Experiments and modelling on vertical flame spread

Experiments and modelling on vertical flame spread

Olavi Keski-Rahkonen, Johan Mangs & Simo Hostikka

VTT Building and Transport

SAFIR Midterm Seminar

20.-21.2.2005, Espoo, Finland

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VTT BUILDING AND TRANSPORT

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Contents

• Experimental• Goals• Research tactics• Cone calorimeter experiments• Flame spread experiments• Heated sample experiments• Heating and autoignition experiments• DNS simulation• Conclusions

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Experiments on vertical flame spread and ignition

Series Sample Variables: range Major targets

l (m) Primary Secondary

W1 – W17 2 D: 4 …21 Wood V(D), instr.

S1 – S22 0.3 ρ: 220…1020 Wood V(ρ), instr.

F1 – F31 0.3 T0: 20...340 ρ: 400...780 V(T0, ρ), instr.

C1 – C14 0.3…0.5 ρ: 120…640 I: 32…72 RHR(ρ, I), instr.

A1 – A15 0.3 ρ: 420…800 Wood Ta(ρ), τa(ρ)

H1 – H4 0.3 RH: 0 …10 Th(ρ), τh(ρ)

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Examples of experimental set-ups

200

500

T1...T6

B

SF

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Goals

• Create from an accurate but still practical model for vertical flame spread to be implemented in CFD numerical fire simulaton tools like LES-model FDS

• Create prototype concepts and models for testing instruments needed for measuring model parameters from industrial objects and materials like cables and building parts

• Carry out the task to implementation during SAFIR-program

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Research tactics

The problem is so complicated, and the goals so demanding, that all possible means has to be used in parallel and in close interaction with each other

• Previous reasearch: Literature studies• Ongoing and planned research: Personal contacts to major operators worldwide• Experimental: for economy smallest possible scale for screening, and modelling the

major physics, then scale up gradually• Modelling: From basic physics to practical validated algorithms

• Analytical models, even crude for understanding major physics• Numerical models and simulation for testing use in practise• Computational physics to find brute force ’right’ solutions using axisymmetric

forced 2D DNS-like simulation at scales possible to calculate• Use modelling results to design experiments, use experimental data to select models• Design of testing equipment: Minimum set of variables measurable well enough• Economy principles:

• Solve easy problems first, and use them for generalization• Use quickiest, cheapest information in Bayesian sense, improve later

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RHR using cone calorimeter

0

1

2

3

4

5

-60 -30 0 30 60

Time (s)

RH

R (

kW)

0

100

200

300

400

-300 0 300 600 900 1200 1500

Time (s)

RH

R (

kW/m

2)

RP1;50;20

RP2;50;15

RP3;35;11

RO1;35;10

0

100

200

300

-300 0 300 600

Time (s)

RH

R (

kW/m

2 )

C11;P1

C12;B1

C13;P2

C14;B2

a c

222)()()( 00 mtt

erfmttt

erfP

dGtPtR ii

• Cone calorimeter time window tolerable

• Cylindrical samples solve the background problem

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Cone calorimeter modelling

0,0

0,2

0,4

0,6

0,8

1,0

-1 0 1 2 3 4 5 6 7

z/h

F

0

1

2

3

4

5

r/h

Radiatingfrustum

Fh

Fv

a

1E-5

1E-4

1E-3

1E-2

1E-1

1E+0

0,01 0,1 1 10 100

z/h

F Fh

FvInversesquare

Inversecubic

b

1;)1()tan(

)tan(

10;1

22

2

22

2

22

2

21

AdA

hF

tan

tantan

)1()tan(

)tan)(1(

1

22

2222

21

arc

F AdAv

• Cone calorimeter to cylinder calorimeter?

• Double cone calorimeter using planar samples better for signal to noise ratio?

• Radiative heat transfer a basic part of flame spread mechanism

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Flame spread experiment W15

a

OA

B

CD

P(,)

S(, )

E

1

b

• Digital photographing a viable screening method

• A flame sheet model worth of trying

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Flame spread experiment W13

• Pine stick 8 mm dia 2 m long

• Ignited from below• Video recording• Digital photographic recording• Digital editing of photographs• Thermocouples close to sample

for temperature recordings

• Thrmocouples a reasonable way to extract essential information

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Temperature modellig using a view factor

0,01

0,1

1

-2 -1 0 1 2 3 4Dimensionless position

Dim

en

sio

nle

ss

te

mp

era

ture

a

OA

B

CD

P(,)

S(, )

E

1

0,01

0,1

1

-2 0 2 4 6 8Dimensionless position

Dim

en

sio

nle

ss

te

mp

era

ture

W10 Pine

1

tanarctanarctan 00 tt

l

vTf

f

• A view factor model a reasonable starting point

• However, needs further refinements

30 )1(

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Flame spread velocity measurements

W15, Pine8 mm dia

0

500

1000

1500

2000

2500

0 120 240 360 480 600Time (s)

Hei

gh

t (m

m)

P FV

BTV

BTP

P FP

FTP

Fit BTP

Fit P FP

Fit FTP

a

W16, Pine21 mm dia

-500

0

500

1000

1500

0 300 600 900 1200 1500 1800Time (s)

Hei

gh

t (m

m)

BTP

PFP

FTP

Fit BTP

Fit P FP

Fit FTP

b

• Long vertical round sticks a good idea to determine flame spread velocity

• Digital photographing and editing works satisfactorily for screening

• Labor intensive for routine production

• Nonlocal burning possible due to ducts in wood

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Timber as modeling material

0,1

1

10

100

200 400 600 800 1 000

Density (kg/m3)V

elo

cit

y (

mm

/s)

Flame tip

Pyrolysis front

Burnthrough

Exp Wood DensityTimber Species kg/m3

S2 Balsa Ochroma lagopos 220S3 Grey alder Alnus incana 410S4 Obeche Triplochiton scleroxylon 420S5 Aspen Populus tremula 480S6 Ramin Gonystylus spp. 500S7 Common alder Alnus glutinosa 500F28 Great sallow Salix caprea 500S1 Scotch pine Pinus sylvestris 520S8 Birch Betula spp. 540S9 Norway spruce Picea abies 540S19 Norway maple Acer platanoides 570F27 Amur maple Acer tataricum ginnala 610F24 Bird cherry Prunus padus 630S10 White oak Quercus robur 710F25 Mountain ash Sorbus aucuparia 720S11 Beech Fagus silvatica 720S12 White oak Quercus robur 740S13 Ash Fraxinus excelsior 768S14 Wenge Millettia spp. 773S15 Courbaril Hymenaea spp. 850S20 Courbaril Hymenaea spp. 860S16 Cabreuva Myroxylon balsamum 880S21 Ipe Tabebuia spp. 990S17 Ipe Tabebuia spp. 990S18 Cumary Dipteryx odorata 1000S22 Cumary Dipteryx odorata 1030

• Timber easy to shape target material

• Charring material; good model for cables

• Density dependence roughly exponential

• Species a minor factor

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Flame spread on heated samples

Obeche

0,1

1

10

100

0 100 200 300

Temperature (oC)

Vel

oci

ty (

mm

/s)

Pine

0,1

1

10

100

0 100 200 300

Temperature (oC)

Vel

oci

ty (

mm

/s)

Oak

0,1

1

10

100

0 100 200 300

Temperature (oC)

Vel

oci

ty (

mm

/s)

Autoignition AutoignitionAutoignition

Flame tip Flame tipFlame tip

Burnthrough

Burnthrough

Burnthrough

Pyrolysis frontPyrolysis front

Pyrolysis front

• Initial heating a quick and cheap way to vary ambient conditions

• Roughly exponential dependence on temperature

• Data needed up to autoignition temperature

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Sample heating and humidity

H4, Pine

0

50

100

150

200

250

0 300 600 900 1 200 1 500

Time (s)

Tem

per

atu

re (

oC

)H3, Pine

0

50

100

150

200

250

0 300 600 900 1 200 1 500

Time (s)

Tem

per

atu

re (

oC

)

0

10

20

30

40

50

60

70

0 200 400 600

Temperature (oC)

DT

A

0

5

10

15

20

25

30

35

TG

A w

eig

ht

loss

(%

)

Al2O3 · 3H2O

ca b

hhh ttTTtxT /)(exp1),( 00 • Two first terms of the series describe heating well

• Humidity significant for wood

• Water effective for cable flame reterdancy

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DNS simulation

• Sample 4 mm diameter birch stick• Fixed pyrolysis temperature assumed• Pyrolysis takes place in a zone travelling within the sample• Axi-symmetric Direct Numerical Simulaton:

• brute force solving of Navier-Stokes equations

• possible for physically small systems

• impossible for real size objects

• gives good guidance for simpler modelling

• Simulations by FDS code in 2D DNS-like mode

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Conclusions

• Screening carried out for flame spread:• Literature study• 103 experiments• simple modeling• DNS simulation

• Timber a good sample material for modelling• Contributed physical phenomena identified• Simplified modelling started• DNS simulations allow admiring ’right’ solutions• Multimethod problem solving speeds progress• New testing instruments identified and drafted

• Modified cone (cylinder) calorimeter• Vertical flame spread test rig with initial heating capability

• Time to proceed from screening to modeling, algorithm building and small scale validation

Experiments and modelling on vertical flame spread

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