TREFLE ENSAM UMR 8508 CNRSTREFLE-ENSAM, UMR 8508 CNRS

RAPSODEE FRE CNRS 3213

Thermography Applied to Anti-Icing Systems

Olivier Fudym

WAS June 1st 2010 Rio de Janeiro Brazil

Centre RAPSODEE

WAS, June 1 2010, Rio de Janeiro, Brazil

1 t COPPE/UFRJ W k h A i ti S f t1st COPPE/UFRJ Workshop on Aviation SafetyInstituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa em Engenharia, COPPE/UFRJ - Universidade Federal do Rio de Janeiro,Brazil

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RAPSODEE FRE 3213 CNRS=

Chemical Engineering Laboratoryfor Finely Divided Solids,Energy & Environment

• South West of France

gy

• Ministère de l’Economie, des Finances, et de l’Industrie

South West of France

• Civil Engineers (Major in Chemical Engineering )

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OUTLINEOUTLINE

1. An overview of Infrared Thermography (IRTh)g p y ( )

2. IRTh applied to anti-icing systems

3. IRTh applied to Fluid flow thermal characterization

4 Conclusion4. Conclusion

4

An overview of IR Thermography = Thermal Images…

MicrocomponentMicrocomponentanalysis

Skin & Fever

Art restoration

Vascular

or Temperature fields ?5Microwave heating

…or Temperature fields ?

IRTh: measurement of infrared radiation by a radiometer

Radiative Heat Transfer

• Surface of bodies

IRTh ll ithi 1 15

• « Instantaneous »• No need for a material medium for propagation

λ (μm)

IRTh: usually within 1 – 15 μm105

107T=5530°C

T=2530K

T=930 K

0,1

10

1000

T=25°CEnergy Signal / Noise

Sensitivity thermal Resolution

10-5

0,001

0,01 0,1 1 10

T= -196 °C

y

6Spectral emissive power of black body λ (μm)

Typical modern IR cameras… fast, sensitive, friendly…

Sensor : InSb, InGaAs, MCT, microbolometric…

F l l A 640 512 i l 320 256 i lFocal plane Array: 640x512 pixels or 320x256 pixels

Thermal sensitivity 30 C: < 20 mK InSb, MCT

< 85 mK bolometric< 85 mK μbolometric

Spectral Sens. : 1 - 5 µm or 3 - 5 µm (InSb), 8 - 12 µm

Typical : 150 400 Hz !!!Typical : 150 – 400 Hz !!!

Integration time: about 10 µs

One pixel Sensor = 15 µmOne pixel Sensor 15 µm

7

Calibration : Black body

Output Signalcamera

V1 0Emissivity

( )objectE Tε =V100

V150

+ T( )j

Black bodyE Tε =

Black Body50 100 150

V50

V100 + T

8

Temperature °C

Infrared measurement : typical situation

1 = Object of interest1 Object of interest2 = Thermal influence of surrounding parts, such as walls, other objects…3 = Surrounding part reflect on the object4 = Object emissive power4 = Object emissive power5 = Partial transmission of the intermediate medium6 = signal arriving to the IR camera7 O t t i l

9

7 = Output signal

Optical properties and spectral range impact on the thermal signal

Combustion 3-5 µm

Methane - Air CO spectralFlame instability

CO2 spectral emissivity

Glass bottle process8 - 12 µm

transparent

8 12 µm

« Spectre » de transmission IR du verre

opaqueBottle Cooling / Surface Temperature

10

Two different approaches for Quantitative IRTh

1/ Qualitative = thermal imaging ???

2/ Quantitative IRTh = Measurements

Temperature measurements

C lib tiCalibrationknown EmissivitiesRadiometric equations ; control of meas. conditions

Temperature fields

2’/ Relative variations of the thermal signal + image processingImages processing

Uniform EmissivityMinimize surrounding effectsProcess temperature or even DL signal

Images processingfor NDT or

Parameter maps

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Process temperature or even DL signal p

Which difference between visible range CCD cameras and IRTh ?

Lighting ThermalIRThg gCCD non-equilibrium:

Passive or ActiveHeat Excitation

Received 2D signal Received 2D signal only

depending on surface information

ece ved s gdependent of

full thermal 3D information

Some subwall information is available !

information

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Some subwall information is available !

( here = fluid flow in the subwall micromixer)

What for ?

Temperature measurements

Retrieve some local properties or parameters

Correlate these parameters to some magnitudes of interest

vertical pixelsimage processing

Inverse methods

15 MB/s !

time

Inverse methods

Compression

13horizontal pixels

Reduction 3D / 2D

OUTLINEOUTLINE

1. An overview of Infrared Thermography (IRTh)g p y ( )

2. IRTh applied to anti-icing systems

3. IRTh applied to Fluid flow thermal characterization

3 Conclusion3. Conclusion

14

IRTh applied to anti-icing systems

Numerous works devotedto thermo-fluid dynamicsto thermo fluid dynamics

- Temperature field measurements- Temperature field measurements- Boundary layers analysis- Impinging jets

Interaction of fl id flo ith the s rface of a bod

Hypersonic compression ramp

- Interaction of fluid flow with the surface of a body

-Heat transfer coefficient measurementsi h h l fl ( i )- High enthalpy flows (supersonic)

- Coupling Shear stress and heat transfer (Reynolds analogy)

C M l d G C l R t d i th f i f dC. Meola and G.Carlomagno, Recent advances in the use of infraredthermography, Review Article, Meas. Sci. Technol. 15 (2004) R27–R58.E. Gaidos, Remote infrared thermography for boundary layer measurements,M t f S I A ti d A t ti MIT 10 d b 1990

15

Master of Sc. In Aeronautics and Astronautics, MIT, 10 december 1990.S. Zuccher and W.S. Saric, Infrared thermography investigations in transitional supersonic boundary layers, Exp. Fluids (2007).

IRTh applied to anti-icing systems

l i j h l i i i ( i il i i l )Multi jets thermal anti-icing systems (wing, tails, engine inlet)Convective heat transfer coefficient mapping

Academic bench results

Von Karman Institute for Fluid Dynamics

Real wing bench results

(VKI, Belgium)

16J. M. Buchlin, Convective Heat Transfer and Infrared Thermography,Journal of Applied Fluid Mechanics, Vol. 3, No. 1, pp. 55-62, 2010

g

IRTh applied to anti-icing systems

h d i i d l ( )IRTh and Icing wind tunnel (ONERA)

Temperature measurements for validation of a thermal simulation codeTemperature measurements for validation of a thermal simulation codefor an anti-icing systems on helicopter rotor blades

Using Radiometric equation Determine radiative properties

…number and size of dropletsBlack paint emissivity Ice emissivity

R. Henry and D. Guffond, Application de la thermographie infrarouge àl’interprétation d’essais dans une soufflerie givrante, Journée SFT, 18/01/1989, Paris.

17

IRTh applied to anti-icing systems

h d i i d l ( )IRTh and Icing wind tunnel (ONERA)

Ice departure

R. Henry and D. Guffond, Application de la thermographie infrarouge à l’interprétationd’essais dans une soufflerie givrante, Journée SFT, 18/01/1989, Paris.

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IRTh applied to anti-icing systems

Airfoil Leading EdgeAirfoil Leading Edge

Convective heat transfer coefficient mapping

Runback flow analysis

pp g

K M Al Khalil & al Validation of NASA thermal ice protection computer codes19

K.M. Al-Khalil & al., Validation of NASA thermal ice protection computer codes part 3 - the validation of antice, AIAA 97-0051, 35th Aerospace Sciences Meeting & Exhibit, January 6-7, 1997.

OUTLINEOUTLINE

1. An overview of Infrared Thermography (IRTh)g p y ( )

2. IRTh applied to anti-icing systems

3. IRTh applied to Fluid flow thermal characterization

3 Conclusion3. Conclusion

20

IRTh applied to Fluid flow thermal characterization

Velocity and heat transfer parameter mappingy p pp g

Thermal Surface MeasurementsThermal Excitation

IRTh

Thermal ExcitationSelf-emission phenomenon

andV i ti i i i itVariations in emissivity

1x y

T T T T T TV V k k k gt x y c x x y y z zρ

⎛ ⎞⎛ ⎞∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂⎛ ⎞ ⎛ ⎞+ + = + + +⎜ ⎟⎜ ⎟ ⎜ ⎟⎜ ⎟∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂⎝ ⎠ ⎝ ⎠⎝ ⎠⎝ ⎠yt x y c x x y y z zρ ⎜ ⎟∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂⎝ ⎠ ⎝ ⎠⎝ ⎠⎝ ⎠

l i d i d i f21

Velocity Conduction and interfaces Heat sourceIn-depth heat transfer

IRTh applied to Fluid flow thermal characterization

Fluid flow troubleshooting IRTh

V

1T T T T T T⎛ ⎞⎛ ⎞∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂⎛ ⎞ ⎛ ⎞1x y

T T T T T TV V k k k gt x y c x x y y z zρ

⎛ ⎞⎛ ⎞∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂⎛ ⎞ ⎛ ⎞+ + = + + +⎜ ⎟⎜ ⎟ ⎜ ⎟⎜ ⎟∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂⎝ ⎠ ⎝ ⎠⎝ ⎠⎝ ⎠

IRTh can be

Change in velocityChange in global heat capacityChange in heat transfer coefficients

External bodiesFoulingIRTh can be

used to detect:Change in heat transfer coefficientsChange in global thermal resistanceChange in mass flow rates

FoulingInclusionsPhase change enthalpy

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Heat source term

Velocity and diffusion mapping for a moving solid

InfraredCamera

“Flash” excitationexcitation

Diffusing pattern after 2D displacement, non-uniform field ( )yxV ,

2D M k2D Mask

Time 0 Time 1X

2D non-uniformly moving semi infinite medium

Y

Z

2 2 2

x y 2 2 2T(x,y,z,t) T(x,y,z,t) T(x,y,z,t) T(x,y,z,t) T(x,y,z,t) T(x,y,z,t)V V a

t x y x y z

⎛ ⎞∂ ∂ ∂ ∂ ∂ ∂+ + = ⋅ + +⎜ ⎟⎜ ⎟∂ ∂ ∂ ∂ ∂ ∂⎝ ⎠

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x y∂ ∂ ∂⎝ ⎠

Velocity and diffusion mapping for a moving solid

Infrared sequence showing a moving and diffusing pattern

Diffusivity and velocity mapping from previous image sequence sampled at 25 Hz

f d C l O d S l l d24

M. Bamford, J.C. Batsale, D. Reungoat, O. Fudym, Simultaneous velocity and diffusivity mapping in the case of 3-D transient heat diffusion: Heat pulse thermography and IR image sequence analysis. QIRT Journal, 5 1 97-126, 2008

Fluid flow in microchannelsT i,j (H2O) pour 5e3(µl/hr)

031 8

14531

31.8

ActiveWith Laser diode145

290

30.2

With Laser diode

0 145 290290

microréacteurmicroréacteur

Flow rate measurementmicroréacteurmicroréacteur

Laser diode

µchannel

Passive

microcanalzone de cristallisation

microcanalzone de cristallisation

Cristalization of nanoparticules

25Serynge pump

Fluid flow in microchannels

Cristalization of nanoparticules

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Peclet field Pei,j estimation

C fidConfidence

zone

( )( )

0j,ij,i

j,i TTT

Peδ

Δ −= ( ) 2

j,i2

j,i T40Pevar −= δσ( )j,i

j Tδ

Pradère C., Joanicot M., Batsale J-C., Toutain J., Gourdon C., Processing of

27temperature field in chemical microreactors with infrared thermography, QIRT Journal (2006) 3 117-135.

Application to a chemical reaction characterizationpp

Temperature field Tc, at Q = 1000 µlh Chemical source term at Q = 1000 µlh

O. Fudym, C. Pradère, J.C. Batsale, An analytical two-temperature model

28for convection-diffusion in multilayered systems. Application to the thermal characterization of microchannel reactors. Chemical Engineering Science, 62 (15) 4054-4064 2007.

Reactive Droplets-Reactive Droplets

Acid injection (HCl) Base injection (NaOH)

Oil Injection

Quasi instantaneous mixing

291 droplet = 1 microreactor

Intensification of the experiments!

Infrared tracking

Infrared CameraPC

Spheres

Oscillating plate

PC

Accelerometer

Loud speaker

Oscillating plate

PowerAmplifier

Function

Controller

Generator Scope

l d d f d k f h l l30

F. Sepúlveda, O. Fudym, Infrared tracking from morphological image processing tools. Application to heat transfer characterization in granular media. Accepted in Heat Transf er Eng., 2010.

Freezing of biological cells: Phase change and thermal diffusivity

mbe

r

20

40

1920

1940

1960

1980pixel 1pixel 2pixel 3

Pix

el n

um 60

80

100

120 1820

1840

1860

1880

1900

Visible microscopic image (x10)

of onionskin

Pixel number20 40 60 80 100 120

120 1820

IR microscopic image (x10) of onionskin during freezing

processp

C P d J T t i J C B t l J M ik E H k T H hi t MiC. Pradere, J. Toutain, J.C. Batsale, J. Morikawa, E. Hayakawa, T. Hashimoto, Micro-scale thermography of freezing biological cells in view of cryopreservation, 9th Int. Conf. on Quantitative InfraRed Thermography, July 2-5, 2008, Krakow - Poland

31

Freezing of biological cells: Phase change and thermal diffusivity

0.15

0.2

0.25pixel 1pixel 2pixel 3

-0.05

0

0.05

0.1

Tt (w

u)-0.2

-0.15

-0.1

kji

kji

kjiji TTFo δ=Φ+Δ 1

,,

±→Δ=Δ

=∑

TTTT

F

kji

kji

F tt δδ

ρ

-1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2-0.25

ΔT (wu)

jijijiji TTFo ,,,, δΦ+Δ 1,,,

, ±→Δ=Δ

= TTTT

Ft

kjiFt

kji

ji δδ

ρ

Analysis of correlations

C P d J T t i J C B t l J M ik E H k T H hi t MiC. Pradere, J. Toutain, J.C. Batsale, J. Morikawa, E. Hayakawa, T. Hashimoto, Micro-scale thermography of freezing biological cells in view of cryopreservation, 9th Int. Conf. on Quantitative InfraRed Thermography, July 2-5, 2008, Krakow - Poland

32

Freezing of biological cells: Phase change and thermal diffusivity

10pixel 1pixel 2

1 2

1.4pixel 1pixel 2

xel n

umbe

r

20

40

605

6

7

8

9pixel 3

0 6

0.8

1

1.2

er n

umbe

r (w

u)

pixel 3

Pix

20 40 60 80 100 120

80

100

1201

2

3

4

0 20 40 60 80 100 1200

0.2

0.4

0.6

Four

ie

Pixel number20 40 60 80 100 120

20 15

20pixel 1pixel 2i l 3

Field of Fourier number at the end of the processing

8

10pixel 1pixel 2

0 20 40 60 80 100 120Time step (wu)

Fourier Number versus time for three pixels.

ixel

num

ber

20

40

60 5

10

15pixel 3

2

4

6

at s

ourc

e (w

u)

pixel 3

P

20 40 60 80 100 120

80

100

120-10

-5

0

0 20 40 60 80 100 120-6

-4

-2

0Hea

Pixel number20 40 60 80 100 120

Field of heat source at the end of the processing

0 20 40 60 80 100 120Time step (wu)

Heat source versus time for three pixels.33

Conclusion and perspectives

Many open problems: icing and fluid flow parameters control

IRTh T fi ldIRTh = Temperature field measurements

But also for thermal parameters mapping… But also for thermal parameters mapping

… Benefit of image processing tools and inverse methodsg p g

… Also inverse technics with single sensors!

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