FIRE STAR

EVG1-CT-2001-00041

D5-02

http://www.eufirestar.org

FIRE STAR:

a decision support system

for fuel management and fire hazard reduction

in Mediterranean wildland - urban interfaces

Deliverable D5-02

Infrared Measurements Methods

adapted to Laboratory Fires of Wildland Fuel

Juan MELENDEZ, Antonio J. DE CASTRO, José M. ARANDA,

Angel M. LERMA, Fernando LOPEZ,

Jean-Luc DUPUY, Philippe VACHET, Joël MARECHAL, Denis PORTIER

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CONTENT LIST

Summary ......................................................................................................................................................... 1

Glossary .......................................................................................................................................................... 1

List of associated documents ............................................................................................................................ 1

1 Recall: Infrared bases.............................................................................................................................. 21.1 Introduction......................................................................................................................................... 21.2 Basic concepts of radiometry ............................................................................................................... 2

1.2.1 Radiometric magnitudes................................................................................................................. 21.2.2 Absorption, reflection and transmission ........................................................................................... 3

1.3 Basic laws of radiation......................................................................................................................... 31.3.1 Planck's law .................................................................................................................................. 31.3.2 Emittance. Kirchhoff's law............................................................................................................... 3

1.4 Emittance of solids and gases .............................................................................................................. 41.4.1 Basic features ............................................................................................................................... 41.4.2 Atmospheric transmittance............................................................................................................. 41.4.3 Infrared emission of fires ................................................................................................................ 4

2 IR instrumentation: general concepts........................................................................................................ 62.1 Introduction......................................................................................................................................... 62.2 IR spectrometers ................................................................................................................................. 6

2.2.1 Dispersive spectrometers ............................................................................................................... 62.2.2 FTIR spectrometers ....................................................................................................................... 72.2.3 Active and passive measurements.................................................................................................. 7

2.3 IR cameras ......................................................................................................................................... 82.3.1 Quantitative measurements with IR cameras ................................................................................... 82.3.2 Case of transparent medium........................................................................................................... 82.3.3 Atmospheric effects ..................................................................................................................... 10

2.4 Imaging spectrometers ...................................................................................................................... 10

3 Determination of pyrolysis gases by FTIR spectrometry ........................................................................... 113.1 Introduction....................................................................................................................................... 113.2 Absorption infrared spectroscopy........................................................................................................ 113.3 FTIR spectroradiometer..................................................................................................................... 123.4 Experimental description.................................................................................................................... 123.5 Quantitative determination of concentrations ....................................................................................... 133.6 Examples of experimental results ....................................................................................................... 14

3.6.1 CO2 and CO analysis................................................................................................................... 143.6.2 CH4 analysis ............................................................................................................................... 15

3.7 Partial conclusion.............................................................................................................................. 16

4 Measurement of fire parameters with bi-spectral system.......................................................................... 174.1 Introduction....................................................................................................................................... 174.2 Instrumentation: IR cameras .............................................................................................................. 174.3 Bi-spectral imaging system ................................................................................................................ 19

4.3.1 Introduction ................................................................................................................................. 194.3.2 Bi-spectral imaging system........................................................................................................... 194.3.3 Pre-processing ............................................................................................................................ 19

4.4 Measurement of fire parameters: general considerations ..................................................................... 204.4.1 Standard methods ....................................................................................................................... 204.4.2 IR imaging methods ..................................................................................................................... 204.4.3 Experimental burns ...................................................................................................................... 20

4.5 Fire scene classification based on bi-spectral images .......................................................................... 224.6 Rate of spread and temperature measurements.................................................................................. 244.7 Radiated power measurements.......................................................................................................... 26

5 Transmission of a flame in the thermal infrared ....................................................................................... 295.1 Introduction....................................................................................................................................... 295.2 Experimental devices and methods .................................................................................................... 29

5.2.1 Fuel and ignition.......................................................................................................................... 295.2.2 Measurements ............................................................................................................................ 295.2.3 Estimation of flame emission and transmission .............................................................................. 315.2.4 Thermocouple measurements. ..................................................................................................... 315.2.5 Video recordings.......................................................................................................................... 32

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5.3 Protocol ............................................................................................................................................ 325.4 Listing of the tests ............................................................................................................................. 33

5.4.1 Table 5.1: Tests of series 2003-02 ................................................................................................ 335.4.2 Table 5.2: Tests of the series 2003-03. ......................................................................................... 33

5.5 Results ............................................................................................................................................. 345.5.1 Mass loss curves ......................................................................................................................... 345.5.2 Transmission (series 2003-02)...................................................................................................... 365.5.3 Transmission, emission and temperature (series 2003-03)............................................................. 40

6 Bibliographical references...................................................................................................................... 44

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SUMMARY

This document presents the activities performed by LIR-UC3M team (P11) and INRA team (P01) to developmethodologies based on infrared measurements to be applied in the framework of Fire Star project.

It covers parts of WP5 activities but also of WP7T1’s ones.

Section 1 is devoted to recall the basis of the infrared methods, concepts and measurements.It gives the definition of basics concepts of radiometry: radiometric magnitudes (radiant flux, irradiance,

exitance, radiance), basic parameters (reflectance, absorptance and transmittance), and the black- and grey-bodies.

It expresses the Planck’s law (from which Stefan-Boltzamn and Wien laws can be derived) and the Kirchhoff’sone describing the emittance of a real body.

It develops the concept of emittance and infrared emission of solid particles and gases during wildland fires andcompares the spectral radiance of a wildland fire to those of blackbodies at different temperatures.

Section 2 is devoted to the general concepts in infrared instrumentation.It describes infrared dispersive or FTIR spectrometers and the passive and active measurements.It describes the different types of infrared cameras, the quantitative measurements, the simple case of

transparent medium, the concepts of Apparent and Brightness temperatures and the perturbing effects of theatmosphere.

It gives some information on imaging spectrometers.

Section 3 presents the approach followed by LIR-UC3M P11 in collaboration with INIA-CIFOR P09 fordetermining pyrolysis gases by FTIR spectrometry.

Section 3.2 presents the basis for the spectroscopy used in these experiments: absorption infraredspectroscopy.

Section 3.3 describes the instrument used for these measurements.Section 3.4 describes the experimental set-up needed to carry out the experiments.Section 3.5 details the methodology used to obtain the so-called normalised concentration for the gases under

study.

Section 4 describes the bi-spectral system provided by LIR-UC3M P11 for measuring the fire parameters on theINIA-CIFOR P09 devices in Madrid.

Section 4.2 details the characteristics of the MIR and TIR camerasSection 4.3 presents the bi-spectral imaging system itself.Section 4.4 gives the general condition for measuring the parameters of the fire.Section 4.5 presents the fire scene classification based on bi-spectral images in order to separate the pixels

concerned by flames, embers, ashes and background.Section 4.6 compares the rate of spread and temperature measurements done classically by INIA-CIFOR P09

(see D7-02).Section 4.7 explains how to deduce the part of energy released by radiation from the IR measurements.

Section 5 presents parts of the experimental work done by INRA-AVI P01 in the framework of infraredmeasurements activities.

Section 5.2 details the experimental devices and methods used by this team, and section 5.3 delivers the mainresults of the two series of tests: mass loss and flame transmission in different burning conditions.

GLOSSARYNone

LIST OF ASSOCIATED DOCUMENTSNone

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1 RECALL: INFRARED BASES

1.1 INTRODUCTION

In the last years, infrared (IR) sensors have beenincreasingly used in fire-related applications.

Pioneering applications were made using theinfrared sensors onboard meteorological satellites.

These satellite observations have proved useful toassess fire risk, monitor fire propagation and followbiomass recovery after fire (Justice et al., 1993).

In the near future, satellite-borne IR sensors mayplay an even more outstanding role as a newgeneration of specific fire satellites, promising early fireoutbreak detection and monitoring, becomes operative(website FUEGO).

In addition, IR sensors are also increasingly used bymany forest services.

Typical ground based devices are IR camerasplaced on rotary platforms on lookout towers; airbornesensors have been also used to help in fire extinction(Young, 1994)

However, the applicability of IR techniques is by nomeans confined to forest fire detection and monitoring.

On the contrary, they offer a large potential to forestfire studies.

In this report, we explain how IR techniques can beused to provide measurements of fire parameters inlaboratory experiments with wildland fuel.

Before describing the instrumentation and themeasurement methods, we will explain in this chapterthe general concepts underlying IR measurements.

dω

θdA

Figure 1-1: Definition of radiance

1.2 BASIC CONCEPTS OF RADIOMETRY

The part of optics devoted to measurement ofradiation is called radiometry.

1.2.1 Radiometric magnitudes

A preliminary step to perform any measurement is toestablish the magnitudes to be measured. (McCluney,1994)

The basic radiometric magnitude is called radiantflux and measures the power transported by theradiation:- symbol: Φ- units Watts

As a rule, the output signal of an infrared detector isproportional to the radiant flux incident on the device.

This radiant flux depends obviously on the size ofthe detector, and hence it may be convenient to workwith the incident power per unit area.

This magnitude is called irradiance:- symbol: E,- units: W/m2.

When radiation emitted, rather than incident, isconsidered, the magnitude is called exitance:- symbol: M,- units: W/m2.

The most basic task when performing IRmeasurements is to estimate the exitance of a givensource, knowing the irradiance it creates on a detector.

In order to solve this problem, it is convenient to useanother magnitude called radiance.

In order to define it, we consider a differential ofarea, dA, of a source, and a differential solid angledω (Figure 1.1).

The angle between dω and the normal vector to thesurface is θ.

Flux contained in dω coming from dA is denoted asd2Φ .

Radiance at dA is defined as:

θω cos

2

dAdd

LΦ= ,

That is to say, flux per unit area per unit of projectedsolid angle.

The definition, stated here for a source, is extendedtrivially for a detector and even for a ray, at any pointalong its path.

Units of radiance are Watts per square meter perstereo radian.

For a source, radiance may vary from point to point,and for a fixed point, it may vary as a function of thedirection.

Thus, it provides the most complete geometriccharacterisation of the emitted radiation.

A simple and important case is that of a Lambertiansource, for which emission is isotropic (L does notdepend on the direction).

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The importance of radiance stems mainly from theinvariance theorem (Nicodemus, 1963) that states that,in any optical system, the radiance along the path of aray is invariant (if the refractive index changes, L mustbe divided by it).

This theorem greatly simplifies radiometriccalculations.

All the radiometric magnitudes introduced here maybe defined as spectral, that is to say, per unitwavelength (or, equivalently, per unit wave number)

1.2.2 Absorption, reflection and transmission

When light strikes a body, a part of it is reflected, apart is absorbed and a part is transmitted.

Three parameters are defined:- ρ = reflectance = fraction of power reflected- α = absorptance = fraction of power absorbed- τ = transmittance = fraction of power transmitted

Obviously,ρ + α + τ = 1

Most solids can be regarded as opaque (τ = 0) andthen

α = 1−ρ (solid)

On the other hand, gases have zero reflectance and

α = 1−τ (gas)

These parameters depend on the material, on thewavelength and, to a certain degree, on temperature.

A body for which α = 1 for all wavelengths (i.e., abody that absorbs all the radiation that strikes upon it) iscalled a blackbody.

A body for which α is constant (<1) for all thewavelengths it is called a grey-body.

Usually, solids can be treated as grey-bodies,whereas gases have a strong spectral structure.

0 . 1 1 10 1 0 00 . 1

1

10

1 0 0

1 0 0 0

S unEarth

Sp

ect

ral i

rra

die

nce

(W/m

2µ

)

W a v e l e n g t h (µm )

0 . 1 1 10 1 0 00 . 1

1

10

1 0 0

1 0 0 0

S un

0 . 1 1 10 1 0 00 . 1

1

10

1 0 0

1 0 0 0

S unEarth

Sp

ect

ral i

rra

die

nce

(W/m

2µ

)

W a v e l e n g t h (µm )

Figure 1-2: Solar spectral irradiance incidenton the Earth (solid black) compared to

Spectral irradiance emitted by the Earth (dashed red)

1.3 BASIC LAWS OF RADIATION

It is a well-known physics fact that all bodies emitcontinuously electromagnetic radiation.

This radiation emission has two very characteristicfeatures:- it increases very fast with temperature, and- it is strongly wavelength-dependent.

For a blackbody in thermal equilibrium, thesefeatures are summarised by two laws:- Stefan-Boltzmann law, stating that the total radiated

power increases as T4 (being T the absolutetemperature of the body), and

- Wien's law, stating that the spectral distribution ofthe emitted radiation has a maximum atλ = 2898/T microns.

1.3.1 Planck's law

Both Stefan-Boltzmann and Wien laws can bederived from the more general Planck's law, whichspecifies the spectral dependence of the blackbodyemission, as a function of temperature.

Planck's law is illustrated, for the specifictemperatures of the Sun (approx. 6000 K) and the Earth(approx. 300 K), in Figure 1.2 (Schowengerdt 1997).

It is clear that most of the radiation that can be seenwhen looking at the earth in the visible spectrum will bereflected solar radiation, whereas in the thermal IRspectrum (from 8 to 12 microns) predominates theemitted radiation.

1.3.2 Emittance. Kirchhoff's law

Blackbodies are idealisations, but the behaviour of areal body can be related to that of a blackbody bymeans of emittance, ε.

Emittance is a function of the wavelength and thetemperature, although usually only the wavelengthdependence is specified: ε(λ).

The spectral power emitted by a real body is:Φ(λ,Τ)=ΦΒΒ(λ,Τ)· ε(λ)

Being Φ ΒΒ(λ,Τ) the spectral power emitted by ablackbody, given by Planck's law.

For a body in thermal equilibrium, emittance equalsabsorptance (Kirchhoff's law):

α(λ) = ε(λ)

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1.4 EMITTANCE OF SOLIDS AND GASES

1.4.1 Basic features

As explained before, the absorptance of solids (andtherefore its emittance) has little spectral dependence.

Hence, the spectral profile of their emission is similarto that of a blackbody.

On the other hand, gases are transparent over mostof the spectrum, but show absorption bands that maybe very strong at specific wavelengths.

These bands are related to specific energy levels ofthe molecules.

In particular, all gases (except those with monatomicor diatomic homonuclear molecules, like O2 and N2)show IR absorption bands related to vibrational levels.

These bands are characteristic for each chemicalspecies, constituting its "IR fingerprint".

According to Kirchhoff's law, emission andabsorption occur at the same wavelengths, and theirintensities are linked: the strongest absorbing bands willbe those with strongest emission

1.4.2 Atmospheric transmittance

These features of IR absorption of gases can beseen clearly in the transmittance of the atmosphere, asshown in Figure 1.3 (Hudson, 1969).

The absorption bands create wide regions in whichthe atmosphere is opaque.

Carbon dioxide and water vapour are the gases withstrongest IR absorption bands in the atmosphere.

In contrast, there are regions, called “windows”, inwhich the atmosphere is nearly transparent.

The most important are:- the Medium IR (MIR) window, from 3 to 5 µ, and- the Thermal IR (TIR) window, from 8 to 14 µ

(although we will restrict it to 8 to 12 microns).Remote optical measurements through the

atmosphere should be performed in those windows (orin visible/near IR).

0.2

0.4

0.6

0.8

1.0

0 1 2 3 4 5 6 7 8 9 10 11 13 14 15

Wavelength(µm)

Atm

osph

eric

trans

mitta

nce

H2O H2O

CO2

CO2

O3

H2O

0.2

0.4

0.6

0.8

1.0

0 1 2 3 4 5 6 7 8 9 10 11 13 14 15

Wavelength(µm)

Atm

osph

eric

trans

mitta

nce

H2O H2O

CO2

CO2

O3

H2O

Figure 1-3: Transmittance profile of the atmosphere(vertical path)

The gases responsible for the main absorption bandsare indicated (O3 refers to stratospheric ozone)

1.4.3 Infrared emission of fires

What we have explained up to now permits alreadyto sketch the main features of the IR emission of fires:a) A fire has a very strong IR emission.b) For solids at approx. 1000ºC, the wavelength of

maximum emission, according to Wien's law, is 4µm, in the centre of the MIR band. Furthermore, atthat temperature, according to Planck's law, 31% ofthe radiated power is emitted in the MIR band, 16%in the TIR band, and only 2·10-7% in the visibleband.

c) There are two contributions to the IR emissionof a fire: that of the emitted gases (mainly CO2 andH2O at the flame) and that of the hot solids (embersand hot ashes). The first one consists of discretebands; the second one is a continuum.

d) The emission of a fire is seen always throughthe atmosphere. As the atmosphere contains CO2and H2O, it will absorb strongly the IR emission ofthose gases.

These qualitative features can be illustrated withreference to Figure 1.4 that shows, in black, anexperimental emission spectrum of a small laboratoryfire, compared to the theoretical emission of ablackbody at different temperatures.

Distance of observation was approximately 6 m.

3 4 5 6 7 8 9 10 11 120,00

0,02

0,04

0,06

0,08

0,10

Fire

725 K BB

575 K BB

425 K BB

Spe

ctra

l Rad

ianc

ie

(W/c

m2·

sr·µ

m)

Wavelength (µm)

3 4 5 6 7 8 9 10 11 120,00

0,02

0,04

0,06

0,08

0,10

Fire

725 K BB

575 K BB

425 K BB

Spe

ctra

l Rad

ianc

ie

(W/c

m2·

sr·µ

m)

Wavelength (µm)

Figure 1-4: Experimental spectral radiance of a firecompared to those of three different blackbodies

The most salient feature of the fire spectrum is adouble peak between 4 and 5 microns.

It is due to the emission of the hot CO2 at the flame.Because of the absorption of the cold atmospheric

CO2, it is divided into two "spikes":- the narrow "blue" spike, at 4.2 µm, and- the wide "red" spike at 4.55 µm)

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As explained by Kirchhoff's law, emission andabsorption by a certain gas occur at the samewavelengths, but temperature widens the bands, andtherefore absorption here only affects the centre of theCO2 band.

There are also wide H2O emission bands at 3 µmand from 5 to 8 µm.

In those bands, also, emission from hot H2O at theflame is mixed with absorption from cold H2O at theatmosphere.

With careful examination of the spectrum, otherminor chemical compounds like CO and hydrocarbonsmay be identified.

The gas-related peaks are superimposed on acontinuum grey-body-like component, due to theemission of hot solids (embers)

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2 IR INSTRUMENTATION: GENERAL CONCEPTS

2.1 INTRODUCTION

Infrared radiation can be detected by a large varietyof IR detectors (Vincent, 1990).

Thermal detectors (bolometers, thermopiles, pyro-electric detectors) are sensitive to the entire IRspectrum and may operate at room temperature.

Quantum detectors (usually, photodiodes or photo-conductors made of semiconductors) are much fasterthan thermal detectors, but they operate only in aspecific spectral range, and usually require cooling atcryogenic temperatures.

Both kinds of detectors are used in IRinstrumentation.

From the point of view of the user, however, themain distinction does not arise from the detector typebut from the instrument conception.

Traditionally, there have been two families of IRinstruments:- Those with spectral resolution (IR spectrometers);

they integrate the incoming radiation over the wholefield of view of the instrument, but discriminatebetween different wavelengths in the spectral rangeof operation; their output is a spectrum.

- Those with spatial resolution (IR cameras); theyintegrate the incoming radiation over the wholespectral range of operation, but discriminatebetween different points within the field of view; theiroutput is an image.

Recently, a new family has appeared that combinesspectral and spatial resolution: the imagingspectrometers.

Their output, sometimes called a spectral cube, canbe viewed as an image for each wavelength of thespectral range or, equivalently, as a spectrum for eachpoint of the field of view.

2.2 IR SPECTROMETERS

A spectrometer is an instrument that can be used tomeasure the spectral positions of features of interest inthe spectrum over a spectral range of interest.

Typical IR detectors integrate the energy received ina wide spectral range, and they can not provide such aspectral information.

Therefore it is necessary an optical element (amonochromator) to discriminate the information inwavelengths to fabricate a spectrometer.

Usually there are two ways to obtain this spectralinformation; these two solutions give rise to twodifferent families of spectrometers.

2.2.1 Dispersive spectrometers

In these instruments, the monochromator is adispersing element: a prism or a diffraction grating.

Figure 2-1: Schematic of a dispersive spectrometer(source http://elchem.kaist.ac.kr/vt/chem-ed/analytic/ac-

meths.htm)

Figure 2-2: Wavelength selectionby using a diffraction grating.

(source http://elchem.kaist.ac.kr/vt/chem-ed/analytic/ac-meths.htm)

Figures 2.1 and 2.2 illustrate a schematic of adispersive spectrometer.

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2.2.2 FTIR spectrometers

These instruments use a Michelson interferometeras the monochromator element.

The detector measures an interference pattern dueto the moving mirror. This pattern corresponds to theFourier transform of the spectral distribution of energy.

moving mirror

sample

beamsplitter

fixed mirror

source

detector

moving mirror

sample

beamsplitter

fixed mirror

source

detector

Figure 2-3: The Michelson interferometer

Major advantages of FTIR spectrometers over adispersive instrument are:- All the wavelengths are measured at the same time

(Fellgett’s advantage)- The simple optical path of the interferometer permits

to reach the detector more energy than in thedispersive spectrometers. That means animprovement of the signal-to-noise ratio (Jacquinot’sadvantage)

- Frequencies are known very precisely, because theinterferometer has a helium-neon laser as aninternal frequency standard (Conne’s advantage)

- The effective spectral resolution is constant over theentire spectrum.

- High acquisition speed, that permits co-addingmultiple scans to improve the signal-to-noise ration

When a spectrometer is calibrated formeasurements of spectral radiant flux, the instrument iscalled a spectroradiometer.

2.2.3 Active and passive measurements.

A spectroradiometer can be used in two differentconfigurations.- The configuration is called passive when the

spectroradiometer points directly to a hot source andmeasures the spectral distribution of the energyemitted by the source.

- The configuration is called active when an artificialIR source is used as the emitter. In this case, thesample (solid or gaseous) is between thespectroradiometer and the source. Then, theinstrument measures the absorbed energy by thesample.

These two configurations appear to be veryinteresting in the study of fires.

Passive measurements can determine the spectraldistribution of energy emitted during a fire, payingattention to the different phases of the burn.

4000 3500 3000 2500 2000 1500 10000.00

2.50x10-5

5.00x10-5

7.50x10-5

1.00x10-4

1.25x10-4

1.50x10-4

fire

unburned mater ia l

H2O

H2O

CO2

straw (F2)Sp

ectra

l rad

ianc

e (W

/ cm

2 sr

cm

-1)

Wavenumber (cm-1)

4000 3500 3000 2500 2000 1500 10000.00

2.50x10-5

5.00x10-5

7.50x10-5

1.00x10-4

1.25x10-4

1.50x10-4

fire

unburned mater ia l

H2O

H2O

CO2

straw (F2)Sp

ectra

l rad

ianc

e (W

/ cm

2 sr

cm

-1)

Wavenumber (cm-1)

Figure 2-4: Spectral radiance measured for:blue: an unburned terrain,

red: the same terrain during a straw burn.

Active measurements are very useful to characterisegases emitted by a biomass sample during a pyrolysisprocess, in-situ and in a non-intrusive way

Determination of these gases and its concentrationsis very important to improve the knowledge ofcombustion chemistry.

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2.3 IR CAMERAS

Three subsystems can be distinguished in any IRcamera: optics, detector, and electronics.

The first designs of IR cameras used a singledetector and had to rely on a complex optical scanningsystem to explore the field of view in order to constructthe image.

In the last years, cameras based on two-dimensionalarrays of detectors (focal plane arrays) have becomethe standard (Wolfe, 1999).

This complication in the detector makes possible agreat simplification the optical subsystem, that now isbasically equal to that of a photographic camera exceptfor the materials (Ge and SeZn are commonly used).

IR cameras usually operate in a spectral range thatcorresponds to one of the atmospheric IR windows,MIR (3 to 5 microns) and TIR (8 to 12 microns).

Most IR cameras have at least two outputs, a videooutput (for visualisation) and a digital output (forquantitative measurements).

2.3.1 Quantitative measurements with IR cameras

When an IR camera images an object (Figure 2.5)its output is a Digital Number (DN) for each point of thefield of view (FOV).

Mo( W / m 2 )

DNO

IR camera

Monitor/PC

Object

Background

Atmosphere

Detector

(FPA)

E o(W/m 2)

exitances

irradiances Digital numbers(DN)

scene

medium sensor system

Mo( W / m 2 )

DNO

IR camera

Monitor/PC

Object

Background

Atmosphere

Detector

(FPA)

E o(W/m 2)

Detector

(FPA)

E o(W/m 2)

exitances

irradiances Digital numbers(DN)

exitances

irradiances Digital numbers(DN)

scene

medium sensor system

scene

medium sensor system

Figure 2-5: A standard measurement with an IR camera

This DN is related to the radiation emitted by eachpoint of the imaged object.

When using the camera for quantitativemeasurements, this relationship must be made explicit.

This is conveniently done by means of two models:

2.3.1.1 A Sensor Model

It relates the irradiance that reaches the detector ofthe camera (we will call it ED) to the digital number itprovides (DN).

For the ordinary conditions of operation, we willassume a linear behaviour for the detector and theelectronics, and thus:

DN=kD·ED+koff (2.1)Where kD is the detector responsivity times the

electronics gain.

2.3.1.2 A Radiometric Model

It relates the exitance at the object (Mo) to theirradiance at the detector (ED).

A complete radiometric model can be very complex(Schowengerdt 1997), and therefore differentapproximations are used depending on theexperimental circumstances.

2.3.2 Case of transparent medium

For a transparent medium (τ=1), the invariance ofradiance guarantees that

Lo = LD

Being Lo the radiance that leaves the object and LDthe incident radiance on the detector.

For ordinary values of f (focal length) and D(diameter of entrance pupil), and for objects at adistance much larger than f, it is easily shown that

LD = ED[2(f/#)]2/πWhere (f/#) is the f-number, defined as (f/#)=f/D.

Therefore,ED=kG· π·Lo (2.2a)

Where kG=[2·(f/#)]−2.

We conclude that there is a fixed proportion betweenED and Lo, and according to (2.1), DN is a linearfunction of Lo.

Thus, if the atmosphere can be considered astransparent (measurements at short distances in an IRwindow), a camera provides a rather directmeasurement of the radiance coming out of the imagedobject.

If, in addition, the object is Lambertian (as for ablackbody), Mo=Lo·π, and a direct relationship existsalso with exitance:

ED=kG·Mo (2.2b)

In order to translate this exitance measurement intoa temperature measurement (thermography), someadditional hypotheses are needed.

We will consider two very simple models, that will becalled zero-order model and first-order model.

2.3.2.1 Thermography: Zero order model

The simplest radiometric model is what we will callZero-order radiometric model.

Here the imaged object is assumed to be ablackbody and the effects of the medium are ignored(i.e., it is considered as a vacuum, with τ=1).

With these approximations, the object exitance issimply (Stefan-Boltzmann law):

Mo=σTo4 (2.3)

Combining this with equation (2.2b) and with thesensor model (2.1), we get

DN=kDkGσTo4 +koff (2.4)

This equation is easily inverted, making possible tomeasure temperatures using an infrared camera.

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The relationship it furnishes between T and DN canbe appreciated in Figure 2.6 for specific (arbitrary)values of kD, kG and koff.

It is clear that the same ∆T gives larger ∆DN at largetemperatures (in fact, ∆DN/DN =4∆T/T)

Zero -o rde r ap p rox ima t i o n

0

1 0 0

2 0 0

3 0 0

4 0 0

5 0 0

0 1000 2000 3000 4000D N

T (

K)

Zero -o rde r ap p rox ima t i o n

0

1 0 0

2 0 0

3 0 0

4 0 0

5 0 0

0 1000 2000 3000 4000D N

T (

K)

Figure 2-6: An example of the relationship betweenDN and temperature in the zero-order model

In the previous equations, we have neglectedspectral dependence.

However, cameras are sensitive only through aspecific spectral range.

Thus, instead of using the Stefan-Boltzmann law,the relevant emittance is given by Planck’s law,integrated over the spectral range of the camera ∆λ.

Therefore,

∫∆

+=λ

λλλλ dkTMkkDN offoBBDG )}(),()({ (2.5)

where:- MBB (To,λ) stands for the spectral exitance of a

blackbody at temperature To, and- the spectral dependence of kD and koff has been

shown explicitly.

In order to invert this equation, it is customary tomake the approximation:

DN=Gain·F(To)+Offset (2.6)where F(T) is an invertible “calibration function”,

usually depending on three parameters, that representsan approximation to the integral of Planck’s functionMBB (To,λ) over the spectral interval ∆λ of the camera .

F i r s t-o rder approx imat ion ( T env= 3 0 0 K )

0

1 0 0

2 0 0

3 0 0

4 0 0

5 0 0

0 1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0D N

T (

K)

ε=1

ε = 0 . 5

F i r s t-o rder approx imat ion ( T env= 3 0 0 K )

0

1 0 0

2 0 0

3 0 0

4 0 0

5 0 0

0 1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0D N

T (

K)

ε=1

ε = 0 . 5

Figure 2-7: An example of the relationship between DNand temperature, for two different object emittances, in

the first order model

2.3.2.2 Camera calibration: apparent brightnesstemperatures

If the calibration function is known, the parametersGain and Offset can be determined by measuring atshort distances a blackbody, and fitting the results (forseveral known blackbody temperatures) by the lastequation.

This procedure is known as camera calibration.After calibration, equation (2.6) can be used to

provide object temperatures, which are called apparentbrightness temperatures.

“Apparent” here means that no atmosphericcorrections have been made.

“Brightness” means that an emittance ε = 1 hasbeen assumed.

2.3.2.3 Thermography: First order model

The zero-order model assumed ε = 1 for the objectand τ = 1 for the atmosphere.

The First-order radiometric model will:- relax the condition ε = 1 for the bodies and- keep the approximation τ = 1 for the atmosphere.

This is correct at short distances for a camera thatoperates in an atmospheric window.

Now the radiation that leaves the object is due notonly to emission but also to reflection from theenvironment.

Assuming an opaque grey-body, ρ = 1-ε, and

Mo = εσTo4+ (1-ε)σTenv4 (2.7)

where Tenv is the temperature of the environment.

Therefore,DN=kDkGσ[εTo4 + (1-ε) Tenv4] + koff (2.8)

(see Figure 2.7)

Clearly, for ε = 1, this model degenerates into thezero-order model.

If we now take into account the limited spectralrange of the camera, a generalisation of equation (2.8)similar to (2.5) can be made.

However, a simple approximation of general validitylike (2.6) will be possible only for grey-bodies.

For the general case, it is necessary to know thedependence ε(λ) to make estimations of temperature.

Temperatures obtained in this model are no longerbrightness temperatures, but are still apparenttemperatures since no correction for the atmosphericeffects has been made.

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2.3.3 Atmospheric effects

When distances are long (i.e., in remote sensingmeasurements) atmospheric effects becomeappreciable even in the IR windows.

Two modifications are needed:- the outcoming exitance is multiplied by the

atmospheric transmittance;- new radiation terms appear.

This point is explained with reference to Figure 2.8.

Latme

Lsunr Lsun

sct+r L sunsct

Loe Lenv

r Latme

Lsunr Lsun

sct+r L sunsct

Loe Lenv

r

Figure 2-8: A scheme of the radiation componentsfor a general remote sensing measurement

(Schowengerdt 1997)

Up to now, in the zero-order model, we haveconsidered the radiance emitted by the object (Lo

e ).In the first-order mode, we took into account also the

radiance from the environment reflected by the object(Lenv

r).In a remote sensing measurement, it may be

important also:- the radiance emitted by the atmosphere between the

object and the camera (Latme),

- the solar radiance reflected by the object to thecamera (solar glints, Lsun

r),- the solar radiance scattered in the atmosphere and

then reflected by the object (Lsunsct+r), and

- the solar radiance scattered by the atmosphere tothe camera (Lsun

sct).

The relative importance of these terms will dependon the specific measurement circumstances.

For an active forest fire, all the terms will benegligible as compared to Lo

e .The atmospheric transmittance factor will be also

negligible at short distances (some meters) but not atmore than 100 m.

On the other hand, if careful temperatureestimations need to be made for an object not very hot,all the terms may have to be reckoned.

2.4 IMAGING SPECTROMETERS

The previous section has shown that standardthermography may give erroneous results when appliedto a fire.

The main difficulties are caused by the spectralstructure of IR emission, and they could be overcome,therefore, if an IR camera could be endowed withspectral resolution.

Such a camera would become then an imagingspectrometer.

The first instruments of this kind were called multi-spectral imagers.

One early example was the Multi-spectral ScannerSystem (MSS) onboard the Landsat satellite, launchedin 1972.

It acquired simultaneous images of a scene at fourdifferent spectral bands, from 0.5 to 1.1 µm.

Since then, instruments with an increasing numberof bands (up to several hundreds in the hyperspectralimagers) have been developed.

Complexity and high cost has confined most of themto military or satellite remote sensing applications.

However, multispectral imaging has potential uses inmany other fields, such as medicine, non-destructivetesting and environmental monitoring.

In order to qualify as an imaging spectrometer, animaging system must provide images in several spectralbands that comply with three requirements:- Co-registration: the fields of view (FOV's) must be

the same in both bands, not only for the wholeimage, but also for corresponding pixels in eachimage.

- Simultaneity: critical for the study of a dynamicalphenomenon like fire.

- Radiometric calibration to ensure that digitalnumbers in each image can be compared.

If these requisites are accomplished, the whole setof images at a specific time is called a "multispectralimage" and it is possible to apply to it the powerfulprocessing techniques developed originally for remotesensing applications

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3 DETERMINATION OF PYROLYSIS GASES BY FTIR SPECTROMETRY

3.1 INTRODUCTION

When a biomass sample is heated at relatively lowtemperatures, a low efficient combustion with theabsence of flames is produced.

Knowledge of gaseous products from thiscombustion is very interesting because it is a source ofinformation to model thermal behaviour of biomass fuel.

The purpose of this chapter is to present anexperimental procedure to determine concentrations ofgases emitted by thermal degradation of biomass.

This experimental procedure is based on remotesensing techniques, more concretely on the infraredabsorption spectroscopy.

The proposed methodology provides an in-situ, non-intrusive procedure to measure these concentrations.

Although the presented methodology is suitable forany gas that present infrared absorption bands, thestudy here will be focused on the most importantcarbon-related gases produced in the pyrolysis process:- carbon monoxide,- carbon dioxide and- methane.

Section 3.2 presents the basis for the spectroscopyused in these experiments: absorption infraredspectroscopy.

Section 3.3 will describe the instrument used forthese measurements.

Section 3.4 is devoted to a description of theexperimental set-up needed to carry out theexperiments.

Section 3.5 describes the methodology used toobtain the so-called normalised concentration for thegases under study.

Section 3.6 presents some experimental resultsobtained by using this methodology within theframework of the FIRESTAR project.

The laboratory LIR-UC3M (Universidad Carlos III deMadrid, Spain) has carried out the experimentspresented in this chapter in collaboration with CIFOR-INIA (Ministry of Science and Technology, Spain)

3.2 ABSORPTION INFRARED SPECTROSCOPY

All polyatomic molecules and heteronuclear diatomicmolecules absorb infrared radiation.

These absorptions are related to transitions betweenthe vibration-rotation energy levels.

The absorption bands depend on the physicalproperties of the molecule.

This means that each absorption spectrum differsfrom all others and can be consider as the infrared“signature” of the molecule.

The basis of experimental determination of gasconcentrations by infrared spectroscopy is the well-known Beer’s law.

According to this law the transmitted radiationintensity I is related to the incident intensity I0 by

[ ]LC)(exp)(I)(I 0 να−ν=ν (3.1)

where α is the gas absorptivity, C is the concentration,L the optical path length, and the wavenumber ν is theinverse of the wavelength λ.

Transmittance τ and absorbance A are defined as:

[ ]

)(log)(A

LC)(expII

)(

10

0

ντ−=ν

να−==ντ(3.2)

Spectrometers measure the spectral distribution of Afollowing the procedure presented in figure 3.1

spectrometer IR source

Figure 3-1: Experimental configurationfor infrared absorption spectroscopy.

An infrared (IR) source emits radiation in all thewavelengths of interest (typically in the range 2-20 µm).

The spectrometer measures the spectral distributionof this radiation (the so-called reference spectrum).

When the gas intercepts the line of sight of theinstrument, a second spectrum is measured (themeasure spectrum).

According to equation 3.2, the ratio between themeasure and reference spectra gives the spectraldistribution of transmittance.

Finally, a simple calculation gives the absorbancespectrum.

The analysis of an absorbance spectrum gives bothqualitative and quantitative information.

The spectral location of the absorption bandsidentifies the gas, whereas the height of the bandprovides the gas concentration (by application of Beer’slaw and a previous knowledge of the optical path L andspectral absorptivity α).

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3.3 FTIR SPECTRORADIOMETER

The spectroradiometric work presented in thisdocument has been carried out with a FTIRspectroradiometer (LIR-UC3M) with the followingcharacteristics (see table 3.1)

When radiometric measurements have beenneeded, the spectroradiometer has been calibrated byusing a blackbody.

Due to the non-linear response of MCT detector tothe energy flux received, a specific calibration has to beperformed.

It will take into account the high level of energy thatis expected to be measured during the burns planned tobe carried out at the CIFOR-INIA facilities.

Figure 3-2: FTIR spectroradiometer (LIR-UC3M)

Table 3-1: Main specifications of the spectroradiometer

Specification Spectroradiometer

Model MIDAC-AM

Detector MCT

Spectral range 2 – 16 µm

Spectral resolution 32 cm-1 - 0.5 cm-1

FOV (semi-angle) 12 mrad

Dimensions 20 × 18 × 34 cm

Weight 11 kg

3.4 EXPERIMENTAL DESCRIPTION

In this section, a description of the methodologyused to measure in-situ gases emitted by thermaldegradation of biomass is presented.

It is completed in D7-02 “Fire Star Laboratory Fires:Methods”, INIA’s contribution (Fire Star, 2003a).

Figure 3.3 presents a scheme of the proposedexperimental set-up.

255 cm 1 cm-1

32 scans1 s/scan

300 W / 330° C

90 W / 140° C

8.5 cm

≈ 4 g

Figure 3-3: Scheme of the experimental set-up

A biomass sample around 4-g weight is placed on aradiator.

Temperature of this surface is fixed at 330°C in ourexperiments.

The FTIR spectroradiometer is placed in a way thatits optical line of sight is a few cm above the sample.

In this way, radiation coming from the solid parts ofthe sample would be avoided, and only absorption ofthe emitted gases would be measured.

In front of the spectroradiometer, an artificial IRsource is placed to perform active remote sensing. Inour experiments, the temperature of this source was140°C.

Distances marked on figure 3.3 are not critical.

The use of an FTIR spectroradiometer (FTIR-SR) isrequired because it presents some importantadvantages:- FTIR-SR performs in-situ, non-intrusive

measurements, avoiding manipulation of the emittedgases.

- FTIR-SR measures simultaneously in the wholerange of wavenumbers. Then, the absorbancespectra of all the emitted gases are measured at thesame time. This point is critical if you want tocompare concentrations of different gases, and itcan not be achieved by dispersive radiometers.

- FTIR-SR acquires spectra fast enough to evaluatetemporal evolution of gas concentrations

Experimental working conditions of the FTIR-SR arealso very important.

Among others, two important parameters have to becarefully selected

Spectral resolution 1 cm-1

This resolution is good enough to determine theabsorption bands of CO2, CO and CH4.

These gases are the most important carbon-relatedproducts of the biomass pyrolysis.

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D5-02.doc 13

Number of scansAn appropriate number of scans has to be selected

in order to guarantee a good signal-to-noise ratio andan acquisition time fast enough to obtain temporalevolution. In our experiments, a number of 32 scans perspectrum has been chosen.

Absorbance spectra of CO2, CO and CH4 can bemeasured in the following spectral windows:

Gas Spectral window

Carbon dioxide 2240 – 2400 cm-1

Carbon monoxide 2050 – 2230 cm-1

Methane 2730 – 3120 cm-1

Figures 3.4 and 3.5 illustrate examples ofabsorbance spectra of these gases.

2500 2400 2300 2200 2100 20000.0

4.0x10-3

8.0x10-3

1.2x10-2

1.6x10-2

2.0x10-2

2.4x10-2

2.8x10-2

CO2

CO

Spe

ctra

l abs

orba

nce

Wavenumber (cm-1)

2500 2400 2300 2200 2100 20000.0

4.0x10-3

8.0x10-3

1.2x10-2

1.6x10-2

2.0x10-2

2.4x10-2

2.8x10-2

CO2

CO

Spe

ctra

l abs

orba

nce

Wavenumber (cm-1)

Figure 3-4: Example of absorbance spectra ofCO2 and CO (spectral resolution 1 cm-1)

3100 3050 3000 2950 2900 2850 2800 2750

0.000

0.002

0.004

0.006

0.008

0.010

0.012

spec

tral a

bsor

banc

e

wavenumber (cm -1)

3100 3050 3000 2950 2900 2850 2800 2750

0.000

0.002

0.004

0.006

0.008

0.010

0.012

spec

tral a

bsor

banc

e

wavenumber (cm -1)

CH4

Figure 3-5: Example of absorbance spectra of CH4.The broad band corresponds to unburned hydrocarbons

(spectral resolution 1 cm-1).

3.5 QUANTITATIVE DETERMINATION OFCONCENTRATIONS

According to Beer’s law, quantitative informationcould be easily obtained on the basis that intensities ofspectral absorption bands are linearly proportional tothe concentration of each gaseous component.

However, different reasons give rise to deviationsfrom Beer’s law linearity.

The most important one is related to the finitespectral resolution of the acquisition system, whichproduces a “smoothing” of the spectrum.

A way to obtain quantitative information is tocompare the experimental spectrum with otherspectrum known as the reference spectrum.

This reference spectrum must fulfil the followingitems:- Reference spectrum has to have the same spectral

resolution than the experimental one.- Concentration of reference spectrum must be

accurately known.- Reference spectrum has to have been measured at

a temperature as close as those of the experiment.

For instance, absorbance spectra that can be usedas reference for different gases and differenttemperatures and spectral resolutions can be found atthis URL: http://www.epa.gov/ttn/emc/ftir/data.html

When a proper set of reference spectra has beenchosen (one spectrum for each different gas), theexperimental spectrum is compared to this set ofreference by using multicomponent analysisprocedures.

One of the most typical solutions for this problem isthe use of classical-least-squares (CLS) regressionanalysis methods.

The laboratory LIR has a commercial software thatperforms CLS analysis by using an advancedmultivariate algorithm developed at NASA’s KennedySpace Centre.

Once the reference has been chosen, it is necessaryto determine the limit of validity for quantitative analysis.

One way to perform this point is to “fabricate” a setof different spectra covering a wide range ofconcentrations.

For this purpose, LIR uses codes based on theHITRAN spectral database.

HITRAN is a compilation of spectroscopicparameters for 32 different molecules present in theatmosphere that a variety of computer codes use topredict and simulate the transmission and emission oflight in the atmosphere.

The database is now being developed at the Atomicand Molecular Physics Division, Harvard-SmithsonianCentre for Astrophysics.

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3.6 EXAMPLES OF EXPERIMENTAL RESULTS

In this section, some results obtained within theframework of Fire Star project will be presented, asexamples of application of the exposed methodology.

Figure 3.6 shows the experimental set-up at INIAlaboratory.

Figure 3-6: Experimental set-upfor pyrolysis experiments

Two radiators have been used:- the vertical radiator is used as an IR source, and- the horizontal radiator is used to heat the samples.

The FTIR-SR is located in front of the verticalradiator.

Temperatures measured for these experiments givevalues at surface of 140°C for the vertical radiator and330°C for the horizontal radiator.

One important point to be determined is thetemperature at which pyrolysis gases are emitted.

This temperature has been measured by using threethermocouples that define a small area located at theheight intercepted by the line of sight of the FTIR-SR.

Figure 3.7 shows the thermocouple arrangement,and figure 3.8 illustrates a typical temporal evolution oftemperature measured by the central thermocouplewhen the biomass sample were Ulex europaeus.

Figure 3-7: Thermocouple arrangement for temperaturemeasurements

0 20 40 60 80 100 120 140 160 180

60

80

100

120

140

160

180

200

220

240

260

280u lex eu ropaeus

TC #2 (center)

tem

pera

ture

(°C

)

time (sec)

Figure 3-8: Temperature measured with centralthermocouple during an experiment with Ulexeuropaeus

Analysis of the different temperature curvesmeasured for the different species will permit to definean averaged value of temperature.

Reference spectra for the quantitative analysis haveto be selected with a temperature as close as possibleto this average.

Other point to be kept in mind concerns the opticalpath length.

As Beer’s law points out, determination of thisparameter is necessary to obtain the value ofconcentration C.

However, for the experiments described in thischapter the optical path length can not be well knowndue to the variability of the spatial location of pyrolysisgases.

One way to reduce errors associated to this is tolocate the line of sight of the FTIR-SR as close aspossible from the sample, but avoiding the infraredemission coming from the embers.

Moreover, calculations have been performed toobtain C×L instead of C.

We will call this multiplication the normalisedconcentration.

Units for C×L will be [ppm m].The use of this normalised concentration will permit

to compare quantitative values for different gases anddifferent species.

3.6.1 CO2 and CO analysis

As was illustrated in figure 3.4, carbon dioxide andmonoxide present isolated absorption bands.

Therefore, quantitative analysis of these gases canbe performed individually.

Figures 3.9 and 3.10 show examples of temporalevolution of normalised concentration of CO2 and COduring an experimental burn of Ulex europaeus.

Reference spectra for quantitative analysis havebeen downloaded from the EPA database previouslycited.

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Before the use of these spectra, a study for the limitsof validity has been performed for both cases, followingthe procedure explained above.

This procedure allows assuring that experimentalabsorbance spectra are within the linearity limits.

0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 0

0

4

8

1 2

1 6

2 0

u l e x e u r o p a e u s

[CO

2] (

pp

m m

)

t i m e ( s e c )

Figure 3-9: Example of temporal evolution of CO2.The burned sample is Ulex europaeus

0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 0

0

1 0

2 0

3 0

4 0

u l e x e u r o p a e u s

[CO

] (p

pm

m)

t i m e ( s e c )

Figure 3-10: Example of temporal evolution of CO. The burned sample is Ulex europaeus

0 2 4 6 8 1 0 1 2 1 4 1 6 1 8 2 00

5

1 0

1 5

2 0

2 5

3 0

3 5

4 0

u l e x e u r o p a e u s

[CO

] (p

pm

m)

[CO 2] ( p p m m )

Figure 3-11: CO-to-CO2 ratio.The burned sample is Ulex europaeus

Figure 3.11 illustrates an example of the importantinformation that can be obtained from this methodology.

Due to the capability of FTIR-SR to measuresimultaneously all the absorption bands, it is possible tocompare concentrations of different gases.

For instance, CO-to-CO2 ratio can be easilymeasured, as shown in figure 3.11, obtaining importantinformation on the combustion process.

3.6.2 CH4 analysis

The study of methane absorbance spectra is not sostraightforward.

As can be seen in figure 3.5, a wide absorption bandsuperimposes the fine structure of methane.

Therefore, two reference spectra (one for puremethane and other associated to the wide band) areneeded in the quantitative analysis.

The wide band is a complex structure thatcorresponds mainly to the stretch of H-C bonds.

This band is not associated to a unique compound,but it is infrared active for different hydrocarbons.

That means it is not possible to find a uniquereference spectrum to take into account this spectralfeature.

3 1 0 0 3 0 0 0 2 9 0 0 2 8 0 0 2 7 0 0

0 . 0 0 0

0 . 0 0 2

0 . 0 0 4

0 . 0 0 6

0 . 0 0 8

0 . 0 1 0

0 . 0 1 2

experim

f i t t ing

peak #1

peak #2

spe

ctra

l a

bso

rba

nce

w a v e n u m b e r ( c m-1

)

Figure 3-12: Mathematical fittingof the HC absorption band

A “synthetic” spectrum has been obtained from anexperimental spectrum with no traces of methane.

The experimental spectrum has been well fitted byusing two Lorentzian curves, as can be seen in figure3.12.

This fitting will be used as an artificial referencespectrum for the wide band in the quantitative analysis.

Reference spectrum for methane has been takenfrom the EPA database.

Figure 3.13 shows as an example the temporalevolution of the CH4 normalised concentration during anexperimental burn of Ulex europaeus.

FIRE STAR

D5-02.doc 16

0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 0

0

2

4

6

8

1 0

u l e x e u r o p a e u s

[CH

4] (

pp

m m

)

t i m e ( s e c )

Figure 3-13: Example of temporal evolution of [CH4].The burned sample is Ulex europaeus

3.7 PARTIAL CONCLUSION

Remote sensing based on absorption infraredspectroscopy appears to be a very useful tool tomeasure in-situ concentration of gases emitted duringthe thermal degradation of biomass.

In this document, some results concerning the mostimportant carbon-related products have beenpresented, but the methodology can be used to detectand measure any other gas, which present absorptionbands in the 2-16 µm spectral range.

For instance, next step within the framework of FireStar project is to identify the emission of volatile organiccompounds (VOC).

For general information on spectroscopic techniquesand Fourier transform based spectroscopy, references(Sigrist, 1994) and (Griffiths & de Haseth, 1986) can beconsulted.

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4 MEASUREMENT OF FIRE PARAMETERS WITH BI-SPECTRAL SYSTEM

4.1 INTRODUCTION

The major aim of the research group at UniversidadCarlos III de Madrid in Fire Star project is to provide IR-based measurement methods for the physicalparameters characteristic of forest fires.

Leaving aside composition-related parameters (thathave been treated in the previous chapter) the mainphysical magnitudes for a fire are:- the position and rate of spread of the fire front,- the height of the flame,- temperatures at different places, and- fire front intensity.

Fire front intensity (measured in kW·m−1) is the heatpower released by the front per unit length. For astationary propagation (v constant), it can be written as:

I = ∆w·h·vwhere

- ∆w is the fuel load consumed by the fire (kg ·m−2),- h is the heat of combustion of the fuel (kJ kg−1) and- v is the rate of spread of the fire front (m s−1).

It is clear that ∆w·h is the heat released per unit areaof fuel bed burnt (q, measured in kJ m−2), and thus

I = q·v

Figure 4-1: MIR band camera (Amber Radiance 1t)

Figure 4-2: TIR band camera (Amber Sentinel)

4.2 INSTRUMENTATION: IR CAMERAS

The following cameras have been used for themeasurements.

MIR band: Amber Radiance 1t- is represented on figure 4-1,- has a 256 × 256 Stirling-cooled InSb focal plane

array (FPA), and- provides digital images with 12 bits radiometric

resolution.The frame rate is 50 frames per second (FPS).

TIR band: Amber Sentinel- is represented on figure 4-2,- has a 320 × 236 uncooled microbolometer FPA and- provides also (via a Digital Interface Unit) digital

images with 12 bits radiometric resolution.The frame rate is 30 FPS.The technical specifications of these cameras are

summarised in Table 4.1

In order to be useful, images must be non-saturatedand radiometrically scaled.

This demand is common to ordinary thermography,but in this application, it poses specific difficulties due tothe large dynamic range of the radiation emitted byfires.

In our system, both cameras were calibrated againsta laboratory blackbody.

In order to avoid saturation, sensitivities can beadjusted over the whole range by means of diaphragmsand neutral density filters.

Consequently, calibration over a very widetemperature range (from room temperature to 1500ºC)is achieved (see Figure 4.3).

1E-3 0.01 0.1 10

200

400

600

800

1000

1200

1400 TMIRmax TMIRmin TTIRmax TTIRmin

Tem

pera

ture

(ºC

)

Optics transmittance

Figure 4-3: Temperature ranges for the MIR and TIRcameras used, as a function of the transmittance of the

optics (normalised to one for a camera with neitherfilters nor diaphragms)

FIRE STAR

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Table 4-1: Main specifications of the IR cameras used

Specification. Radiance 1t Sentinel

Detector InSb Microbolometers VOx

Pixel number 256x256 (65536) 320x240 (76800)

Pixel size 38x38 µm 50x50 µm

Pixel pitch 38 µm 50 µm

Spectral band 3 – 5 µm 8 – 12 µm

Refrigeration Linear Stirling Cycle Thermoelec. Stabiliz.

NEDT < 0.025 K @ 300 K < 0.1 K @ 298 K

Detector temp. 70 – 80 K 298 K

Detectivity D*>2·10 11 cm Hz1/2/W @ 80 K D*>5·10 8 cm Hz1/2 / W @ 298 K

Dynamic Range 12 bits 12 bits

Frame rate 50 fps 30 fps

Video output PAL/CCIR RS-170

Digital output HSVB (RS-422 @ 3.44 MHz) HSVB (RS-422 @ 3.068 MHz)

Focal length 50 mm 50 mm

F number 2.3 0.7

Entrance pupil 21.7 mm 71.4 mm

FOV 11.2º x 11.2º 13.5º V x 18.2º H

IFOV 0.76 mrad 1.0 mrad

Nominal power 40 W 6 W

Weight 4.5 kg 3 kg

Dimensions 112x183x262 mm 160x105x283 mm

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4.3 BI-SPECTRAL IMAGING SYSTEM

4.3.1 Introduction

The key to reduce complexity and cost ofmultispectral systems is to reduce the number of bands.

In fact, many of the applications of multispectralimages can be faced up with comparatively simple Bi-spectral instruments.

Bi-spectral imaging is particularly relevant to fireresearch.

This approach has been implemented in the BIRDsatellite, launched in October 2001.

It uses simultaneous images of an area in themedium infrared (MIR) and thermal infrared (TIR) bandsin order to spot forest fires and estimate their size andtemperature.

A similar scheme will be employed by the FUEGOmini satellite constellation, to be launched by 2006(website FUEGO)

4.3.2 Bi-spectral imaging system

The two cameras have been integrated into a Bi-spectral imaging system by controlling them with acomputer provided with two digital frame grabberboards.

In our system, a perfect simultaneity is not possibledue to the different frame rates of the cameras

Nevertheless, the computer allows the maximumpossible degree of simultaneity, acquiring single framesor pre-programmed sequences of MIR and TIR images,at a maximum rate of 30 FPS and a minimum rate of afew frames per hour.

More than 100 images can be acquired in a singlesequence.

Cameras are mounted on a platform that keeps thetwo cameras rigidly aligned and allows easy pointing.

In addition to the IR cameras, an ordinary videocamera provided also a visible record of the fires,although neither calibration nor simultaneousacquisition were available in this band.

A scheme of the acquisition system can be seen inFigure 4.4.

T I R c am era

MIRca mera

1 2 bits

12 bits

F r a m egrabber A

F r a m eg r ab ber B

Pla tform

Co m puter

T I R c am era

MIRca mera

1 2 bits

12 bits

F r a m egrabber A

F r a m eg r ab ber B

Pla tform

Co m puter

Figure 4-4: Scheme of the bi-spectral image acquisitionsystem

4.3.3 Pre-processing

As acquired, images are not co-registered, becausethe cameras have different fields of view (see table 4.1).

However, their relative positions are kept fixed bythe supporting platform, therefore making possible co-registration with subsequent processing.

This operation is a part of what is usually called inremote sensing “pre-processing”, a concept thatincludes all the initial, device-specific steps of digitalimage processing.

This generally includes (Campbell, 1996) correctionof image defects (due to “bad pixels”, detector drift, etc)and reduction of image noise.

It includes also geometric pre-processing, in orderto:- co-register images in each band, or- register images to a map (geo-reference).

In our case, this is done by selecting “ground controlpoints” (GCPs) (Mather, 1989) in the MIR and TIRimages and applying an algorithm that deforms one ofthe images in order to force the GCPs to coincide.

This process is specific for the measurementgeometry, and must be repeated if the orientation of thecameras or distance to the object vary.

Radiometric pre-processing, in order to translatedigital values into reflectance, radiance, or temperaturevalues.

As explained in section 2.3, this requires a sensormodel (camera calibration) and a radiometric model(atmospheric effects).

In our case, two kinds of radiometric images areobtained: radiance images and brightness temperatureimages.

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4.4 MEASUREMENT OF FIRE PARAMETERS:GENERAL CONSIDERATIONS

4.4.1 Standard methods

The standard methods to measure these parametersare as follows.

Rate of spread and flame height are measured byvisual inspection.

Temperatures are measured, as a rule, bythermocouples.

Fire front intensity is calculated using the previousequations, which require knowing ∆w and h.

The heat of combustion of the fuel h is known for themain vegetal species.

A pre-burn fuel inventory permits to estimate w (pre-burn fuel load) for the specific plot to be burnt, and apost-burn inventory gives ∆w (for laboratory burns, itcan be measured directly with a balance).

These standard methods are conceptuallystraightforward but show some practical drawbacks.

Visual determination of fire front position is not veryaccurate and may be hindered by smoke.

Thermocouples are rugged (if they are not very thin),inexpensive and easy to use but they are not well suitedto field experiments.

They provide only point measurements, and in orderto measure temperatures for the whole fire, a grid ofsensors needs to be deployed in advance over the areato be burnt; in addition, they may alter fire, and have aslow response time.

In order to minimise these effects, very thin (andtherefore fragile) thermocouples must be used.

Power related measurements (fire front intensity andheat release per unit area) require fuel inventories thatare tedious, can be done only in prescribed burns (notin wild fires) and are not practical when vegetation isnot homogeneous.

Often post-burn inventories are not done, and anestimation of the percentage of consumption of fuel isused instead, based on the size of the branches (thesmaller the diameter, the most complete theconsumption).

On the whole, power related measurements arequite indirect, labour consuming and prone to errors.

4.4.2 IR imaging methods

The drawbacks just described make IR imaging anappealing alternative to the standard methods.

The fire front can be easily visualised throughsmoke.

A calibrated camera gives a direct measurement ofthe radiance (power per unit area per unit solid angle)emitted by each point of the field of view, in the spectralband of operation of the camera.

This magnitude is related to temperature and, afterintegration over an area and a solid angle, to emittedpower, and therefore to fire front intensity.

Cameras provide all this information with very goodspatial and temporal resolution.

In order to be useful for this application, however, IRimaging must overcome some difficulties that stem fromthe physical properties of forest fires; in particular, fromthe strong spectral structure of their IR emission.

Only instruments with a large degree of spectralresolution can manage fully these complexities.

However, ordinary spectrometers have no spatialresolution and hence cannot deal properly with theheterogeneous and dynamic nature of forest fires.

The prohibitive cost and complexity of imagingspectrometers have leaded us to explore thecapabilities of a relatively simple bi-spectral system.

Methods and results exposed in this chapter willrefer to that system, although some of the techniquesare obviously applicable to any IR camera.

4.4.3 Experimental burns

For simplicity and clarity of exposition, nearly all theresults described correspond to a single experimentalburn, performed at the CIFOR-INIA facilities (SpanishMinistry of Science & Technology).

The burning area is 8.5 m long and composed byeight wagons, each 100 cm long and 80 cm wide.

Using a fan located at the tunnel entrance cansimulate wind speeds between 1 and 7 m/s.

The forest fuel was Pinus pinea needles with amoisture content of 7.76%.

The fuel bed was spread over the wagons 4 to 7,with a thickness of 9.3 cm, and a fuel load of 1.5 Kg/m2.

Wind velocity was 1m/s.

The IR cameras were located on top of the fanstructure (approximate height: 225 cm), pointing to thesixth wagon, although the FOV covered also most ofwagons 5 and 7

FOV

e m b e r sa s h e s fire front u n b u r n e d f u e l

B i-spect ra l imager

FOV

e m b e r sa s h e s fire front u n b u r n e d f u e l

B i-spect ra l imager

Figure 4-5: Scheme of the experimental burns studied

The burn progressed moving away from thecameras, i.e., from bottom to top in the images, andtook approximately 8 minutes to reach the end of thefuel.

Bi-spectral images were acquired at a rate of oneeach two seconds, but in order to reduce time ofprocessing, only one image each 30 s has been usedsubsequently.

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Figure 4.6 shows a typical example of bi-spectral MIR-TIR images.As already explained, for flames Treal > TMIR > TTIR.This means that flames are hardly visible in TIR images, and they can be mistaken for relatively cold soil in MIR

images.For instance, in Figure 4.6 the brightness MIR temperature is around 750 K for flames and about the same for

the burned soil at the bottom of the image.

Figure 4-6: (Top) Simultaneous MIR (left) and TIR (right) images obtained with the bi-spectral imager in a burn atthe fire tunnel.

(Bottom) The same images converted to temperature units.The false-colour scale is in Kelvin degrees.

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4.5 FIRE SCENE CLASSIFICATION BASED ON BI-SPECTRAL IMAGES

Differences in brightness MIR and TIR temperatures are the basis for the application of classification techniquesto bi-spectral fire images. Classification is best explained with reference to the “MIR-TIR scatterplot” (Figure 4.7).

Figure 4-7: (Left) MIR-TIR scatterplot for the images of Figure 4.6.Each point stands for a pixel, with the MIR and TIR brightness temperatures on the y and x-axis, respectively.

The different colours indicate the point density

(Right) Cumulative ("global") MIR-TIR scatterplot for all the images acquired during the burn.

In this plot, each point stands for a pixel of the bi-spectral image, its abscissa being TIR brightnesstemperature and its ordinate being MIR brightnesstemperature.

Pixels with black- or grey-body behaviour will givepoints along the diagonal line (TMIR=TTIR).

Flame pixels, with TMIR>TTIR, will lie over that line.For a complex scene, each region with more-or-less

homogeneous radiative properties will generate a more-or-less compact cluster of points in the scatterplot.

These are the “classes” into which we want toclassify the scene.

In order to assign each pixel to a specific class, analgorithm, called a “classifier”, must be used.

If the statistical distribution of the pixels within eachclass is known, the best option is a maximum likelihoodalgorithm (Richards & Xia, 1999).

In the multispectral satellite images used in remotesensing applications, the statistical distribution of eachclass is ascertained by defining on the image regions ofinterest (“training sites”) that are known to belong to thatclass.

This process is called “training the classifier”.

In our case, this procedure is impractical because nowell-defined training sites are usually available in a firescene and, in addition, the training process is too time-consuming to be useful for most fire applications.

Therefore, a different approach has beenundertaken: to identify training regions on thescatterplot rather than on the image.

This is difficult to do for more than two dimensions,but bi-spectral images give 2-D scatterplots whereclusters of points are easily pointed out.

Several well-separated sub-clusters have beenselected as training regions.

Figure 4.8 shows these training regions on theglobal scatterplot, together with a legend that indicatestheir correspondence to classes on the image.

The rest of the pixels are assigned to one of theseclasses by a maximum likelihood algorithm.

Consequently, the whole sequence of the burnbecomes classified.

Results of this classification are shown in Figure 4.9.The upward progress of the fire is obvious in these

images.In the first two frames, the fire front has not reached

yet the FOV, and only flames are seen.The fire front reaches the top of the FOV at frame

13.After that, the progressive cooling of the embers is

clearly appreciated.

It must be pointed out that the classificationprocedure just described is quite robust.

Calibration inaccuracies may alter the values of TMIRand TTIR, causing a shift or re-scaling of the axis.

This may shift, for instance, the grey-body pointsaway from the TMIR=TTIR line, but it does not changeappreciably the shape of the scatterplot and thereforedoes not alter significantly the classification results

TTIR (K)

TMIR (K)

TTIR (K)

TMIR (K)

TTIR (K)

TMIR (K)

TTIR (K)

TMIR (K)

TTIR (K)

TMIR (K)

TTIR (K)

TMIR (K)

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Classes Colour

Hot embers Blue

Embers with flame Red

Cold embers Green

Ashes Cyan

Intense flame Orange

Medium flame Yellow

Cold flame Magenta

Background Maroon

Figure 4-8: (Left) Training areas selected on the global scatterplot (Right) Key to the colours

Figure 4-9: Classified sequence of an experimental burn. Time between consecutive frames is 30 s

Classification provides an objective method toidentify the different regions that make up a forest firescene.

This has direct applications, like, for instance, toidentify the fire front position in a propagating fire, or tolocate points of re-ignition in an area of embers.

But, in addition, as will be explained in the followingsections, classification is a necessary starting point formost forest fire measurements based on IR imaging: itmakes possible to include the heterogeneity of theforest fire scene.

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4.6 RATE OF SPREAD AND TEMPERATUREMEASUREMENTS

As explained before, visual inspection is thestandard procedure to measure the rate of spread of afire front: IR cameras provide two additional methods:

For images like those of Figure 4.6, vertical profile ofbrightness temperatures shows a sharp increase at theflame/embers interface.

The change is more abrupt in the TIR images, dueto the lower brightness temperature of flames in thatband. In practice, this interface can be located at the800 K isotherm for the fire studied in this work (Figure4.10, left).

Therefore, the time evolution of this isotherm givesthe time evolution of the fire front.

Classification of the whole sequence of a burnprovides a series of images in which the position of theflame/embers interface can be easily followed.

Both methods require a previous geo-referencing ofthe images.

This term refers to a “geometric calibration” in orderto translate pixel co-ordinates into ground co-ordinates(measured in meters).

This can be done by an algorithm similar to the oneused for image co-registration.

A comparison of the three methods (visualinspection, TIR brightness temperature andclassification) is shown in Figure 4.10 (right).

Clearly, all three methods agree, but some practicaldifferences must be pointed out:

Visual inspection provides less frequentmeasurements.

Obviously, this is not the case for a video recording,but smoke obscuration is then usually a problem.

The method based on TIR brightness temperaturesworks well for the fire studied in this work.

Nevertheless, no single value of temperature workswell for all forest fires, and the “fire front isotherm” canappear at other points in the embers.

In practice, then, the fire front in this method must belocalised manually.

In contrast, the method based on classification canbe applied, in principle, automatically by a softwareroutine, but more work is still needed to test itsrobustness for other geometries of observation.

On the left-hand side of Figure 4.10, several TIRbrightness temperatures profiles along the centre of theburning area are shown.

For each profile, the point where the fire front startshas been marked:- Points at smaller y values are embers or ashes

(“solid” classes).- Points at larger y values are not yet burnt, but the

temperature they show is due to the flames.

For the “solid” classes, MIR and TIR brightnesstemperatures are similar, and both are a goodestimation of the real surface temperature.

Thus, concerning measurement of this parameter,IR cameras clearly do better than thermocouples,because they give almost instantaneous coverage ofthe whole 2-D area.

For the flames, however, brightness temperaturesare very different from the real value, and, in addition,they represent a line-of-sight average for a three-dimensional body.

More IR-methods that are complex are needed inorder to get a good measurement of the flametemperature.

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0 1 2 3 4 5 6 7 8

3

4

5

6

7

Visual Classification TIR edge

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fron

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e of

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in)

t (min)

Figure 4-10: (Left) Brightness TIR temperature profiles along the centre of the wagons at different times during theburn. Fire progresses towards increasing "y" values. The vertical bar marks the beginning of the fire front, roughly

at the 800K isotherm.(Right) Fire front position and rate of spread versus time, as obtained by three different methods: visual inspection

during the burn, classification, and time evolution of the TIR profile.

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4.7 RADIATED POWER MEASUREMENTS

As explained in section 2.3, IR cameras provide adirect measurement of the radiance emitted from eachpoint in the FOV.

In order to estimate the total power of a forest fire,an important intermediate result will be the powerradiated in the spectral band of the camera.

To obtain a value for this parameter requires severalsteps that, for definiteness, we will explain for the MIRcamera:- 1. Calculation of emitted MIR exitance (W/m2) for

each pixel. Assuming Lambertian radiation doesthis: values of radiance are simply multiplied by π.

- 2. Calculation of the area of each pixel, for eachclass and each image. For the “solid” classes, this isa by-product of geo-referencing. For flames, adifferent calculation is made: the flame is consideredto be in a vertical plane, located at the position of thefire front.

- 3. A pixel-per-pixel multiplication (exitance timesarea) of the previous images gives the powerradiated by each pixel.

- 4. A spatial integration gives the MIR power emittedby each class, as measured by the camera.

An example of the results of this procedure is shownin Figure 4.11 (all the “solid” classes have been mergedinto a single “embers” class, and all the flame classesinto a single flame class).

The measured values can be viewed as a“convolution” of the “real” fire values with the FOV“window”.

This has been pointed out by the annotations inFigure 4.11.

Each point corresponds to one of the frames inFigure 4.9.

Before the fire front enters the FOV, the measuredpower is due to flames.

As the fire progresses, the flame area viewedincreases and its power increases.

When the full flame area is seen in the images, theflame power it is kept more or less constant untilt = 5 min, when the top of the flames goes out of theFOV.

The embers region keeps entering the images fromt = 1.4 min to t = 4 min, and the power keeps risingaccordingly.

The further power increase after t = 4 min isexplained because the embers area grows wider as thefire propagates.

Shortly after the fire front left the FOV, the fireextinguished because it reached the end of the fuelbed. The progressive cooling means a fast decrease inthe MIR radiated power.

Measured MIR power - Back view

0

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Top o f f lames exitsF O V

Top o f f lames exitsF O V

End o f embers entersF O V

End o f embers entersF O V

Fire front enters F O VFire front enters F O V

Figure 4-11: MIR power emitted by embers and flame as measured by the camera, as a function of time(“Back view” means that fire propagated away from the cameras).

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The evolution just described can be betterappreciated with reference to Figure 4.12, showing theareas and average MIR exitances of the “embers” and“flame” classes.

The calculation summarised by points (1) to (4)provides the power measured by the cameras in theMIR band.

In order to estimate the power emitted by the fire inthe full spectrum, two important corrections must bemade:

For flame classes, the measured emissioncorresponds only to one side of the flame.

This must be corrected multiplying by a flame-specific geometric factor.

The fraction of the total radiated power that isemitted in a particular band is different for each class.

To estimate the power in the full spectrum from thepower in a band, this spectral fraction must be known.

In must be remarked that both the geometric factorand the spectral fraction are different for the flame andfor the “solid” classes.

For this reason, the previous (1) to (4) calculationsmust be made separately for each class.

As a first approximation, a geometric factor of 2 hasbeen used for the flame.

The spectral fractions have been estimated basedon spectroradiometric measurements.

For the MIR band, a value of 45% has been used forthe flame, and 35% for the solid classes (this numbercan be refined, because it will depend on the classtemperature, but the final results are not very sensitiveto it).

Values of spectral fractions for the TIR band havenot been estimated because the calibration of thespectroradiometer in that region was not reliable atpresent.

By correcting the MIR power calculated in step (4)with the geometric factor (a) and spectral fractions (b),an estimation of the total power radiated by each class,at each moment, is achieved.

Obviously, this power refers to the imaged area: thearea of the burn within the FOV of the camera.

If, at a time when the full fire is within the field ofview, this power is divided by the length of the fire front,a value of the radiant intensity of the fire front isobtained.

For the burn studied here, time = 4.4 min wasselected (it corresponds to the image in Figure 4.6 andto frame 9 in Figure 4.9).

A value of 41 kW/m was obtained.This must be compared to the fire front intensity

obtained by the standard method involvingmeasurements of rate of spread, dry fuel loadconsumed and heat of combustion of the fuel.

This value was 253 kW/m.Thus, radiant intensity was 17% of the total intensity.

If the method is consistent, this fraction should bequite close to the radiated fraction of the total heatreleased.

The total heat released per unit area was, accordingto the fuel consumption and its heat of combustion,28179 kJ/m2.

The total energy radiated per unit area during theburn has been calculated by a time integration of thepower images.

A value of 5730 kJ/m2 has been obtained.This amounts to a 20% of the total heat release, in

good agreement with the previous 17% value.Values for this fraction that range from 20% to 60%

have been reported (Trabaud, 1992), depending on thesize of the fire; small fires like the one studied hereshould be on the lower end of this interval.

Experimental work, studying laboratory fires withdifferent fuel loads and rates of spread is presentlyunder way in order to check the validity of these results.

Area within the field of view

0 . 0 0

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Figure 4-12 (Left) Areas of the classes "embers" and "flame" within the FOVof the bi-spectral imaging system.

(Right) Average exitance of those classes.

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4 5 0 0 4 0 0 0 3 5 0 0 3 0 0 0 2 5 0 0 2 0 0 0 1 5 0 0 1 0 0 0

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(b)(b)

W a venumber (cm)Figure 4-13: Spectral radiance emitted by two different regions of an experimental forest fire:

(a) flames (b) embers without flame.Spectra were measured with a Fourier Transform Infrared spectroradiometer.

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5 TRANSMISSION OF A FLAME IN THE THERMAL INFRARED

5.1 INTRODUCTION

This series of experiments (n°2003-02 and n°2003-03) realised by INRA from May to July 2003 aimed atcharacterising the transmission in the thermal infrared,of a flame from an axis-symmetric burner, filled with aMediterranean fuel (Pinus pinaster).

The first series of transmission experiments, forFIRESTAR, was carried out in 2002 (n°2002-02).

The method of estimation of the transmission wasbased on the expected symmetry of the signal withrespect to the vertical axis of the device.

The results were not satisfactory because thissymmetry was not good.

The present report gives the results obtained with anew method.

5.2 EXPERIMENTAL DEVICES AND METHODS

5.2.1 Fuel and ignition

The fuel was oven-dried needles of Pinus pinaster, atypical Mediterranean species.

The burners were 20-cm high cylindrical baskets,made of metallic meshes.

Three basket diameters were used: 20, 28, 40 cm.The baskets contained respectively: 125g, 250g,

and 500g of dry fuel.For more details about the experimental apparatus

and results previously obtained with it, refer to (Dupuyet al, 2003)

The fuel was ignited at the lower circumference ofthe basket using alcohol.

The apparatus was used without the plate at thebase oh the basket.

5.2.2 Measurements

The mass loss was measured with a balance, whichhad precision of 1g.

The balance display was set to zero just before that,alcohol was put for ignition of the fuel.

Thus, the first record of the balance indicates theinitial mass of alcohol.

The mass loss was recorded at 1 Hz, during 2 minfor each test.

The mass loss data were stored in the filemasse03s02.csv.

The infra-red measurements were done using thecamera described in (Fire Star, 2003a)

The sampling frequency was 50 images/s.The range of measurement was 0-500°C (brightness

temperature).

The origin and the duration of the recording for thebalance and the camera were the same (use of atrigger).

The calibration was done before each test, and wasdisabled during the tests.

The atmospheric parameters were updated beforeeach test in the data logging software of the camera(Thermacam Researcher): atmospheric temperature,ambient temperature, and air humidity.

All temperature values were set to the drytemperature given by the (ventilated) psychrometer.

Air humidity was calculated from dry and wettemperatures of air measured by the psychrometer.

The infra-red data (320*240 pixels plus additionalinformation, for each image) were stored as follows:- a first file (IRaammjjnn.img) contains the image

without flame, and- a second file (IRaammjjnn.seq) contains the 2-min

recording.

Then they were saved on DVD disks.

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A 2-min recording with the camera (50 Hz) gave a1 Go file, each DVD disk (DVD-R 4.7 Go) contained theuncompressed files of four tests.

The camera set at 1.5 m of the symmetry axis of thedevice (vertical axis of the basket) at a height h fromthe top of the basket (Figure 5.1).

The measurements were done at three heights:50 cm, 100 cm, and 150 cm.

In the previous series (2002-02), the camerafocused on the blackbodies (at 3.0 m).

In this series, it focused on the basket axis (at1.5 m).

This change increases the precision by reducing thethickness of beams in the basket axis plan.

The thickness of an elementary beam correspondingto one pixel in the image depends on the aperture of thecamera lens (about 4.5 cm), the focus-distance, and thedistance from the camera.

In these experiments, the thickness of theelementary beam is 2 mm at 1.5 m (basket), andaround 5 cm at 3 m (blackbodies).

Three blackbodies were used: a hot (at 250°C +-1°C, with a PID controller), and two cold (at ambienttemperature, filled with water).

The hot blackbody was at height h at 1.5 m from thebasket axis (3.0 m from the camera).

The size of the hot surface was 10*10 cm.It was painted with a special resistant and black

paint.As the paint can be considered as perfectly

absorbent, the emissivity of the surface is consideredequal to 1.

It was checked before the experiments bycomparing the brightness temperature and thetemperature measured by a thermocouple.

The cold blackbodies were tanks filled with water.The surface treatment was the same as that of the

hot blackbody.The cold blackbodies set 17 cm ahead of the hot

blackbody in the direction of the line of sight of thecamera.

In a vertical plane, the two cold blackbodies wereover and under the line of sight of the camerarespectively, so that only a 5-cm high hot area is visiblefrom the camera.

This height is equal to the thickness of anelementary beam of the camera.

Hence, an elementary beam coming from the coldarea located at h+5cm, in the hot blackbody plan, cameonly from this upper cold area.

A symmetric device was applied to the lower coldblackbody.

basket axis

infrared camera

basket top of the basket

h

80cm

ground

150cm 150cm

up cold area

down cold area

hot area

balance

Figure 5-1: Scheme of the experimental device (n° 2003–02)

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The two beams at +-6.4cm (+- 16pixels, Figure 5.2) were used to estimate the emission (see below).Notice that the two previous beams were 12.8 cm apart in the blackbodies plan.Thus, they were 6.4 cm apart in the basket plan at 3.2 cm from the beam that crossed the centre of the hot

blackbody.

Figure 5-2: The three pixels of measurement in two samples of infrared images: without and with flame.

5.2.3 Estimation of flame emission andtransmission

The unit of the infrared images is OS (ObjectSignal).

This unit is linked with the infrared camera and itsrange of measurement.

It is the total intensity integrated over the cameraspectrum, after the correction for the atmosphericabsorption.

As the object distance (3 m) is very short, and therelative humidity is often low (about 50%), theatmospheric correction in the unit OS is negligible.

As the emissivity was set to 1 in ThermacamResearcher, there was no object-emissivity correction.

Thus, OS can be considered as the amount ofenergy in the thermal infrared (7.5 to 13 µm) emitted inthe camera direction.

The relation between the Object Signal and thebrightness temperature (in Kelvin) is the following:

( ) FeBOS

TR −

= (5.1)

(Numerical values of the parameters B, R and F areprovided by the manufacturer).

This formula is commonly used for infrared camera(Gaussorges, 1989) and is the equivalent of theStefan’s law applied to the thermal infrared.

The parameters of the formula are valid for a rangeof temperature measurement (here 0-500°C inbrightness temperature).

Because the position of the blackbodies with respectto the camera can slightly move from one test toanother one, the centre of the hot area was detected inthe images without flame (.img files).

It was given by the average position of the pixels, ofwhich the values was more than a threshold (1000 OS).

The area of the centre pixel was the area ofmeasurement onto the hot blackbody.

Its value gave the sum of the energy emitted by thehot background and transmitted by the flame, and of theflame emission.

Without flame it was named « L,h,nof » and duringthe test « L,h,f ».

The flame emission was given by the average of thevalues of the two pixels of measurement onto the twocold blackbodies.

The upper one was named « L,c,up,nof » and« L,c,up,f », the lower one « L,c,down,nof » and« L,c,down,f » (respectively without and with flame).

The flame emissions « L,c,nof » and « L,c,f » wereestimated by the arithmetic means:

L,c,nof = (L,c,up,nof+L,c,down,nof)/2L,c,f = (L,c,up,f+L,c,down,f)/2

The flame transmission is given for each image of atest by the formula :

Tr = (L,h,f- L,c,f) / (L,h,nof - L,c,nof)

Then the emission and the transmission werefiltered with a moving average over 1 s (50 previouspoints at 50 Hz), and under-sampled at 5 Hz.

The values for the two series were stored in the files0302EmissionFlame.csv , 0302TransmissionFlame.csv.

5.2.4 Thermocouple measurements.

In a complementary series of experiments (n°2003-03), two thermocouples of diameter 50 µm (K type)were added on the axis basket to measure the flametemperature.

They were over and under the two beams that wentthrough the two cold areas of measurement,respectively.

The thermocouple set at h + 5 cm and h – 5 cm andthus did not disturb the measurement of the cold areaemission.

The mean of temperatures of the two thermocouplesprovides an estimation of the flame temperature atheight h.

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The sampling frequency (50 Hz), the origin (bytriggering), and the duration (2 min) of the recordingwere the same as those of the infrared acquisition.

The temperature data were stored in the filesth030703nn.csv.

See (Fire Star, 2003b) for more details about thethermocouples description.

For this series, the infrared data were stored in:- 0303EmissionFlame.csv and- 0303TransmissionFlame.csv.

The mass loss was stored in masse03s03.csv.

5.2.5 Video recordings

A video camera (CCD-VX1E-Hi8) recorded theimages in the visible wavelength of each test. Twovertical scales on both sides of the basket enabled toderive the flame height from these recordings. Thevideo recording was started before each test. The timeof ignition announced by an operator was recorded onthe audiotape. The internal clock of the camera enablesto date all events.

The adjustments of the camera (lens aperture,focus, shutter speed) were done before each test andautomatic procedure was disabled.The contents of the Hi8 videotapes were copied toSVHS videotapes, digitalized as MPEG2 files andsaved on DVD.

Photographs of the device and of the experimentswere also taken with a digital camera.

5.3 PROTOCOL

The protocol of the experiments n°2003-02 is acombination of the three basket diameters (20, 28, and40cm), the three measurement heights (50, 100, and150cm) and three replications (27 valid tests).

The protocol of the complementary experimentsn°2003-03 is a combination of the three basketdiameters (20, 28, and 40cm), one measurement height(50cm), and 2 replications (6 valid tests).

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5.4 LISTING OF THE TESTS

5.4.1 Table 5.1: Tests of series 2003-02

Bench Fuel Air

Test number(aammjjnn)

Measurementheight(cm)

Basketdiameter

(cm)Humidity

(%)

Drytemperature

(°C)

Relativehumidity

(%)

03052104 50 20 0.1 19.8 42.903052105 50 20 0.1 19.8 42.903052106 50 20 0.2 20.0 41.103052102 50 28 0.2 19.5 42.403052103 50 28 0.1 20.0 44.603070307 50 28 26.5 40.803052201 50 40 0.4 19.0 54.103052202 50 40 0.1 19.7 52.003052203 50 40 0.2 20.1 54.703052307 100 20 1.4 23.7 51.203052308 100 20 0.4 24.0 54.103052309 100 20 0.3 24.5 51.503052304 100 28 0.3 22.5 54.503052305 100 28 0.2 23.5 51.703052306 100 28 0.5 24.0 49.703052301 100 40 0.2 20.5 58.803052302 100 40 0.2 20.2 63.703052303 100 40 0.3 22.0 54.003060501 150 20 0.4 25.0 61.603060502 150 20 0.3 25.2 54.103060503 150 20 0.1 26.0 50.003060504 150 28 0.2 26.2 49.603060505 150 28 0.1 26.5 51.703060506 150 28 0.2 27.0 44.203060507 150 40 0.2 27.0 49.303060508 150 40 0.3 27.0 44.203060509 150 40 0.2 27.0 47.0

5.4.2 Table 5.2: Tests of the series 2003-03.

Bench Fuel Air

Test number(aammjjnn)

Measurementheight(cm)

Basketdiameter

(cm)Humidity

(%)

Drytemperature

(°C)

Relativehumidity

(%)

03070301 50 40 0.4 24.1 41.003070302 50 40 0.3 25.5 39.603070303 50 20 0.4 24.9 42.003070304 50 20 0.7 26.0 43.003070305 50 28 0.4 26.0 40.203070306 50 28 0.5 26.5 38.1

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5.5 RESULTS

5.5.1 Mass loss curves

The recordings of mass loss were performed at afrequency of 50 Hz.

Starting from these raw data, a file was created; itcontains the temperature at a frequency of 1 Hz.

Indeed, the recording at a frequency of 50 Hz has nointerest for this measurement (the balance has acharacteristic response time of about 1 s and theduration of the recording is 120 s).

The analysis was done on these data.

The balance was set to a mass of zero just after thatthe fuel was put into the basket and just before thealcohol was put on the ignition device.

Thus the first record of mass is positive andcorresponds to the alcohol initial mass (malcohol).

After ignition (time 0 of the recordings), the massdecreases and becomes negative.

Figure 5.3 represents the mass of fuel (and alcohol)consumed as a function of time (from 0 to 120 s) foreach test of the series (values are a 5 s movingaverage of the consumed mass).

The consumed mass m is deduced from therecorded mass mrec as follows:

m(t) = − (mrec(t) − malcohol) / (minit + malcohol) * minit,where

- malcohol = mrec(0) and- minit is the initial mass of dry fuel, 125, 250 and 500

g respectively for the diameters 20, 28 et 40 cm.This correction of the raw data makes the

comparisons of all the tests possible.

We can conclude from Figure 5.3 that the evolutionof the mass of fuel is very similar from one test toanother, the diameter of the basket being given.

Thus we can reasonably assume that themeasurements of IR emission (and the calculatedvalues of transmission) by the IR camera and oftemperature by thermocouples (series 2003-03 only),performed at different heights above the basket (50,100 and 150 cm), were done on true replications of thesame conditions.

In order to characterise the combustion regime ofeach test, the maximum value of the rate of mass lossvmax was calculated.

This maximum is calculated from smoothed data(5 s moving average).

The maximum heat release rate Qmax was deducedusing 16 kJ/g as heat of combustion.

Results are reported in Table 5.3.

The mass loss curves we obtained here are alsovery similar to those obtained in year 2000 for a seriesof tests that serve as reference data.

The maximum heat release rates were the same,the diameter of the basket being given.

This previous series of tests enabled to determinevertical and horizontal profiles of temperature.

Table 5.3: Maximum heat release rate Qmax.

TestNumber

Height ofmeasurement

(cm)

Basketdiameter

(cm)

Qmax(kW)

Series 030203052104 50 20 7003052105 50 20 6303052106 50 20 6803070307 50 28 9603052102 50 28 9903052103 50 28 9703052201 50 40 16503052202 50 40 15003052203 50 40 14103052307 100 20 5703052308 100 20 7203052309 100 20 6403052304 100 28 10803052305 100 28 8703052306 100 28 10003052301 100 40 13903052302 100 40 15703052303 100 40 17803060501 150 20 5103060502 150 20 5603060503 150 20 5603060504 150 28 10803060505 150 28 10803060506 150 28 11303060507 150 40 15003060508 150 40 15403060509 150 40 162

Series 030303070303 50 20 7003070304 50 20 6203070305 50 28 9103070306 50 28 9703070301 50 40 15903070302 50 40 152

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D = 20 cm

0

20

40

60

80

100

120

0 20 40 60 80 100 120Time (s)

Mas

s lo

ss (

g)

series 0302series 0303

D = 28 cm

0

50

100

150

200

250

0 20 40 60 80 100 120Time (s)

Mas

s lo

ss (

g)

series 0302series 0303

D = 40 cm

0

50

100

150

200

250

300

350

400

450

500

0 20 40 60 80 100 120Time (s)

Mas

s lo

ss (

g)

series 0302series 0303

Figure 5-3: Mass loss as a function of time

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5.5.2 Transmission (series 2003-02)

The transmittance of the medium (flame, smokeplume) was defined as the portion of the energyradiated by the hot blackbody that:- goes through this medium and- is received and measured by the detectors of the IR

camera.

We remind that the hot blackbody had a temperatureof 523 K and the camera had a spectral range of 7.5-13 µm.

For each test, the transmittance was determinedaccording to the method of calculation described above(see 5.2.3).

This calculation requires the estimation of theemission of the medium (flame or smoke plume) asmeasured by the IR camera.

Therefore, values of emission are also available.Emission is not expressed in standard units but in OS(Object Signal) unit.

This unit is that of a radiative intensity (W/m²sr).

The Object Signal is related to the temperature T ofa blackbody that would emit radiation towards thecamera.

This relation of calibration is given in &2.3. Thetemperature T is the brightness temperature.

Raw data of emission were recorded at a frequencyof 50 Hz, but smoothed values are reported in thefollowing figures (1 s-period moving averages ofemission and transmittance were calculated andsampled at a frequency of 5 Hz).

Figure 5.4 shows the time-evolution of thetransmittance and of the emission measured at 50-cmheight (diameter 40 cm).

For this test, the flame developed above the basketabout 10 s after the ignition (time 0) and was fullydeveloped up to about 55 s.

At this time, the flame partially extinguished andthen, up to the time 90 s, the flame height slightlydecreased and was often inclined from the vertical axisof the device.

Therefore, the point of measurement was out of thevisible flame intermittently.

We will focus on the first step of the combustion(between 10 and 50 s) when the visible flamedeveloped above the basket and remained vertical.

As expected, we observe that the transmittance andthe emission of the flame are correlated and that thiscorrelation is generally negative.

Indeed, when the amount of soot, other solidparticles and gases increases, flame emission isexpected to increase and flame transmission isexpected to decrease.

However, we also observe that for a short duration(mark 2 on Figure 5.4), both transmission and emissiondecrease.

We assume that this trend is due to thesimultaneous decrease of temperature, which causes adecrease of the flame emission, and increase of theconcentration of soot and gases.

The transmission then reaches a minimum value(mark 3 on Figure 5.4), but this value does notcorrespond to the maximum value of emission (mark 1).

Hence, we defined two different characteristic valuesof transmittance for each test: the minimum value τm oftransmittance and the value τEmax of transmittancereached when flame emission is at its maximum.

Figure 5.4 also shows the symmetric behaviour oftransmission and emission when flame extinguishes(mark 4).

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 20 40 60 80 100 120Time (s)

Tra

nsm

itta

nce

0

500

1000

1500

2000

2500

3000

3500

Em

ission

(OS

= W

/m²sr)

TransmissionEmission

12

3 4

Figure 5-4: Time-evolution of flame transmission and flame emission (D = 40 cm, H = 50 cm).

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Figure 5.5 shows an example of the time-evolutionof transmittance for the three basket diameters and thethree heights of measurement.

We can see that minimum values of transmissioncan be reached since the flaming period of thecombustion has finished.

These values correspond to smoke puffs.

This observation reinforce the need for the definitionof two minimum values of transmittance τm and τEmax,which are characteristic of two distinct phenomena.

We can see on Figure 5.5 that as expected themedium (flame or plume) has a higher transmittancewhen the diameter is lower and the point ofmeasurement is higher.

Emission of course has the opposite behaviour.

0.5

0.6

0.7

0.8

0.9

1

0 20 40 60 80 100 120Time (s)

Tran

smitt

ance

D = 20 cmD = 28 cmD = 40 cm

Height of measurement : 50 cm

0.5

0.6

0.7

0.8

0.9

1

0 20 40 60 80 100 120Time (s)

Tran

smitt

ance

D = 20 cmD = 28 cmD = 40 cm

Height of measurement : 100 cm

Smoke/No flame

0.5

0.6

0.7

0.8

0.9

1

0 20 40 60 80 100 120Time (s)

Tran

smitt

ance

D = 20 cmD = 28 cmD = 40 cm

Height of measurement : 150 cm

Smoke puff

Figure 5-5: Time-evolutions of transmittance.

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Table 5.4 reports the minimum values oftransmittance.

The maximum values of emission Eflame are alsoreported, although their unit is not standard.

All these values were calculated on a 5 s-periodmoving average of the data (smoothing of peaks).

Flame and plume properties measured at a givenheight z above a burner are often expressed as afunction of a dimensionless height defined as the ratioof the height z to a theoretical flame height Zf l.

It was shown that the usual law

( )Qmaxfl .Z 52

20= holds for the flames we obtain on

our device (Dupuy et al, 2003).

Table 5.4: Minimum values of transmittance and maximum values of emission.

Test NumberHeight of

measurement(cm)

BasketDiameter

(cm)

Qmax(kW) τEmax τm

Eflame(OS)

Eflame (1)

(K)

03052104 50 20 70 0.82 0.82 1873 54003052105 50 20 63 0.86 0.86 1676 52003052106 50 20 68 0.83 0.83 1736 52603070307 50 28 96 0.77 0.81 2102 56203052102 50 28 99 0.76 0.80 2340 58403052103 50 28 97 0.78 0.78 2116 56303052201 50 40 165 0.58 0.70 2954 63803052202 50 40 150 0.67 0.79 2602 60803052203 50 40 141 0.61 0.72 2759 62103052307 100 20 57 0.92 0.96 813 41603052308 100 20 72 0.94 0.96 786 41203052309 100 20 64 0.96 0.96 837 42003052304 100 28 108 0.88 0.92 1409 49103052305 100 28 87 0.91 0.92 1330 48203052306 100 28 100 0.83 0.92 1393 48903052301 100 40 139 0.88 0.88 1770 53003052302 100 40 157 0.84 0.86 2005 55303052303 100 40 178 0.82 0.85 2260 57703060501 150 20 51 0.95 0.98 264 31503060502 150 20 56 0.91 0.98 302 32403060503 150 20 56 0.96 0.97 286 32103060504 150 28 108 0.95 0.95 593 38203060505 150 28 108 0.87 0.95 586 38103060506 150 28 113 0.94 0.95 752 40703060507 150 40 150 0.92 0.92 1101 45503060508 150 40 154 0.93 0.93 1091 45403060509 150 40 162 0.93 0.93 1248 473

(1) Emission is expressed as a brightness temperature.

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Figure 5.6 shows the values τm and τEmax oftransmittance as a function of this dimensionless heightof measurement.

We observe that for τflame a single curve fits the datawell.

In fact, we plotted the absorptance 1-τEmax, whichwas found to fit an exponential decay of thedimensionless height and then we deduced a simplelaw for 1-τEmax.

Although the general trend of τm with thedimensionless height is the same as above, dataremain more scattered and the fitting is not so good (R-squared of 0.72 instead of 0.92).

Again, it appears that the minimum value τm oftransmittance is more related to an erratic phenomenon(smoke puff) than the characteristic value τEmax relatedto the maximum of emission.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.00 0.50 1.00 1.50 2.00

Dimensionless height of measurement

Tra

nsm

itta

nce

Figure 5-6: Transmittance as a function of the dimensionless height of measurement(circles and fitted curve: τEmax ; diamonds: τm)

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5.5.3 Transmission, emission and temperature(series 2003-03)

The complementary series of tests (series 2003-03)was performed to study the flame emission in detail.

Measurements were performed at 50-cm heightabove the basket only, because this position ofmeasurement is always inside the visible (and highlyemissive) flame during the stage of fully developedflame of the test.

Two thermocouples were used to estimate thetemperature of the medium at the height ofmeasurement on the vertical axis of the basket device,in addition to the device of emission/transmissionmeasurement.

Indeed the flame temperature is one of the variablesthat strongly influence the IR emission, the other onebeing the concentration of soot and participating gases.

Figure 5.7

Figure 5.7 shows the time-evolution of the rate ofmass loss, of the brightness temperature and of thethermocouple temperature, for the three basketdiameters.

For the diameter 20 cm, the rate of mass loss fastdecreases after it reached a maximum value.

Simultaneously, the flame temperature remainssteady (up to 40 s), while the brightness temperature,which characterises the IR emission of the flame, alsodecreases.

For the diameter 40 cm, the rate of mass lossreaches a maximum value and remains steady over aperiod from about 15 to 35 s.

Simultaneously, the flame temperature decreases,while the brightness temperature remains steady (orslightly increases by the end of this period).

The heat release rate of the fire – here proportionalto the rate of mass loss– and the IR emission of theflame – here shown through the brightness temperature- are well correlated.

On the contrary, this heat release rate of the fire isnot correlated with the flame temperatures

Figure 5.8

Figure 5.8 shows the time-evolution of thetransmittance, of the brightness temperature and of thethermocouple temperature, for the three basketdiameters.

Transmission and emission (brightness temperature)generally exhibit ‘symmetric’ trends.

For the diameter 40-cm, the flame temperature andthe transmittance show very similar trends with timeduring the main period of flaming combustion (from 15to 50 s).

We must notice that these two quantities come fromindependent measurements.

In particular transmittance and flame temperaturesimultaneously decrease towards a minimum valuereached at 40 s, while IR emission (brightnesstemperature) is almost constant.

This should be due to an increase of concentrationof soot and participating gases over this period, which isa factor of IR emission increase, since the decrease oftemperature is a factor of IR emission decrease.

To analyse the results, we calculated from therelation of calibration of the IR camera the Object Signalthat the surface of a blackbody at the flame temperature(measured by thermocouples) would cause.

We called this value of emission, expressed in OS,the blackbody emission of the flame.

Then we calculated the ratio of the IR emissionactually measured by the camera to the blackbodyemission of the flame.

This ratio of emissions defines an effectiveemissivity of the flame.

Figure 5.9

Figure 5.9 shows the time-evolution of the ratio ofemissions and of the absorptance, for each basketdiameter.

Absorptance on Figure 5.9 is the complement to oneof the transmittance (1 - τ).

Hence it is intended to include scattering effects, ifany, on the absorption of radiation.

In the same way, flame emission includes radiationscattered in the direction of measurement, if any.

Of course, we are not able to separate the eventualscattering component of these terms through ourexperiments (see below for additional information).

As expected, Figure 5.9 shows that the ratio ofemissions (‘effective emissivity’) is not a constant, evenin the flaming period.

However, during the main period of flamingcombustion, the ratio of emissions and the absorptanceexhibit very similar trends and in addition have veryclose values.

The values of these two variables were obtainedfrom independent measurements.

This suggests that the model of a grey absorbingand emitting medium without significant scatteringeffects is a good candidate for modelling radiation of theflame medium.

In that case, the absorptance is actually thecomplement to one of the transmittance.

Conclusion

We remind that the results are only valid in thespectral band of the IR camera (7.5- 13 µm).

In this band, the contribution of carbon dioxide andwater vapour (main combustion products) to radiationare expected to be negligible compared to the sootcontribution.

According to the Mie theory and available data onsoot properties, scattering of soot should be negligiblewith respect to absorption for the range of wavelengthsof interest in flames (Siegel & Howel, 1992).

This is in accordance with the above results.

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D = 20 cm

0

200

400

600

800

1000

1200

0 20 40 60 80 100 120Time (s)

Tem

per

atu

re (

°C)

0

2

4

6

8

10

12

Mass lo

ss rate (g/s)

Brightness temperature (°C)

Thermocouple Temperature (°C)

Rate of mass loss (g/s)

D = 28 cm

0

200

400

600

800

1000

1200

0 20 40 60 80 100 120Time (s)

Tem

per

atu

re (

°C)

0

2

4

6

8

10

12

Mass lo

ss rate (g/s)

Brightness temperature (°C)

Thermocouple Temperature (°C)

Rate of mass loss (g/s)

D = 40 cm

0

200

400

600

800

1000

1200

0 20 40 60 80 100 120Time (s)

Tem

per

atu

re (

°C)

0

2

4

6

8

10

12

Mass lo

ss rate (g/s)

Brightness temperature (°C)

Thermocouple Temperature (°C)

Rate of mass loss (g/s)

Figure 5-7: Mass loss rate, temperature and emission as a function of time (height of measurement 50cm, series 2003-03)

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D = 20 cm

0

200

400

600

800

1000

1200

0 20 40 60 80 100 120Time (s)

Tem

per

atu

re (

°C)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Tran

smittan

ceBrightness temperature (°C)Thermocouple Temperature (°C) Transmission

D = 28 cm

0

200

400

600

800

1000

1200

0 20 40 60 80 100 120Time (s)

Tem

per

atu

re (

°C)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Tran

smittan

ce

Brightness temperature (°C)Thermocouple Temperature (°C) Transmission

D = 40 cm

0

200

400

600

800

1000

1200

0 20 40 60 80 100 120Time (s)

Tem

per

atu

re (

°C)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Tran

smittan

ce

Brightness temperature (°C)Thermocouple Temperature (°C) Transmission

Figure 5-8: Transmittance, temperature and emission as a function of time(height of measurement 50cm, series 2003-03).

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D = 20 cm

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 20 40 60 80 100 120Time (s)

Ratio of emissionsAbsorption

D = 28 cm

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 20 40 60 80 100 120Time (s)

Ratio of emissionsAbsorption

D = 40 cm

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 20 40 60 80 100 120Time (s)

Ratio of emissionsAbsorption

Figure 5-9: Ratio of emissions and absorption as a function of time(height of measurement 50cm, series 2003-03).

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6 BIBLIOGRAPHICAL REFERENCES

(Aranda el al ,2000) Aranda J M, S Briz, J Meléndez, AJ de Castro and F López, 2000. Flame analysis byIR thermography and IR hyperspectral imaging.Proceedings of QUIRT2000 (Quantitative InfraredThermography 5), pp 337-342; Reims, France,2000.

(Campbell, 1996) Campbell, James B. “Introduction toRemote Sensing”. 1st ed., The Guilford Press, NewYork (USA) 1996

(Dupuy et al, 2003) Dupuy, Maréchal, et Morvan.Combustion and Flame (in press)

(Fire Star, 2003a) ) Fire Star project: Deliverable D7-02.FireStar laboratory fires: methods. (experimentsn°2002-01)

(Fire Star, 2003b) Fire Star project: Deliverable D5-03.Laser Dopler Velocimetry and Particles ImagesVelocimetry adapted to laboratory fires of wildlandfuel. (experiments n°2003-01)

(Gaussorgues 1989) Gaussorgues, Gilbert. “LaThermographie Infrarouge. Principes TechnologiesApplications”. Techniques et DocumentationLavoisier, 3e éd., Paris (France) 1989.

Griffiths & de Haseth, 1986) Peter R. Griffiths & JamesA. de Haseth, Fourier Transform InfraredSpectrometry, Wiley & Sons, New York, 1986

(Hudson, 1969) Hudson, Richard D., Jr, Infrared systemengineering, 1st. ed., John Wiley & sons, New York,USA (1969)

(Justice et al, 1993) Justice, C.O., Malingreau, J.-P.,and Setzer, A.W.: “Satellite Remote Sensing ofFires: Potential and Limitations”, in Fire in theEnvironment: the Ecological, Atmospheric, andclimatic Importance of Vegetation Fires. Edited byP.J. Crutzen and J.G. Goldammer, 1993, John Wiley& Sons.

(McCluney, 1994) Ross McCluney, Introduction toradiometry and photometry, Artech House, Boston,1994.

(Mather, 1989) Mather, Paul M. “Computer Processingof Remotely–Sensed Images. An Introduction”. JohnWiley and Sons, 2nd ed. Chichester (England)1989.

(Nicodemus, 1963) Fred E. Nicodemus, ARadiance@,Am. J. Phys., 31, 5, p368-377 (1963)

(Richards & Xia, 1999) Richards, J.A.; Jia, Xiuping.“Remote Sensing Digital Image Analysis”. 3rd ed.Springer-Verlag, Berlin, Germany, 1999.

(Schowengerdt, 1997) Robert A. Schowengerdt,Remote Sensing. Models and methods for imageprocessing, 2nd ed. Academic Press, San Diego,1997