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![Page 1: DEVELOPMENT OF NDE METHODS FOR CERAMIC COATINGS AND MEMBRANES Library/Events/2009/fem/Sun-Fossil... · DEVELOPMENT OF NDE METHODS FOR CERAMIC COATINGS AND MEMBRANES J. G. Sun Nuclear](https://reader034.fdocuments.net/reader034/viewer/2022051600/5a842c387f8b9aa5408b78fd/html5/thumbnails/1.jpg)
DEVELOPMENT OF NDE METHODS FOR CERAMIC
COATINGS AND MEMBRANES
J. G. Sun
Nuclear Engineering DivisionArgonne National Laboratory
Argonne, IL 60439
23rd Annual Conference on Fossil Energy MaterialsPittsburgh, PA
May 12-14, 2009
Work supported by U.S. Department of Energy,Office of Fossil Energy,
Advanced Research-Materials Program
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Outline
Background
Thermal imaging methods for TBCs– Multilayer thermal modeling method
• Determine TBC thermal properties (e.g., conductivity)– Thermal tomography
X-ray microCT for ceramic gas-separation membranes
Summary
Planned future efforts
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Background
Thermal barrier coatings (TBCs) are “prime reliant” material for turbine engine components – evaluation of their conditions by NDE is important
NDEs may be used to:– Assess TBC process reliability and product quality (specs, defects, etc)
• Current TBC processing relies on robust process conditions– Monitor TBC degradation and predict TBC lifetime
• As-processed TBC TBC sintering TBC degradation (internal cracking) TBC delamination TBC spallation (failure)
– Detect FOD (foreign object damage)
NDE detection of delamination is relatively easy: thermal imaging, optical, etc
TBC thickness = 0.29 mm
Delaminations
TBC spalled
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Background - continued
Few NDEs were reported to monitor TBC condition and predict TBC lifetime– Mid-IR reflectance (J. Eldridge of NASA)
• Reflectance increases with damage– Electrical impedance spectroscopy (P. Xiao of Univ. Manchester)
• Resistance (cracking), modulus (TGO growth), capacitance (delamination)
Current methods are not quantitative and difficult to be generalized!
60
62
64
66
68
70
72
74
76
0 50 100 150 200 250Number of cycles
Ref
lect
ance
(%)
solid: with bond coatdashed: no bond coat
From: J. Eldridge and C. Spuckler, Int. J. Appl. Ceram. Technol., Vol. 3, pp. 94-104, 2006
Spallation
As-processed
Small change in reflectance
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A Quantitative Approach for TBC Lifetime Prediction
Based on NDE measurement of TBC conductivity k– TBC conductivity is the most important thermophysical parameter
• Measured for all coatings• Used in component design
– TBC conductivity evolution has characteristic features (many studies)• As-processed TBC: baseline conductivity• TBC sintering: conductivity increase• TBC degradation (internal cracking): conductivity decrease• TBC delamination: significant conductivity drop• TBC spallation (failure)
k
TBC life
As-processed
SinteringDegradation and micro-cracking
Delamination
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NDE Development for TBCs
NDE development at ANL is focused on thermal imaging methods– For quantitative measurement of TBC thermal properties, e.g., conductivity
• TBC conductivity is measured mostly by laser flash method which is a two-sided thermal method and not suitable for NDE of real component
– For evaluation of TBC degradation and prediction of TBC lifetime
– Applicable to TBCs of all types (APS or EBPVD) and conditions (e.g., thickness)
Multilayer thermal modeling method (2D imaging):– TBC thermal conductivity and thickness distribution
• Accuracy is most important!– TBC cracking and delamination
Thermal tomography method: – 3D imaging of TBC structure and property distribution– Determination of TBC thickness and damage depth
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One-Sided Flash Thermal Imaging Setup for Testing of a TBC Coated Turbine Blade
Image entire surface (100% inspection)Fast (a few seconds for testing, up to a few minutes for data processing)Data processing is completely automated (no operator adjustment)
Flash lamp IR camera
Monitor
Turbine blade
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Typical Raw Thermal Imaging Data
High temperatureLow temperature
Total time period is ~0.1 sAPS TBC of 1” diameter
Flash lamp at left side
A typical image
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Characteristics of Thermal Imaging Data (1- and 2-layer)
Thermal imaging data, i.e., surface temperature and its slope at each surface pixel, are significantly different for 1- and 2-layer materials
Characteristics in thermal data allow for direct calculation of coating thickness and thermal properties, as well as substrate thickness
High detection sensitivity for TBC due to large thermal-property disparity between coating and substrate
0.1
1
10
100
0.001 0.01 0.1 1 10
Single layerTwo layer
T (°C)
t (s)
Typical surface temperature and its slope at a single pixel
-1.00
-0.75
-0.50
-0.25
0.00
0.001 0.01 0.1 1 10t (s)
)(ln)(ln
tdTd
Single layerTwo layer Coating
thickness
Substrate thickness
Coating property
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2D multilayer thermal modeling method– Two issues identified last year were resolved:
• (1) nonlinear temperature reading• (2) two independent parameters in solution
– Automated data-processing software was established• Speed was not optimized• Coating optical property was not yet accounted for
– Calibration tests are underway for both APS and EBPVD TBCs
3D Thermal tomography method– Data-processing software was improved
Recent Thermal Imaging NDE Development for TBCs
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2D Multilayer Thermal Modeling for TBC Systems
Repeat this process for every pixel
Time
Measured data T(x,y,t):Time series of 2D thermal
(surface temperature) imagesSurface temperature variation at pixel (x,y)
(x,y)
T
t
Multilayer TBC model
T
t
Numerical solution of surface temperature variation
Coating 1:k, (ρCp,) L, (α, ε, τ)
Substrate 2:fixed properties
1: k, ρCp, L, (α, ε, τ)
2: k, ρCp, L
Flash heating
Fixed
Fitting numerical result with exp. data to derive correct TBC parameters:
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Formulation for Multilayer Thermal Modeling Method
Two independent parameters for coating: e (effusivity) and η = L/α1/2, which can derive coating conductivity k and heat capacity ρc (or thickness L)
( ) ( )( ) ( ) ⎥
⎦
⎤⎢⎣
⎡⎟⎟⎠
⎞⎜⎜⎝
⎛−
++
++
+= ∑∞
=∞ 2
2
2
1 222111
2211
21
2211 expcoscos
coscos21)(ηγ
γωωγωωγωγωωω t
xxxx
xxxxTtT K
K KK
KK
( ) ( ) 0sinsin 2211 =+ γωγω xx
xi = e12 − (−1) i ei = kiρicii = 1, 2
ω i = η12 − (−1)i ηi = Li α i i = 1, 2
e12 = e1 e2 η12 =η1 η2
where γK is the K-th positive root of the following equation,
and
Governing heat transfer equation (1D):
2
2
zTk
tTc
∂∂
=∂∂ρ
Analytical solution (2 layers, no interface resistance):
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TBC Sample Preparation for Thermal Imaging Test
For thermal imaging, TBC surface needs to be coated black– Same requirement as for laser flash, the current standard method for TBC
conductivity measurement– This requirement can be removed if an optical model for TBC is developed
Tested EBPVD TBC sample (black coated)– 1” diameter, coating thickness = 0.127mm– Sample curtsey of Dr. A. Feuerstein, Praxair Surface Technologies, Inc.– Image acquisition rate was at 1068 frames/second
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Multilayer Modeling Prediction for TBC Thermal Properties
Average TBC conductivity: 1.65 W/m-K (1.71)Average TBC heat capacity: 3.3 J/cm3-K (high)
– Higher prediction in heat capacity data is likely due to uncertainty in TBC thickness and other parameters discussed later
TBC conductivity k(W/m-K)
TBC heat capacity ρCp(J/cm3-K)
2 J/cm3-K1 W/m-K
2 W/m-K 4 J/cm3-K
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Typical Line Plot of Predicted TBC Thermal Conductivity
Gross conductivity change is likely due to thickness variation
Pixel 0
Pixel 1281.0
1.2
1.4
1.6
1.8
2.0
0 25 50 75 100 125
Pixel number
k (W
/m-K
)
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Typical Fitting Results for One Pixel
Good fitting agreement!
Temperature vs. time Temperature slope vs. time
0.1
1
10
100
0.001 0.01 0.1 1 10
ExperimentTheory
t (s)
T (°C)
t (s)
)(ln)(ln
tdTd
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.001 0.01 0.1 1 10
ExperimentTheory
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Thermal Conductivity Based TBC Lifetime Prediction
Initial TBC conductivity (baseline)Conductivity increase in early life – due to sintering
– Many studies (NASA, SUNY, DLR, etc); e.g., the increase may be modeled by a Larson-Miller parameter (LMP)
Conductivity decrease in mid-to-late TBC life – due to micro-cracking– NASA, etc
Significant conductivity decrease in late TBC life – due to delamination
Con
duct
ivity
k
TBC life
As-processed
Sintering increases conductivity
Degradation and micro-cracking decreases conductivity
Delamination
100.0 um100.0 um100.0 um100.0 um100.0 umsubstrate
bond coat
TBCCracking
100 μm 200 µm
Delamination
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0
0.5
1.5
2.5
3.0
2.0
1.0
0 33 67 100
Prediction of TBC Property for Thermally-Cycled Samples
Based on quantitative measurement of TBC conductivity valuesSample curtsey of Mr. A. Luz, Imperial College London
Con
duct
ivity
k (W
/m-K
)
TBC life (%)
As-processed
Sintering
Degradation and micro-cracking Regions
delaminated
Spalled
Delaminated
Thermal conductivity images
Regionsstill bonded
0 3 W/m-K
5 mm
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Thermal Tomography Images of 3D TBC Structure
Thermal tomography algorithm was improved to enhance image qualityHowever, major development is needed to eliminate artifacts
CoatingSubstrate
Flaws near TBC surface
Crack at interface
5 mm
Plane images at mid coating depth
Plane images at interface depth (shown locations of cross-section slices)
Cross-sectional slices
0% life 33% life 67% life 100% lifeBack oxidation
0.3mm/pixel
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Current Effort: Calibration of Multilayer Modeling Method
Current computer algorithm is robust with consistent convergence in predicted TBC thermal properties (complete automated prediction)
Current effort is focused on calibrating predicted TBC results– With literature values– With laser flash measurements
Broad collaborations were established with industry and academics– Siemens, Praxair, Rolls Royce, DLR, SUNY, NASA, UCSB, etc
When successful, this new technology may be widely used for bothfundamental thermal property measurement and for NDE
– Only laser flash method is currently available– Other reported methods are not expected to be widely adopted
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Preliminary Calibration Findings
For APS TBCs, good agreements in both predicted thermal properties were obtained – work ongoing
– TBC coatings from 0.2 to ~1mm thickness– Different substrates– Also for metal coatings (with high conductivity)
For EBPVD TBCs, predicted thermal properties were higher– Possible cause: infiltration of black paint inside feathers and TBC columns
• This effect is being assessed– No painting is possible if a long-IR camera is used?
Painted sample Substrate
Bond coat
EBPVD YSZ
Black paint
Paint infiltration
Feathers
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NDE Investigation for Gas-Separation Membranes
Gas-separation membranes are being developed for FE applications– For example, hydrogen and oxygen membranes
NDE opportunities are investigated for ceramic membranes– For materials– For components
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Gas-Separation Membranes
Gas-separation membranes are being developed to produce hydrogen– E.g., ceramic oxygen-transport membrane via water splitting. Hydrogen is produced
when the membrane removes oxygen from the water dissociation zone– Membrane structure: a thin film supported by a porous ceramic substrate
* Hydrogen is used as a model gas to maintain high pO2 gradient.
high pO2 low pO2
H2, H2O, CO, CO2 etc.
O2-N2, H2O = H2 + 1/2 O2
N2, H2O, H2
e-
Syngas, CO, CO2, EtOH, H2*
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NDE Investigation for Ceramic Membranes
NDE method: 3D synchrotron microtomography (microCT) at ANL’s Advanced Photon Source (APS)
NDE possibilities for materials and components:– dimension, – film/substrate microstructure, – defects (leaking)
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3D MicroCT System at APS Synchrotron Beamline 2-BM
Sample
ScintillatorLens
Rotation-translation stages
CCDcamera
X-ray direction
X-ray source: monochromatic energy (~25keV), Detector: consists of a scintillation plate, a microscope lens (eg, 10X) and a digital camera of size 2048x2048 pixels
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Typical Membrane Samples
Three small membrane samples (~2mm in dimension)MicroCT voxel dimension was 1.48 µm/pixel
1 inch
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Typical Cross-Sectional Slices for Sample 1
Pd(60vol%)/CeO2(40vol%) composite thin film on porous alumina (Al2O3) substrate CT image shows that the film is porous and at ~22 µm thickness
Substrate has low “density”
100µm
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Typical Cross-Sectional Slices for Sample 2
SrFeCo0.5Ox (CFC2) thin film on porous CFC2 substrateCT image shows that the film is dense and between 32-38 µm thickness
100µm
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Typical Cross-Sectional Slices for Sample 3
BaCe0.8Y0.2O3 (BCY) film on BCY/NiO substrate (porous) CT image shows that the BCY film is dense and at ~10 µm thicknessCT resolution is not sufficient to determine the pore size distributions
NiO/BCY substrate
BaCe0.8Y0.2O3-δ (BCY) Film
Image curtsey of Dr. T. Lee, ANL
Film
CT image SEM image
50µm
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MicroCT Imaging of Cracks in Silicon Nitride Subsurface
Large crack0.6 μm opening
Image curtsey of Dr. C. Vieillard, SKF
MicroCT may detect very thin cracks (@ ~1/10 of the 0.74 µm pixel size)
30Small crack0.1 μm opening100µm
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Summary for NDE Developments for TBCs
Multilayer modeling method was developed for quantitative imaging of TBC parameters
– Current software is robust to predict TBC thermal properties– Capable to predict evolution of TBC conductivity for entire TBC life cycle
• Conductivity increase due to sintering• Conductivity decrease due to cracking/delamination
– Calibration of predicted thermal properties is ongoing• Predicted values for APS TBCs are accurate (comparable to laser flash)• Predicted values for EBPVD TBCs are higher
– Possible effects were identified and being investigated• Prediction of conductivity variation along coating depth is being verified
Thermal tomography method was improved to show enhanced 3D structural image
Collaborations were established with industry and academics for relevant technology development and potential applications
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Summary for NDE Investigation for Ceramic Membranes
Synchrotron x-ray microCT was used to study ceramic membrane materials– High resolution, fast data acquisition an processing (<20min each)– It can resolve micron-sized volumetric features
• May determine membrane film and substrate dimension and structure– However, not sufficient resolution for fundamental membrane study
• May detect film defect and leaking– Can detect crack-type defect with gap-opening in the order of 1/10 of the voxel
dimension (i.e., <0.1µm)
For NDE of membrane components, other NDE methods may be needed– E.g., thermal imaging for leaking and cracking detection
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Planned Future Efforts
Further development of thermal imaging methods for TBCs– Continue calibration of predicted TBC properties with laser flash results– Apply to wider TBC parameter range: thin/thick, graded/layered, etc– For EBPVD TBCs, investigate effects that affect prediction accuracy– Develop additional models, such as for 2-layer coating system and coating
transparency– Establish an accurate and reliable instrumentation for TBC characterization
Validation of NDE model for TBC lifetime prediction– In collaboration with partners, perform TBC life-cycle tests and collect NDE
data– Correlate TBC parameter change with TBC degradation state– Compare with destructive examination results
Correlation between different NDE methods– Work with collaborators who are developing other methods