Climate loads and their effect on building envelopes - an overview

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Climate loads and their effect on building envelopes - an overview

Climate loads and their effect on building envelopes -an overview

Cornick, S.M.

www.nrc.ca/irc/ircpubs

Climate loads and their

effect on building envelopes

- an overview

Steve Cornick

Methodology

Q

Ideal Wall

Climate

Climate

characterisation

_{characterisation}

Model Simulation

hygIRC

Output

Full-scale tests DWTF

Full-scale tests DWTF

Material properties

Material properties

Benchmarking

Benchmarking

Building practice

Building practice

Response curve

Climate Severity

---INDEX OF CLIMATE SEVERITY--->

---INDEX OF WALL RESPONSE--->

Q

Outline

1. Review Climate Study Objectives

2. Develop the Moisture Index

approach

3. Apply the MI approach for selecting

Moisture Reference Years

Climate Study Objectives

• Main objectives for Climate Study

were:

– Develop a method for classifying climate

– Use the method to select locations of

interest for MEWS Modeling as well

Moisture Reference Years

A Moisture Index Approach

• How can we classify climates?

• A quantitative method rather

qualitative

• Long history of climate classification

• Classic example is Köppen's scheme

• Focus is on habitability and

A Moisture Index Approach

Groups Types

A Tropical Humid Climates Ar Tropical Wet

Aw Tropical Wet and Dry

B Dry Climates BW Desert or Arid

BS Steppe or Semiarid

C Subtropical Climates Cs Subtropical Dry Summer

Cf Subtropical Humid

D Temperate Climates Do Temperate Oceanic

Dc Temperate Continental

E Boreal Climate E Boreal

F Polar Climates Ft Tundra

Fi Ice Cap

H Highland Climates H Undifferentiated Highland Climates

A modified Köppen Scheme

N.B. the definition of dry climates

B

= evaporation exceeds

precipitation

A Moisture Index Approach

• For buildings a few schemes exist

• They are based on combinations of

Temperature and Rainfall

• Two examples are:

– Russo has a scheme for construction

A Moisture Index Approach

Scheffer’s Index

=

Σ

Dec

_Jan

(T -2)(D-3)/16.7

Marching Orders:

4 Points

A Moisture Index Approach

• Scheffer’s marching orders:

– Use available climate data

– Use as few elements as possible

– Range for 0 to 100 for rapid recognition

– Correlate to observed field data

A Moisture Index Approach

• Additional Mews marching orders

– climate study should be conducted

independent of the wall characteristics

• Use a Moisture Index (MI) approach

• Goes back to Köppen

• MI relates evaporation and

precipitation

A Moisture Index Approach

• For MEWS the MI is a function of

wetting and drying

• Hypothesis:

– The higher the value of the MI the higher

the moisture-loading

A Moisture Index Approach

• Possible wetting functions are:

– average annual rainfall (mm/m2)

– driving-rain index (DRI)

– derivative DRI approaches such as Lacy

• For MEWS wetting was defined as:

– average annual rainfall (climate normal)

A Moisture Index Approach

• The drying portion of the MI was a

modification of Hagentoft and

Harderup's

Π

-factor approach.

• Briefly:

– Drying = difference between saturation

mixing ratio and ambient mixing ratio

– This is proportional to the potential

A Moisture Index Approach

Drying Index versus Wetting Index (Rain)

0 20 40 60 80 100 120 140 160 180 0 200 400 600 800 1000 1200 1400 1600 1800 Rain (mm) D ry ing I nde x ( k g w a te r/ k g a ir ) Phoenix AZ Wilmington NC Seattle WA Ottawa ON Winnipeg MB

A Moisture Index Approach

Drying Index versus Wetting Index (Rain)

0 20 40 60 80 100 120 140 0 200 400 600 800 1000 1200 1400 1600 1800

Wetting Index (Rain mm)

D ryin g In d e x ( k g w a te r/ kg air )

cold dry cold wet

hot wet

hot dry _Phx

Wpg

Ott

A Moisture Index Approach

• How is MI defined?

• First we normalize both measures,

Why?

– Relative comparison of climates

– Normalize wetting: rainfall/maximum

– Normalize drying: drying index/maximum

value in the sample set

A Moisture Index Approach

– For wetting a value of 1.0 indicates

maximum wetting

– For wetting a value of 0 indicates minimum

wetting

– For drying a value of 1.0 indicates

maximum drying

– For drying a value of 0 indicates minimum

drying

A Moisture Index Approach

Drying Index versus Wetting Index

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

normalized Wetting Index (Precipitation)

1 n o rm al iz ed D ryi n g I n d ex MI = (x2 + y2)0.5 Phx Wil Win Ott _Sea

Suppose we plot:

1 -normalized drying

vs. normalized wetting

Suppose we calculate

MI as RMS

More wetting

Less drying

A Moisture Index Approach

• Origin (0, 0) -> MI = 0

• implies maximum drying and minimum wetting

• least severe climate

• Top right (1,1) -> MI =square root(2) =

1.414

• implies minimum drying and maximum wetting

• most severe climate

Drying Index versus Wetting Index

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1 nor m a li z e d D ry ing I n de x MI = (x2 + y2)0.5 Phx Wil Win Ott Sea

A Moisture Index Approach

• 7 cities were selected in the climate

study for detailed treatment

City

MI

T ype

Wilmington NC

1.13

hot & wet

Seattle WA

0.99

mild & wet

Ottawa ON

0.93

cold & wet

Winnipeg MB

0.86

cold & dry

San Diego CA

0.74

hot & dry

Fresno CA

0.49

hot & dry

A Moisture Index Approach

0

20

40

60

80

100

120

140

160

0

0.2

0.4

0.6

0.8

1

1.2

MI

aver

ag

e R

H

T

(80)

/100

reference properties no deficiency … 2211 best properties w ith deficiency ……..… 2213 reference properties w ith deficiency ….. 2211

Phoenix Fresno San

Diego W’peg Ottawa Seattle Wilmington Hot-Dry Cold-Dry Cold-Wet Mild-Wet Hot-Wet

BLUE RED

GREEN

Does it Work? Yes!

Wall P

e

r

A Moisture Index Approach

Division

Classification

Colour

MI >= 1.0

Zone 1

Red

MI >= 0.9 but <1.0

Zone 2

Orange

MI >= 0.8 but < 0.9

Zone 3

Yellow

MI >= 0.7 but < .0.8

Zone 4

Green

Moisture Reference Years

Drying Index versus Wetting Index (Rain)

0 20 40 60 80 100 120 140 160 180 0 200 400 600 800 1000 1200 1400 1600 1800 Rain (mm) D ryi n g I n d ex ( k g w a te r/ kg ai r) Phoenix AZ Wilmington NC Seattle WA Ottawa ON Winnipeg MB

Moisture Reference Years

• Suppose we applied the MI method

to every year in the sample for each

city

• Hypothesis is the same

– The higher the value of MI, the more

severe the year

Moisture Reference Years

Year

MI

Year

MI

Year

MI

Year

MI

1980

1.113039

1968

0.85872

1976

0.706572

1992

0.556734

1983

1.09571

1974

0.805049

1969

0.702709

1977

0.423853

1981

1.086941

1982

0.800553

1988

0.691339

1990

0.396009

1963

1.07145

1955

0.798624

1957

0.681264

1989

0.348897

1966

0.994037

1954

0.791871

1959

0.677381

1975

0.347732

1964

0.990617

1972

0.744142

1978

0.652115

1958

0.334702

1953

0.953268

1991

0.740474

1993

0.610896

1973

0.332317

1967

0.90811

1986

0.739438

1971

0.609301

1970

0.313151

1961

0.901427

1965

0.735586

1956

0.598004

1987

0.285926

1962

0.888426

1979

0.708837

1960

0.57169

1985

0.205171

1984

0.871172

For Vancouver

Moisture Reference Years

• Let’s define for each city:

– Wet year as the year with highest MI

(red)

– Average year as the year closest to the

mean MI

(blue)

Moisture Reference Years

For Vancouver

Year

MI

Year

MI

Year

MI

Year

MI

1980

1.113039

1968

0.85872

1976

0.706572

1992

0.556734

1983

1.09571

1974

0.805049

1969

0.702709

1977

0.423853

1981

1.086941

1982

0.800553

1988

0.691339

1990

0.396009

1963

1.07145

1955

0.798624

1957

0.681264

1989

0.348897

1966

0.994037

1954

0.791871

1959

0.677381

1975

0.347732

1964

0.990617

1972

0.744142

1978

0.652115

1958

0.334702

1953

0.953268

1991

0.740474

1993

0.610896

1973

0.332317

1967

0.90811

1986

0.739438

1971

0.609301

1970

0.313151

1961

0.901427

1965

0.735586

1956

0.598004

1987

0.285926

1962

0.888426

1979

0.708837

1960

0.57169

1985

0.205171

1984

0.871172

Moisture Reference Years

• For example - Vancouver wet year

– 1980

– avg T = 9.5 C (normal 9.9 C) colder

– avg RH = 81.9 % (normal 80.2 %) more

humid

Moisture Reference Years

• Why do this?

– Allows us to classify years as W, A, or D

– Allows for statistical analysis of MRYs

• 1 in 10, 1 in 30, 1 in 100

– Allows us to construct sequences of years

for analysis

Summary

• Method for classifying climates w.r.t

Moisture loading

– Can begin define hazard zones

• Have a method for defining MRY

– Can begin to define return periods and test

reference years for design

Wall Performance

---INDEX OF CLIMATE SEVERITY--->

---INDEX OF WALL RESPONSE--->

Q

Ideal Wall