms in hygrothermal modelling have not been studied with respect to their effects on durability metrics such as mold index.

6.0 Conclusions From the parametric analysis, 864 total scenarios were evaluated over a five-year period using WUFI software. Parameters included wall type, climate, driving rain calculation method, air cavity air change rate, insulation thickness, elevation, and sorption isotherm of wood sheathing. The output data was compared using RHT values and Mold Index values. There were five sorption isotherms examined: a base case (using default WUFI sorption isotherm values), two cases where the sorption isotherm was decreased for the most hygroscopic material in the wall, and two cases where the sorption isotherm was increased for the most hygroscopic material in the wall. The results show that there are scenarios where changes to sorption isotherm result in changes to durability of wood sheathing. Increasing the sorption isotherm of the wood sheathing by 10% can result in a material having little mold growth risk to having definite mold growth risk. The variability in porosity in wood and variability from manufacturing of sheathing, combined with the results of this parametric study, suggest that model results can vary significantly with respect to mold growth risk. Other factors affecting sorption isotherms that will be examined in future work are temperature and ageing of wood sheathing.

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Highlights: • • • •

Hygrothermal modelling can be a powerful predictor of durability Moisture sorption isotherms are a key input into hygrothermal modelling Moisture sorption isotherms are not well measured Inaccurate data can make model results critical with respect to mold risk

Declarations Kevin Zhang Sept 6, 2018 For the manuscript submissions entitled “Parametric Analysis of Moisture Sorption Isotherms of Wood Sheathing with Hygrothermal Modelling”, I, Kevin Zhang as the primary author, state that I have no declarations of interest. Furthermore, I declare that this manuscript has not been previously submitted elsewhere.

Kevin Zhang PhD Candidate Ryerson University