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SINTEF REPORTTITLE

Nordtest project 1526-01 “Using the Cone Calorimeter forscreening and control testing of fire retarded wood products”.

AUTHOR(S)

Bjarne Kristoffersen, Anne Steen-Hansen, Tuula Hakkarainen,Birgit Östman, Patrik Johansson, Martin Pauner, Ondrej Grexa,Per Jostein HovdeCLIENT(S)

Norges branntekniskelaboratorium as(Norwegian Fire ResearchLaboratory)

Address: NO-7465 Trondheim, NorwayLocation: Tiller Bru, Tiller

Telephone:+47 73 59 10 78Fax:+47 73 59 10 44E-mail: [email protected]: nbl.sintef.no

Enterprise No.: NO 982 930 057 MVA

NORDTEST

REPORT NO. CLASSIFICATION CLIENTS REF.

NBL A03119 Unrestricted Jan FjeldkaasaCLASS. THIS PAGE ISBN PROJECT NO. NO. OF PAGES/APPENDICES

Unrestricted 107109 63 + 2 appendicesELECTRONIC FILE CODE PROJECT MANAGER (NAME, SIGN.) CHECKED BY (NAME, SIGN.)

I:\Pro\107109\Rapport\107109_reportNBLA03119.doc Bjarne Kristoffersen Anne Steen-HansenFILE CODE DATE APPROVED BY (NAME, POSITION, SIGN.)

2003-09-01 Kjell Schmidt PedersenABSTRACT

Fire retarded treated (FRT) wood products resemble a group of building products that during the lastdecades has become more common in use. The products are however inhomogeneous materials, andsmall specimens cut from the same batch or plank may show different fire characteristics. Theharmonized European classification system requires that most building products (excluding floorings)are tested according to a relatively time and cost consuming medium scale test method, the SingleBurning Item test. A simplified and cheaper method for documenting and evaluating the fireproperties of these products is therefore required.

This project has explored the possibilities of using the cone calorimeter test method to predict theclassification of FRT wood products. The following procedures are suggested:� Procedure for sampling ISO 5660 test specimen from FRT wood products� Procedure for testing FRT wood products according to ISO 5660� Procedure for predicting the classification of FRT wood products after running ISO 5660 tests� Procedure for selection of test method for FRT wood products for different application

The work has shown that the cone calorimeter, together with simple prediction criteria based on ISO 5660test parameters, except for classification purposes, are well suited to predict the classification of FRT woodproducts.

KEYWORDS ENGLISH NORWEGIAN

GROUP 1 Fire BrannGROUP 2 Testing PrøvingSELECTED BY AUTHOR Reaction to fire Bidrag til brann

ISO 5660 ISO 5660SBI prediction SBI prediksjon

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Nordtest project 1526-01; Using the Cone Calorimeter for screening and control testing of fire retarded wood

TABLE OF CONTENTS

PREFACE ...........................................................................................................................4ACKNOWLEDGEMENTS ..........................................................................................................4SUMMARY ...........................................................................................................................5SAMMENDRAG PÅ NORSK – NORWEGIAN SUMMARY..................................................71 ORGANISATION ................................................................................................................102 SELECTION AND DESCRIPTION OF TEST SPECIMENS........................................11

2.1 PRODUCTS ...................................................................................................................112.2 PREPARATION OF TEST SPECIMEN FOR ISO 5660 ..............................................112.3 PREPARATION OF TEST SPECIMEN FOR prEN 13823..........................................12

3 TEST PROGRAMME .........................................................................................................133.1 TEST METHODS ..........................................................................................................133.2 PRELIMINARY TESTING ACCORDING TO ISO 5660............................................13

3.2.1 ISO 5660 tests to decide test parameters .........................................................133.2.2 ISO 5660 tests with reference materials ..........................................................13

3.3 ISO 5660 TESTS IN THE MAIN PROJECT ................................................................143.3.1 Introduction .....................................................................................................143.3.2 Calibrations and conditioning..........................................................................143.3.3 Choice of heat flux level in the cone calorimeter test .....................................143.3.4 Testing .............................................................................................................15

3.4 SBI TESTS IN THE MAIN PROJECT..........................................................................153.4.1 Introduction .....................................................................................................153.4.2 Calibrations and conditioning..........................................................................153.4.3 Specimen installation.......................................................................................153.4.4 Testing .............................................................................................................16

3.5 DISTRIBUTION OF WORK IN THE MAIN PROJECT .............................................16

4 TEST RESULTS ..................................................................................................................174.1 TEST RESULTS ACCORDING TO ISO 5660.............................................................174.2 TEST RESULTS ACCORDING TO prEN 13823 SBI .................................................23

5 EVALUATION OF TEST RESULTS................................................................................245.1 PROCEDURES FOR TEST SPECIMEN SAMPLING.................................................24

5.1.1 General ............................................................................................................245.1.2 Sampling positions ..........................................................................................245.1.3 Visual evaluation of surface during sampling .................................................295.1.4 Fixed versus Optimum test specimens ............................................................335.1.5 Discussion of the sampling procedure.............................................................36

5.2 VARIATION OF ISO 5660 TEST RESULTS...............................................................365.2.1 General ............................................................................................................365.2.2 Time to ignition, tign.........................................................................................365.2.3 Maximum heat release rate, HRRmax...............................................................385.2.4 Total heat released 300 s after ignition, THR300..............................................395.2.5 Average smoke production rate, SPRavg..........................................................415.2.6 Total mass loss ................................................................................................425.2.7 Discussion of the variation in the explored test results ...................................43

5.3 VARIATION OF SBI RESULTS ..................................................................................44

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6 PREDICTING SBI TEST RESULTS BY CALCULATION MODELS ........................456.1 Prediction of heat release by a one-dimensional thermal flame spread model...............456.2 Prediction of heat release by a convolution model.........................................................466.3 Prediction of classification based on time to flashover in the Room Corner test...........476.4 Prediction of smoke production by a convolution model...............................................496.5 Prediction of smoke production by a multivariate statistical model...............................506.6 Prediction of SBI results - discussion and conclusions ..................................................51

7 PREDICTING SBI TEST RESULTS BY SIMPLE CRITERIA ....................................527.1 GENERAL......................................................................................................................527.2 CRITERIA USED FOR PREDICTION OF HEAT RELEASE CLASSIFICATION ...52

7.2.1 Prediction procedures developed by VTT.......................................................527.2.2 Prediction Procedures from this project ..........................................................53

7.3 CRITERIA USED FOR PREDICTION OF SMOKE CLASSIFICATION ..................55

8 SELECTION OF TEST METHODS FOR DIFFERENT USE.......................................568.1 General 568.2 Selection of test methods for different use – discussion and conclusions......................56

9 CONCLUSIONS AND RECOMMENDATIONS.............................................................589.1 CONCLUSIONS ON ISO 5660 RESULTS...................................................................589.2 CONCLUSIONS ON SBI RESULTS............................................................................589.3 CONCLUSIONS ON PREDICTING SBI RESULTS ...................................................599.4 CONCLUSIONS ON SAMPLING ................................................................................609.5 CONCLUSIONS ON SELECTION OF TEST METHOD............................................609.6 RECOMMENDATIONS FOR FURTHER WORK ......................................................61

REFERENCES………………………………………………………………………………… 62

APPENDIX I SBI test instructions (6 pages)

APPENDIX II Prediction of Euroclasses of fire retarded wood products using a one-dimensional thermal flame spread model (12 pages)

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PREFACE

During the 1990’s, there has been a considerable development regarding performance basedbuilding regulations. These new regulations increase the opportunities for architects, consultantsand constructors to use new building materials and technical solutions. To a large extent, theregulations contain no specific requirements for specific materials or components to be used. Thishas increased the possible use of wood as a building material.

Fire retarded treated (FRT) wood products resemble a group of building products that has becomemore common in use during the last decades. FRT wood products are inhomogeneous materials,and specimens cut from the same batch, and even the same plank, may show different firecharacteristics. Different number and sizes of knots, varying resin content, and from where in thelog the plank is cut are parameters that may cause variation of fire properties even within the sameplank. These parameters will most likely have an influence on the uptake of fire retardantchemicals during the manufacturing process of vacuum-pressure impregnation.

Until the latest years, indicative fire tests on FRT wood products have been performed accordingto NT FIRE 004 /1/ and ISO 5660 (for simplification this report uses ISO 5660 as a collectiveterm for the standards ISO 5660-1 /2/ and ISO 5660-2 /3/). These small-scale methods are wellsuited, partly because of the small required amount of test material and relatively low costs.Additionally, the former method is used for classification of surface materials in the Nordiccountries. In the latest years, a new test method has been developed for classification of surfacematerials. The method is described in EN 13823 Single Burning Item /4/ (SBI), and will be validfor all nations part of the European Community and European Economic Community. SBI testingrequires large test specimens and is quite expensive compared with the former Nordic methods.

It is therefore of great interest to develop calculation tools for predicting SBI results from small-scale test results. For many years, the cone calorimeter test performed according to ISO 5660 hasbeen used in research and development projects. The cone calorimeter provides a lot of test data,requires small test specimens and is inexpensive compared to the SBI. Already, calculation toolsare developed to predict SBI results from ISO 5660 results. Some of them have been used in thisproject to assess their validity for FRT wood products.

Hopefully, this project may ease the use of ISO 5660 to predict SBI test results, not only givingsimple test procedures and criterion but also describing how to make test specimens representingthe impregnated batch of FRT wood in a satisfactory way.

ACKNOWLEDGEMENTS

The authors would like to thank Nordtest, Impregnum AB, Moelven FireGuard AS and TrysilSkog Brannimpregnering AS for their support of this project.

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SUMMARY

Five FRT wood products have been tested according to ISO 5660 and EN 13823 to explore thepossibilities of using the cone calorimeter for fire testing and prediction of classes in theharmonised European classification system. Please note that the use of ISO 5660 is not intendedfor classification purposes.

Running tests according to EN 13823 and EN ISO 11925 will be both time and cost consuming. Asimplified and cheaper method is therefore required - especially in connection with applications asexternal audit, factory production control and product development. This project has evaluated thepossibilities of using the ISO 5660 test method to predict the classification if the same FRT woodproduct were tested according to the SBI method described in EN 13823. Due to small testspecimens, the cone calorimeter test results for these products are very dependent on where thespecimens are cut, and from which plank they are selected. These matters have all been part of theevaluation of test results. Various prediction models and simple prediction criteria have also beenassessed.

The work has shown that the cone calorimeter, together with simple prediction criteriabased on ISO 5660 test parameters, except for classification purposes are well suited topredict the classification if FRT wood products were tested according to EN 13823.

It is recommended to concentrate any further work on the development of existing and newprediction tools – especially simple prediction criteria for smoke evaluation.

Recommended procedures for sampling, testing and prediction criteria – all related to ISO 5660are shown below:

Sampling procedure in connection with ISO 5660 tests:

� The distribution of non-ignited specimens and comparison of fire properties betweenoptimized specimens and product average both indicate the same: optimizedspecimens do not have substantially better fire properties, and may therefore beused to represent the product.

� The influence of knots depended on its position on the specimen. Knots positioned onthe specimen side and covered by the specimen frame did not seem to have a certaininfluence on the results. Specimens with knots positioned near the centre did howeverhave worse fire properties than the average product level. To be on the safe side, it isrecommended to avoid knots on the specimen surface as far as possible.

� Finally, the results from test specimens cut in fixed mutual distances have been usedto find the optimal area on the plank to cut the 100 mm x 100 mm samples. Theevaluations indicate that specimens should be cut approximately 1 meter from theends of the plank.

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Procedure for predicting the classification of FRT wood products after running conecalorimeter tests at heat exposure level of 50 kW/m2:

� Determine the ignition time tign� if tign < 30 s → the specimen is most likely not class B – proceed for at least

1200 seconds and use the HRRmax criterion below to substantiate the prediction

� if tign > 200 s → the specimen is most likely class B – proceed for at least 1200seconds and use the HRRmax criterion below to substantiate the prediction

� if tign > 600 s → the specimen is predicted as class B and the test may beterminated

� Determine the maximum heat release rate HRRmax� if HRRmax < 80 kW/m2 → the specimen is predicted as class B

� Determine the average smoke production rate SPRavg� if SPRavg > 0,0025 m2/s → the specimen will most likely obtain s2 or s3

� if SPRavg < 0,0015 m2/s → the specimen may obtain s1

Procedure for selection of test method for FRT wood products:� Classification purposes

� Tests performed according to test methods EN 13823 and EN ISO 11925-2.At least duplicate ISO 5660 tests should be conducted on specimens from thesame batch – cut in accordance with the sampling procedures recommendedin this report. The ISO 5660 test results will be used as reference for futurecone calorimeter tests.

� External audit� At least duplicate tests according to ISO 5660 + prediction model or –criteria

� One single SBI test is conducted every second year

� Factory production control� At least duplicate tests according to ISO 5660 + prediction model or –criteria

� Product development� One single test according to ISO 5660 + prediction model or –criteria as a

first screening tool

� Duplicate tests according to ISO 5660 + prediction model or –criteria arerequired at the late stage of the development.

� The need for an indicative SBI test mostly depends on the need to know exactsmoke production results – i.e. the SBI test may be found necessary forproducts aiming for s1 class.

It is very important that the evaluator of the predictions is aware of the uncertainty in the results.Furthermore is it of great importance to understand that products not meeting or meeting therequired prediction criteria still may obtain other classes when tested according to EN 13823.

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SAMMENDRAG PÅ NORSK – NORWEGIAN SUMMARY

Fem produkter av brannimpregnert trevirke har blitt testet i henhold til ISO 5660 og EN 13823 forå undersøke muligheten til å bruke konkalorimeteret til brannteknisk prøving og prediksjon avklassifisering i det harmoniserte europeiske klassifikasjonssystemet. Vær oppmerksom på at ISO5660 er ikke tenkt benyttet til klassifisering.

Brannteknisk prøving i henhold til EN 13823 og EN ISO 11925-2 vil være både tidkrevende ogkostbart. Det er derfor ønskelig å utvikle en enklere og billigere metode – særlig i forbindelse medtilvirkningskontroll, produksjonskontroll og produktutvikling. Dette prosjektet har undersøktmuligheten til å bruke ISO 5660 til prediksjon av klassifiseringen dersom det sammebrannimpregnerte trevirket prøves i henhold til SBI metoden. På grunn av små prøvestykker vilresultatene fra prøving i henhold til ISO 5660 være avhengig av hvor prøvestykkene er tatt. Dettehar også blitt evaluert i prosjektet. Ulike prediksjonsmodeller og enkle prediksjonskriterier harogså blitt vurdert.

Arbeidet har vist at konkalorimeteret, sammen med enkle predisjonskriterier basert på ISO5660 parametere, egner seg til prediksjon av klassifiseringen dersom det sammebrannimpregnerte trevirket prøves i henhold til EN 13823. Bruken av ISO 5660 ogprediksjonskriterier kan ikke brukes for klassifisering.

Det er anbefalt å arbeide videre med utvikling av eksisterende og nye prediksjonsverktøy – og dasærlig enkle prediksjonskriterier røykproduksjon.

Anbefalte prosedyrer for prøveuttak, prøving og prediksjonskriterier – alle relatert til ISO 5660 ervist nedenfor:

Prosedyre for prøveuttak i forbindelse med ISO 5660 prøving:

� Fordelingen av prøvestykker som ikke gikk til antennelse, samt sammenligning avde branntekniske egenskapene til optimale prøvestykker og produktetsgjennomsnitt viser begge det samme: optimale prøvestykker har ikke vesentligbedre branntekniske egenskaper, og er derfor representative for produktet.

� Innvirkningen av kvist avhenger av hvor de befinner seg på prøvestykket. Kvist påsiden av prøvestykket, samt skjult av prøveholderen, ser ikke ut til å ha vesentliginnvirkning på resultatene. Prøvestykker med kvist nært senter ser imidlertid ut tilå ha dårligere branntekniske egenskaper enn produktets gjennomsnittlige nivå. Forå være sikker anbefales det så langt det lar seg gjøre å unngå kvist påprøvestykket.

� Resultatene fra prøvestykker tatt med samme innbyrdes avstand har blitt brukt til åbestemme hvor på planken det er best å ta de 100 mm x 100 mm storeprøvestykkene. Vurderingen indikerer at prøvestykkene bør tas omtrent 1 meterfra enden av planken.

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Prosedyre for prediksjon av klassifiseringen til brannimpregnert trevirke etter prøvingi konkalorimeteret ved 50 kW/m2:

� Bestem tid til antennelse tig� dersom tig < 30 s → prøvestykket tilfredsstiller trolig ikke klasse B – fortsett

prøving i minimum 1200 sekunder og bruk HRRmax kriteriet til å underbyggeprediksjonen

� dersom tig > 200 s → prøvestykket tilfredsstiller trolig klasse B – fortsett prøvingi minimum 1200 sekunder og bruk HRRmax kriteriet til å underbyggeprediksjonen

� dersom tig > 600 s → prøvestykket antas å tilfredsstille klasse B og prøvingen kanavsluttes

� Bestem maksimal varmeavgivelseshastighet HRRmax� dersom HRRmax < 80 kW/m2 → prøvestykket antas å tilfredsstille klasse B

� Bestem gjennomsnittlig røykproduksjon SPRavg� dersom SPRavg > 0,0025 m2/s → prøvestykket tilfredsstiller trolig s2 eller s3

� dersom SPRavg < 0,0015 m2/s → prøvestykket kan oppnå s1

Prosedyre for å velge testmetode for brannimpregnert trevirke:� Klassifisering

� Prøving gjennomføres i henhold til EN 13823 og EN ISO 11925-2. Minimumto ISO 5660 prøvinger bør gjennomføres med prøvestykker fra samme batch –prøveuttak utføres i henhold til den anbefalte prosedyren beskrevet tidligere.ISO 5660 resultatene skal brukes som referanse ved senere prøvinger ikonkalorimeteret.

� Tilvirkningskontroll� Minimum to ISO 5660 tester + prediksjonsmodell eller -kriterier

� En enkelt EN 13823 test gjennomføres annenhvert år

� Produksjonskontroll� Minimum to ISO 5660 tester + prediksjonsmodell eller -kriterier

� Produktutvikling� En enkelt ISO 5660 test + prediksjonsmodell eller –kriterier i

innledningsfasen

� Minimum to ISO 5660 tester + prediksjonsmodell eller –kriterier ernødvendig i den siste fasen av utviklingsarbeidet

� Behovet for en enkelt EN 13823 test avhenger først og fremst pånødvendigheten for å vite eksakte resultater for røykproduksjonen – det vil siat SBI test kan være nødvendig for produkter der ønsket er s1 klassifisering

Det er viktig at den som evaluerer prediksjonene er klar over usikkerheten i resultatene. Videre erdet viktig å forstå at produkter som ikke tilfredsstiller eller tilfredsstiller prediksjonskriterienelikevel kan oppnå andre klasser når prøvet i henhold til EN 13823.

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SCOPE

The scope of this Nordtest project was first of all to explore the possibilities of using the ISO 5660test method for fire testing of fire retarded treated (FRT) wood products in connection withproduct development, screening tests and tests related to external audits.

When impregnating a batch of wood panels with FR liquid, the local variation of FR content ineach plank may be very large. Some decisive parameters may be content of knots and resin, andthe place in the log where the panel is cut. In the future, ISO 5660 tests will most likely be run topredict SBI results, both in connection with development and external audits. Compared with themethods NT FIRE 004 /1/, EN 13823 /4/ and EN ISO 11925-2 /5/ used today, the specimens aremuch smaller, which means that the test results are more influenced by the sampling procedure. Itis therefore of great interest to know how to select the test specimens, and also how to use the testresults. Furthermore, knowing the precision of the assessments will be very valuable.

A procedure for selection of test specimens so that they represent the product as good as possibleis also suggested. In addition to ISO 5660 testing, tests were performed according to prEN 13823(SBI) /6/. The latter test results were used as reference for the ISO 5660 tests and the preliminaryversion of the standard was used as the EN version /4/ was issued later. Furthermore, the projectevaluates calculation tools that use ISO 5660 results to predict the results if an SBI-test wereperformed on the same product. Whether these tools are suitable for FRT wood products isdiscussed on the basis of correctly predicted classes from ISO 5660 tests versus obtained classesfrom the SBI-tests. Finally, the project suggests how to predict heat release and smoke classes inthe new European system on the basis of single ISO 5660 parameters.

The outcome of this project is important for the manufacturers of FRT wood products – both withregard to development of new products, but also when adjusting existing products for the newEuropean test methods. Finally, the project may be used in connection with product control testingrelated to external audits. Conclusions from the project will therefore be of interest for bothindustry and fire laboratories.

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1 ORGANISATION

Project co-ordinator:

Norges branntekniske laboratorium as (NBL)Tiller bru7465 TrondheimNORWAY

Project partners:

NordtestDanish Institute of Fire Technology, Denmark (DIFT)Norwegian University of Science and Technology, Norway (NTNU)State Forest Products Research Institute, Slovakia (SFPRI)Swedish National Testing and Research Institute, Sweden (SP)Trätek, Swedish Institute for Wood Technology Research, Sweden (Trätek)VTT Building and Transport – Fire Technology, Finland (VTT)

Industrial partners:

Impregnum AB, SwedenMoelven FireGuard AS, NorwayTrysil Skog Brannimpregnering AS, Norway

The industrial partners are all manufacturers of FRT wood that contributed with test materials forfire testing.

Project group:

Project leader: Bjarne Kristoffersen, NBL

Project group: Anne Steen-Hansen, NBLMartin Pauner, DIFTPer Jostein Hovde, NTNUOndrej Grexa, SFPRIPatrik Johansson, SPBirgit Östman, TrätekTuula Hakkarainen, VTT

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2 SELECTION AND DESCRIPTION OF TEST SPECIMENS

2.1 PRODUCTSA total of 5 different products were tested in the project. For all products, the fire retardant liquidswere inserted by a vacuum-pressure method. The products were manufactured by 3 differentcompanies and the table below shows the product information received:

Table 2-1 Wood products tested during the project

Label Wood species Dimensions(thickness x

width)[mm]

Density aftertreatment 1

[kg/m3]

FR content

A Pine for interior usewithout surface treatment 15 x 145 571 356 kg/m3

B Spruce for exterior usewithout surface treatment 22 x 148 516 676 kg/m3

C Spruce for exterior usewith surface treatment 2 22 x 148 470 68 l/m3

D Pine for interior usewithout surface treatment 14 x 120 490 130 l/m3

E Pine for interior usewithout surface coating 12 x 120 824 603 kg/m3 3

2.2 PREPARATION OF TEST SPECIMEN FOR ISO 56603 planks from each product, 4-5 m long and labelled 1, 2 and 3, are used for the cone calorimetertesting. From each plank, the following test specimens are prepared:

� Minimum 5 specimens are cut with a fixed (F) mutual distance of 1,0 m. These specimens areprepared without considerations of surface appearance such as knots and high resin content,and are labelled- 1ccF1, 1ccF2….., 1ccF5- 2ccF1, 2ccF2….., 2ccF5- 3ccF1, 3ccF2….., 3ccF5

Planks of minimum 4 m length made it possible to obtain at least 5 specimens. All specimenswith a label that ends with number “1” and “5” were cut from the end of their respectiveplanks. As described in the next chapter, 5 of the specimens labelled “F” were tested for eachplank.

1 Measured before testing according to prEN 138232 The surface coating was a stain of quality Gori 730 – amount unknown3 The content of FR was calculated as the average FR content of the 12 planks picked for testing. The content of eachplank varied between 444 kg/m3 and 692 kg/m3

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� Minimum 5 specimens from each plank were cut to represent the part of the panel that mostlikely holds the highest FR content. These specimens are cut as far from knots and visibleresin as possible. Thus, they are considered to be the optimized (O) specimens of their plankswith the following labelling- 1ccO1, 1ccO2….., 1ccO5- 2ccO1, 2ccO2….., 2ccO5- 3ccO1, 3ccO2….., 3ccO5

As described in the next chapter, 5 of the specimens labelled “O” were tested for each plank.

Figure 2-1 Example of how the test specimens are cut from a 4 meter plank. The fixedspecimens with 1 meter mutual distance and the optimized specimens cut withconsideration of the look of the surface. The black dots simulate knots of differentsize.

2.3 PREPARATION OF TEST SPECIMEN FOR prEN 13823The required amount of planks for 2 SBI tests was cut from each product – i.e. 1,5 m length andplanks sufficient to make the large and small wing described in section 3.4.3.

The preparation of test specimens was performed without regard to surface differences (knots,resin etc.) as the larger specimen size was assumed to average out the variability of the material.

F1 F2 F3 F4 F5O1 O2

O3

O4O5

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3 TEST PROGRAMME

3.1 TEST METHODSThe tests conducted in this project were run according to two different test methods:� ISO 5660-1 (/2/)� prEN 13823 (/6/)

The latter standard is later issued as a European Standard EN 13823 /4/. In this report, themethods will also be referred to as cone calorimeter and SBI (single burning item) respectively.Both methods are regarded as well known and test principles and drawings of apparatuses aretherefore not part of the report.

3.2 PRELIMINARY TESTING ACCORDING TO ISO 5660

3.2.1 ISO 5660 tests to decide test parametersThe objective of performing preliminary tests according to ISO 5660 was to decide which testconditions to use during the testing of products in the cone calorimeter. The tests were run on FRTwood products with� Various heat flux exposure� Various test duration� FRT wood products with or without surface treatment

The test results were discussed among the project group that concluded with theISO 5660-test procedures described in section 3.3.

3.2.2 ISO 5660 tests with reference materialsPrior to the testing, the 4 laboratories running ISO 5660-tests participated in a simplified RoundRobin exercise. 3 single tests on non-treated particleboard were performed to identify thereproducibility and repeatability among and within the laboratories. The tests were run to findobvious deviations that might be caused by the different apparatuses. Furthermore, the resultsmight give answers to possible variation of test results in the main project.

The particleboard was tested at 50 kW/m2 for 1800 seconds, and the variations were calculated inaccordance with ISO 5725-2 /7/, which was also used to calculate Mandels k (within lab) andMandels h (between lab) statistics for both 1 % and 5 % significant levels.

The results showed that the within- and between-laboratory variance were at an acceptable level.The largest variations were, as expected, found for the smoke measurements.

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3.3 ISO 5660 TESTS IN THE MAIN PROJECT

3.3.1 IntroductionAs described in chapter 2, three planks were used for each batch of FRT wood. From each plank,two categories of test specimen were cut – “Fixed” (same mutual distance without considering thelook of the surface) and “Optimized” (specimen cut as far as possible away from knots and visiblesurface resin). As earlier described, 10 cone calorimeter tests were performed for each plank, thusa total of 30 tests per product.

3.3.2 Calibrations and conditioningPrior to testing, the test specimens were conditioned according to the procedures in the relevantISO 5660 standard, i.e. conditioned to constant mass at a temperature of (23 ± 2) °C, and arelative humidity of (50 ± 5) %. All laboratories were asked to perform calibration proceduresaccording to the standard. In addition, the simplified Round Robin exercise described in 3.2.2showed satisfactory reproducibility and repeatability.

3.3.3 Choice of heat flux level in the cone calorimeter testIn the SBI the test specimen is subjected to a heat flux of approximately 40 kW/m2 in the cornerbehind the burner flames. This relatively high heat flux level is localised in a relatively small areacompared with the whole specimen, and the heat flux decreases rapidly with increasing distancefrom the burner. When the SBI specimen is ignited, the total heat flux to the surface is the sum ofthe heat flux from the burner flames and the heat flux from the flames from the burning specimen.Small flames from the specimen will therefore result in relatively low heat flux some distanceaway from the corner.

In the cone calorimeter test, the total specimen surface is exposed to an evenly distributed heatflux, which in this project has been 50 kW/m2.

This implies that for some products with a limited combustibility, the 50 kW/m2 exposure in thecone calorimeter means more severe combustion conditions than the heat exposure in the SBI test.As a consequence of this, the fire retardant system in some of the products may then be overcomeby the thermal decomposition conditions in the cone calorimeter, but not in the SBI. This againmeans that prediction based on a cone calorimeter test at 50 kW/m2 may be problematic for suchproducts. Predictions based on cone calorimeter results from tests at lower levels of heat flux maytherefore be more optimal for FRT wood products. Using lower heat flux levels than 50 kW/m2 incone tests used as a basis for SBI-predictions, may, however, introduce new problems forproducts that do not ignite when tested below 50 kW/m2.

We have chosen to apply the 50 kW/m2 heat flux level in the cone calorimeter tests of the FRTproducts studied in this project. By applying this rather high level of heat exposure, we force alarger part of the tested specimens to ignite and undergo combustion, which in turn means a largeramount of test data on heat release rate to analyse, and a broader basis for discrimination ofdifferent larger-scale fire behaviour. Another argument for choosing this heat flux level, is that theprediction models applied here are based on input data from cone tests at 50 kW/m2.

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3.3.4 TestingThe ISO 5660 test instructions sent to the relevant laboratories described the test specimens,distribution of work, tests to be run on each product, and the test conditions. The latter werechosen on the basis of the preliminary tests described in 3.2.1 and the previous section:

� Test specimen positioning: horizontally� Heat flux exposure: 50 kW/m2

� Test duration: 1800 seconds 4� The specimen holder and frame shall be used� The grid shall not be used� Observations regarding specimen behaviour and the time this occurs shall be reported

3.4 SBI TESTS IN THE MAIN PROJECT2 single tests were performed on each product 5 according to prEN 13823 (SBI test). The testspecimens were cut from the planks without any special consideration regarding knots, resincontent etc. The following sub-chapters summarise the test instructions prepared by TuulaHakkarainen for the laboratories running the SBI-tests. The installation procedure for interior andexterior products are enclosed in Appendix I.

3.4.1 IntroductionIn order to obtain good-quality SBI test data for comparison to the cone calorimeter test results,the FRT wood products selected for the project were tested in two replicates using the SBI testprocedure according to the prEN 13823 standard. Special attention was paid to the specimeninstallation to ensure the comparability of tests performed in different laboratories.

3.4.2 Calibrations and conditioningThe calibrations were performed according to Annex C of prEN 13823, with special attention tothe heat release rate calibration.

The products were conditioned before testing according to EN 13238 /8/. That is, FRT woodproducts shall be conditioned to a constant mass or at least for 2 months.

3.4.3 Specimen installationInterior productsThe following materials and accessories are needed for each test:

� backing boards: larger wing 1500 mm × 1000 mm, smaller wing 1500 mm × ca. 600 mm� the product to be tested: a sufficient number of planks of length 1500 mm to cover the widths

of 500 mm and ca. 1000 mm of the specimen wings� screws or nails for attaching the specimen planks on the backing boards

For detailed installation procedure – see Appendix I.

4 Product E was tested for 1200 seconds5 Three replicate tests were performed on product A and B.

16


Exterior productsThe following materials and accessories are needed for each test:

� backing boards: larger wing 1500 mm × 1000 mm, smaller wing 1500 mm × ca. 600 mm� the product to be tested: a sufficient number of planks of length 1500 mm to cover the widths

of ca. 500 mm and ca. 1000 mm of the specimen wings� battens 22 mm × 50 mm for forming an air gap of 22 mm: 2 lengths of ca. 500 mm for the

smaller wing and 2 lengths of ca. 1000 mm for the larger wing� 2 wooden stripes (length: 1500 mm, width: batten thickness + plank thickness) for closing the

outer edges of the specimen wings� screws or nails for attaching

- the battens on the backing boards- the specimen planks on the battens- the wooden edge stripes to the outer edges of the specimen wings

3.4.4 TestingPrior to the test, the specimens were photographed to show the details of the specimen assembly.The width of wooden material on each specimen wing was measured, and after installing thespecimen on the SBI trolley, the specimen assembly was photographed as defined in section 5.3.3of prEN 13823.

The tests were run according to section 8 of prEN 13823, with special attention to visualobservations. The observations were recorded with occurrence times. The only exception to thenormal test procedure according to prEN 13823 was the following: photograph the specimenassembly half-way through the test (i.e. 600 seconds after ignition of the main burner).

After the test, the end of test conditions according to section 8.3.5 of prEN 13823 were recordedand the specimens photographed to record the extent of the damage.

3.5 DISTRIBUTION OF WORK IN THE MAIN PROJECT

Table 3-1 Distribution of work in the main project. Calculations include the work with the raw data files.

FIRE TESTSINSTITUTEISO 5660 SBI

CALCULATIONS/ EVALUATIONS

NTNU - - -VTT 1 product 1 product xSP - 2 products xTrätek 2 products - xDIFT - 2 products xSFPRI 1 product - xNBL 1 product - x

In addition to the work distribution above, VTT was responsible for SBI mounting instructionsand for predicting the SBI results on the basis of ISO 5660 results according to the calculationtool described in Section 6.

17


4 TEST RESULTS

4.1 TEST RESULTS ACCORDING TO ISO 5660The results from testing according to ISO 5660 are shown in the following figures as averagevalues for the fixed and optimized specimens for each of the three planks per product. Theaverage results for all specimens for each plank are also included. The variation of ISO 5660 testresults is described and discussed in Section 5.2. After looking at many different parameters, theones below were chosen due to their influence in evaluations and predictions later in the report –also discussed in Section 5.2.

� Time to ignition [s] – tign.� Maximum heat release rate [kW/m2] – HRRmax� Total heat release rate 300 seconds after ignition [MJ/m2] – THR300� Average smoke production rate [m2/s] – SPRavg.� Mass loss [%] - ML

In addition to the tables, this section includes heat release rate and smoke production rate curvestypical for each product. The former variable is shown as a 30 seconds sliding average value.

Please note that the test specimens that did not ignite during 1800 seconds testing are not used inthe calculation of the average heat release related parameters. This simplification was madebecause these specimens were predicted class B without further evaluation.

18


Table 4-1 Average ISO 5660 test results for product A –tested at 50 kW/m2 for 1800 seconds.

Test parametersPlank Sampling t ign.

[s]HRR max[kW/m2]

THR 300[MJ/m2]

SPR avg.[m2/s]

Mass loss[%]

Fixed 6 1029 33 7,7 0,004 79,3Optimized 7 No ignition No ignition No ignition 0,004 No ignition1

Avg. all 1029 33 7,7 0,004 79,3Fixed 8 637 35 8,3 0,003 61,5

Optimized 8 623 33 8,9 0,003 68,72Avg. all 630 34 8,6 0,003 65,1Fixed 8 847 41 10,4 0,003 71,4

Optimized 8 771 37 8,8 0,003 61,43Avg. all 809 39 9,6 0,003 66,4

Product A - Heat Release Rate - 30s sliding average [kW/m2]

0

5

10

15

20

25

30

35

40

0 120 240 360 480 600 720 840 960 1080 1200 1320 1440 1560 1680 1800 1920 2040

Time [s]

SFA2CCO1 SFA2CCO2 SFA2CCO3 SFA2CCO4 SFA2CCO5

Product A - Smoke Production Rate [m2/s]

0

0,002

0,004

0,006

0,008

0,01

0,012

0,014

0,016

0 120 240 360 480 600 720 840 960 1080 1200 1320 1440 1560 1680 1800 1920 2040Time [s]

Figure 4-1 Typical heat release rate and smoke production rate curves for product A.

6 4 of 5 specimens did not ignite – i.e. results calculated from 1 single test7 None of the specimens ignited8 2 of 5 specimens did not ignite – i.e. results calculated from 3 tests

19


Table 4-2 Average ISO 5660 test results for product B – tested at 50 kW/m2 for 1800 seconds.


[s]HRR max[kW/m2]

THR 300[MJ/m2]

SPR avg.[m2/s]

Mass loss[%]

Fixed 9 94 70 12 0,004 70,2Optimized 9 200 66 9 0,003 70,01

Avg. all 147 68 11 0,003 70,1Fixed 9 306 75 8 0,003 71,2

Optimized 257 82 14 0,003 73,02Avg. all 279 79 11 0,003 72,2Fixed 10 870 65 14 0,004 63,6

Optimized 697 62 9 0,004 64,43Avg. all 746 63 11 0,004 64,2

Product B - Heat Release Rate - 30s sliding average [kW/m2]

0

10

20

30

40

50

60

70

80

90

0 120 240 360 480 600 720 840 960 1080 1200 1320 1440 1560 1680 1800 1920 2040Time [s]

Product B - Smoke Production Rate [m2/s]

0,000

0,005

0,010

0,015

0,020

0,025

0,030

0 120 240 360 480 600 720 840 960 1080 1200 1320 1440 1560 1680 1800 1920 2040Time [s]

Figure 4-2 Typical heat release rate and smoke production rate curves for product B.

9 One specimen did not ignite – i.e. result calculated from 4 tests10 3 of 5 specimens did not ignite – i.e. result calculated from 2 tests

20


Table 4-3 Average ISO 5660 test results for product C – tested at 50 kW/m2 for 1800 seconds.


[s]HRR max[kW/m2]

THR 300[MJ/m2]

SPR avg.[m2/s]

Mass loss[%]

Fixed 19 164 21 0,002 74,2Optimized 17 181 20 0,001 75,71Avg. all 18 172 21 0,002 74,9Fixed 18 200 13 0,002 71,2

Optimized 14 200 16 0,001 72,12Avg. all 16 200 14 0,001 71,6Fixed 21 192 16 0,002 75,6


Product C - Heat Release Rate - 30s sliding average [kW/m2]

0

20

40

60

80

100

120

140

160

0 120 240 360 480 600 720 840 960 1080 1200 1320 1440 1560 1680 1800 1920 2040Time [s]

Product C - Smoke Production Rate [m2/s]

0,000

0,005

0,010

0,015

0,020

0,025

0 120 240 360 480 600 720 840 960 1080 1200 1320 1440 1560 1680 1800 1920 2040

Time [s]

Smok

e Pr

oduc

tion

Rat

e [m

2/s]

Figure 4-3 Typical heat release rate and smoke production rate curves for product C.

21


Table 4-4 Average ISO 5660 test results for product D – tested at 50 kW/m2 for 1800 seconds. The results from specimen D2CCF5 were lost, and do therefore not exist.


[s]HRR max[kW/m2]

THR 300[MJ/m2]

SPR avg.[m2/s]

Mass loss[%]




Product D - Heat Release Rate - 30s sliding average [kW/m2]

0

10

20

30

40

50

60

70

80

90

100

0 120 240 360 480 600 720 840 960 1080 1200 1320 1440 1560 1680 1800 1920Time [s]

Product D - Smoke Production Rate [m2/s]

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

0 120 240 360 480 600 720 840 960 1080 1200 1320 1440 1560 1680 1800 1920Time [s]

Figure 4-4 Typical heat release rate and smoke production rate curves for product D.

22


Table 4-5 Average ISO 5660 test results for product E – tested at 50 kW/m2 for 1800 seconds.


[s]HRR max[kW/m2]

THR 300[MJ/m2]

SPR avg.[m2/s]

Mass loss[%]




Product E - Heat Release Rate - 30s sliding average [kW/m2]

0

50

100

150

200

250

0 120 240 360 480 600 720 840 960 1080 1200 1320Time [s]

Product E - Smoke Production Rate [m2/s]

0

0,002

0,004

0,006

0,008

0,01

0,012

0,014

0,016

0,018

0,02

0 120 240 360 480 600 720 840 960 1080 1200 1320Tid [s]

Figure 4-5 Typical heat release rate and smoke production rate curves for product E.

23


4.2 TEST RESULTS ACCORDING TO prEN 13823 SBITwo replicate tests were performed according to prEN 13823 on product C, D and E as describedin chapter 3.4. Please note that product A and B was tested in three replicate tests.

The test results are shown as calculated values and classification in the table below. As the tableshows, product C was the only product that did not meet the class B criteria. Section 2.1 describethat product C was the only surface treated product in the project. Product E was the only one tomeet the criteria to smoke classification s1.

Table 4-6 Summary of SBI test results (calculated from raw data).

Testparameter

PRODUCT

Test no.

A 1) B 1) C D E

FIGRA0.2MJ[W/s]

Test 1Test 2Test 3

Average

00134

97894075

211193−

202

4963−56

7556−66

FIGRA0.4MJ[W/s]

Test 1Test 2Test 3

Average

00134

69652653

147139−

143

2942−36

4469−57

THR600s[MJ]

Test 1Test 2Test 3

Average

0.50.51.40,8

3.43.51.92,9

3.63.1−

3,4

2.33.0−

2,7

3.64.5−

4,1SMOGRA[m2/s2]

Test 1Test 2Test 3

Average

8898

1813913

1618−17

1814−16

21−2

TSP600s[m2]

Test 1Test 2Test 3

Average

77828682

108736582

6362−63

8466−75

3937−38

Class B-s2,d0 B-s2,d0 C-s2,d0 B-s2,d0 B-s1,d0

1) CO2-data corrected by smoothing.

24


5 EVALUATION OF TEST RESULTS

5.1 PROCEDURES FOR TEST SPECIMEN SAMPLING

5.1.1 GeneralWhen cutting specimens for ISO 5660 test, the 100 mm x 100 mm piece of wood is taken from aplank that may have a length of 4-5 m. Knowing what parts of the plank that coincide satisfactorywith the average fire characteristics would be of great interest – both for fire laboratories andmanufacturers of FRT wood products.

To be able to evaluate the above-mentioned matter, the test specimens in this project were cutaccording to the description in 2.2. In this way, we hoped to answer some main questionsregarding sampling:

� Would specimens with no visible defections such as knots and resin, labelled “O” (Optimum)have substantially better test results than the product average?

� Would specimens with knots and visible resin have substantial poorer test results than theproduct average?

� What positions in the plank coincide satisfactory with the average fire characteristics of theproduct?

5.1.2 Sampling positionsThe specimens labelled “Fixed” were cut with a mutual distance of approximately 1 meter. Thiswill be used in the evaluation of what positions that coincide satisfactory with average firecharacteristics of the plank.

The specimens labelled “F1” and “F5” are cut from each edge of the plank. The other three werethen cut in between with 1 meter mutual distance without considering the look of the surface. Dueto this sampling procedure, most of the planks used in this project were approximately 4 meterslong.

To evaluate the different sampling positions on the plank, the test results from the fixed specimenswere compared with the product average. For simplification, the results for each of the fivepositions are shown as average values. Each average value is calculated from the three specimenscut in the respective positions in the three planks per product. The total average value used forcomparison is then calculated from all of the specimens tested for the product in question – i.e.both fixed and optimum. The specimens that did not ignite are still left out of the averagecalculations, except for the average smoke production rate.

The comparison was used to evaluate the best positions for sampling as well as assessing whetherthe results in the fixed positions were satisfactory close to the product average. Visual assessmentof the diagrams was used for the former, and for the latter we defined results within +/- 10 % ofproduct average as representative for the product.

25


The graphic presentations show the average value for each position and the total average value forthe product. After evaluating the influence of many different test parameters, the ones below wereused for each product:

� Time to ignition, tign [s]� Maximum Heat Release Rate, HRRmax [kW/m2]� Average Smoke Production, SPRavg [m2/s]� Mass loss [%]� Total Heat Release 300 seconds after ignition, THR300 [MJ/m2]

To simplify the evaluation, the figures also include the polynomial trendlines for the averageresults of each position.

600

700

800

900

1000

1 2 3 4 5

Position (1 and 5 at each end)

tign

[s] Average Fixed

Product avg.

Trendline

0

10

20

30

40

50

1 2 3 4 5


HR

RM

ax [k

W/m

2]

Average Fixed

Product avg.

Trendline

0

0,001

0,002

0,003

0,004

0,005

1 2 3 4 5


SPR

avg

[m2/

s]

Average Fixed

Product avg.

Trendline

50

55

60

65

70

75

80

1 2 3 4 5

Pos ition (1 and 5 at each end)

Mas

s Lo

ss [%

]

Average Fixed

Product avg.

Trendline

0

2

4

6

8

10

12

1 2 3 4 5


THR

300

[MJ/

m2]

Average Fixed

Product avg.

Trendline

Figure 5-1 Product A - comparison between average values for each fixed position and the product average. No specimen ignited at position 1.

Product A showed very good fire characteristics and 17 of 30 specimens did not ignite during1800 seconds test duration. The specimens that did not ignite were evenly distributed among thefixed (8 specimens) and optimum (9 specimens) positions. Of the fixed specimens, none of thethree specimens in position 1 ignited. Furthermore, 1, 2, 1 and 1 specimens did not ignite inpositions 2, 3, 4 and 5 respectively.

26


With exception of the values for mass loss, the tendency for product A is evident – especiallywhen looking at the trendlines. The specimens cut from the end of the planks in position 5 showthe best fire properties, while positions 2, 3 and 4 all seem to coincide with the total averagevalue. The fact that none of the specimens in position 1 ignited substantiates the former assertion.When looking at the mass loss diagram, the only position with good coincidence is position 4 thatalso meet the criteria to satisfactory representation of the product.

The form of the trendlines indicates the same – the trendline for the time to ignition parameter is adescending shaped curve while the others have upward trendlines. This indicates that the fireproperties decrease from the short ends to the centre of the plank – probably connected with thetrend for the FR content in the plank.

0100200300400500600700

1 2 3 4 5


tign

[s] Average Fixed

Product avg.

Trendline

50

60

70

80

90

1 2 3 4 5


HR

RM

ax [k

W/m

2]

Average Fixed

Product avg.

Trendline

0,0000

0,0010

0,0020

0,0030

0,0040

0,0050

1 2 3 4 5


SPR

avg

[m2/

s]

Average Fixed

Product avg.

Trendline

60

65

70

75

80

1 2 3 4 5


Mas

s Lo

ss [%

]

Average Fixed

Product avg.

Trendline

02468

101214

1 2 3 4 5


THR

300

[MJ/

m2]

Average Fixed

Product avg.

Trendline

Figure 5-2 Product B - comparison between average values for each fixed position and the product average. Only 1 specimen ignited at position 5 – this value istherefore used in the graph.

24 of the 30 specimens tested from product B ignited. The specimens that did not ignite arerepresented by 5 fixed specimens and 1 optimized specimen. 3 of these fixed specimens werefrom positions 1 or 5 – i.e. specimens cut from the end of the planks showed better results than thetotal average of the plank.

27


Product B mainly shows the same trend as the discussed product A. Positions 2 and 4 show theoverall best coincidence with the product average results. As for satisfactory results comparedwith the product average, none of the positions met the +/- 10 % criteria regarding time toignition. Only position 2 represented the average product for all of the other parameters exceptTHR300.

0

5

10

15

20

25

1 2 3 4 5


tign

[s] Average Fixed

Avg. all

Trendline

150

160

170

180

190

200

1 2 3 4 5


HR

RM

ax [k

W/m

2]

Average Fixed

Product avg.

Trendline

0,000

0,001

0,002

0,003

0,004

0,005

1 2 3 4 5


SPR

avg

[m2/

s]

Average Fixed

Product avg.

Trendline

60

65

70

75

80

1 2 3 4 5


Mas

s Lo

ss [%

]

Average Fixed

Product avg.

Trendline

0

4

8

12

16

20

1 2 3 4 5


THR

300

[MJ/

m2]

Average Fixed

Product avg.

Trendline

Figure 5-3 Product C - comparison between average values for each fixed position and the product average.

All of the specimens from product C ignited early during the 1800 seconds test duration, mostlikely due to their surface coating. When looking at the results from the fixed specimens, positions2 and 4 does not show the same clear trend as the products described earlier. The form of thetrendline for HRRmax is even curved in the other direction than the others, showing highest heatrelease rate values for positions 1 and 5. For the other parameters, Figure 5-3 indicates thatposition 5 seems to have the best coincidence with the product average. Position 5 is also the onlyposition that satisfactory represents product C for all parameters by using the +/- 10 % tolerancecriterion.

28


0

100

200

300

400

500

600

1 2 3 4 5


tign

[s] Average Fixed

Product avg.

Trendline

50

60

70

80

90

1 2 3 4 5


HR

RM

ax [k

W/m

2]

Average Fixed

Product avg.

Trendline

0,000

0,001

0,002

0,003

0,004

0,005

1 2 3 4 5


SPR

avg

[m2/

s]

Average Fixed

Product avg.

Trendline

70

72

74

76

78

80

1 2 3 4 5


Mas

s Lo

ss [%

]

Average Fixed

Product avg.

Trendline

0

5

10

15

20

1 2 3 4 5


THR

300

[MJ/

m2]

Average Fixed

Product avg.

Trendline

Figure 5-4 Product D - comparison between average values for each fixed position and the product average.

As all 30 specimens from product D ignited, the values for each position are calculated as theaverage of three single tests. The diagrams in Figure 5-4 show that position 1 has the bestcoincidence with the product average. However, positions 2 and 4 do also seem to coincide well.All results from position 1 satisfy the +/- 10 % tolerance criterion except THR300, while the timeto ignition parameter is the only one not satisfactorily represented by the results in position 3.

29


0

100

200

300

400

500

600

1 2 3 4 5


tign

[s] Average Fixed

Product avg.

Trendline

50

60

70

80

90

1 2 3 4 5


HR

RM

ax [k

W/m

2]

Average Fixed

Product avg.

Trendline

0,000

0,001

0,002

0,003

0,004

0,005

1 2 3 4 5


SPR

avg

[m2/

s]

Average Fixed

Product avg.

Trendline

65

67

69

71

73

75

1 2 3 4 5


Mas

s Lo

ss [%

]

Average Fixed

Product avg.

Trendline

20

24

28

32

36

40

1 2 3 4 5


THR

300

[MJ/

m2]

Average Fixed

Product avg.

Trendline

Figure 5-5 Product E - comparison between average values for each fixed position and the product average.

All of the 30 specimens tested for product E ignited. For product E, position 1 showed resultsalmost equal to the average value of the product for all 4 parameters. Except for the mass lossparameter, all of the other positions are assessed to have results further away from the productaverage.

5.1.3 Visual evaluation of surface during samplingThe possible influence of knots and resin content on the surface was evaluated in the ISO 5660test series. As the fixed specimens were cut without consideration of the look of the surface, thetest procedure included a description of each test specimen surface. The surfaces were describedeither by using a camera or putting the specimen in a copy machine.

30


The initial wish to assess the influence of resin content on the surface showed to be impossible asjust a few specimens had this characteristic surface. The specimens with knots were divided intothe following two classes:

� Knot class I – specimens with knot(s) distributed on the sides – i.e. more or less covered bythe frame, examples are shown on the two upper photos below.

� Knot class II – specimens with knot(s) distributed near the centre – i.e. not covered by theframe, examples are shown on two lower photos below.

Photo 1 Photographs showing the two “classes” used to divide specimens with knots –knot class I at the top and knot class II at the bottom.

By evaluating the surface descriptions we found that knot class I and II were best represented inproduct A and D respectively – each of the classes counting 5 and 4 specimens. The number ofspecimens with knots from product A was initially larger, but the ones that did not ignite are notpart of this evaluation.

The results from the above-mentioned test specimens were compared with the average resultsfrom their respective products. The evaluation is based on the same parameters as in Section5.1.2.

31


0

200

400

600

800

1000

1 2 3 4 5

Position

tign

[s] Avg. product

Knot class 1

0

10

20

30

40

50

1 2 3 4 5

Pos ition

HR

RM

ax [k

W/m

2]

Avg. product

Knot class 1

0,000

0,001

0,002

0,003

0,004

0,005

0,006

1 2 3 4 5

Position

SPR

avg

[m2/

s]

Avg. product

Knot class 1

50,0

60,0

70,0

80,0

1 2 3 4 5

Pos ition

Mas

s Lo

ss [%

]

Avg. product

Knot class I

0

2

4

6

8

10

12

1 2 3 4 5

Position

THR

300

[MJ/

m2]

Avg. product

Knot class I

Figure 5-6 Product A - comparison between specimens of knot class I and product average.

Even if the evaluated specimens were few, the 5 specimens represent almost 40% of the numberof specimens used to calculate the product average – i.e. excluding the specimens that did notignite. However, Figure 5-6 shows no clear trend when assessing the influence of knots defined asknot class I. The results are spread on both sides of the product average, indicating that specimenswith knots defined in surface class I may be used for tests to predict the product fire properties inlarger scale.

32


050

100150200250300350

1 2 3 4 5

Position

tign

[s] Avg. product

Knot class II

0

20

40

60

80

100

120

1 2 3 4 5

Position

HR

RM

ax [k

W/m

2]

Avg. product

Knot class II

0,000

0,001

0,002

0,003

0,004

1 2 3 4 5

Position

SPR

avg

[m2/

s]

Avg. product

Knot class II

60,0

70,0

80,0

90,0

1 2 3 4 5

Pos ition

Mas

s Lo

ss [%

]

Avg. product

Knot class II

0

5

10

15

20

1 2 3 4 5

Pos ition

THR

300

[MJ/

m2]

Avg. product

Knot class II

Figure 5-7 Product D – comparison between specimens of knot class II and product average.

Only 4 of the specimens from Product D were classified as knot class II – representingapproximately 13% of the total number of specimens. Still, Figure 5-7 indicates that the fireproperties of specimens in knot class II are worse than the product average. The lower smokeproduction is most likely caused by the fact that the smoke production of both untreated and FRTwood is at its maximum in the smouldering phase before ignition. Thus, early ignition will lead todecreased smoke production. Despite the small basis for comparison, the evaluation still indicatethat specimens with knots defined in surface class II have worse fire properties than the averageproduct level. These specimens should therefore be avoided when sampling for ISO 5660 tests.

33


5.1.4 Fixed versus Optimum test specimensThe influence of where the specimens are cut on the plank is discussed in Section 5.1.2 by usingdata from the fixed samples. Another matter that should be evaluated is whether the optimizedspecimens have fire characteristics better than the average fire properties of the product. Thissubject is partly discussed in section 5.2 as well.

When looking at the number of specimens that did not ignite, it seems to be evenly distributedbetween fixed and optimized specimens. Of the 17 specimens from product A that did not ignite,the distribution between fixed and optimum was 8 and 9 respectively. Of the 6 none-ignitionspecimens from product B, only 1 was an optimized specimen. This simple evaluation indicatesthat the specimens cut as far from visible knots and resin as possible do not have any better firecharacteristics than the specimens cut without such considerations.

The figures below show the average value of the optimized specimens per plank compared withthe product average. Using a graphical presentation will ease the evaluation of this item, and theparameters used are the same as in the previous section. The product average does not, except forthe smoke parameter, include the specimens that did not ignite.

500

600

700

800

1 2 3

Plank

tign

[s] Average Optimzed

Product avg.

32

33

34

35

36

37

38

1 2 3

Plank

HR

RMax

[kW

/m2]

Average Optimized

Product avg.

0,0000

0,0010

0,0020

0,0030

0,0040

0,0050

1 2 3

Plank

SPRa

vg [m

2/s]

Average Optimized

Product avg.

50

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Figure 5-8 Product A – comparison between optimized specimen and product average. Nooptimized specimens from plank 1 ignited – i.e. only the smoke parameter has avalue for this plank.

34


0100200300400500600700800

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Figure 5-9 Product B – comparison between optimized specimen and product average.

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Figure 5-10 Product C – comparison between optimized specimen and product average.

35


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Figure 5-11 Product D – comparison between optimized specimen and product average.

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Figure 5-12 Product E – comparison between optimized specimen and product average.

36


All of the figures show the average optimized values distributed on both sides of the productaverage. This trend is the same for all products and all variables analysed, showing that the resultsfrom optimized specimens were not substantially better than the average of the product.

5.1.5 Discussion of the sampling procedureThe figures in Section 5.1.2 were used to assess which positions of the plank that had the bestcoincidence with the product average. This is a simple evaluation method, which for this matterseems to indicate a certain pattern. Figure 5-1 and Figure 5-2 clearly showed positions 2 and 4 tocoincide best with the horizontal line that represents the product average. While product C seemsto be best represented in positions 3 and 4, products D and E deviates from this trend. Both Figure5-4 and Figure 5-5 show that specimens from product D and E should be cut in position 1.However, position 2, and especially position 4, still seem to have satisfactory coincidence withthese products as well. The above-mentioned evaluations indicate that FRT wood products aresatisfactorily represented by specimens cut in positions 2 and 4. That is, specimens takenapproximately 1 meter from the end of the plank.

The influence of knots on the specimen surface was assessed in Section 5.1.3 – indicating thatspecimens with knots distributed on their sides showed no clear trend in decreased fire properties.Knots positioned near the centre of the specimen did, however, have worse fire properties than theaverage product level. To be on the safe side, the recommendation for sampling is as far aspossible to avoid knots on the specimen surface.

Section 5.1.4 evaluates if test results from specimens cut as far from visible knots and resin aspossible are substantially better than the product average. The distribution of non-ignitedspecimens and comparison of fire properties between optimized specimens and product averageboth indicate the same: optimized specimens do not have substantially better fire properties, andmay be used to represent the product.

5.2 VARIATION OF ISO 5660 TEST RESULTS

5.2.1 GeneralBefore predicting the performance of a product in the SBI test based on cone calorimeter testresults, we need to know something about the statistical spread of test results for differentmeasured and calculated variables. If the standard deviation is large for a variable with significantinfluence on the predicted results, the inaccuracy of the predicted results can be large.

In the following sections, we have investigated the spread of results of several variables that arefound to be important for prediction of test results in larger-scale tests, like in prediction ofperformance in the SBI test. The parameters we have explored are time to ignition, maximum heatrelease rate, total heat release 300 seconds after ignition, average smoke production rate for thewhole test period and total mass loss. The results from the exploration are presented in thesections below.

5.2.2 Time to ignition, tign

Time to ignition is considered to be an important test result from the cone calorimeter test, and itsvalue has a great importance in several models where large-scale test results are predicted fromcone calorimeter test results. The models used in this project (see Section 6) are no exceptions.

37


However, time to ignition may for some products be one of the test results that has the largestvariation, and FRT wood is often found to be in this group of products. The results on time toignition from the 5 products tested in this project are shown in Table 5-1 below. Only the caseswhere the specimens were ignited during the test period of 30 minutes are included in the table.For product A, 17 of 30 specimens did not ignite, and for product B 6 of 30 specimens did notignite.

Table 5-1 Variation in time to ignition, tign, for the five FRT wood products tested in the cone calorimeter at 50 kW/m2.

Product Mean[s]

Min[s]

Max[s]

Stdev[%]

95% confidenceinterval [s]

A(n=13) 743 362 1029 23 639 - 848

(mean value ± 14%)B

(n=24) 371 7 870 80 246 - 497(mean value ± 34%)

C(n=30) 18 11 35 37 16 - 21

(mean value ± 14%)D

(n=30) 297 56 700 61 225 - 364(mean value ± 23%)

E(n=30) 41 25 51 17 38 - 43

(mean value ± 6%)

According to Table 5-1 there obviously is a large spread in the values of time to ignition. Thelargest spread is for product B, where the shortest time to ignition was 7 seconds, and the largestvalue was 870 seconds, and where 6 of the 30 specimens not ignited at all. The width of the 95 %confidence intervals varies from product to product. A graphical presentation of the 95 %confidence intervals for the five products is given in Figure 5-13.

0100200

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Tim

e to

igni

tion

[s]

A

B

D

15

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30

35

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45

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e to

igni

tion

[s]

E

C

Figure 5-13 A graphical presentation of the 95 % confidence intervals for time to ignition forthe five tested FRT wood products. The mean value for each product is markedwith a solid dot, while the surrounding rectangles represent the width of theintervals. Because of different levels of time to ignition values, the products arepresented in two separate diagrams. Only confidence intervals for products B and Doverlap each other.

38


The smallest spread in times to ignition was found for product E, with a standard deviation of 17%. Product C has the lowest mean value of time to ignition, and was also the only product that didnot satisfy class B when tested in the SBI apparatus. Even if the content of FR seemed extremelylow for this product, the surface treatment may also had influence on the test results. However, themaximum measured values of time to ignition are in the same range as the minimum values forsome of the other products, and this will make it difficult to base a prediction of the SBI heatclassification on a single value of time to ignition only.

There is no obvious trend in the standard deviation with respect to products with high or lowmean values for time to ignition. The spread of time to ignition values was quite similar for testspecimens taken in fixed and optimized positions for each of the five tested products.

5.2.3 Maximum heat release rate, HRRmax

Maximum rate of heat release in the cone calorimeter may be an indicator of large-scale firebehaviour of a product. The statistical spread of this variable for the tested FRT wood products istherefore explored here, and the results are shown in Table 5-2 below. Only the cases where thespecimens were ignited during the test period of 30 minutes are included in the table. For productA, 17 of 30 specimens did not ignite, and for product B 6 of 30 specimens did not ignite.

Table 5-2 Variation in maximum heat release rate, HRRmax, for the five FRT wood productstested in the cone calorimeter at 50 kW/m2

Product Mean[kW/m2]

Min[kW/m2]

Max[kW/m2]

Stdev[%]

95% confidenceinterval [kW/m2]

A(n=13) 36 22 56 26 30 - 42

(mean value ± 17 %)B

(n=24) 70 52 89 18 65 - 76(mean value ± 8 %)

C(n=30) 184 123 241 16 174 - 195

(mean value ± 6 %)D

(n=30) 71 44 102 19 66 - 76(mean value ± 7 %)

E(n=30) 184 134 237 15 174 - 195

(mean value ± 6 %)

According to Table 5-2 above, the standard deviation for all the five tested products are moderate,i.e. in the order of 20% from the mean value, independent of the different levels of HRRmax. The95 % confidence intervals are relatively narrow for all the five tested products. A graphicalpresentation of the 95 % confidence intervals for the five products is given in Figure 5-14.

39


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Figure 5-14 A graphical presentation of the 95 % confidence intervals for HRRmax for the fivetested FRT wood products. The mean value for each product is marked with a soliddot, while the surrounding rectangles represent the width of the intervals. Becauseof different levels of HRRmax values, the products are presented in two separatediagrams. Confidence intervals for products B and D, and also for C and E, overlapeach other.

Product C was the only product that did not satisfy class B when tested in the SBI apparatus. Thisproduct, together with product E, obtained the highest values of HRRmax. It is not possible todistinguish between these two products on the basis of the HRRmax values alone.

5.2.4 Total heat released 300 s after ignition, THR300

The total heat released in a fixed period of time in the cone calorimeter may also indicate large-scale fire behaviour of a product. We have chosen to explore how the heat released during the first300 seconds after ignition, THR300, vary within each of the five tested products. THR300 is usedas an input variable in the model by Östman and Tsantaridis /9/, and is indirectly one of thevariables to be reported according to ISO 5660 (ISO 5660 requires that the average HRR duringthe first 300 s after ignition shall be reported). The statistical spread of this variable for the testedFRT wood products is therefore explored here, and the results are shown in Table 5-3 below. Onlythe cases where the specimens were ignited during the test period of 30 minutes are included inthe table. For product A, 17 of 30 specimens did not ignite, and for product B 6 of 30 specimensdid not ignite.

40


Table 5-3 Variation in total heat released 300 s after ignition, THR300, for the five FRT wood products tested in the cone calorimeter at 50 kW/m2

Product Mean[MJ/m2]

Min[MJ/m2]

Max[MJ/m2]

Stdev[%]

95% confidenceinterval [MJ/m2]

A(n=13) 7,5 3,4 14,0 49 5,3 - 9,7


(n=24) 10,8 6,2 15,5 30 9,5 - 12,3(mean value ± 13 %)

C(n=30) 17,0 9,8 23,6 22 15,6 - 18,4


(n=30) 14,7 11,5 17,6 11 14,1 - 15,3(mean value ± 4 %)

E(n=30) 26,9 21,8 32,6 9 26,0 - 27,8

(mean value ± 3 %)

According to the table above, there is generally a large spread in the values of THR300. Someproducts have a low spread (products D and E), some have a moderate spread (products B and C)while product A has a large spread in the measured THR300 values.

Product C was the only product that did not satisfy class B when tested in the SBI apparatus.However, it is product E that obtained the highest values of THR300, while product C has thesecond highest values.

A graphical presentation of the 95 % confidence intervals for the five products is given inFigure 5-15.

0

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00 [M

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Figure 5-15 A graphical presentation of the 95 % confidence intervals for THR300 for the fivetested FRT wood products. The mean value for each product is marked with a soliddot, while the surrounding rectangles represent the width of the intervals.Confidence intervals for products A and B slightly overlap each other.

41


5.2.5 Average smoke production rate, SPRavg

For almost all products tested in the SBI, the total smoke production during the first 600 seconds,TSP600s, will be determining for the smoke classification. The products tested in this project areno exceptions from this rule. All of them had a SMOGRA value well below the limit for s1, butthe total smoke production was exceeding the s1 criterion for four of five products. Only Eproduct satisfied both criteria to s1.

As total smoke production is the determining variable in the larger-scale test, it seems logical thattotal smoke production measured in the cone calorimeter may be an indicator of large-scale smokebehaviour. We have therefore chosen to explore how the average smoke production rate duringthe whole test, SPRavg, varies within each product and between the different products. The resultsare shown in Table 5-4 below. All cases, both ignited and not ignited are included in the table.

Table 5-4 Variation in average smoke production, SPRavg, for the five FRT wood productstested in the cone calorimeter at 50 kW/m2. All values are multiplied by a factor of 104.

Product Mean[m2/s]

Min[m2/s]

Max[m2/s]

Stdev[%]

95 % confidenceinterval [m2/s]

A(n=30) 40 24 64 21 37 - 44


(n=30) 32 15 56 35 28 - 36(mean value ± 13 %)

C(n=30) 16 9 21 22 15 - 17


(n=30) 30 7 68 51 24 - 35(mean value ± 18 %)

E(n=30) 13 1 81 137 6 - 19

(mean value ± 50 %)

The table shows that there generally is a large spread in the values of SPRavg. Some products havea moderate spread (products A and C) while the products B, D and E have a large spread in themeasured SPRavg values. Product E has the lowest mean value of SPRavg, and was also the onlyproduct to satisfy the s1 classification when tested in the SBI apparatus. However, the maximummeasured values of SPRavg are in the same range as for all the other products, and this will make itdifficult to base a prediction of the SBI smoke classification on a single value of SPRavg. Inaddition, the standard deviation for SPRavg for product E is extremely high.

A graphical presentation of the 95 % confidence intervals for the five products is given inFigure 5-16.

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101520253035404550

SPR

avg

[m2 /s

] x 1

04

A

B D

C E

Figure 5-16 A graphical presentation of the 95 % confidence intervals for SPRavg for the five tested FRT wood products. The mean value for each product is marked with a solid dot, while the surrounding rectangles represent the width of the intervals.Confidence intervals for products B and D, and also for products C and E, overlap each other.

5.2.6 Total mass lossThe statistical spread of the total mass loss during the whole test period for the tested FRT woodproducts is shown in Table 5-5 below. Only the cases where the specimens were ignited duringthe test period of 30 minutes are included in the table. For product A, 17 of 30 specimens did notignite, and for product B 6 of 30 specimens did not ignite.

Table 5-5 Variation in total mass loss for the five FRT wood products tested in the cone calorimeter at 50 kW/m2

Product Mean[%] Min [%] Max

[%]Stdev[%]

95% confidenceinterval [MJ/m2]

A(n=13) 67 52 82 19 59 - 75


(n=24) 69 63 76 6 68 - 71(mean value ± 2 %)

C(n=30) 74 66 79 4 73 - 75


(n=30) 75 70 80 3 73 - 75(mean value ± 1 %)

E(n=30) 70 69 71 1 70 - 71

(mean value ± 1 %)

According to the table, there is generally small spread in the values of total mass loss. There areapparently no large differences in the total mass loss between the five different products.

A graphical presentation of the 95 % confidence intervals for the five products is given in Figure5-17.

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50

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tota

l mas

s lo

ss [%

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Figure 5-17 A graphical presentation of the 95 % confidence intervals for the total mass loss forthe five tested FRT wood products. The mean value for each product is markedwith a solid dot, while the surrounding rectangles represent the width of theintervals. There is considerate overlap between the different confidence intervals.

5.2.7 Discussion of the variation in the explored test resultsThe exploration of the different variables have shown that there is no significant difference inneither the mean values nor in the spread of the values between results from specimens taken infixed positions along the planks and results from specimens selected from optimal areas.However, as shown in Section 5.1.2, the variation in the values of test results is linked to theposition along the boards. Specimens taken one meter from the end of a board will have testresults closer to the mean value than a specimen taken from one of the ends or from the middle ofthe plank.

Time to ignition is a variable with relatively large standard deviation. For product B, time toignition varied between 7 seconds and no ignition at all. For products C and E that had shortertimes to ignition, the 95 % confidence intervals are narrow, with a width of only 5 seconds.Predicting the SBI test results based on a single value of time to ignition is not recommendable,because of the large standard deviation found for some products. However, looking at the meanvalues for time to ignition, there may seem to be a correlation to the SBI-classification. Product Chas a low mean value of 18 s, and did not satisfy class B based on the SBI test results. Product Aobtained the best results in the SBI, and also had the largest mean value of time to ignition amongthe five products.

Maximum heat release rate has a moderate spread in measured values compared to time toignition. The ranking of the five tested products based on HRRmax corresponds mainly to theranking based on THR600s measured in the SBI. The exception is product E, which obtained thesame high mean value of HRRmax in the cone calorimeter as product C did. It is not possible todistinguish between these two products on the basis of HRRmax, even though product C obtainedclass C in the SBI and product E obtained class B.

44


The ranking of products according to THR300 is mostly consistent with the ranking of the sameproducts according to the values of THR600s obtained in the SBI test. Once again, it is the valuesfor product E that are complicating the comparison. Product E has the largest measured values forTHR300, but it is classified as B according to the SBI test results. The spread of the measuredvalues is rather large for the five products tested, and this indicates that the variable is unreliableas an indicator of the final classification based on SBI test results.

The values of total mass loss in percentage of original mass are not very different among the fivetested products. The largest spread in values is for product A, which had very low heat releaseresults, and also performed best in the SBI test. It is not possible to distinguish product C from theother products based on the values on total mass loss.

It is no surprise that the largest variation in measured test results lies in the variables related tosmoke production. Average smoke production has a moderate to high standard deviation forproducts A, B, C and D, while the standard deviation for product E is extremely high. Product Ewas the only one to satisfy smoke classification s1 based on SBI test results. Looking at the meanvalues for SPRavg, we see that product E has the lowest value, but the 95 % confidence interval israther wide, and in fact is overlapping the 95 % confidence interval of product C. The TSP600s measured in the SBI was not very far from satisfying class s1 for product C. This means thatSPRavg measured in the cone calorimeter could be an indicator of the smoke classification basedon SBI test results, but because of the large expected spread in results, it would not be a veryprecise indicator if the predictions were based on this variable alone.

5.3 VARIATION OF SBI RESULTSThe test results from each single SBI test is shown in Table 4-6. Calculation of standard deviationand 95 % confidence interval as for the ISO 5660 results is not relevant based on 2 or 3 singletests.

However, the table shows no variation of classes obtained for each of the parallel tests run on thesame product. The variation in FR content shown in Table 2-1 is visible in the ISO 5660 testresults, but does not seem to influence the SBI test results substantially. Further work wastherefore not prioritised regarding sampling procedures for use in SBI tests.

An interesting aspect showed by the SBI tests, is that 4 of 5 products did not meet the s1 criterionfor total smoke production. According to the classification system based on NT FIRE 004 /1/testing, the smoke classes are defined as “low smoke production” and “normal smokeproduction”. Most FRT wood products tested according NT FIRE 004 have met the criteria to lowsmoke production – often with good margin. In the harmonized European system, the Nordiccountries have all replaced “low smoke production” with the s1 class. FRT wood products thatonly satisfy s2 classification in the new European system will have their internal applications inNordic buildings substantially decreased compared with today’s possibilities for “low smokeproduction” products.

One “solution” mentioned by manufacturers of FRT wood products is related to the fact thatdecreased FR content and fire properties will also decrease the amount of smoke production. Asmentioned above, many of today’s products have good margins to the heat release criteria,allowing them substantially decrease of FR content. This is of course an undesirable development,as it will lead to products having worse fire properties regarding ignitability and heat release thantoday. The “smoke problem” may generally be vital for the business sector of FRT woodproducts, and comprehensive product development may be required.

45


6 PREDICTING SBI TEST RESULTS BY CALCULATION MODELS

6.1 Prediction of heat release by a one-dimensional thermal flame spread modelThe heat release related classification of a construction product in an SBI test can be predicted onthe basis of cone calorimeter test data using a one-dimensional thermal flame spread model/10,11/. The model is adjusted in this project to be able to handle prediction of FRT woodproducts, and the model and its modifications are described in detail in Appendix II.

An overview over the predicted classification for the five FRT wood products based onFIGRA0,2MJ value alone is shown in Figure 6-1. The predicted classification based on THR600svalue alone is shown inFigure 6-2, and the prediction of the final classification is shown in Figure 6-3. The matrices inthe figures show both correctly classified cases and misclassifications.

Actual class Actual class# cases B C % B C

B 119 14 B 100 47Predicted C 0 16 Predicted C 0 53class D 0 0 class D 0 0

E 0 0 E 0 0

Figure 6-1 Results from classification based on the predicted FIGRA0.2MJ value for the one-dimensional thermal flame spread model. The numbers of predicted cases areshown in the left matrix, while the shares of the total number are shown in the rightmatrix. The numbers presented in squares with heavy borderlines are the correctlyclassified cases, while the other numbers represent the misclassified cases.

Actual class Actual class# cases B % B

B 149 B 100Predicted C 0 Predicted C 0class D 0 class D 0

E 0 E 0

Figure 6-2 Results from classification based on the predicted THR600s value for the one-dimensional thermal flame spread model. The numbers of predicted cases areshown in the left matrix, while the shares of the total number are shown in the rightmatrix. The numbers presented in squares with heavy borderlines are the correctlyclassified cases, while the other numbers represent the misclassified cases.

46




E 0 0 E 0 0

Figure 6-3 Results from classification in the European system based on the predicted valuesof FIGRA0.2MJ and THR600s for the one-dimensional thermal flame spread model.The numbers of predicted cases are shown in the left matrix, while the shares of thetotal number are shown in the right matrix. The numbers presented in squares withheavy borderlines are the correctly classified cases, while the other numbersrepresent the misclassified cases.

6.2 Prediction of heat release by a convolution modelThis prediction model for heat release results was presented in detail in reference /12/. The modelis based on a prediction model presented by Messerschmidt, Van Hees and Wickström, whichuses cone calorimeter test results to predict test results in the SBI test /13/. This model uses astatistical multivariate method to select the proper calculation algorithm for a certain set of inputdata from a cone calorimeter test.

The predicted classification for the five FRT wood products based on FIGRA0,2MJ value alone isshown in Figure 6-4, the predicted classification based on THR600s value alone is shown in Figure6-5 and the prediction of the final classification is shown in Figure 6-6. The matrices in the figuresshow both correctly classified cases and misclassifications.



E 0 0 E 0 0

Figure 6-4 Results from classification based on the predicted FIGRA0.2MJ value for theconvolution model. The numbers of predicted cases are shown in the left matrix,while the shares of the total number are shown in the right matrix. The numberspresented in squares with heavy borderlines are the correctly classified cases, whilethe other numbers represent the misclassified cases.

47


Actual class Actual class# cases B % B

B 70 B 47Predicted C 71 Predicted C 48class D 7 class D 5

E 0 E 0

Figure 6-5 Results from classification based on the predicted THR600s value for theconvolution model. The numbers of predicted cases are shown in the left matrix,while the shares of the total number are shown in the right matrix. The numberspresented in squares with heavy borderlines are the correctly classified cases, whilethe other numbers represent the misclassified cases.



E 0 0 E 0 0

Figure 6-6 Results from classification in the European system based on the predicted valuesof FIGRA0.2MJ and THR600s. The numbers of predicted cases are shown in the leftmatrix, while the shares of the total number are shown in the right matrix. Thenumbers presented in squares with heavy borderlines are the correctly classifiedcases, while the other numbers represent the misclassified cases.

6.3 Prediction of classification based on time to flashover in the Room Corner testA calculation model that is capable of predicting time to flashover in the ISO 9705 room cornertest /14/ from cone calorimeter test results could be used to predict the European classification ofa product. According to the classification standard EN 13501-1 /15/, the correlation between thenew European classes and time to flashover is as shown in Table 6-1 below.

Table 6-1 Correlation between time to flashover in the ISO 9705 room corner test and thedifferent classes in the European system.

Time to flashover in the ISO 9705 test Euroclass accordingto EN 13501-1

No flashover during 20 minutes test BNo flashover during 10 minutes test CNo flashover during 2 minutes test DFlashover before 2 minutes E

48


Östman and Tsantaridis have reported a very simple empirical linear regression model forprediction of time to flashover in the room corner test /9/. The model is based on empirical data,and was found to predict time to flashover with good accuracy for 28 products. Cone calorimeterresults from tests at 50 kW/m2 are used as input data to this model, which also requiresinformation about mean density of the outer 10 mm of the tested product. The regression model isexpressed in the following way:

6007.0 3.1300

7.125.0

+⋅

⋅=THR

tt ig

FO

ρ (1)

wheretFO = time to flashover in the room corner test [s]tig = time to ignition in the cone calorimeter at 50 kW/m2 [s]THR300 = total heat release during 300 s after ignition at 50 kW/m2 [MJ/m2]ρ = mean density [kg/m3]

The model was applied to the set of test data from FRT wood products. Data from specimens thatdid not ignite are not used as input to this model.

The prediction results are shown in the matrix in Figure 6-7 below. The matrices in the figureshow both correctly classified cases and misclassifications.



E 0 0 E 0 0

Figure 6-7 Results from prediction of classification in the European system based on time toflashover in the room corner test for the regression model by Östman andTsantaridis. The numbers of predicted cases are shown in the left matrix, while theshares of the total number are shown in the right matrix. The numbers presented insquares with heavy borderlines are the correctly classified cases, while the othernumbers represent the misclassified cases.

49


6.4 Prediction of smoke production by a convolution modelThe prediction model for SBI smoke production results is based on the heat release predictionmodel described in section 6.2 above. The model is described in detail in reference /16/.

The predicted classification based on SMOGRA value alone is shown in Figure 6-8, the predictedclassification based on TSP600s value alone is shown in Figure 6-9, and the prediction of the finalsmoke classification is shown in Figure 6-10. The matrices in the figures show both correctlyclassified cases and misclassifications.

Actual class Actual class# cases s1 s2 % s1 s2

s1 70 0 s1 47 0Predicted s2 78 0 Predicted s2 53 0class s3 0 0 class s3 0 0

Figure 6-8 Results from smoke classification based on the predicted SMOGRA value for theconvolution model. The numbers of predicted cases are shown in the left matrix,while the shares of the total number are shown in the right matrix. The numberspresented in squares with heavy borderlines are the correctly classified cases, whilethe other numbers represent the misclassified cases.



Figure 6-9 Results from smoke classification based on the predicted TSP600s value for theconvolution model. The numbers of predicted cases are shown in the left matrix,while the shares of the total number are shown in the right matrix. The numberspresented in squares with heavy borderlines are the correctly classified cases, whilethe other numbers represent the misclassified cases.

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Figure 6-10 Results from smoke classification based on predicted values for SMOGRA andTSP600s for the convolution model. The numbers of predicted cases are shown inthe left matrix, while the shares of the total number are shown in the right matrix.The numbers presented in squares with heavy borderlines are the correctlyclassified cases, while the other numbers represent the misclassified cases.

6.5 Prediction of smoke production by a multivariate statistical modelA prediction model for the SBI smoke classification based on a multivariate statistical methodwas developed in a NORDTEST project that was reported in 2002 /17/. Later on, this model wasmodified based on more data from SBI tests and cone calorimeter tests. The statistical model usedin this report is described in reference /16/.

The prediction of the final smoke classification is shown in Figure 6-11. The matrices in thefigure show both correctly classified cases and misclassifications.


s1 4 0 s1 13 0Predicted s2 26 118 Predicted s2 87 100Class s3 0 0 class s3 0 0

Figure 6-11 Results from prediction of smoke classification for the statistical multivariatemodel. The numbers of predicted cases are shown in the left matrix, while theshares of the total number are shown in the right matrix. The numbers presented insquares with heavy borderlines are the correctly classified cases, while the othernumbers represent the misclassified cases.

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6.6 Prediction of SBI results - discussion and conclusionsIn the sections above we have used the cone calorimeter test results as input to differentcalculation models to predict the products' classification based on SBI test results. The differentmodels have shown both weaknesses and strengths.

The results presented in section 4.2 show that the classification based on heat release in the SBI istotally determined by the FIGRA0.2MJ value for all the five tested FRT wood products. All valuesfor THR600s are well below the class B limit of 7,5 MJ. Correspondingly, the smoke classificationis determined by the TSP600s value. All SMOGRA values are well below the s1 limit of 50 m2/s2.This means that if a calculation model is able to predict the value of FIGRA0.2MJ, it will alsopredict the resulting heat release classification satisfactorily. And if a calculation model canpredict the TSP600s precise enough, it will implicitly predict the smoke classification.

The one-dimensional thermal flame spread model presented in section 6.1 had the overall bestprediction results regarding variables connected to heat release in the SBI. The total heat release,THR600s was predicted correctly in all of the 149 calculated cases. The FIGRA0,2MJ value wascalculated correctly in 135 of 149 cases. However, only one product, i.e. product C, had aFIGRA0,2MJ value in class C, and that means that of the 149 cases, 119 belonged to class Bproducts while the 30 cases left belonged to a class C product. Of these 30 cases, only about halfwere predicted to belong to the correct class by the one-dimensional thermal flame spread model.

The convolution model presented in section 6.2 has a tendency to predict both FIGRA0.2MJ andTHR600s in a worse class than the values actually measured in the SBI. Looking at the FIGRA0.2MJvalue, about 3/4 of the cases in class B are correctly predicted, but only 1/4 of the cases in class C.The model would need some adjustments to be able to handle prediction SBI classification forFRT wood products.

The regression model presented in section 6.3 was not capable to handle the test results from thefive FRT wood products very well. Of 95 products in class B in the European system, only 5 werepredicted to have a time to flashover in the room corner test above 1200 seconds. The product inclass C was also not correctly predicted by this model, 30 of 30 different calculations predictedclass D for this product. The regression model is predicting too much on the safe side and willobviously have to be modified to be able to predict classification of FRT wood products correctly.

The smoke prediction model presented in section 6.4 was able to predict the TSP600s value at thecorrect level for 29 of the 30 cases in class s1, and for more than 2/3 of the s2 products. This is arather promising result, which indicates that this model can be useful for predicting the smokeclassification for FRT wood products.

The multivariate statistical smoke prediction model presented in section 6.5 is predicting the s2classification for all the tested specimens of the four s2 products correctly. However, from inputvalues from cone tests of the 30 specimens of the s1 product, only 4 were predicted correctly. 87% of these cases were misclassified as s2 products. This model needs some modification to beuseful. One way to improve it, would be to use data from more cone calorimeter tests of differentFRT wood products in the development of the model. As this is a purely statistical model, itspredictability will be closely dependent on the quality of the data set on which the model is based.The larger the data set is, and the more different products of the same kind that are represented inthe data set, the larger validity the statistical model will get. An option is to develop a modelintended for wood based products only.

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7 PREDICTING SBI TEST RESULTS BY SIMPLE CRITERIA

7.1 GENERALMany of the SBI prediction methods described in Section 6 require either access to a calculationtool or additional calculations that may be time consuming. Using such methods is of even lessinterest if they do not give satisfactory predictions.

It is therefore of great interest to have simple criteria related to the ISO 5660 test results. Suchcriteria should be used to predict heat release related European class and the additional smokeclass without the need for additional calculations. The sections below indicate how to use some ofthe cone calorimeter test results to predict the classification if the same product were testedaccording to EN 13823.

7.2 CRITERIA USED FOR PREDICTION OF HEAT RELEASE CLASSIFICATION

7.2.1 Prediction procedures developed by VTTSection 6.1 describes a procedure of using the cone calorimeter test results for the prediction ofreaction to fire classes of FRT wood products. The procedure does not include any additionalcalculations and is based on cone calorimeter tests at a heat exposure level of 50 kW/m2:

� determine the maximum heat release rate HRRmax (first peak for a multi-peak curve)� if HRRmax < 75 kW/m2 → the specimen is predicted as class B

� determine the ignition time tig,50 as the moment when the heat release rate per unit areareaches 50 kW/m2

� if tig,50 > 570 s → the specimen is predicted as class B

Specimens having a maximum heat release rate higher than 75 kW/m2, or that reache 50 kW/m2

before 570 seconds of testing time cannot be predicted by this procedure. In this project, theprocedure was used for the following products:

� product A: all of the 30 tests� product B: 21 of 30 tests� product C: none� product D: 18 of 30 tests� product E: none

When using this procedure, all of the specimens listed above were correctly predicted. The criteriadescribed, using the HRRmax and tig,50 variables, are therefore highly recommended for FRT woodspecimens.

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7.2.2 Prediction Procedures from this projectThe ISO 5660 test results were also evaluated to find simple criteria for predicting the heat releaseclass if SBI tests were performed on the same product. The five products tested represents 3different manufacturers and FR chemicals, and the conclusions from the project should thereforebe considered as satisfactory representative for FRT wood products of planks for internal andexternal use in buildings. The remaining uncertainty in the work should however decrease with alarger number of products.

When evaluating the parameters described in Section 5.2, we decided to use product A, B and Dthat all were well inside the class B criteria, both for the FIGRA and THR600s variables. The figurebelow shows a graphical presentation of the 95 % confidence intervals for all products – for timeto ignition and HRRmax. The other variables are not used, as they proved to be less suitable for thispurpose.

0100200

300400500600

700800900

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Figure 7-1 Graphical presentation of the 95 % confidence intervals for all FRT wood productstested. The mean value for each product is marked with a solid dot, while thesurrounding rectangles represent the width of the intervals. The confidenceintervals are shown in two separate diagrams for both time to ignition and HRRmax,because of the large spread in values.

Section 4.2 showed that product E obtained class B when tested according to prEN 13823, havingtest results in the same range as the other class B products. However, the cone calorimeter clearlyshowed large differences between E and the other products. When using only product A, B and Dthe figure above may be used to define limits related to the ISO 5660 test results.

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The HRRmax value can also be used to predict the classification of the tested products. Figure 7-1shows that products obtaining HRRmax values below 80 kW/m2 can be predicted as class B. Thissubstantiates the corresponding criterion described in Section 7.2.1, where a product obtaining aHRRmax less than 75 kW/m2 is predicted in class B. The ISO 5660 test results also show that someof the HRRmax values do not occur until almost after 1200 seconds test duration. Tests run forprediction purposes should therefore have at least 1200 seconds duration.

Products that ignite before 30 seconds will most likely not have class B characteristics, whileignition after 200 seconds seems to indicate class B. These statements are, however, onlyindications as the project showed large spread in time to ignition results.

As described earlier, the time to ignition criterion described in Section 7.2.1 predicted correctclass for all specimens evaluated. Only one of the ignited products was ignited after 600 seconds.I.e. specimens that do not ignite before 600 seconds will most likely not ignite at all, and are mostprobably class B products. This also means that the ISO 5660 test may be terminated after 600seconds if ignition has not occurred.

As a result of the figures and discussion above, a suggested procedure for predicting theclassification of FRT wood products from ISO 5660 test results is found:

After running cone calorimeter tests at heat exposure level of 50 kW/m2:

� Determine the ignition time tign� if tign < 30 s → the specimen is most likely not class B – proceed for at least 1200

seconds and use the HRRmax criterion below to substantiate the prediction� if tign > 200 s → the specimen is most likely class B – proceed for at least 1200

seconds and use the HRRmax criterion below to substantiate the prediction� if tign > 600 s → the specimen is predicted as class B and the test may be terminated


The criteria above are most applicable to be used to predict distinct class B products. It is of greatimportance to understand that products not meeting or meeting the prediction criteria still mayobtain other classes when tested according to EN 13823. Furthermore, the user of these criteriashould be aware of the fact that the time to ignition value has the largest variation in the ISO 5660tests. At least the two first above-mentioned criteria related to this parameter should therefore notbe used as a sole criterion.

Another aspect of this analysis is that the short time to ignition for product C probably is a resultof its surface treatment. In this project, the surface treated product also showed poor firecharacteristics that anyway would have resulted in a class C prediction. None of the time toignition parameters above should therefore be used as a sole criterion for surface treated products.

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7.3 CRITERIA USED FOR PREDICTION OF SMOKE CLASSIFICATIONWhen using the smoke results from the SBI-tests, the products may be divided into 2 categories.Product A, B and D were all well above the TSP600 criteria and obtained smoke class s2. On theother hand, product C and E produced substantially less smoke. Even if product C obtained smokeclass s2 and Product E obtained s1, the critical TSP600 results were in the same area – just overand just below the criterion. As in the previous section, the 95 % confidence intervals for theSPRavg parameter is used to try to define criteria for smoke classification predictions.

05

101520253035404550

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avg

[m2 /s

] x 1

04

A

B D

C E

Figure 7-2 A graphical presentation of the 95 % confidence intervals for SPRavg for the fivetested FRT wood products. The mean value for each product is marked with a soliddot, while the surrounding rectangles represent the width of the intervals.

The figure shows that all SPRavg results are in the order of 0,0005 – 0,0045 m2/s, which meansthat the smoke production is low, compared to many other building products. Looking at thedifference between the two smoke categories, the figure may seem to indicate a certain limit in therange of 0,002 m2/s. Additionally, product E was only tested for 1200 seconds in the conecalorimeter. Testing the product E specimens for 1800 seconds would most likely further havedecreased the average smoke production rate.

A suggestion for a simple indication of smoke class after running cone calorimeter tests at heatexposure level of 50 kW/m2 for at least 1200 seconds is:

� Determine the average smoke production rate SPRavg� if SPRavg > 0,0025 m2/s → the specimen will most likely obtain s2 or s3 in the

SBI-test� if SPRavg < 0,0015 m2/s → the specimen may obtain s1 in the SBI-test

Using average smoke production values in the range around 0,002 m2/s is of course accompaniedwith a rather large uncertainty, and should only be used in connection with product development –preferrably together with SBI-tests for verification. Because of the large expected spread inresults, SPRavg would not be a very precise indicator if the predictions were based on this variablealone. Whether SPRavg values calculated after 600, 900 or 1200 seconds are better suited forpredicting smoke classes is not evaluated in this project. This may however be an interestingaspect to assess in the future.

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8 SELECTION OF TEST METHODS FOR DIFFERENT USE

8.1 GeneralThe purpose of this project has among others been to evaluate if fire tests may be run according toISO 5660 to predict SBI results. Different calculation models are assessed in Section 6, whileSection 7 describes simple prediction criteria. Both ways of predicting SBI results are based onISO 5660 test results – the former being more time consuming than the latter, and also requiringthe calculation tools in question.

Fire tests on FRT wood products can be divided into four main applications:� Tests for classification� Tests in connection with external audits� Tests for factory production control� Tests for product development

8.2 Selection of test methods for different use – discussion and conclusionsThe fire tests applied for fire classification of certain product groups will in the future bedescribed in European standards (hEN). There is reason to believe that no such hEN will beprepared specifically for FRT wood products similar to the ones tested and evaluated in thisproject. However, the FRT wood product group is expected to meet the same requirements asmost other surface and cladding building materials. This means that the reaction to fireperformance for products not meeting the criteria for products classified without further testingshall be classified according to EN 13501-1 /15/ - i.e. tested according to either EN 13823 /4/ orEN ISO 11925-2 /5/ or both.

As for testing for external audits, factory production control and product development, norequirements exist, and the selection of test method may differ. The main objective of fire tests isto document fire properties of the product as accurate as possible. However, the costs will also bean argument when selecting test methods, especially as the application changes from being usedby approval bodies to internal use by the manufacturer. The Nordic laboratories can offer 2-3single cone calorimeter tests for the cost of one single SBI test. Duplicate tests according to ISO5660 therefore represent lower costs than one single SBI test. Considering the costs related to theamount of product required for the test specimens substantiates the argument for using the conecalorimeter as often as possible for these applications. Another argument for using the conecalorimeter is that there are several mounting possibilities in the SBI test that may affect theresult.

Section 7 shows that simple prediction criteria based on ISO 5660 parameters may be used toestimate the heat release class. Especially products far from the classification limit can bepredicted with satisfactory accuracy. The prediction of smoke class is however connected withlarger uncertainty. Using more advanced prediction models will most likely be more timeconsuming and will not necessarily have better accuracy. Regardless of using simple predictioncriteria or advanced models, it is of great importance that the evaluators are aware of theweaknesses and limitations related to the different prediction criteria and -models.

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The following procedure is suggested for the four different applications of predictions:

� Classification purposes� Tests performed according to test methods EN 13823 and EN ISO 11925-2. At

least duplicate ISO 5660 tests should be conducted on specimens from the samebatch – cut in accordance with the sampling procedures recommended in thisreport. The ISO 5660 test results will be used as reference for future conecalorimeter tests.

� External audit� At least duplicate tests according to ISO 5660 + prediction model or -criteria� One single SBI test conducted every second year

� Factory production control� At least duplicate tests according to ISO 5660 + prediction model or -criteria

� Product development� One single test according to ISO 5660 + prediction model or -criteria as a first

screening tool – this requires that the evaluator is aware of the uncertainty in theresults.

� Duplicate tests according to ISO 5660 + modelling are required at the late stage ofthe development.

� The need for an indicative SBI test mostly depends on the need to know exactsmoke production results – i.e. an SBI test may be found necessary for productsaiming for s1 class

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9 CONCLUSIONS AND RECOMMENDATIONS

9.1 CONCLUSIONS ON ISO 5660 RESULTSA total of 150 tests from 5 different products have been conducted according to ISO 5660. Due tosmall specimens, the cone calorimeter test results for FRT wood products are very dependent onwhere the specimens are cut, and from which plank they are selected.

The exploration of the different variables have shown that there is no significant difference inneither the mean values nor in the spread of the values between results from specimens taken infixed positions along the planks and results from specimens selected from optimal areas. Thevariation of FR content along a plank results in equal variations in the fire properties of the sameplank. As expected, the project found the largest variation in measured test results in the variablesrelated to smoke production.

The recommended test parameters when performing fire tests on FRT wood productsaccording to ISO 5660 are:

� Heat flux: 50 kW/m2

� Test duration: At least 1200 seconds to measure the HRRmax value for the first of thetwo characteristic peaks. The test may however be terminated after 600 seconds ifignition has not occurred. For the latter case, the specimen is predicted class B.

9.2 CONCLUSIONS ON SBI RESULTSTwo or three single SBI tests have been performed on each of the products. The results showed novariation in classes obtained for each of the parallel tests run on the same product. The variation inFR content in and between planks visualised by the cone calorimeter test results does however notseem to influence the SBI test results substantially. The project did therefore not evaluate varioussampling procedures for use in SBI tests.

The SBI test results showed that the classification of FRT wood products is determined by theFIGRA0,2MJ parameter, while TSP600 is determining for the smoke classification. An importantaspect discovered from the SBI tests is that 4 of 5 products did not meet the s1 criterion for totalsmoke production. All the Nordic countries have replaced the Nordic smoke class “low smokeproduction” with the s1 class in the new European classification system. The fact that the s1 classwill be hard to obtain will substantially decrease the internal applications for FRT wood products,which again may be vital for many actors in this business sector.

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9.3 CONCLUSIONS ON PREDICTING SBI RESULTSSBI results can be predicted by either simple criteria related to the cone calorimeter test variableswithout additional calculations, or by prediction models that require either access to calculationtools or additional calculations that may be time consuming. This project has both evaluatedexisting calculation models and recommended simple prediction criteria. The latter has beenfound to be easier to use, more assessable, and less time and cost consuming.

The SBI test results showed that if the value of FIGRA0,2MJ can be predicted, the resulting heatrelease class will also be satisfactory predicted. Likewise, if prediction criteria or models canpredict the TSP600 precise, it will also predict the smoke classification. The different predictionmodels evaluated in this project have all shown both weaknesses and strengths. As expected is thesmoke classification is the most difficult result to predict. Furthermore, surface coating may oftenlead to a sharp peak in heat release rate in the early phase of a cone calorimeter test, which in turnleads to notable scattering of predicted results.

The evaluation of test results in this project has lead to the following suggested procedure forpredicting the classification of FRT wood products from ISO 5660 test results:

Procedure for predicting the classification of FRT wood products after running conecalorimeter tests at heat exposure level of 50 kW/m2:

� Determine the ignition time tign� if tign < 30 s → the specimen is most likely not class B – proceed for at least

1200 seconds and use the HRRmax criterion below to substantiate the prediction

� if tign > 200 s → the specimen is most likely class B – proceed for at least 1200seconds and use the HRRmax criterion below to substantiate the prediction

� if tign > 600 s → the specimen is predicted as class B and the test may beterminated


� Determine the average smoke production rate SPRavg� if SPRavg > 0,0025 m2/s → the specimen will most likely obtain s2 or s3

� if SPRavg < 0,0015 m2/s → the specimen may obtain s1 in the SBI-test

It is very important that the evaluator of the predictions is aware of the uncertainty in the results.Furthermore it is of great importance to understand that products not meeting or meeting therequired prediction criteria to cone calorimeter test results still may obtain other classes whentested according to EN 13823. The calculation models and simple prediction criteria cannot beused for classification purposes, but may be useful as indicative tools.

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9.4 CONCLUSIONS ON SAMPLINGThe test results have also been used to develop a procedure for selection of representative testspecimens of a product. SBI test results showed that no sampling procedure was required for theSBI test specimen. The large variation of ISO 5660 results clearly showed that this was not thecase for cone calorimeter sampling:

Sampling procedure in connection with ISO 5660 tests:

� The distribution of non-ignited specimens and comparison of fire properties betweenoptimized specimens and product average both indicate the same: optimizedspecimens do not have substantially better fire properties, and may therefore beused to represent the product.

� The influence of knots depended on its position on the specimen. Knots positioned onthe specimen side and covered by the specimen frame did not seem to have a certaininfluence on the results. Specimens with knots positioned near the centre did howeverhave worse fire properties than the average product level. To be on the safe side, it isrecommended to avoid knots on the specimen surface as far as possible.

� Finally, the results from test specimens cut in fixed mutual distances have been usedto find the optimal area on the plank to cut the 100 mm x 100 mm samples. Theevaluations indicate that specimens should be cut approximately 1 meter from theends of the plank.

9.5 CONCLUSIONS ON SELECTION OF TEST METHODThe selection of test method for FRT wood products first of all depends on the application. Themain objective with the fire tests is of course to document the fire properties of the product asaccurate as possible. The costs will however also be an argument when selecting test methods,especially as the application changes from being used by approval bodies to internal use by themanufacturer. Using calculation models and/or simple prediction criteria requires that theevaluator is competent and aware of the uncertainty in the results. Based on the work performedin this project, the following procedure is suggested:

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Procedure for selection of test method for FRT wood products:� Classification purposes

� Tests performed according to EN 13986 – i.e. test methods EN 13823 andEN ISO 11925-2. At least duplicate ISO 5660 tests should be conducted onspecimens from the same batch – cut in accordance with the samplingprocedures recommended in this report. The ISO 5660 test results will be usedas reference for future cone calorimeter tests.

� External audit� At least duplicate tests according to ISO 5660 + prediction model or –criteria.

� One single SBI test is conducted every second year

� Factory production control� At least duplicate tests according to ISO 5660 + prediction model or –criteria

� Product development� One single test according to ISO 5660 + prediction model or –criteria as a

first screening tool – this requires that the evaluator is aware of theuncertainty in the results.

� Duplicate tests according to ISO 5660 + modelling are required at the latestage of the development.

� The need for an indicative SBI test mostly depends on the need to know exactsmoke production results – i.e. an SBI test may be found necessary forproducts aiming for s1 class.

9.6 RECOMMENDATIONS FOR FURTHER WORKIt is recommended that further work is concentrated on the development of existing and newprediction tools – particular simple criteria that does not require any software programs and areeasy to use for the manufacturers as well. The prediction of smoke production is a matter thatshould be especially looked into.

Furthermore, the test results from this project may be used to find criteria related to HRRmax, tign,SPRavg, or other variables evaluated after shorter test periods than the recommended 1200seconds.

Finally, the Nordic manufacturers of FRT wood products may find it useful to work together withthe Nordic fire laboratories to solve the future challenge of obtaining the required s1 class.

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REFERENCES

/1/ NT FIRE 004 Building products: Heat Release and Smoke Generation, Edition 2Approved 1985-11 (Identical with the standard INSTA 412 – i.e. agrees with the Danishstandard DS-INSTA 412, Swedish standard SS 0248 23, Finish standard SFS 4192:E andthe Norwegian standard NS-INSTA 412).

/2/ ISO 5660-1 Reaction-to-fire tests – Heat release, smoke production and mass loss rate –Part 1: Heat release rate (Cone calorimeter method). Second edition 2002-12-15.ISO International Organization for Standardization, Geneve, Switzerland.

/3/ ISO 5660-2 Reaction-to-fire tests – Heat release, smoke production and mass loss rate –Part 2: Smoke production rate (dynamic measurement). First edition 2002-12-15.ISO International Organization for Standardization, Geneve, Switzerland.

/4/ EN 13823 Reaction to fire tests for building products. Building products excludingfloorings exposed to the thermal attack by a single burning item. February 2002.CEN European Committee for Standardization, Brussels, Belgium.

/5/ EN ISO 11925-2 Reaction to fire tests – Ignitability of building products subjected todirect impingement of flame – Part 2: Single-flame source test (ISO 11925-2:2002).February 2002. CEN European Committee for Standardization, Brussels, Belgium.

/6/ prEN 13823:1999 Reaction to fire tests for building products. Building productsexcluding floorings exposed to the thermal attack by a single burning item. PreliminaryStandard. CEN European Committee for Standardization, Brussels, Belgium.

/7/ ISO 5725-2 Accuracy (trueness and precision) of measurement methods and results –Part 2: Basic method for the determination of repeatability and reproducibility of astandard measurement method. First edition 1994-12-15.ISO International Organization for Standardization, Geneve, Switzerland.

/8/ EN 13238 Reaction to fire tests for building products. Conditioning procedures andgeneral rules for selection of substrates. May 2001.CEN European Committee for Standardization, Brussels, Belgium.

/9/ Östman, B A-L. Tsantaridis, L D. Correlation between Cone Calorimeter Data andTime to Flashover in the Room Fire Test. Fire and Materials 1994, Vol 18, pp 205-209.

/10/ Hakkarainen, T. & Kokkala, M. A. Application of a one-dimensional thermal flamespread model on predicting the rate of heat release in the SBI test. Fire and Materials,2001. Vol. 25, No. 2, pp. 61–70.

/11/ Kokkala, M., Baroudi, D. & Parker, W. J. Upward flame spread on wooden surfaceproducts: Experiments and numerical modelling. In: Hasemi, Y. (ed.). Fire SafetyScience – Proceedings of the 5th International Symposium. Melbourne, Australia, 3–7March 1997. International Association for Fire Safety Science, 1997. Pp. 309–320. ISBN4-9900625-5-5

/12/ Hansen, A.S: Prediction of heat release in the single burning item test. Fire andMaterials 2002, Vol 26, pp 87-97.

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/13/ Messerschmidt, B. Van Hees, P. Wickström U. Prediction of SBI (SingleBurning Item) test results by means of Cone Calorimeter test results. ConferenceProceedings, Volume 1, Interflam ’99, 8th International Fire Science & EngineeringConference, Edinburgh, Scotland, 29th June-1st July 1999, pp 11-22.

/14/ ISO 9705 Fire tests – Full-scale room test for surface products. First Edition 1993-06-15.Corrected and reprinted 1996-03-01. International Organization for Standardization,Geneve, Switzerland.

/15/ EN 13501-1 :2002 E. Fire classification of construction products and building elements –Part 1: Classification using test data from reaction to fire tests. European Committee forStandardization (CEN), Brussels, Belgium, 2002.

/16/ Hansen, A E S: No fire without smoke. Prediction models for heat release and smokeproduction in the SBI test and the Room Corner test based on Cone Calorimeter testresults. ISBN 82-471-5441-2. Dr. ing thesis from Department of Building andConstruction Engineering, Norwegian University of Science and Technology (NTNU),Trondheim, Norway, 2002

/17/ Van Hees, P., Hertzberg, T. & Steen-Hansen, A., Development of a screening methodfor the SBI and room corner using the cone calorimeter. Borås 2002. Swedish NationalTesting and Research Institute, SP Report 2002:11. 103 p. NT Project No. 1479-00.

sintef report - forside · sintef report title nordtest project 1526-01 “using the cone...

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