6.2 Room corner test method

Transcription

Development of a Screening Method
for the SBI and Room Corner using
the Cone Calorimeter
SP Fire Technology
SP REPORT 2002:11
model
exp
SBI Test
Room Corner Test
ConeTools Program
Cone Calorimeter
Nordtest project 1479-00
SP Swedish National Testing and Research Institute
Patrick Van Hees, Tommy Hertzberg
Anne Steen Hansen, SINTEF
2
Abstract
With the implementation of the classes of reaction to fire performance for construction
products, the so-called “Euroclasses”, a new test method has been introduced, namely the
SBI test method (EN 13823). This method is an intermediate scale test and it has been
developed to allow harmonization of the reaction to fire classification in Europe. As the
test is new and needs rather large samples there will be a need from the industry to have
an easy and cost effective tool for product development and quality control with respect
to this method. In the near future also the possibility of using an appeal procedure by
running the reference scenario test, ISO 9705, will be possible. Also for this test it would
be an advantage to have a screening method.
In this project such a tool has been developed by using cone calorimeter data according to
ISO 5660. The tool allows prediction of the major classification parameters for HRR and
SPR in the SBI and room corner. Within the project a software tool has also been
developed. The results of the project show that the predictions are satisfactory and that
the tool will be powerful for the product development by industry. This project can be
extended for other building products (e.g. cables, pipe insulation) once an European
classification system is in place for these products.
Key words: cone calorimeter, SBI, room tests, modeling, wall and ceiling linings
SP Sveriges Provnings- och
Forskningsinstitut
SP Rapport 2002:11
ISBN 91-7848-904-0
ISSN 0284-5172
Borås 2002
SP Swedish National Testing and
Research Institute
SP Report 2002:11
Postal address:
Box 857, SE-501 15 BORÅS
Sweden
Telephone: +46 33 16 50 00
Telefax: +46 33 13 55 02
E-mail: info@sp.se
Internet: www.sp.se
3
Contents
Abstract
2
Contents
3
Preface
5
Sammanfattning
5
1
Background
7
2
Scope
9
3
3.1
11
3.5
3.6
Research programme
WI 1 Development of screening protocol in ISO 5660 and fine
tuning of models
WI 2 Additional testing in ISO 5660 and SBI
WI 3 Validation of the models for prediction of FIGRA
WI 4 Development and validation of a prediction model for
SMOGRA in SBI and RCT
WI 5 Development of the software
WI 6 Conclusions and reporting
4
4.1
4.2
4.3
Overview of test methods
Cone Calorimeter test
SBI test method
Room corner test method
13
13
13
15
5
Euroclass system
17
6
6.1
6.1.1
6.1.2
6.1.2.1
6.1.2.2
6.1.2.3
6.1.3
6.1.4
6.2
6.2.1
6.2.2
21
21
21
25
25
26
28
28
28
30
30
6.2.3
Development of models for FIGRA and HRR
SBI test method
Description of model
Sensitivity study of model
Influence of HRR threshold and ignition time
Influence of backing board
Shiny materials
Guidance and description testing protocol
Comparison and discussion of simulation results
Description of model(s)
Description of test protocol in ISO 5660 used for the
simulations
7
7.1.1
7.2
7.3
7.3.1
7.3.2
7.3.3
7.4
7.4.1
Development of models for Smoke production
Statistical method
Test results from ISO 5660 used in the prediction models
Prediction of smoke production in the SBI test
General
Predicting the level of SMOGRA
Predicting the smoke classification s1, s2 or s3
Prediction of smoke production in the Room Corner test method
Flashover is determining for the smoke production
39
39
40
41
41
43
44
46
46
3.2
3.3
3.4
11
11
11
11
12
12
37
37
4
7.4.2
7.4.3
7.4.4
46
47
7.5
How predictable is the Room Corner SMOGRA value?
The EUREFIC classification system
Prediction of maximum smoke production rate in the Room
Corner test
Prediction of average smoke production rate in the Room
Corner test
Implementation of the prediction models
8
8.1
8.2
8.2.1
8.2.2
8.2.3
8.2.4
8.2.5
8.2.6
8.2.7
8.2.8
8.2.9
8.3
8.3.1
8.3.2
8.3.3
Software development
Principle
Different Button menus
Open file menu
Calculate menu
Select what to plot menu
Compare menu
Save SBI and SBI RCT menu
Print menu
Types menu
Help menu
Exit menu
Different Scroll menus
File menu
Simulation menu
Help menu
55
55
56
56
57
58
59
60
61
62
63
64
64
64
64
64
9
Conclusions
65
Annex A
Simulation results for cone-RCT model for HRR
The Eurefic project
The SBI research project
67
67
72
Annex B
Simulation results for Cone-SBI HRR model
Eurefic data
SBI RR materials
Additional data
87
87
88
102
Annex C
Generic description of SBI RR materials and Eurefic data
105
References
109
7.4.5
48
51
53
5
Preface
This work was sponsored by Nordtest as Nordtest project 1479-00. The authors would
like to thank all industrial sponsors for their support. Also the staff conducting the tests
and this work at SP, Interscience and SINTEF are thanked. Special thanks to Birgitte
Messerschmidt (Rockwool International) for her efforts in the first version of the software
and to Jesper Axelsson (SP) for the user interfaces in the software.
Sammanfattning
Införandet av nya europaklasser för byggnadsmaterial har medfört implementering av en
ny testmetod, SBI (Single Burning Item) enligt Europanorm EN 13283. Metoden är att
karakterisera som en metod i mellanskala och den har utvecklats främst för att möjliggöra
en harmonisering av brandklasser för ytmaterial i Europa.
Då metoden är ny och SBI medför relativt omfattande experiment, finns behov av att ta
fram enkla, småskaliga och kostnadseffektiva verktyg för industrin att använda i sin
produktutveckling och kvalitetsövervakning. Det finns även ett behov av att ta fram ett
sådant verktyg för det större referensscenariotestet, ISO 9705, vilket kommer att kunna
användas för att ’överklaga’ en produktklassificering som erhållits enligt SBI, EN 13283.
I detta projekt har ett simuleringsverktyg baserat på ett småskalig test utvecklats.
Metoden baseras på konkalorimeter test enligt internationell standard ISO 5660.
Verktyget möjliggör att uppskatta viktiga materialparametrar som styr
produktklassificeringen enligt EN 13283 eller ISO 9705.
Modelleringsverktyget visar en mycket god överensstämmelse mellan prediktion baserat
på småskaliga konkalorimeterförsök och experiment utförda i stor (ISO 9705) eller
mellan (EN 13283) skala och kommer att kunna nyttjas för ett stort antal industriella
produkter. Projektet och verktyget kan dessutom utvidgas till att omfatta andra
byggnadsprodukter såsom t.ex. kablar eller rörisoleringsmaterial, så fort en motsvarande
klassificering finns på plats för dessa produkter.
6
7
1
Background
Within the Euroclass system two methods are important with respect to classification of
wall and ceiling linings. On one hand there is the SBI (Single Burning Item) test, which is
the key test for Euroclasses A2 to D. On the other hand there is the room corner test (ISO
9705 or Nordtest NT FIRE 025), which is used as the reference scenario for the
Euroclasses. Both tests, however, cannot be considered as small-scale tests. Hence a need
for a small-scale test is necessary for screening but also as a tool for production control
and as a tool for product development. The cone calorimeter test according to ISO 5660 is
the most appropriate choice.
At the same time it should be noted that the room corner test results are used for
determining fire restricting materials in the HSC (High Speed Craft) Code within the
IMO regulations. Also in this case it is desirable with a small scale screening method for
production control and product development.
An important factor for the worldwide application of a combination of small and largescale tests is that the room corner test recently has been used as reference test for the
classifications system in the Japanese building code.
8
9
2
Scope
This project will develop a screening procedure for wall and ceiling lining testing in the
SBI and room corner tests. The outcome of this project will be a testing protocol and also
a multifunctional user-friendly software package allowing users to predict SBI and room
corner test results by means of cone calorimeter tests.
10
11
3
Research programme
The research programme sponsored by Nordtest had the following work items. In this
chapter an overview of these work items is given, and also a reference to the parts of this
report that are dealing with the specific work items.
3.1
WI 1 Development of screening protocol in ISO
5660 and fine tuning of models
This work package would develop a cone calorimeter test protocol for the screening test.
A number of materials from the RR in the SBI project would be chosen to optimise the
model. Following items will be investigated:
1.
2.
3.
4.
5.
The optimum heat flux level in the cone calorimeter
The optimum sample preparation for testing
The optimum substrate for sample preparation
The optimum surface emissivity for testing shiny materials
The optimum ignition properties of the materials
In total a maximum of 35 cone calorimeter tests were planned.
The work item will also include the fine-tuning of the models i.e. the cone-sbi and the
cone-room corner conetools model so that they can be used at the same heat flux level
and with the same sample preparation. One of the items in the cone-sbi model is the
improvement of the lateral flame spread routine. One of the items in the cone-rct model is
the adaptation of the model to use the same heat flux level as in the cone-sbi model.
The work with respect to this work item is summarised in chapter 6
3.2
WI 2 Additional testing in ISO 5660 and SBI
Five more materials would be checked and also the remaining RR materials of the SBI
project will be retested with the optimum test protocol defined in WI1, if necessary. The
five additional materials will also be tested in the SBI apparatus.
This part is included in the work described in chapter 6. More materials were provided
thanks to industrial support.
3.3
WI 3 Validation of the models for prediction of
FIGRA
This work item would validate the model for prediction of the FIGRA index for the SBI
test and for the room corner test. It will also validate the HRR curve obtained and fine
tune the model additionally if necessary. This work item is described in chapter 6.
3.4
WI 4 Development and validation of a prediction
model for SMOGRA in SBI and RCT
This work item would develop a first prediction model for the SMOGRA in the SBI and
the RCT. It will be based on a correlation model with different parameters from the cone
calorimeter test. As smoke prediction are very difficult to perform between different
scales of testing a modest approach should be taken.
This work item is described in chapter 7.
12
3.5
WI 5 Development of the software
In this work item a multifunctional and user-friendly software package will be developed.
The request for Nordtest support was limited and more internal research resources were
used.
The software shall have the following properties:
1. user-friendly interface
2. multifunctional input routine i.e. different type of input files should be possible e.g.
Excel, FDMS and commercial software file formats
3. standard output report with links to Word/Excel
The development is described in chapter 8.
3.6
WI 6 Conclusions and reporting
This work package draws conclusions and gives a proposal for the screening test and a
manual for the software. These items are described in chapter 6, 7, 8 and 9.
13
4
Overview of test methods
4.1
Cone Calorimeter test
The cone calorimeter is described in ISO 56601. The test method describes a test
specimen with an area of 100 mm x 100 mm, which is exposed to a constant radiant heat
flux. The heat flux can be adjusted from 10 kW/m2 to 100 kW/m2. A spark plug
positioned over the test specimen ignites any flammable gasses produced by the test
specimen. The effluents from the test is collected in a hood and transported through a
duct. In the duct there is a thermocouple, a pressure sensor, smoke measurement system
and a sample probe. Furthermore the test specimen is positioned on a load cell, so the
mass loss of the test specimen can be recorded during the test. The test equipment is
shown in Figure 1.
The test results are heat release rate (calculated using oxygen depletion), time to ignition,
smoke production and weight loss.
Figure 1
Cone Calorimeter.
4.2
SBI test method
The Single Burning Item (SBI) test2 is developed by the Official Laboratories Group
(OLG) based on the guidelines set out by the EU Regulatory Group (RG). The SBI test is
one of the test methods to be used to determine the classification of building products in
the future European classification system.
The SBI test simulates a single burning item (e.g. a waste paper basket) burning in a
corner of a room. The test rig is shown in Figure 2. The dimensions of the test specimen
are 1.0 m x 1.5 m and 0.5 m x 1.5 m. The two wings of the test specimen overlap in the
14
corner behind the burner. Products are tested in their end use conditions as far a possible.
The burner is triangular with a side length of 250 mm, and is a diffusion burner supplied
with propane. The output of the burner is 30 kW for 21 minutes. The classification results
are only evaluated over a maximum of 20 minutes. There is a floor in the test
configuration but no ceiling. The effluents from the fire are collected in the hood and
transported through the duct. In the duct thermocouples, a pressure sensor, a smoke
measurement system and a sample probe are mounted. The test rig is placed in an
enclosure in order to avoid any draft around the test specimen and to protect the operator
from the produced smoke.
Gas analysis
(O 2 , CO, CO 2 )
Sm oke
measurement
Exhaust
gases
Fl ow
measurement
Enclosure
Ignition
source
Trolley with
specim en holder
Figure 2
Single Burning Item test.
The test results are heat release rate (calculated using oxygen depletion), lateral flame
spread on the large wing of the test specimen, smoke production and burning
droplets/particles. From the results parameters such as FIGRA and SMOGRA are
calculated. An overview how the parameters are used inside the Euroclass system is given
in chapter 5.
The FIGRA(SBI) index is defined as the maximum value of 30 second averaged heat
release rate divided by time. The calculation of the FIGRA(SBI) index is described in
detail in EN 13823. The SMOGRA(SBI) is defined as the maximum value of 60 second
averaged smoke production rate divided by time.
15
4.3
Optical density
(lamp/photocell)
Gas analysis (O2, CO, CO2)
Volume flow
Temperature and differential pressure
Exhaust gases
2,40m
Exhaust hood
3,0x3,0x1,0
Gas
burner
3,60m
Figure 3
Doorway 0,8m x 2,0m
0m
2,4
Room Corner Test.
Room tests are performed according to ISO 9705, Room Corner Test3. The test room has
nominal internal dimensions of 3.6 m by 2.4 m by 2.4 m (length by width by height). The
test material is mounted so that the three inner walls and the ceiling in the room are
covered. Smoke gases are vented and air is let in through the door opening. The ignition
source is a gas burner, which is placed in one of the inner corners. The burner heat output
is 100 kW for the first ten minutes and then 300 kW for another ten minutes. The smoke
gases coming out through the doorway are collected by a hood and exhaust system from
where samples are taken for gas analysis. Heat release rate and smoke production rate are
measured continuously. A schematic drawing is given in Figure 3.
16
17
5
Euroclass system
The European Commission published the classes of reaction to fire performance of
construction products, the so called “Euroclasses” on February 8, 2000. Reaction to fire
testing will follow a new concept compared to existing procedures in Europe. Seven main
classes are introduced, the Euroclasses. These are A1, A2, B, C, D, E and F. A1 and A2
represent different degrees of limited combustibility. For linings, classes C to E represent
products that may go to flashover in a room and at certain times. F means that no
performance is determined. B means no flashover in a room corner test. Thus there are 7
classes for linings and 7 classes for floor coverings. Additional classes for smoke
production and any occurrence of burning droplets are also given, see Table 1 and Table
2.
18
Table 1
Classes of reaction to fire performance for construction products
excluding floorings (*)
Class
Test method(s)
Classification criteria
A1
EN ISO 1182 (1);
And
∆T ≤ 30°C; and
∆m ≤ 50%; and
tf = 0 (i.e. no sustained flaming)
PCS ≤ 2.0 MJ.kg-1 (1); and
PCS ≤ 2.0 MJ.kg-1 (2) (2a); and
PCS ≤ 1.4 MJ.m-2 (3); and
PCS ≤ 2.0 MJ.kg-1 (4)
EN ISO 1716
A2
EN ISO 1182 (1);
Or
EN ISO 1716;
and
EN 13823 (SBI)
B
C
D
E
F
EN 13823 (SBI);
And
EN ISO 11925-2(8):
Exposure = 30s
EN 13823 (SBI);
And
EN ISO 11925-2(8):
Exposure = 30s
EN 13823 (SBI);
And
EN ISO 11925-2(8):
Exposure = 30s
EN ISO 11925-2(8):
Exposure = 15s
No performance determined
∆T ≤ 50°C; and
∆m ≤ 50%; and tf ≤ 20s
PCS ≤ 3.0 MJ.kg-1 (1); and
PCS ≤ 4.0 MJ.m-2 (2); and
PCS ≤ 4.0 MJ.m-2 (3); and
PCS ≤ 3.0 MJ.kg-1 (4)
Additional classification
-
-
FIGRA ≤ 120 W.s-1; and
LFS < edge of specimen; and
THR600s ≤ 7.5 MJ
THR600s ≤ 7.5 MJ
Fs ≤ 150mm within 60s
Smoke production(5); and
Flaming droplets/ particles (6)
THR600s ≤ 15 MJ
FIGRA ≤ 750 W.s-1
(*) The treatment of some families of products, e.g. linear products (pipes, ducts, cables etc.), is still under review
and may necessitate an amendment to this decision.
(1) For homogeneous products and substantial components of non-homogeneous products.
(2) For any external non-substantial component of non-homogeneous products.
(2a) Alternatively, any external non-substantial component having a PCS ≤ 2.0 MJ.m-2, provided that the product
satisfies the following criteria of EN 13823(SBI) : FIGRA ≤ 20 W.s-1; and LFS < edge of specimen; and THR600s ≤
4.0 MJ; and s1; and d0.
3
( ) For any internal non-substantial component of non-homogeneous products.
(4) For the product as a whole.
(5) s1 = SMOGRA ≤ 30m2.s-2 and TSP600s ≤ 50m2 ; s2 = SMOGRA ≤ 180m2.s-2 and TSP600s ≤ 200m2; s3 = not s1 or s2.
(6) d0 = No flaming droplets/ particles in EN13823 (SBI) within 600s; d1 = No flaming droplets/ particles persisting
longer than 10s in EN13823 (SBI) within 600s; d2 = not d0 or d1; Ignition of the paper in EN ISO 11925-2 results in
a d2 classification.
(7) Pass = no ignition of the paper (no classification); Fail = ignition of the paper (d2 classification).
(8) Under conditions of surface flame attack and, if appropriate to end–use application of product, edge flame attack.
19
Table 2
Classes of reaction to fire performance for floorings.
Class
Test method(s)
Classification criteria
Additional classification
A1FL
EN ISO 1182 (1);
And
∆T ≤ 30°C; and
∆m ≤ 50%; and
-
EN ISO 1716
tf = 0 (i.e. no sustained flaming)
PCS ≤ 2.0 MJ.kg-1 (1); and
-
PCS ≤ 2.0 MJ.kg (2); and
PCS ≤ 1.4 MJ.m-2 (3); and
-1
PCS ≤ 2.0 MJ.kg-1 (4)
A2FL
EN ISO 1182 (1);
Or
EN ISO 1716;
and
BFL
CFL
DFL
EFL
EN ISO 9239-1 (5)
EN ISO 9239-1 (5)
and
EN ISO 11925-2(8):
Exposure = 15s
EN ISO 9239-1 (5)
And
EN ISO 11925-2(8):
Exposure = 15s
EN ISO 9239-1 (5)
And
EN ISO 11925-2(8):
Exposure = 15s
EN ISO 11925-2(8):
Exposure = 15s
No performance determined
∆T ≤ 50°C; and
∆m ≤ 50%; and
tf ≤ 20s
PCS ≤ 3.0 MJ.kg-1 (1); and
PCS ≤ 4.0 MJ.m-2 (2); and
PCS ≤ 4.0 MJ.m-2 (3); and
PCS ≤ 3.0 MJ.kg-1 (4)
Critical flux (6) ≥ 8.0 kW.m-2
-
-
Smoke production (7)
-
FFL
(1) For homogeneous products and substantial components of non-homogeneous products.
(2) For any external non-substantial component of non-homogeneous products.
(3) For any internal non-substantial component of non-homogeneous products.
(4) For the product as a whole.
(5) Test duration = 30 minutes.
(6) Critical flux is defined as the radiant flux at which the flame extinguishes or the radiant flux after a test period of 30
minutes, whichever is the lower (i.e. the flux corresponding with the furthest extent of spread of flame).
(7) s1 = Smoke ≤ 750%.min; s2 = not s1.
(8) Under conditions of surface flame attack and, if appropriate to the end–use application of the product, edge flame
attack.
Symbols. The characteristics are defined with respect to the appropriate test method.
temperature rise
∆T
mass loss
∆m
duration of flaming
tf
PCS
gross calorific potential
FIGRA
fire growth rate
total heat release
THR600s
LFS
lateral flame spread
SMOGRA
smoke growth rate
TSP600s
total smoke production
Fs
flame spread
20
Definitions
Material : A single basic substance or uniformly dispersed mixture of substances, e.g.
metal, stone, timber, concrete, mineral wool with uniformly dispersed binder, polymers.
Homogeneous product : A product consisting of a single material, of uniform density
and composition throughout the product.
Non-homogeneous product : A product that does not satisfy the requirements of a
homogeneous product. It is a product composed of one or more components, substantial
and/or non-substantial.
Substantial component : A material that constitutes a significant part of a nonhomogeneous product. A layer with a mass per unit area ≥ 1.0 kg/m2 or a thickness ≥ 1.0
mm is considered to be a substantial component.
Non-substantial component : A material that does not constitute a significant part of a
non-homogeneous product. A layer with a mass per unit area < 1.0 kg/m2 and a thickness
< 1.0 mm is considered to be a non-substantial component.
Two or more non-substantial layers that are adjacent to each other (i.e. with no substantial
component(s) in-between the layers) are regarded as one non-substantial component and,
therefore, must altogether comply with the requirements for a layer being a nonsubstantial component.
For non-substantial components, distinction is made between internal non-substantial
components and external non-substantial components, as follows :
Internal non-substantial component : A non-substantial component that is covered on
both sides by at least one substantial component.
External non-substantial component : A non-substantial component that is not covered
on one side by a substantial component.
An Euroclass is intended to be declared as for example Bd1s2. B represents the main
class, d1 means droplets/particles class no 1 and s2 means smoke class no 2. This gives
theoretically a total of about 40 classes of linings and 11 classes of floor coverings to
choose from. However, each country is expected only to use a very small fraction of the
possible combinations.
21
6
Development of models for FIGRA and
HRR
6.1
SBI test method
6.1.1
Description of model
The calculation model4,5 presented here uses ignition time as well as the complete heat
release rate curve from the Cone Calorimeter. In principle results from a single small
scale test is used to predict the first part of the heat release rate curve in the SBI and
hence the FIGRA(SBI) index. Other models6,7 exist but for this project the abovementioned model was used.
The calculation model, based on the conetools model8 for the room corner, is described in
detail below in sections containing principles, area growth, criterion for flame spread and
heat release rate, respectively.
Principles of prediction model
Three major assumptions have been made in the prediction model of heat release rate in
the SBI test:
1)
The burning area growth rate and the heat release rate are decoupled.
2)
The burning area growth rate is proportional to the ease of ignition, i.e. the inverse
of the time to ignition in small scale.
3)
The history of the heat release rate per unit area at each location in the SBI test is
the same as in small scale.
Burning area growth rate
The fire spread can follow two different routes as shown in Figure 4. All products start to
spread along route I. A product is assumed to continue to spread along route III if the
calculated sustained flame height is at least 1.5 m, which is equal to the height of the test
sample. Otherwise the product is assumed to spread along route II. The calculation of
flame height will be outlined below.
22
0.8
0.7
Area [m2]
0.6
III
0.5
0.4
0.3
II
I
0.2
0.1
0
0
100
200
300
400
500
600
Time [sec.]
Figure 4
Burning area curve modelled for the SBI.
Within the different flame spread regimes the burning area growth rate of a product
depends on ignitability, i.e. time to ignition in the Cone Calorimeter. Once the flame
spread rate is determined the heat release rate is calculated assuming that products always
give the same heat release rate per unit area as a function of time in small scale as in the
SBI test. In other words all parts of the tested product are assumed to burn in the same
way in the SBI as in small scale. This is of course a simplification. The heat release rate
depends more or less on the actual heat flux level received by the product as a function of
time. However, the experience so far of the model shows, that the errors average out and
can be included in the empirical constants.
The flame spread of the product is described by an S-shaped curve, which is a function of
time. The curve represents a step response of second order system. In the beginning of the
test, the product ignites at one point on the test sample. This ignition time is assumed to
be a time equal to half of the ignition time found in the Cone Calorimeter at 40 kW/m2.
Immediately after ignition, the area growth rate of the product is slow. The area growth
rate will then accelerate, depending on the time to ignition in the Cone Calorimeter, until
the involved area gets close to its maximum value. Then the area growth rate slows down
again.
The area growth rate is described by the following function:
 
t ign
  t−
2
A(t) = A max ⋅ 1 − 1 +

t

ign
 
t



 t − ign
2
 ⋅ exp

 t ign










[1]
where Amax is the maximum area involved and tign is time to ignition in the Cone
Calorimeter.
23
In the beginning of the test, all products are assumed to follow the same area growth
function. However, if the sustained flame height reaches the top of the test specimen,
which is 1.5 m, then the maximum area in the area growth function changes. This is the
only parameter that is changed when changing from one flame spread regime to another.
The sustained flame height is a function of the calculated total heat release in the test as
explained in the section about criteria for flame spread.
The area growth function and the different values for the maximum area are empirically
chosen. However, they agree very well with those observed during the SBI round robin
test series at both SP and Danish Institute of Fire Technology (DIFT).
The maximum area is assumed to be 0.35 m2 for the products, which do not have a
sustained flame height of 1.5 m. This area is roughly equal to the area behind the burner
flames. For products where the sustained flame height exceeds 1.5 m the maximum area
is 0.60 m2. This maximum area is chosen based on the configuration of the SBI test. The
burner has a side length of 250 mm and is positioned at a distance of 40 mm from the test
specimen. If the flames were spreading to the top of the test specimen in the entire width
of the burner the maximum area should be 0.87 m2. However, since the burner is
triangular, the thickness of the flame varies. During the tests, it was observed that the
flame leaned into the corner and in no way has a width identical to the width of the burner
in its entire height. Using these areas as maximum areas for flame spread the model gives
good agreement with observations during tests.
Some products will spread the flames more than 0.6 m2 before reaching their first peak in
heat release rate. These are products with an extremely short ignition time in the Cone
Calorimeter, or thermoplastic products, which create pool fires before reaching their
maximum heat release. The model does therefore not give correct results for these types
of products when it comes to peak heat release. But as will be shown later, the
FIGRA(SBI) index for these products is predicted quite good by the model which is due
to a good estimation of the initial inclination of the heat release curve.
Criteria for flame spread
As shown in Figure 4 the flames will either spread over a small area or over a larger part
of the test sample. The criterion used in this model to decide what the maximum area of
the flame spread will be, is the sustained flame height in the corner.
In the original Cone Tools model the criteria used to determine if flames were spreading
over a larger area than what was initially involved, is an assumed surface temperature.
This assumed surface temperature depended on the temperature of the combustion gasses
passing over the surface and on the thermal response of the product surface. This
approach agrees well with the fact that there is a hot gas layer under the ceiling in the
Room Corner. However in the SBI test there is no ceiling and hence no hot gas layer to
heat the product. The temperature of the surface of the product in the SBI test depends
primarily on the radiation and convective heat transfer from the flame.
Using the flame height to determine the maximum area over which the flames will spread
is based on observations from SBI tests, and on the assumption that the part of the
product behind the sustained flame will receive a heat flux from the flame sufficient to
ignite that part of the product. In the SBI test the product will first ignite in the corner
behind the burner flames. The involved area will then spread mostly upwards behind the
flame from the burner. Depending on the burning behaviour of the product, the heat
release from the burner and the product can be high enough to create a flame in the
24
corner, which will have a sustained height equal to, or higher, than the height of the test
sample. If this is the case the upward flame spread will continue to the top of the test
specimen.
The flame height in a wall corner geometry is given as9:
H
& *2/3
= 3⋅ Q
D
[2]
where
5/3
&* =Q
&
Q
⋅1110)
total ⋅ (D
[3]
D is the diameter of the burner, which was assumed 150 mm considering that the burner
&
is triangular. Using these expressions gave the criteria that the total heat release Q
total
shall be greater than 59 kW if the flames shall spread to the top of the specimen. This
criterion also agrees well with what was observed in the SBI tests.
Calculation of heat release rate
The total heat release from the SBI test is obtained by summing up the contributions from
each part of the total burning area and the burner.
&
&
&
Q
total = Q product + Q burner [4]
&
&
Q
burner is constant at 30 kW while Q product varies with time as the fire spreads, the
involved area A(t) increases as described above and the burning intensity at each position
&
is time dependent. Q
product is obtained by adding the contributions from burning parts
which have started to burn at various times. The heat release rate of the specimen at each
location is then assumed to go through the same history as was measured in bench-scale,
i.e. the Cone Calorimeter.
&
Q
product is calculated using the Duhamel´s integral:
t
Q& product = ∫ A& (τ ) q&bs′′ (t − τ ) dτ
0
[5]
& is the time derivation of the burning area, t is time, q& ′′ is the heat release per
where A
bs
unit area as recorded in the Cone Calorimeter and τ is a dummy variable.
The following very simple numerical solution to the Duhamel's integral is the approach
used in this model:
Q& product = ∑ ∆Ai q&bs′′ N −i
[6]
25
′′ N −i is
Where ∆A i is the incremental burning area growth at the time increment i, and q&bs
the heat release rate per unit area after (N-i) time increments as recorded in the Cone
Calorimeter.
Correction for cone calorimeter data obtained at other heat flux levels
The model has been developed to use cone calorimeter at a heat flux level of 50 kW/m2.
In order to be able to use the model also with cone resultants different from the preset
value a correction was introduced for both the ignition time and the HRR level. The
correction is based mainly on fine-tuning the results:
tignCorr = tignCone.* (ConeFlux / SBIFlux)
HRRCorr = HRRCone(SBIFlux / ConeFlux)^0.5
Where:
tignCorr:
tignCone
HRRCorr
HRRCone
ConeFlux
SBIFlux
Corrected ignition time used in the model
Ignition time in the cone calorimeter test
Corrected heat release rate
Heat release rate in the cone calorimeter test
Flux level in the cone calorimeter test
Corresponding reference flux for the cone-SBI model being 40 kW/m2
It is understood that for ignition the correction is based on thermally thin theory but this
has shown to give the best simulation results. The exponent for the HRR correction was
determined in a similar way.
6.1.2
Sensitivity study of model
6.1.2.1
Influence of HRR threshold and ignition time
The main input parameters to the model are the ignition time and the heat release curve.
The HRR curve is automatically registered by a computer, but the ignition time is
obtained from visually observing the experiment. In this project also a HRR threshold
value was used to investigate whether it is possible to run this as an alternative. This is
especially interesting for material with heavy flashing behaviour (FR materials) and for
materials with low HRR levels where maybe even no ignition occurs. From the results in
Table 3 it can be seen that a threshold of 10 kW/m2 can be used as alternative for a visual
ignition time. Using 50 kW/m2 as a threshold gives mainly lower Figra values i.e. results
with a better classification. However, the use of a HRR threshold value should also be
done after studying the actual HRR curve. This can be done in the conetools software
package before the calculations are performed.
From our experience it is also advisable to run a small sensitivity study on the ignition
time in order to investigate whether it has a great influence on the result. If so it can be
advisable to run at another flux level. This is mainly the case for materials with short
ignition times.
26
Table 3
Results of 5 materials with visual ignition time and HRR threshold
values as input for the ignition time.
Material
CP1
Cone test
Test 1
CP1
Test 2
CP2
Test 1
CP2
Test 2
CP3
Test 1
CP3
Test 2
CP4
Test 1
CP5
Test 1
Ignition criterion
Visual = 7s
HRR= 50 kW/m2
HRR= 10 kW/m2
Visual = 10s
HRR= 50 kW/m2
HRR= 10 kW/m2
Visual = 11s
HRR= 50 kW/m2
HRR= 10 kW/m2
Visual = 14s
HRR= 50 kW/m2
HRR= 10 kW/m2
Visual = 15s
HRR= 50 kW/m2
HRR= 10 kW/m2
Visual = 17s
HRR= 50 kW/m2
HRR= 10 kW/m2
Visual = None
HRR= 50 kW/m2
HRR= 10 kW/m2
Visual = None
HRR= 50 kW/m2
HRR= 10 kW/m2
FIGRA*
383
254
383
255
185
255
79
NA1
79
47
NA1
47
62
NA1
59
46
NA1
34
NA2
NA1
27
NA2
NA1
19
FIGRA0.2
274
176
274
197
135
197
67
NA1
67
43
NA1
43
44
NA1
41
36
NA1
27
NA2
NA1
25
NA2
NA1
0
FIGRA0.4
77
58
77
79
63
79
32
NA1
32
27
NA1
27
0
NA1
0
14
NA1
11
NA2
NA1
17
NA2
NA1
0
THR
0.61
0.61
0.61
0.68
0.68
0.68
0.58
NA1
0.58
0.61
NA1
0.61
0.45
NA1
0.45
0.53
NA1
0.53
NA2
NA1
1.1
NA2
NA1
0.72
* without threshold level of THR, only HRR > 3kW
NA1: not applicable since HRR is lower than the threshold (Values would be zero)
NA2: not applicable since no visual ignition occurred (Values would be zero)
6.1.2.2
Influence of backing board
Figure 5 and Figure 6 give the difference between a sample preparation with and without
the standard backing board used in the SBI. It can be seen that this improves the quality
of the simulation, especially in the second part of the SBI curve. It is hence advisable to
use as often as possible a backing board or substrate identical to the one that will be used
in the SBI test.
27
M22 with backingboard
140
120
hrrSBIsim(kW)
HRRSBI
100
80
60
40
20
0
0
30
Figure 5
60
90
120
150
180
210
240
270
300
330
360
390
420
450
480
510
540
570
600
Simulation of particle board (M22) with backing board.
M22
140
hrrSBIsim(kW)
120
HRRSBI
100
80
60
40
20
0
0
30
Figure 6
60
90
120
150
180
210
240
270
300
330
360
390
420
450
480
510
Simulation of particle board (M22) without backing board.
540
570
60
28
6.1.2.3
Shiny materials
The total heat flux towards the specimen in case of the cone calorimeter consists mainly
of radiation (more than 90%). This means that materials with a shiny surface such as M4
and Insulation material 2 in Table 4 will reflect a large part of the incident heat flux from
the cone heater. In the SBI test, however, the radiation will be lower than in the cone
calorimeter as a larger part of the incident heat flux is based on convection. Moreover, the
materials will be sooted very fast and hence receive more radiation energy due to an
increase of the surface emissivity. This could be observed for the two above-mentioned
materials. Without a sooted or painted surface the materials did not ignite at a heat flux
level of 50 kW/m2 (Insulation material 2) or showed a very high ignition time (M4)
resulting respectively in a >B and C classification.
6.1.3
Guidance and description testing protocol
The following guidance can be given when preparing test specimens in the cone
calorimeter:
1. Materials should by preference be tested at 50 kW/m2 unless very short ignition
times (less than 5s) are observed. In this case a lower heat flux level can be
chosen
2. The preparation of the sample should closely follow the mounting as in the SBI
test. So it is advisable to run the materials in the cone calorimeter with the
backing boards described in the SBI standard.
3. Shiny materials, e.g materials with Aluminium foil facing, should also be tested
with the surface sooted or blackened by paint (with limited combustibility e.g.
heat flux meter paints). The results may in this case be more conservative, but
will allow a better overall prediction.
4. If very short ignition times (less than 5 s) are obtained at the cone heat flux level,
it can be advisable to reduce the heat flux level in the cone calorimeter.
6.1.4
In annex B an overview is given of the graphs comparing the SBI test results with the
prediction of the conetools model. The data given here are all SBI RR materials (except
cables and pipes), one Eurefic material (used as market place material in the SBI project),
5 ceiling panels and 3 insulation materials. The generic description of the RR materials is
given in annex C.
From the results in Table 4 it can be seen that a satisfactory prediction tool has been
developed. The marked materials are those where a wrong classification is obtained. In
two cases the materials are melting products (M2 and M7). Here some more research is
needed to try to improve the model if possible.
In the two other cases the results are so-called borderline results (M5 and M26).
29
Table 4
Summary of simulation results for the cone-SBI model.
Material
FIGRA_02
(MW/S2)
FIGRA_04
(MW/S2)
THR (MJ)
M01
M02*
M03
M04
M05*
M06
M07*
M08
M09
M10
M11
M12
M13
M14
M15
M16
M19
M20
M21
M22
M23
M24
M25
M26*
M27
M28
M29
M30
Eurefic3
Ceiling P1
Ceiling P2
Ceiling P3
Ceiling P4
Ceiling P5
Insulation 1
Insulation 2
Insulation 3
28
262
1554
2109
1212
0
428
37
147
675
60
592
47
96
0
335
10
361
6
473
430
450
421
734
42
21
153
2236
476
236
55
34
25
0
1404
767
448
6
262
1554
2109
1212
0
428
24
114
658
35
592
28
88
0
335
7
361
3
473
430
450
421
734
36
3
127
2227
476
78
30
6
17
0
1052
747
448
0.5
0.6
19.0
0.6
26.0
0.4
0.6
0.35
1.1
6.0
0.35
23.9
0.35
4.9
0.1
0.6
0.35
0.6
0.35
33.6
0.6
31.0
36.7
35.0
0.35
0.35
1.45
0.6
3.2
0.65
0.60
0.49
1.1
0.59
7.6
4.5
16.7
* Simulation too severe
Euroclass
according
to
simulation
≥B
D
E
E
E
≥B
D
≥B
C
D
B
D
≥B
≥B
≥B
D
≥B
D
≥B
D
D
D
D
D
≥B
≥B
C
E
D
C
≥B
≥B
≥B
≥B
E
D
D
Euroclass
according
to test
result
≥B
>B
E
E
D
≥B
>B
≥B
C
D
B
D
≥B
≥B
≥B
D
≥B
D
≥B
D
D
D
D
E
≥B
≥B
C
E
D
C
≥B
≥B
≥B
≥B
E
D
D
30
6.2
6.2.1
Description of model(s)
In the Wickström/Göransson model8 ignition time as well as the complete heat release
rate curve from the Cone Calorimeter are used. In principle results from a single smallscale test could be used to predict the full-scale behavior of a product.
Certainly there are lots of products for which it is not possible to predict their behavior in
large scale based on small-scale tests. Examples are products with a protective surface or
with joints, which after some heating suddenly cracks and exposes highly flammable
materials. They need to be tested in large scale to get results that can be used for
evaluating their potential fire hazard.
The calculation model is described in detail below in four sections containing principles,
area growth, heat release rate and criterion for flame spread, respectively. Finally
calculated full-scale heat release rates are shown and compared with measured values.
Principles of prediction model
Three major assumptions have been made in the prediction model of heat release rate in
full scale:
1) The burning area growth rate and the heat release rate are decoupled.
2) The burning area growth rate is proportional to the ease of ignition, i.e. the inverse of
the time to ignition in small scale.
3) The history of the heat release rate per unit area at each location in full scale is to be
the same as in small scale.
As shown Figure 7 the fire spread may follow three different routes. A product is
assumed to spread along routes II and V if a calculated fictitious surface temperature is
higher than a critical value. The calculation is based on data from the Cone Calorimeter
as will be outlined below.
Within the different flame spread regimes the burning area growth rate depends on
ignitability, i.e. time to ignition in the Cone Calorimeter. Once the flame spread rate is
determined the heat release rate is calculated assuming that products always give the
same heat release rate per unit area as a function of time in small and large scale. In other
words all parts of the tested product is assumed to burn in the same way in full scale as in
small scale. This is of course a vast simplification. The heat release rate depends more or
less on the actual heat flux level received by the product as a function of time. The
experience so far of the model shows, however, that the errors average out and can be
included in empirical constants.
31
15
2
Burning area (m )
II
V
10
B
VI
5
IV
A
III
I
0
0
5
10
15
20
Time (min)
Figure 7
Area growth as a function of time for the model.
Burning area growth rate depends on the time to ignition
The flame spread at the beginning of the test is divided into two subsequent parts. First
the area in the corner behind the burner is ignited. The size of the area is assumed to be
the same for all products. In the second part, the burning area is assumed to grow
according to a given function of time. It will, however, start to grow only if a fictitious
surface temperature is reached. This assumed temperature depends on ignitability as well
as on heat release properties of the product. These parameters are obtained from the Cone
Calorimeter and together they give sufficient information for the model to predict the
flame spread development.
Figure 7 shows how the area behind the burner first ignites first (I) and burns at a certain
heat release rate. As mentioned above products then behave in one of two ways; either
there is a progressive flame spread that eventually will involve the entire room (II), or
there is no further flame spread outside the burner flame region (III). Which category the
product belongs to be determined by the flame spread propagation criterion, which is
described below.
When the burner heat output is raised to 300 kW the flames will become bigger and a
larger surface area will almost immediately be involved (IV). The situation is then similar
to the one at the beginning of the test. Either there is a progressive flame spread (V), or
not (VI).
The two continuous flame spread developments, (II) and (V), are assumed to follow given
functions of time as shown below in this section.
32
A more detailed description of how the flame-spread rate is obtained in the model follows
now. The formulae are empirical and must be seen as proposals that at least with the
current experience have proven to give acceptable results.
First the area behind the burner ignites, see Figure 7. The size of this area (2 m2) is
assumed to be the same for all products while the growth rate of the burning area is
assumed to vary with time normalized with the ignition time, tign, of the product
considered:
A(t) =4(
t
) -1
tign
(1)
Along routes (II) and (V) in Figure 7 the involved area as a function of time is written as,
A(t) = A0 (1 + a
(t-tx)2
)
tign
(2)
A0 is the area behind the burner and a is an empirical constant found to be 0.025 s-1 for
route (II) and tx is chosen so that the burning area growth rate is the same as in eq (1)
when equation (2) starts to be used, i.e. when the surface temperature criterion is
exceeded.
After 10 minutes when the burner output is raised, the burning area is initially expressed
as:
A(t) = 2 +
24
(t-t10)
tign
(3)
until A(t) is 5 m2; t10 = 600 s (10 minutes). If the surface temperature criterion is
reached, the burning area growth will continue as in equation (2) with the parameters A0
= 5 m2 and a = 0.1 s-1 and tx determined as above.
Calculation of the heat release rate
The total heat release from the room is obtained by summing the contributions from each
part of the total burning area and the burner.
Qtotal = Qproduct + Qburner
(4)
Qburner is constant while Qproduct varies with time as the fire spreads; the involved area
A(t) increases as described above and the burning intensity at each position is time
dependent. Qproduct is obtained by adding the contributions from burning parts, which
have started to burn at various times. The heat release rate of the specimen at each
location is then assumed to go through the same history as was measured in bench-scale,
i.e. the Cone Calorimeter.
A time incremental approach is used to calculate Qproduct. At each time step ∆t, an area
increment ∆A is calculated and the Qproduct is obtained by adding the contributions from
each area increment. Then we get at time t = N ∆t
33
N
Qproduct = Σ ∆Ai qbsN-i
i=1
(5)
In an alternative form this equation may be written as an integral (Duhamel´s integral):
t
Qproduct = eq ⌠
⌡ A'(τ) qbs(t-τ) dτ
0
(6)
where A' is the time derivative of the burning area and τ is a dummy variable. No closed
form solution may, however, be obtained unless qbs is given a suitable analytical form.
To understand the summation in the equation (5) we will go through a simple numerical
example. Assume that the heat release rate as measured in the Cone Calorimeter and the
involved area are as shown in Figure 8 and Figure 9, respectively. The incremental
contribution can then be calculated and added as shown in Table 5. The total heat release
rate at the third time increment may for instance be calculated as:
(0.2)(250) + (0.45)(125) + (0.65)(50) = 50 + 56 + 32 = 138.
This incremental technique is computationally very fast and may be carried out with such
a small time increment that the details of the small-scale heat release curve are adequately
considered.
475
600
2
Heat release rate (kW/m )
500
400
225
250
300
125
200
50
50
100
0
t
Figure 8
1
ign
2
3
4
5
6
Time increments (delta t)
Schematic curve from heat release rate as measured in the cone
calorimeter.
34
8
5.95
7
Burning area (m )
6
2
5
3.5
4
2.15
3
0.65
1.3
2
0.2
1
0
1
2
3
4
Time increment (delta t)
5
6
Figure 9
Burning area growth rate described by parabolic relation.
Table 5
Example of heat release rate calculation.
Time
s
1∆t
2∆t
3∆t
4∆t
5∆t
6∆t
A
m2
0.2
0.65
1.3
2.15
3.5
5.95
∆A
m2/s
0.2
0.45
0.65
0.85
1.35
2.45
qbs
Incremental contributions kW
2
3
4
5
kW/m2 1
50
10
125
25
22
250
50
56
32
475
95
112
81
42
225
45
214
162
106
68
50
10
101
309
212
169
6
122
Total
kW
10
47
138
330
595
914
Legend to Table 5: qbs column is the cone calorimeter result, the A column is the
estimated burning area growth. By using the superposition technique with respect to when
the different areas are ignited the total heat release rate is obtained.
Time to ignition plays an important role in this analysis as it governs the growth rate of
the burning area. An alternative calculation form can be obtained by differentiating
equation (2):
∆A = (2A0a/tign) t ∆t
(7)
Thus the involved area for a given time is proportional to the inverse of the ignition time.
The summation (equation 5) may then be written in a more convenient form as [7]
35
Qproduct = (2A0 a / tign) Σ(ti qbsN-i ∆t)
(8)
This formula reveals how sensitive the calculated heat release rate is to the ignition time
measured in small scale. Therefore several tests ought to be carried out to achieve reliable
ignition time results. For this, some kind of averaging technique need to be developed so
that results from various irradiance levels can be considered. Note that all results reported
here are based on single tests at an irradiance of 25 kW/m2 in the Cone Calorimeter.
Criterion for flame propagation is a fictive surface temperature
As indicated in Figure 7 the fire may or may not spread away from the vicinity of the
burner at the beginning or after 10 minutes when the burner output is raised to 300 kW,
respectively. The criterion assumed in this model for flame spread to occur is that the
surface temperature, θs, at an imaginary point just beyond the flame front at some time
reaches a certain critical value. The calculation procedure as outlined below is very
schematic but it is found for the tested products that when the calculated surface
temperature is above a particular value, flame spread occurs.
The surface temperature, θs, depends on the temperature of the combustion gases passing
by, θgas, and on the thermal response of the product surface. θgas in turn is in this model
assumed to depend on the sum of the heat release rate from the burner and the specimen
product in the vicinity of the burner, i.e. Qtotal.
The heat release from the product is calculated with involved areas as outlined above. A
fictitive gas temperature is then in these calculations obtained as
θgas = γ Qtotal2/5
(9)
where γ is a proportionality factor here empirically chosen to be 50 and 35 K/W2/5 at the
burner rates of 100 and 300 kW, respectively. The expression could be compared with the
plume temperatures beyond the flame (the intermittent regime) as calculated by for
instance McCaffrey. The power 2/5 is derived for free flames but as the flames in this
case reaches the ceiling a somewhat higher value could possibly be argued for.
The thermal response of the surface is assumed to be determined by the ignition time in
the Cone Calorimeter only. That gives combined information about the thermal response
of the surface and the ignition temperature. It is assumed in the calculation that the
product is semi-infinite i.e. thermally thick. The thermal response may then be expressed
by the thermal inertia (kρc). This parameter varies with orders of magnitude while the
ignition temperature lies within a relatively narrow range. The ignition time gives a
measure mainly of the thermal response characteristics.
Below the thermal response is calculated assuming gas passing by with a temperature
varying with time as shown above. The thermal response of the surface is then calculated
by superposition. The response function η is chosen assuming that the body is semiinfinite and that kρc is proportional to the ignition time measured in the Cone
Calorimeter. Other response functions would of course be more adequate in many cases
but for the final result it seem from our experience not necessary to employ other
functions.
36
The thermal inertia, kρc, which in theory is relevant only for semi-infinite homogeneous
products, is here replaced by what we call IRV = Ignition Response Value. IRV is
assumed proportional to tign and by matching it with known thermal inertia of wood we
get IRV = 1250 tign, where tign is measured in the Cone Calorimeter at an irradiance
level of 25 kW/m2. It would of course also be possible to obtain the IRV value at other
irradiance levels or even by other methods.
The response function of the surface, η, expression for semi-infinite bodies exposed to a
constant gas temperature θg, as taken from textbooks, is now employed:
η=
θs
t
= [1 - exp( erfc((
θg
τ
t
)]
τ
(10)
where
τ = IRV/h2
(11)
The convection heat transfer coefficient, h, is for the numerical calculation assumed to be
50 W/m2K. This rather high value is deemed reasonable for a point near the burner
plume.
For gas temperature varying with time, surface temperature can now be obtained by
superposition expressed as (the same numerical technique as outlined above for
calculating heat release rate):
N
θs(t) = Σ ∆θgi ηN-i
i=1
(12)
where θgi is the gas temperature at the i:th time increment and ηN-i the response function
at time N-i.
Correction for cone calorimeter data obtained at other heat flux levels
The original model has been developed to use cone calorimeter at a heat flux level of 25
kW/m2. In many cases however materials do not ignite at this flux level or the
experiments show poor repeatability. For this reason it was investigated to adapt the data
so that it could be used at higher heat flux levels.
In order to be able to use the model also with cone results different from the preset value
of 25 kW/m2 a correction was introduced for both the ignition time and the HRR level.
The correction is based on fine-tuning the results for the HRR and assuming materials are
semi-infinite for ignition properties:
tignCorr = tignCone.* (ConeFlux / RCTFlux)^2
HRRCorr = HRRCone(RCTFlux / ConeFlux)^0.33
Where:
tignCorr:
tignCone
HRRCorr
HRRCone
ConeFlux
RCTFlux
Corrected ignition time used in the model
Ignition time in the cone calorimeter test
Corrected heat release rate
Heat release rate in the cone calorimeter test
Flux level in the cone calorimeter test
Corresponding reference flux for the cone-SBI model being 25 kW/m2
37
6.2.2
Description of test protocol in ISO 5660 used for the
simulations
In the simulation of the room corner test all materials were tested according the ISO 5660
without any specific change in sample preparation. The materials were wrapped in
Aluminum foil and placed on ceramic wool. All tests were conducted with the retainer
frame in place and without grid. The tests were conducted at 50 kW/m2.
6.2.3
In Table 6 the results are given for the simulation of the SBI RR materials and a number
of the Eurefic test data. All simulations were made with a cone calorimeter test results
obtained at 50 kW/m2. For the Eurefic data a number of test results were not taken into
the table, as the cone calorimeter data were not of sufficient quality (too few data points).
The results show that the prediction level is of the same order of success as previously
been shown with the model with input data at 25 kW/m2. This shows that the model has
been adapted successfully for use with cone calorimeter data at 50 kW/m2 or even other
flux levels could be used. This is important as it gives the user a larger flexibility.
The marked results are those were the model does not give the correct class. In two cases
it concerns thermoplastics with heavy melting behavior (M2 and M7). In two other cases
it concerns sandwich panels where it can be expected that joint behavior etc are more
important and where the model has its limitation (M21 and Eurefic 9). In the other two
cases it concerns materials going to flashover at 300 kW (M14 and M15). Here it maybe
should be investigated in the future whether even higher heat flux levels should be used
as input. For the Euroclass of a material in the room corner the procedure outlined in the
EN standard for classification of construction products and building elements has been
used. This means the following classification:
Class B or higher: No flashover
Class C Flashover time larger than 10 minutes
Class D Flashover time between 2 and 10 minutes
Class E Flashover less than 2 minutes
38
Table 6
Material
M01
M02*
M03
M04
M05
M06
M07*
M08
M09
M10
M11
M12
M13
M14**
M15**
M16
M19
M20
M21**
M22
M23
M24
M25
M26
M27
M28
M29
M30
Eurefic1
Eurefic2
Eurefic3
Eurefic4
Eurefic5
Eurefic6
Eurefic9*
Eurefic10
Eurefic11
*
**
Summary of simulation results for the cone room-corner model.
Euroclass
according
to
simulation
≥B
D
E
E
E
≥B
D
≥B
≥B
C
≥B
D
≥B
≥B
≥B
D
≥B
D
≥B
D
D
D
D
E
≥B
≥B
≥B
E
≥B
D
C
≥B
≥B
C
E
C
E
Euroclass
according
to test
result
≥B
≥B
E
E
E
≥B
≥B
≥B
≥B
C
≥B
D
≥B
C
C
D
≥B
D
C
D
D
D
D
E
≥B
≥B
≥B
E
≥B
D
C
≥B
≥B
C
D
C
E
Simulation more severe
Simulation less severe
39
7
Development of models for Smoke
production
7.1.1
Statistical method
It has earlier been shown that statistical information from Cone Calorimeter tests can be
used to predict time to flashover in the Room Corner test10. Smoke production in the SBI
test and in the Room Corner test has also been predicted with good precision using
multivariate statistical methods11,7. The same techniques are used in this project, but on a
more extensive set of test results than in earlier projects. This may have led to some
differences between the calculation models developed here and the models presented in
the earlier studies. However, as the models presented here are built on information from a
larger number of results from fire tests, we believe that the present models will have a
broader range of validity than the previously published models.
Multivariate statistical methods may find links between different variables that are not
obvious to the investigator. In a single Cone Calorimeter test of a product several
variables are recorded, like time to ignition, smoke gas concentrations, heat release rate,
specimen mass loss and optical smoke density. Other parameters are used to describe the
product before testing, like density and thickness. Since a test in the Cone Calorimeter
clearly is a multivariate phenomenon, the test results should be well suited for a
multivariate statistical analysis. Applied on a set of Cone Calorimeter test results a
multivariate statistical analysis may be able to find ways of combining relevant variables
that could be used to predict large-scale fire behaviour.
Multiple discriminant function analysis, abbreviated MDA, is a multivariate statistical
method used to classify cases into groups12,13. One case is in our analysis defined as
results from one Cone Calorimeter test. The groups are determined based on a categorical
dependent variable, i.e. a variable that shows discrete values that can be assigned to
discrete classes.
MDA can be used to
• classify cases into groups
• investigate differences between groups
• detect variables that are important for distinguishing between groups
• discard variables that are irrelevant for group distinctions
When a relation between groups and variables exist, MDA will find the simplest way of
assigning cases to a set of predetermined groups. The classification is then governed by
functions, which include only the variables that are most strongly related to the group
distinction.
A discriminant function is analogous to multiple regression in that it creates a linear
function between the latent variable L and the n different independent variables x1,
x2,…,xn that are found to be relevant for distinguishing between groups:
L = b1x1 + b2x2 + … + bnxn
+ c
(1)
where c is a constant. If there are g different predetermined groups and p different
discriminating variables, a set of the lesser of (g-1) or p functions will be evaluated. Each
function is orthogonal to the others, i.e. all functions are independent of the other
functions.
40
Before performing a discriminant analysis, some assumptions concerning the cases in the
data set must be validated through statistical examination. Population sizes should not
differ too much, and all cases should be independent. The variables are assumed to follow
a multivariate normal distribution, and within-group distributions should be symmetric.
Different populations should have about equal spread of variance for each variable.
Transformation of variables may be necessary to improve normality, stabilise variance
and make distributions more symmetric. However, MDA is said to be relatively robust
against modest violations of these assumptions.
MDA was applied to the data set, to evaluate if this tool could be used to predict the
levels of smoke production in the SBI test and in the Room Corner test with satisfactory
accuracy. The software program SPSS 9.014 gives the option to choose development of
Fisher’s linear discriminant functions for classification of cases. The result of this
analysis is a set of g linear functions, one for each of the g predetermined groups. The
Fisher’s linear discriminant functions are combinations of the p variables z1…zp that are
found to discriminate between the groups. The functions are expressed in the format
Fi = ai·z1 + bi ·z2 +…+ ci ·zp + constanti ,(i=1,..,g)
(2)
All functions from F1 to Fg are calculated for each case. A new case will be associated to
the group which classification function obtains the highest value.
About 20 different variables assumed to be important for predicting smoke production in
large scale was calculated from the Cone Calorimeter test results. These variables gave
information connected to smoke production, production of CO, HRR, time to ignition,
time to extinction etc. Some of the variables had to be transformed to make them fulfil the
criteria set to variables suited for an MDA. The software program SPSS gives the option
to choose an automatic stepwise selection of variables that are able to distinguish between
the predetermined groups. The variables are chosen based on their statistical significance
as discriminators in the actual analysis.
7.2
Test results from ISO 5660 used in the prediction
models
The Cone Calorimeter test results are recorded until 2 minutes after extinguishment. For
test specimens with a long burning period, only test results from the first 15 minutes are
used in the calculations, otherwise results from the complete test are used.
The statistical models use the recorded arrays of heat release rate, HRR [kW/m2] and
smoke production rate, SPR [m2/s] as a base for the calculation of the following variables:
•
•
•
•
•
Time to ignition, tign [s], which is determined as the moment when HRR exceeds 50
kW/m2. This value has to be compared to the observed time to ignition, to avoid noise
influencing the determination of the parameter.
SPRmax [m2/s], which is the maximum value of SPR.
HRRmax [kW/m2], which is the maximum value of HRR.
THR300s [MJ/m2], which is the total heat released during 300 s after time to ignition.
TSP [m2], which is the total smoke production calculated over the total test period, or
alternatively over 15 minutes of testing time for tests with longer duration.
41
•
•
SMOGRAcc [m2/s2], which is the maximum value of the ratio between SPR and time
when SPR was measured.
FIGRAcc [kW/m2s], which is the maximum value of the ratio between HRR and time
when HRR was measured.
The mean density of the outer 10 mm of the tested product, ρmean [kg/m3], is also an
important parameter used in the models.
7.3
Prediction of smoke production in the SBI test
7.3.1
General
In the new European system for testing and classification of reaction-to-fire properties15,
products are categorised into 3 subclasses according to their smoke production in the SBI
test. The criteria for the additional classifications s1, s2 and s3 are based on the
SMOGRA index and on TSP600s as presented in Table 7.
Table 7
Criteria to parameters related to smoke production in the SBI test in the
European classification system15.
Smoke class
s1
s2
s3
SMOGRA [m2/s2]
30
180
-
TSP600s [m2]
50
200
-
From Table 4 we see that test results of both SMOGRA and TSP600s can be divided into
three groups in this system. Our aim is to be able to predict the correct level of both
SMOGRA and TSP600s, and to predict the final level of smoke classification.
The set of products that the prediction model development is based on is collected from
different research projects, and are described in references 10, 11,16, and 17. Three additional
products are included in this study, namely the PIR with aluminium facing (Insulation
material 1), PIR with glass fibre facing (Insulation material 2) and polystyrene (Insulation
material 3), all products described elsewhere in this report. 35 products are used for
development of the SBI smoke prediction models, and a number of 116 single Cone
Calorimeter tests have been analysed. For the development of the Room Corner models,
results from a total of 152 Cone Calorimeter tests of 53 different products have been the
base. New Cone Calorimeter test data have been included in this project compared to the
previously published studies10,11,.
A point that should be mentioned is that the SBI smoke measurement system was
modified after the SBI round robin was finished. This was done to avoid problems with
sooting of the lenses. A possible result of the sooting may have been that a higher smoke
density than actually present in the exhaust system was measured for some of the SBI
round robin products. The improvement in the smoke measurement system may lead to a
change in the obtained smoke classification for some products, most likely an improved
result. Future work with validation and refinement of the models will take account of the
possible effects the modifications may have had on the products’ smoke performance in
the SBI apparatus. However, the distribution of smoke performance in the available data
set is rather skewed, with the majority of products in class s1, and we therefore believe
that the changes in the SBI apparatus will have negligible effect on the SBI smoke
prediction models presented here.
42
The smoke classification for all of the products tested in the SBI round robin was
determined by the value of TSP600s15. The same is also the case for 8 of the 9 additional
products used in this analysis. The only case where SMOGRA was governing for the final
smoke class was for the product Insulation material 1, where the SMOGRA value
exceeded the s2 limit with a few units, leading to a final s3 classification of smoke.
However, this conclusion was drawn from a single SBI test only. Because this single test
result would affect the final models to a very high degree, we have chosen to omit it from
this analysis. We do not exclude the possibility that such results may occur from SBI tests
of other products as well, the event is, however, assumed to be rare.
5 variables were found to be able to distinguish between both the three levels of
SMOGRA-values and between the three smoke classes. The selected parameters were:
SPRmax
)
HRRmax
•
z1 = ln(
•
•
•
•
z2 = TSP
z3 = ln(SMOGRAcc)
z4 = ρmean [kg/m3]
z5 = tign
The data set was divided into one test set containing 89 cases for building the functions,
and one validation set containing 27 cases for testing the model precision afterwards.
43
7.3.2
Predicting the level of SMOGRA
The three classification functions are expressed as follows:
FSMOGRA1 = - 26.300·z1 + 3.851·z2 - 11.006·z3 + 0.004352·z4 + 0.05247·z5 - 173.937
FSMOGRA2 = - 21.501·z1 + 5.801·z2 - 8.629·z3 + 0.003791·z4 + 0.03838·z5 - 119.595
FSMOGRA3 = - 21.566·z1 + 6.845·z2 - 11.745·z3 - 0.006831·z4 + 0.04458·z5 - 140.686
All functions FSMOGRA1, FSMOGRA2 and FSMOGRA3 are calculated for the case to be predicted.
The predicted SMOGRA level is determined as the level, which associated Fisher’s
function, gives the highest result. If e.g. FSMOGRA3 gives a higher result than both FSMOGRA1
and FSMOGRA2, the case is predicted to obtain a SMOGRA level s3 in the SBI.
Figure 10 shows how the model described by equation (3) is able to discriminate between
members of the three SMOGRA levels.
Canonical Discriminant Functions
3
2
2
1
1
0
SMOGRA level
-1
3
Function 2
-2
Group Centroids
3
-3
2
-4
1
-4
-2
0
2
4
6
Function 1
Figure 10
The statistical classification model’s ability to separate cases belonging
to different levels of SMOGRA measured in the SBI test. The diagram
shows the scores for each of the 89 cases in the test set for the two
canonical discriminant functions. The circular dark spots show the
group centroids, i.e. the centre of gravity for each cluster.
(3)
44
In Figure 11 the predictability for the different levels of SMOGRA for cases in the test set
and cases in the validation set are presented in confusion tables18.
Test set (n=89)
1
Actual
level
2
3
Figure 11
Validation set (n=27 )
Predicted level
1
2
3
65
2
0
(97%)
11
0
5
(69%)
5
1
0
(83%)
1
Actual
level
2
3
Predicted level
1
2
3
18
3
0
(95%)
2
1
0
(67%)
2
0
1
(67%)
Confusion tables18 for prediction of SMOGRA level in the SBI test.
The results in Figure 10 and Figure 11 show that the members of levels 1 and 2 of
SMOGRA can be separated quite well based on combinations of these parameters, while
SMOGRA level 3 (only 6 cases in the test set) is more difficult to sort out by this model.
7.3.3
Predicting the smoke classification s1, s2 or s3
The three classification functions are expressed as follows:
Fs1 =
- 47.981·z1 + 13.874·z2 - 12.564·z3 + 0.005609·z4 - 0.191·z5 - 289.047
Fs2 =
- 38.197·z1 + 12.385·z2 - 10.345·z3 + 0.004679·z4 - 0.133·z5 - 189.621
Fs3 =
- 39.698·z1 + 20.439·z2 - 13.200·z3 - 0.007048·z4 - 0.156·z5 - 233.990
All functions Fs1, Fs2 and Fs3 are calculated for the case to be predicted. The predicted
result is determined as the class, which associated Fisher’s function gives the highest
result. If e.g. Fs3 gives a higher result than both Fs1 and Fs2, the case is predicted to obtain
smoke classification s3 in the SBI.
members of the three smoke classes.
(4)
45
6
4
2
2
1
0
Smoke class
3
Function 2
-2
Group Centroids
s3
-4
s2
s1
-6
-6
-4
-2
0
2
4
6
Function 1
Figure 12
The statistical classification model’s ability to separate cases belonging
to the different smoke classes determined from SBI test results. The
diagram shows the scores for each of the 89 cases in the test set for the
two canonical discriminant functions. The circular dark spots show the
In Figure 13 the predictability for the different smoke classes s1, s2 and s3 for cases in
the test set and cases in the validation set are presented in confusion tables.
Test set (n=89)
Actual
class
Figure 13
Predicted class
s1
s2
s3
56
0
0
s1
(100%)
19
0
0
s2
(100%)
13
1
0
s3
(93%)
Validation set (n=27 )
s1
Actual
class
s2
s3
Predicted class
s1
s2
s3
16
0
1
(94%)
5
0
0
(100%)
4
0
1
(80%)
Confusion tables for prediction of the smoke classification determined
from SBI test results.
The results in Figure 12 and Figure 13 show that the members of s1, s2 and s3 can be
separated quite well based on combinations of the parameters in equation (4).
46
7.4
Prediction of smoke production in the Room
Corner test method
7.4.1
Flashover is determining for the smoke production
It has earlier been shown that the event of flashover is crucial for the resulting smoke
production in the Room Corner test11,19,20,21. The products are therefore grouped according
to in which time interval the flashover, if any, occurs. We have chosen to name these
possible groups FO-categories10, and the separation criteria are as follows:
•
•
•
•
FO-category 1: products not reaching flashover during 1200 s of testing time
FO-category 2: 600 s ≤ tFO <1200 s
FO-category 3: 120 s ≤ tFO < 600 s
FO-category 4: tFO < 120 s
Before the statistical models for smoke prediction in the Room Corner test can be applied,
the most probable FO-category must be predicted. There are several efficient models
available for this purpose10.
7.4.2
How predictable is the Room Corner SMOGRA value?
That the Room Corner test has status as the reference scenario for the SBI test implies
that ranking of materials according to test results from the two methods should be more or
less equivalent. A good correlation between ranking order has been found for heat release
results, using FIGRA from the SBI test and FIGRA from the Room Corner test. No
obvious and simple correlation has been found between SMOGRA values from the two
methods, and the ranking based on smoke production is very different from ranking based
on heat release. There is at the time being no classification system based on SMOGRA
calculated from Room Corner test results.
When SMOGRA is calculated from Room Corner test results for the products included in
this study, the values cover a broad range, from values near zero to values in the order of
magnitude 102. Smoke production during the very first minutes of the test will be
determining for the final SMOGRA value. We therefore suggest that, like in the
SMOGRA calculations from SBI test results, threshold values should be applied in the
Room Corner calculations. Threshold values could be defined to prevent calculation of
SMOGRA before SPR exceeds a predefined value and the total smoke production is
above a certain level. Because of the uncertainty with regard to the calculation and
applicability of the Room Corner SMOGRA value, we have concentrated on prediction of
other smoke related parameters measured in the Room Corner test, namely the maximum
and average smoke production rate, SPRmax and SPRavg.
47
7.4.3
The EUREFIC classification system
A product’s performance in the Room Corner test apparatus can be evaluated according
to the classification system proposed through the EUREFIC programme17. Time to
flashover, maximum heat release rate (HRRmax) and average heat release rate (HRRavg)
form the basis for the EUREFIC-classes, while the classification of smoke production is
based on maximum smoke production rate (SPRmax) and average smoke production rate
(SPRavg). Averaging is performed over the classification period.
The EUREFIC-classes and requirements for heat release- and smoke production
parameters are shown in Table 8 below.
Table 8
Classification criteria for smoke production, together with the
corresponding EUREFIC-classes and requirements for heat release
parameters17.
EUREFIC
class
A
Minimum tFO1)
[s]
1200
HRRmax2)
[kW]
300
HRRavg2)
[kW]
50
SPRmax3)
[m2/s]
2.3
SPRavg3) [m2/s]
B
1200
700
100
16.1
1.2
C
720
700
100
16.1
1.2
D
600
900
100
16.1
1.2
E
120
900
No
requirement
16.1
No
requirement
0.7
1)
tFO : time to flashover
Heat release rate from burner not included
3)
Smoke production rate from burner not included
Based on this system, both SPRmax and SPRavg can be divided into three levels:
2)
SPRmax [m2/s] :
Level 1:
Level 2:
Level 3:
SPRmax ≤ 2.3
2.3 < SPRmax ≤ 16.1
SPRmax ≥ 16.1
SPRavg [m2/s] :
Level 1:
Level 2:
Level 3:
SPRavg ≤ 0.7
0.7 < SPRmax ≤ 1.2
SPRmax ≥ 1.2
Each set of data in the FO-categories was divided into a test set and a validation set, and
MDA was then performed separately on each group. For FO-category 2, the number of
cases was too small to allow for any separation into subsets. The validation of the
prediction rules for FO-category 2 products is therefore only made through crossvalidation18 (also called jack-knifing).
48
5 variables were found to be able to distinguish between the three levels of both SPRmax
and SPRavg. The selected parameters were:
•
•
•
•
w1 = ρmean [kg/m3]
w2 = THR300s [MJ/m2]
w3 = ln(tign)
w4 = ln(FIGRAcc)
•
w5 = ln(
7.4.4
SPRmax
)
HRRmax
Prediction of maximum smoke production rate in the
Room Corner test
The variables w1,…,w5 are used to build sets of Fisher’s discrimination functions, one set
for each FO-category. We have chosen to name the functions Fi-max_k. i is a reference to
the level of smoke performance (1, 2 or 3), max refers to prediction of maximum SPR
and k is a reference to the FO-category.
The three classification functions for FO-category 1 are expressed as follows:
F1-max1 = - 0.004348·w1 +1.070·w2 +6.230·w3
+12.940·w4
F2-max1 = - 0.009168·w1 + 0.846·w2 +9.014·w3
+ 12.513·w4 -12.376·w5 -69.726
F3-max1 = - 0.003952·w1 + 1.432·w2 +17.014·w3 +20.660·w4
-15.094·w5 -87.792
(5)
-13.653·w5 -110.025
All functions F1-max1, F1-max3 and F1-max3 are calculated for the case to be predicted. (The
case must, of course, first be predicted to not reaching flashover). The predicted level of
SPRmax is then determined as the level, which associated Fisher’s function gives the
highest result.
F1-max2 = No products in FO-category 2 in the test set belonged to level 1 of SPRmax.
F2-max2 = 0.04986·w1
+ 1.326·w2 -7.169·w3 + 0.294·w4
-14.104·w5 -75.944
F3-max2 = 0.04096·w1
+ 1.297·w2 -6.775·w3 -1.175·w4
-13.913·w5 -68.389
The prediction of SPRmax level is then performed as for the FO-category 1 cases.
It was not possible to obtain any good prediction models for products in FO-category 3.
However, all of the cases in this category in the available data set belonged to either level
2 or level 3 regarding SPRmax.
(6)
49
members of the three smoke classes for non-flashover (i.e. FO-category 1) products.
4
3
2
2
1
0
1
SPRmax
3
-1
Function 2
Group Centroids
-2
3
-3
2
-4
1
-4
-2
0
2
4
6
8
Function 1
Figure 14
The statistical classification model’s ability to separate cases in FOcategory 1 belonging to the three different levels of SPRmax. The
diagram shows the scores for each of the 39 cases in the test set for the
two canonical discriminant functions. The circular dark spots show the
50
In Figure 15 the predictability of the levels of SPRmax for FO-categories 1 and 2 for cases
in the test sets and cases in the validation sets are presented.
FO-category =1:
Test set (n=39)
Actual
level
Validation set (n=10)
Predicted level
1
2
3
22
0
0
1
(100%)
10
3
0
2
(77%)
4
0
0
3
(100%)
Actual
level
Predicted level
1
2
3
6
0
0
1
(100%)
3
0
1
2
(75%)
3
-
-
-
FO-category =2:
Test set (n=25)
Actual
level
1
2
3
Predicted level
1
2
3
0
0
0
10
(100%)
3
12
(80%)
Test set (n=25, cross validated)
Predicted level
1
2
3
0
0
1
0
Actual
2
10
level
(100%)
4
3
11
(73%)
FO-category =3: No prediction model for SPRmax available.
Figure 15
Confusion tables for Classification rules 1and 2 for prediction of SPRmax
level in the Room Corner test. The grey shaded areas indicate the levels
where no classification rules apply.
51
7.4.5
Prediction of average smoke production rate in the
Room Corner test
The variables w1,…,w5 are used to build sets of Fisher’s discrimination functions, one set
for each FO-category. We have chosen to name the functions Fi-avg_k. i is a reference to
the level of smoke performance (1, 2 or 3), avg refers to prediction of average SPR and k
is a reference to the FO-category.
F1-avg1 = 0.008004·w1 +0.07154·w2 -0.227·w3 +9.976·w4
F2-avg1 = 0.01445·w1
-18.308·w5 -94.359
+0.09239·w2 +1.611·w3 + 10.318·w4 -14.280·w5 -71.270
F3-avg1 = 0.002022·w1 +0.01573·w2 +4.466·w3 +9.735·w4
(7)
-13.128·w5 -62.885
All functions F1-avg1, F1-avg3 and F1-avg3 are calculated for the case to be predicted. (The
case must, of course, first be predicted to not reaching flashover). The predicted level of
SPRavg is then determined as the level, which associated Fisher’s function gives the
highest result.
F1-avg2 = 0.02123·w1
+0.158·w2
-3.567·w3 +7.893·w4
-23.272·w5 -116.940
F2-avg2 = No products in FO-category 2 in the test set belonged to level 2 of SPRavg.
F3-avg2 = 0.002587·w1 +0.599·w2
-4.577·w3 +3.910·w4
-19.343·w5 -84.499
The prediction of SPRavg level is then performed as for the FO-category 1 cases.
All products in FO-category 3 belonged to SPRavg level 3.
(8)
52
members of the three smoke classes for non-flashover (i.e. FO-category 1) products.
4
3
2
2
1
1
SPRavg
0
Function 2
Group Centroids
3
-1
3
-2
2
-3
1
-4
-2
0
2
4
Function 1
Figure 16
The statistical classification model’s ability to separate cases in FOcategory 1 belonging to the three different levels of SPRavg. The diagram
shows the scores for each of the 39 cases in the test set for the two
canonical discriminant functions. The circular dark spots show the
53
In Figure 17 the predictability of the levels of SPRavg for FO-categories 1 and 2 for cases
in the test sets and cases in the validation sets are presented.
FO-category =1:
Test set (n=39)
Actual
level
Validation set (n=10)
Predicted level
1
2
3
18
3
0
1
(82%)
1
4
0
2
(8%)
4
0
9
3
(100%)
Actual
level
Predicted level
1
2
3
6
0
0
1
(100%)
1
0
0
2
(25%)
3
0
3
0
FO-category =2:
Test set (n=25)
Actual
level
Predicted level
1
2
3
0
0
1
10
(100%)
0
0
2
0
3
2
0
13
(87%)
Test set (n=25, cross validated )
Predicted level
1
2
3
0
0
1
10
(100%)
0
0
Actual
2
0
level
2
0
3
13
(87%)
FO-category =3: All cases at SPRavg level 3.
Figure 17
Confusion tables for Classification rules 1and 2 for prediction of SPRavg
level in the Room Corner test. The grey shaded areas indicate the levels
where no classification rules apply.
7.5
Implementation of the prediction models
The predictions of smoke production are easily calculated on an ordinary PC. The
statistical classification models have been implemented as simple calculation formulas in
an Excel worksheet, and may also be implemented as an algorithm of a computer
program.
54
55
8
Software development
Within the project it was envisaged to develop a user-friendly software package, which
would calculate the HRR part of the SBI and Room corner models. At a later stage the
smoke model could be incorporated. In the next chapter the different options of the model
are explained which can be used as manual for the programme.
8.1
Principle
The software package is based a Visual Basic programme written for use under the
Windows environment. The outlook of the programme is given in Figure 18 and Figure
19. Figure 18 shows the menu fields while Figure 19 gives the screen when the
programme has started from the windows system. Both a number of scroll down menus
and menu buttons are available which are explained in the following chapters.
Figure 18
Menus in the cone tools software package.
Figure 19
Screen after start-up of the programme.
56
8.2
Different Button menus
8.2.1
Open file menu
The open file menu, see Figure 20, allows the user to import a cone calorimeter file for
processing within the programme. The browsing function allows selecting the cone
calorimeter. At the same type the user can choose the type of input file. A number of
standard input file types are given such as FDMS export file and a CSV file adapted to
the FTT software. Besides that the user can defines its own type of import file provided
the files are vector files. For sequential data files only the FDMS format is possible. The
user can define is own type of file under the types menu button, see 8.2.7. The load cone
data menu is given in
Figure 20
Open file dialog box.
From the moment a file is selected, the cone calorimeter data is imported in the software
and the screen will shown the HRR curve of the cone calorimeter test. Here the user can
investigate e.g. the HRR threshold for ignition. The user can also investigate the quality
of the cone calorimeter data (noise, drift etc.).
57
Figure 21
Screen after import of cone calorimeter data.
8.2.2
Calculate menu
Once a cone calorimeter file is imported the ”calculate” menu button can perform
calculations. First the heat flux level has to be introduced. If the input file is an FDMS file
this can be taken from the file immediately. Else the user types in the heat flux level in
the cone calorimeter test. Then the user should indicate whether a HRR threshold or a
visual ignition time is used for the ignition properties. Finally the user can choose the
simulate SBI or room corner test results or both. Clicking the calculate button starts the
calculation and brings the user back into the main menu with the results as shown in
Figure 23. Left of the graph the scalar simulation results can be seen.
58
Figure 22 Calculate dialog box.
Figure 23
Result screen.
8.2.3
Select what to plot menu
By selecting the scroll menu “select what to plot” the user can examine the results on the
screen and show what vector data to plot on the screen e.g. simulated HRR in the SBI, see
Figure 24.
59
Figure 24
Example of selection of what to plot.
8.2.4
Compare menu
Figure 25
Compare dialog box.
60
Figure 26
Example of a comparison of two cone calorimeter data sets.
With the compare button it is possible to compare e.g. two cone calorimeter data sets. The
user should indicate which vector the HRR data contains and whether some rows have to
skipped (offset). The dialog menu is given in Figure 25
The results of a comparison are shown in Figure 26.
8.2.5
Save SBI and SBI RCT menu
The results from either a SBI or a RCT simulation can be saved as a vector data. The
vector data file contains also a number of scalars such as FIGRA, THR etc. The dialog
box is shown in Figure 27. The data is saved as a comma separated file or the separator
defined in windows under the national settings. In certain countries this can be a semicolon.
61
Figure 27
Save data dialog box.
8.2.6
Print menu
Figure 28
Print menu.
62
With the print button the actual chosen graphs and the scalar data left of the graph are
printed on the standard printer configured for your computer.
8.2.7
Types menu
An important powerful menu in the programme is the types menu. In this menu you can
define the type of file which is to be used for data import. The dialog box is given in
Figure 29. Either a new type can be added by using the “add new type” button or a
selected type can be deleted by using the “delete this type” button.
Figure 29
Select type dialog box.
The dialog box ”create new file type, see Figure 30, allows the user to define, within
certain limites, his own file type. First the name of the file type you want is given. Then
the format of your file type is defined by given the first and last row of the data. This is
especially important if header are used in the file. Then the column for time and HRR
vectors are given together with the used units.
The next items are the column separator and the decimal separator used.
Finally the user can define the position of heat flux level and ignition time if necessary.
The creation of a new file type assumes that your data is in a vector format and not in a
sequential format such as FDMS. For FDMS the FDMS file type should be used. In this
case the so called export files should be used (EXP extension).
63
Figure 30
Create new file type dialog box.
8.2.8
Help menu
In the help menu a short summary of the different function of the model are included. The
dialog box in the help menu is shown in Figure 31.
Figure 31
Help screen.
64
8.2.9
Exit menu
To exit the conetools programme. The user should be aware to have saved all possible
runs.
8.3
Different Scroll menus
8.3.1
File menu
The file menu contains the following scroll items:
1.
2.
3.
4.
5.
Open cone file, see 8.2.1
Edit file type, see 8.2.7
Save SBI data, see 8.2.5
Save RCT data, see 8.2.5
Print data and diagram, see 8.2.6
8.3.2
Simulation menu
The simulation menu contains the following scroll items:
1. Calculate, see 8.2.2
2. Plot comparison, see 8.2.4
8.3.3
Help menu
The help menu contains the following scroll items:
1. Help, see 8.2.8
2. About conetools: information on the version of the programme and the system
information
65
9
Conclusions
In this project a screening protocol for prediction the SBI and room corner test results
have been developed. The major achievements are:
1. A cone-SBI numerical calculation model for prediction the main HRR
classification parameters of the SBI test methods, namely FIGRA and THR. As
input cone calorimeter data at only one user-defined heat flux level is necessary.
This report also quotes a number of advice for the sample preparation of the cone
calorimeter test results.
2. A statistical calculation model for the smoke parameters in SBI and room corner
test by means of the cone calorimeter test results.
3. The extension of the original conetool model (cone-room corner model) by
introducing a correction of the data for heat flux levels other than the original 25
kW/m2.
4. Development of a user-friendly software programme in Windows environment.
The major implementation of the results of this project is its use for cost effective product
development and quality control. This cost effective tool is combined with a considerable
reduction of scale and exhaust of combustion gases.
66
67
Annex A Simulation results for cone-RCT model
for HRR
The Eurefic project
Eurefic1
1000
hrr (kW)
800
600
Conetools
data
400
200
0
0
2
4
6
8
10
12
14
16
18
20
time (min)
Painted gypsum plaster board
Eurefic2
1000
Conetools
data
hrr (kW)
800
600
400
200
0
0
2
4
6
8
10
time (min)
Ordinary Birch Plywood
12
14
16
18
20
68
Eurefic3
1000
Conetools
800
hrr (kW)
data
600
400
200
0
0
2
4
6
8
10
12
14
16
18
20
18
20
time (min)
Textile wall covering on gypsum paper plasterboard
Eurefic4
1000
hrr (kW)
800
600
Conetools
data
400
200
0
0
2
4
6
8
10
time (min)
Melamine faced high density non-combustible board
12
14
16
69
Eurefic5
1000
hrr (kW)
800
600
data
Conetools
400
200
0
0
2
4
6
8
10
12
14
16
18
14
16
18
20
time (min)
Plastic faced steel sheet on mineral wool
Eurefic6
1000
Conetool
data
hrr (kW)
800
600
400
200
0
0
2
4
6
8
10
time (min)
FR particle board type B1
12
20
70
Eurefic9
1000
data
hrr (kW)
800
600
Conetools
400
200
0
0
2
4
6
8
10
12
14
16
18
20
time (min)
Polyurethane foam covered with steel sheets
Eurefic10
1000
Conetools
data
hrr (kW)
800
600
400
200
0
0
2
4
6
8
10
time (min)
PVC wall carpet on gypsum plasterboard
12
14
16
18
20
71
Eurefic11
1000
800
hrr (kW)
Conetools
600
data
400
200
0
0
2
4
6
8
10
time (min)
FR polystyrene foam
12
14
16
18
20
72
The SBI research project
M01
1000
hrr (kW)
800
600
Conetools
400
data
200
0
0
2
4
6
8
10
12
14
16
18
20
time (min)
Plasterboard
M02
1000
900
800
Conetools
700
hrr (kW)
600
500
data
400
300
200
100
0
0
2
4
6
8
10
time (min)
FR PVC
12
14
16
18
20
73
M03
1000
Conetools
hrr (kW)
800
data
600
400
200
0
0
2
4
6
8
10
12
14
16
18
20
12
14
16
18
20
time (min)
FR extruded polystyrene board
M04
1000
data
900
Conetools
800
700
hrr (kW)
600
500
400
300
200
100
0
0
2
4
6
8
10
time (min)
PUR foam panel with alu foil faces
74
M05
1000
Conetools
800
hrr (kW)
data
600
400
200
0
0
2
4
6
8
10
12
14
16
18
20
18
20
time (min)
Varnished mass timber, pine
M06
800
data
700
600
hrr (kW)
500
400
Conetools
300
200
100
0
0
2
4
6
8
10
time (min)
FR chip board
12
14
16
75
M07
1000
Conetools
data
hrr (kW)
800
600
400
200
0
0
2
4
6
8
10
12
14
16
18
20
16
18
20
time (min)
FR polycarbonate panel 3
M08
1000
800
hrr (kW)
600
Conetools
400
200
data
0
0
2
4
6
8
10
time (min)
Painted plasterboard
12
14
76
M09
1000
800
hrr (kW)
data
600
Conetools
400
200
0
0
2
4
6
8
10
12
14
16
18
20
14
16
18
20
time (min)
Paper wall covering on plasterboard
M10
1000
Conetools
800
hrr (kW)
data
600
400
200
0
0
2
4
6
8
10
time (min)
12
77
M11
1000
800
600
hrr (kW)
data
400
200
Conetools
0
0
2
4
6
8
10
12
14
16
18
20
14
16
18
20
time (min)
Plastic-faced steel sheet on mineral wool
M12
1000
Conetools
800
hrr (kW)
data
600
400
200
0
0
2
4
6
8
10
time (min)
Unvarnished mass timber
12
78
M13
1000
800
600
hrr (kW)
Conetools
400
200
data
0
0
2
4
6
8
10
12
14
16
18
20
18
20
time (min)
Plasterboard on polystyrene
M14
1000
800
hrr (kW)
data
Conetools
600
400
200
0
0
2
4
6
8
10
time (min)
Phenolic foam
12
14
16
79
M15
1000
data
hrr (kW)
800
600
Conetools
400
200
0
0
2
4
6
8
10
12
14
16
18
20
14
16
18
20
time (min)
Intumescing coating on particleboard
M16
1000
data
800
Conetools
hrr (kW)
600
400
200
0
0
2
4
6
8
10
time (min)
Melamine faced MDF board
12
80
M19
400
data
350
300
Conetools
hrr (kW)
250
200
150
100
50
0
0
2
4
6
8
10
12
14
16
18
20
12
14
16
18
20
time (min)
Unfaced rockwool
M20
1000
data
800
Conetools
hrr (kW)
600
400
200
0
0
2
4
6
8
10
time (min)
Melamine faced particleboard
81
M21
1000
800
data
hrr (kW)
600
Conetools
400
200
0
0
2
4
6
8
10
12
14
16
18
20
12
14
16
18
20
time (min)
Steel clad expanded polystyrene sandwich panel
M22
1000
data
800
Conetools
hrr (kW)
600
400
200
0
0
2
4
6
8
10
time (min)
Ordinary particle board
82
M23
1000
data
800
hrr (kW)
600
Conetools
400
200
0
0
2
4
6
8
10
12
14
16
18
20
12
14
16
18
20
time (min)
Ordinary plywood (birch)
M24
1000
Conetools
data
800
hrr (kW)
600
400
200
0
0
2
4
6
8
10
time (min)
Paper wall covering on particleboard
83
M25
1000
Conetools
hrr (kW)
800
600
data
400
200
0
0
2
4
6
8
10
12
14
16
18
20
12
14
16
18
20
time (min)
Medium density fiberboard
M26
1000
Conetools
800
hrr (kW)
data
600
400
200
0
0
2
4
6
8
10
time (min)
Low density fiberboard
84
M27
1000
800
600
hrr (kW)
Conetools
400
data
200
0
0
2
4
6
8
10
12
14
16
18
20
18
20
time (min)
Plasterboard/FR PUR foam core
M28
1000
800
Conetools
hrr 8kW)
600
400
data
200
0
0
2
4
6
8
10
time (min)
Acoustic mineral fiber tiles
12
14
16
85
M29
1000
800
hrr (kW)
Conetools
data
600
400
200
0
0
2
4
6
8
10
12
14
16
18
20
time (min)
Textile wall covering on calcium silicate board
M30
1000
hrr (kW)
800
600
Conetools
400
data
200
0
0
2
Paper-faced glass wool
4
6
8
10
12
time (min)
14
16
18
20
86
87
Annex B
Simulation results for Cone-SBI HRR
model
Eurefic data
Eurefic3-SBI
40
35
data
30
hrr (kW)
25
20
15
10
Conetools
5
0
0
100
200
300
time (s)
Textile wall covering on gypsum plaster board
400
500
600
88
SBI RR materials
M01-SBI
20
18
16
14
hrr (kW)
12
10
8
6
data
Conetools
4
2
0
0
100
200
300
400
500
600
400
500
600
time (s)
Plasterboard
m02-SBI
50
45
Conetools
40
35
hrr (kW)
30
25
20
15
data
10
5
0
0
100
200
300
time (s)
FR PVC
89
M03-SBI
250
200
data
Conetools
hrr (kW)
150
100
50
0
0
50
100
150
200
250
300
500
600
time (s)
M04-SBI
180
160
140
data
hrr (kW)
120
100
80
60
Conetools
40
20
0
0
100
200
300
time (s)
PUR foam panel with Alu-foil facing
400
90
M05-SBI
80
70
60
Conetools
hrr (kW)
50
40
data
30
20
10
0
0
100
200
300
400
500
600
time (s)
m06-SBI
7
6
hrr (kW)
5
4
data
3
2
Conetools
1
0
0
100
200
300
time (s)
FR chip board
400
500
600
91
M07-SBI
120
100
Conetools
hrr (kW)
80
60
data
40
20
0
0
100
200
300
400
500
600
500
600
time (s)
FR polycarbonate panel
M08-SBI
7
6
data
Conetools
hrr (kW)
5
4
3
2
1
0
0
100
200
300
time (s)
400
92
M09-SBI
18
16
14
data
hrr (kW)
12
10
Conetools
8
6
4
2
0
0
100
200
300
400
500
600
400
500
600
time (s)
Paper wall covering on plasterboard
M10-SBI
30
25
data
hrr (kW)
20
15
10
Conetools
5
0
0
100
200
300
time (s)
93
M11-SBI
7
data
6
hrr (kW)
5
4
3
Conetools
2
1
0
0
100
200
300
400
500
600
400
500
600
time (s)
Plastic-faced steel sheet on mineral wool
M12-SBI
80
70
Conetools
60
hrr (kW)
50
data
40
30
20
10
0
0
100
200
300
time (s)
Unvarnished mass timber
94
M13-SBI
5
4.5
4
Conetools
3.5
hrr (kW)
3
2.5
data
2
1.5
1
0.5
0
0
100
200
300
400
500
600
400
500
600
time (s)
M14-SBI
10
Conetools
9
8
7
hrr (kW)
6
5
data
4
3
2
1
0
0
100
200
300
time (s)
Phenolic foam
95
m15-SBI
12
10
data
hrr
8
6
4
Conetools
2
0
0
100
200
300
400
500
600
400
500
600
time (s)
Intumescing coating on particleboard
M16-SBI
80
Conetools
70
60
hrr (kW)
50
data
40
30
20
10
0
0
100
200
300
time (s)
96
M19-SBI
1.8
1.6
Conetools
1.4
hrr (kW)
1.2
1
0.8
data
0.6
0.4
0.2
0
0
100
200
300
400
500
600
400
500
600
time (s)
Unfaced rockwool
M20-SBI
100
80
Conetools
hrr (kW)
60
40
data
20
0
0
100
200
300
time (s)
Melamine faced particleboard
97
SBI-21
3
2.5
data
hrr (kW)
2
1.5
Conetools
1
0.5
0
0
100
200
300
400
500
600
500
600
time (s)
Steel clad expanded polystyrene sandwich panel
M22-SBI
90
data
80
70
Conetools
hrr (kW)
60
50
40
30
20
10
0
0
100
200
300
time (s)
Ordinary particleboard
400
98
M23-SBI
160
140
Conetools
120
hrr (kW)
100
80
60
data
40
20
0
0
100
200
300
400
500
600
time (s)
Ordinary plywood (Birch)
M24-SBI
90
80
70
data
hrr (kW)
60
50
Conetools
40
30
20
10
0
0
100
200
300
400
time (s)
Paper wall covering on particle board
500
600
700
99
M25-SBI
140
120
100
hrr (kW)
Conetools
80
data
60
40
20
0
0
100
200
300
400
500
600
400
500
600
time (s)
Medium density fibreboard
M26-SBI
120
100
data
hrr (kW)
80
60
40
Conetools
20
0
0
100
200
300
time (s)
Low density fiberboard
100
M27-SBI
7
6
Conetools
hrr (kW)
5
4
data
3
2
1
0
0
100
200
300
400
500
600
400
500
600
time (s)
Plasterboard/FR PUR foam core
M28-SBI
6
5
Conetools
4
hrr (kW)
data
3
2
1
0
0
100
200
300
time (s)
Acoustic mineral fiber tiles
101
M29-SBI
16
14
12
hrr (kW)
10
data
8
6
4
Conetools
2
0
0
100
200
300
400
500
600
400
500
600
time (s)
Textile wall covering on calcium silicate board
M30-SBI
160
140
120
data
hrr (kW)
100
80
60
40
Conetools
20
0
0
100
200
300
time (s)
102
Additional data
CP-1
9
8
7
6
hrr (kW)
data
5
Conetools
4
3
2
1
0
0
100
200
300
400
500
600
400
500
600
time (s)
Ceiling panel 1
CP-2
7
Conetools
6
hrr (kW)
5
4
3
data
2
1
0
0
100
200
300
time (s)
Ceiling panel 2
103
CP-3
4.5
4
Conetools
3.5
3
hrr (kW)
data
2.5
2
1.5
1
0.5
0
0
100
200
300
400
500
600
400
500
600
time (s)
Ceiling panel 3
CP-4
4
data
3.5
3
Conetools
hrr (kW)
2.5
2
1.5
1
0.5
0
0
100
200
300
time (s)
Ceiling panel 4
104
CP-5
4
3.5
Conetools
3
data
hrr (kW)
2.5
2
1.5
1
0.5
0
0
100
200
300
time (s)
Ceiling panel 5
400
500
600
105
Annex C Generic description of SBI RR materials
and Eurefic data
Code
Product name
Thickness
Density
Surface
(mm)
(kg/m3)
weight
(g/m2)
M01
Plasterboard
13
700
-
M02
FR PVC
3
1180
-
M03
40
32
-
M04
PUR foam panel with alu foil
faces
40
PUR:40
-
M05
10
380
-
M06
FR chip board
12
780
-
M07
FR polycarbonate panel 3
layered
16
175
-
M08
13
700
Paint:145
M09
Paper wall covering on
plasterboard
13
700
Paper:200
M10
PVC wall carpet on
plasterboard
13
700
PVC: 1500
M11
Plastic-faced steel sheet on
mineral wool
0,15 + 1 +50
Min.wool:
160
-
M12
Unvarnished mass timber,
spruce
10
450
-
M13
13 + 100
700/20
-
M14
Phenolic foam
40
-
-
106
Code
Product name
Thickness
Density
Surface
(mm)
(kg/m3)
weight
(g/m2)
M15
Intumescent coat on particle
board
12
700
Paint:500
M16
12
MDF: 750
Mel.:120
M19
Unfaced rockwool
50
145
-
M20
Melamine faced particle board
12
-
-
M21
Steel clad expanded
polystyrene sandwich panel
0,5 + 100
EPS:20
-
M22
Ordinary particle board
12
700
-
M23
Ordinary plywood ( Birch )
12
650
-
M24
Paper wall covering on particle
board
12
700
Wallpaper:
200
M25
Medium density fibre board
12
700
-
M26
Low density fibre board
12
250
-
M27
Plasterboard/FR PUR foam
core
13 + 87
PUR:38
-
M28
Acoustic mineral fibre tiles
18
Min.wool:
220
-
M29
Textile wall paper on calcium
silicate board
CaSi.10
CaSi.875
Wallpaper:
400
M30
100
18
90
COMMENTS:
Plasterboard is in all cases in this report gypsum plasterboard.
Products are not fire retardant treated unless specified with ”FR”
107
Code
Product name
Thickness
Density
Surface
(mm)
(kg/m3)
weight
(g/m2)
E01
Painted gypsum paper
plasterboard
12
700
100
E02
Ordinary birch plywood
12
600
-
E03
Textile wall covering on
gypsum plasterboard
1+12
700
505
E04
Melamine faced high density
non-combustible board
1.5+12
640
-
E05
Plastic faced steel sheet on
mineral wool
0.15+0.7+23
640
-
E06
FR particle board, type B1
16
630
-
E07
Combustible faced mineral
wool
1+30
87
-
E08
FR particle board
12
750
E09
Plastic faced steel sheet on
polyurethane foam
0.1+1+40
160
E10
PVC wall carpet on gypsum
paper plaster board
0.9+12
700
1250
E11
FR extruded polystyrene foam
25
37
-
108
109
References
1
ISO 5660; ”Fire Tests – Reaction to Fire – Rate of Heat Release from building
products”, International Standards Organisation (ISO), 1991.
2
EN 13823 Reaction to fire tests for building products - Building products
excluding floorings - exposed to the thermal attack by a single burning item,
CEN, February 2002.
3
ISO 9705:1993(E), Fire Tests - Full-scale room test for surface products, ISO
1993.
4
B. Messerschmidt, P. Van Hees, U. Wickström, Prediction of SBI (Single
burning item) test results by means of Cone Calorimeter Test results,
Interflam proceedings 1999 pp 11-22, Interscience communications Ltd,
London 1999.
5
P. Van Hees, The need for of a screening method for the major Euroclass
methods, Flame Retardants Conference proceeding 2002.
6
T. Hakkarainen, Correlation studies of SBI and Cone Calorimeter test results
Interflam proceedings 2001 pp 519-530, Interscience communications Ltd,
London 2001.
7
A. Steen Hansen, P. Hovde, Prediction of smoke production based on statistical
analyses and mathematical modelling, Interflam proceedings 2001 pp 113124, Interscience communications Ltd, London 2001.
8
Wickström U. and Göransson U, Full-Scale/Bench-Scale Correlations of Wall
and Ceiling linings”, Journal of Fire and Materials, vol. 16, 1992.
9
McCaffrey B, Flame Height, The SFPE Handbook of Fire Protection
Engineering, 2nd edition, Chapter 2-1.
10
Hansen, A S. Hovde P J. Prediction of time to flashover in the ISO 9705 Room
Corner test based on Cone Calorimeter test results. Submitted to Fire and
Materials, May 2001. Revised January 2002.
11
Hansen, A S. Hovde P J. Prediction of Smoke Production in Large and
Intermediate Scale Tests based on Bench Scale Test Results. A Multivariate
Statistical Analysis. Proceedings of Fire and Materials 2001 Conference,
January 22-24 2001, San Francisco, USA, pp 363-374.
12
Kinnear, P R. Gray, C D. SPSS for Windows made simple. Release 10.
Psychology Press Ltd, Publishers, East Sussex, UK, 2000, pp 319-331.
13
Garson D. Notes to course PA 765 Quantitative Research in Public
Administration, at website http://www2.chass.ncsu.edu/garson/pa765/mda.htm,
(February 2001) North Carolina State University, USA.
14
SPSS Inc. 1999: SPSS Base 9.0 Applications Guide ISBN 0-13-020401-3,
Chicago, USA, 1999, p 248.
15
EN 13501-1 :2001 E. Fire classification of construction products and building
elements – Part 1: Classification using test data from reaction to fire tests.
European Committee for Standardization (CEN), Brussels, Belgium,
February 2002.
16
Östman, B A-L. (2001) Wooden facades in multi-storey buildings. Proceedings
of Fire and Materials 2001 Conference, January 22-24 2001, San Francisco,
USA, pp 185-196.
110
17
Wickström, U. (editor) (1991) Proceedings of the International EUREFIC
Seminar 11-12 September 1991, Copenhagen, Denmark. ISBN 0 9516320 19.
Interscience Communications Limited, London, England.
18
Johnson, R A. Wichern, D W. (1998) Applied Multivariate Statistical Analysis.
Chapters 11.4-11.6. Fourth edition. ISBN 0-13-834194-X Prentice-Hall, Inc.,
New Jersey, USA.
19
Östman, B. Tsantaridis, L. Stensaas, J P., Hovde, P J. (1992) Smoke Production
in the Cone Calorimeter and the Room Fire Test for Surface Products –
Correlation Studies. Trätek, Report I 9208053, Stockholm 1992.
20
Heskestad, AW. Hovde, PJ. (1993) Evaluation of smoke test methods for
classification of building products. Nordtest Technical Report 220, Approved
1993-10.ISBN 82-91412-00-6. University of Trondheim, Norway.
21
Heskestad, AW. Hovde, PJ. (1999) Empirical Prediction of Smoke Production
in the ISO Room Corner Fire Test by Use of ISO Cone Calorimeter Fire Test
Data. Fire and Materials, 23, 193-199.
SP Swedish National Testing and Research Institute develops and transfers
technology for improving competitiveness and quality in industry, and for safety,
conservation of resources and good environment in society as a whole. With
Swedens widest and most sophisticated range of equipment and expertise for
technical investigation, measurement, testing and certfication, we perform
research and development in close liaison with universities, institutes of technology
and international partners.
SP is a EU-notified body and accredited test laboratory. Our headquarters are in
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6.2 Room corner test method

Transcription

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