opening new doors with chemistry think simulation! advances in corrosion simulation technology 24 th...
Post on 26-Mar-2015
219 Views
Preview:
TRANSCRIPT
Opening new doors with Chemistry
THINK SIMULATION!
Advances in Corrosion Simulation Technology
24th Conference October 23-24, 2007
Andre AnderkoGeorge
Engelhardt Margaret Lencka
Scope
• Structure of corrosion simulation technology
• General corrosion model
• Repassivation potential model
• Predicting the effects of heat treatment
• Modeling the propagation and time evolution of localized corrosion
• Development plans
Hierarchy of models for simulating aqueous corrosion
OLI’s Corrosion Simulation Technology
• Stability diagrams• Based entirely on thermodynamics• Predict the tendency of metals to corrode, passivate or remain
immune to corrosion
• General corrosion model• Based on surface electrochemistry• Predicts the rate of general corrosion and corrosion potential
• Repassivation potential model• Based on electrochemistry of local corrosive environments• Predicts the threshold potential above which stable localized
corrosion may occur
• Corrosion propagation and damage evolution model• Based on damage function analysis and deterministic extreme
value statistics• Predicts long-term damage based on short-term data
Electrochemical model for predicting general corrosion rate and corrosion potential
• Partial electrochemical processes in the active state:• Cathodic reactions (e.g., reduction of protons, water
molecules, oxygen, etc.)• Anodic reactions (e.g., oxidation of metals)• Adsorption phenomena
• Active-passive transition influenced by• Acid/base properties of passive oxide films• Temperature• Additional aggressive or inhibitive species
• Synthesis of the processes using mixed potential theory
General corrosion model:Application highlights
• Corrosion of stainless steel in nonoxidizing acids
• Active-passive transition and prediction of depassivation pH
• Effect of oxygen concentration on corrosion potential of a passive alloy
Modeling general
corrosion
• Corrosion rates and corrosion potential of 316L SS in HF solutions
• Prediction is based on calculating partial cathodic and anodic reactions in the active state
0.001
0.01
0.1
1
10
100
0.01 0.1 1 10m HF
Co
rr. R
ate
(m
m/y
)
Pawel (1994) 297 K
Schmitt (2004) 298 K
Ciaraldi et al. (1982) 298 K
Pawel (1994) 323 K
Pawel (1994) 349 K
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
0 2 4 6 8 10 12 14m HF
Eco
rr (
V/S
HE
) Schmitt (2004) 298 K,aerated
Schmitt (2004) 298 K,deaerated
Ciaraldi et al. (1982)366 K, deaerated
Corrosion potential Corrosion rate
Corrosion potential and depassivation
pH
• Corrosion potential of 304L SS in aerated solutions
• Predicted polarization curves include active-passive transition and partial processes of O2, H+ and H2O reduction
(3)
(1)
(2)(4)
(3)
(1)
(2)(4)
pH=0.8
pH=1.8
-0.20-0.15-0.10-0.050.000.050.100.150.200.250.300.35
0 1 2 3 4 5 6
pH
Eco
rr /
SH
E
Corrosion potential as a
function of dissolved O2
• Transition between controlling cathodic processes (H2O and O2 reduction) explains the dependence of corrosion potential on dissolved O2
pH=0.013 ppm
pH=0.096 ppm
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
1.E-07 1.E-06 1.E-05 1.E-04 1.E-03m O2
Ec
orr (
SH
E)
Ecorr, exp
Ecorr, cal
Calculating repassivation potential
• Threshold condition: Potential above which localized corrosion can be stabilized
• The model simulates electrochemical processes in a pit or crevice in the limit of repassivation
• It relates the repassivation potential to solution chemistry
Repassivation potential model:Alloys 22, 825, and 316L
• The slope changes as a function of chloride activity
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
0.0001 0.001 0.01 0.1 1 10
aCl
Erp
(S
HE
)
23 C, exp
60 C, exp
95 C, exp
23 C, cal
60 C, cal
95 C, cal
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
0.0001 0.001 0.01 0.1 1 10
a Cl
Erp
(S
HE
)
368 K, exp
353 K, exp
333 K, exp
323 K, exp
303 K, exp
423 K, cal
368 K, cal
353 K, cal
333 K, cal
323 K, cal
303 K, cal
316L825
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
0.0001 0.001 0.01 0.1 1 10 100 1000
a Cl-
Erp
(S
HE
)313 K, exp
333 K, exp
353 K, exp
368 K, exp
378 K, exp
383 K, exp
398 K, exp
423 K, exp
448 K, exp
313 K, cal
333 K, cal
353 K, cal
368 K, cal
378 K, cal
383 K, cal
398 K, cal
423 K, cal
448 K, cal
22
Repassivation potential for mixed chloride – oxyanion systems
• A steep change in slope indicates inhibition at a certain oxyanion concentration
• The transition depends on Cl- concentration and temperature
• At high Cl- concentration, inhibition may not be achieved due to solubility limits
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
0.0001 0.001 0.01 0.1 1 10
M OH-
Erp
(S
HE
)23 C, 0.004 M Cl - exp
23 C, 0.5 M Cl - exp
23 C, 4 M Cl - exp
60 C, 0.04 M Cl - exp
60 C, 0.42 M Cl - exp
23 C, 0.004 M Cl - cal
23 C, 0.5 M Cl - cal
23 C, 4 M Cl - cal
60 C, 0.04 M Cl - cal
60 C, 0.42 M Cl - cal
Erp values above ~0.7 V indicate lack of localized corrosion
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
0.0001 0.001 0.01 0.1 1 10
M NO3-
Erp
(S
HE
)
23 C, 0.42 M Cl - exp
23 C, 3 M Cl - exp
23 C, 4 M Cl - exp
60 C, 0.04 M Cl - exp
95 C, 0.42 M Cl - exp
23 C, 0.42 M Cl - cal
23 C, 3 M Cl - cal
23 C, 4 M Cl - cal
60 C, 0.04 M Cl - cal
95 C, 0.42 M Cl - cal
316L in Cl- + OH-
316L in Cl- + NO3
-
Effect of molybdates on Erp of various alloys:Similar patterns
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
0.0001 0.001 0.01 0.1 1 10
M MoO42-
Erp
(S
HE
) 23 C, 4M NaCl, exp
60 C, 0.04 M NaCl, exp
23 C, 4M NaCl, cal
60 C, 0.04 M NaCl, cal
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
0.0001 0.001 0.01 0.1 1 10
M MoO42-
Erp
(S
HE
) 60 C, 0.4M Cl, exp
60 C, 4M Cl, exp
60 C, 0.4M Cl, cal
60 C, 4M Cl, cal
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
0.0001 0.001 0.01 0.1 1 10
M MoO42-
Erp
(S
HE
)
23 C, 4M NaCl, exp
60 C, 0.04 M NaCl, exp
60 C, 4 M NaCl, exp
23 C, 4M NaCl, cal
60 C, 0.04 M NaCl, cal
60 C, 4 M NaCl, cal
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
0.0001 0.001 0.01 0.1 1 10
M MoO42-
Erp
(S
HE
) 60 C, 0.4M Cl, exp
60 C, 4M Cl, exp
60 C, 0.4M Cl, cal
60 C, 4M Cl, cal
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
0.0001 0.001 0.01 0.1 1 10
M MoO42-
Erp
(S
HE
)23 C, 0.004 M Cl - exp
23 C, 0.04 M Cl - exp
23 C, 0.42 M Cl - exp
23 C, 4 M Cl - exp
60 C, 0.04 M Cl - exp
60 C, 0.42 M Cl - exp
23 C, 0.004 M Cl - cal
23 C, 0.04 M Cl - cal
23 C, 0.42 M Cl - cal
23 C, 4 M Cl - cal
60 C, 0.04 M Cl - cal
60 C, 0.42 M Cl - cal
316L
600
690
254SMO
2205
Generalized correlation for predicting Erp of stainless steels and nickel-base alloys
• The correlation has been verified for 13 alloys
• It also includes Fe (carbon steel) and Ni as limiting cases
• Correlation includes the effect of oxyanions (OH-, MoO4
2-, VO3-,
NO3-, SO4
2-)
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
0.0001 0.001 0.01 0.1 1 10
aCl
Erp
(SH
E)
22, exp22, generalized276, exp276, generalized625, exp625, generalized825, exp825, generalized690, generalized600, exp600, generalized800, generalized254SMO, exp254SMO, generalizedAL6XN, expAL6XN, generalized2205, generalized316L, exp316L, generalized304L, generalizeds-13Cr, exps-13Cr, generalized
T = 368 K
Effects of heat treatment• Formation of carbides, intermetallics, etc. changes the
microchemistry of alloys and affects corrosion resistance
• A model has been developed to predict alloy composition profiles in the vicinity of the grain boundary as a function of temperature and time of heat treatment• Formation of carbides (M7C3 or M23C6) at the grain
boundaries in Fe-Cr-Ni-Mo-W-N-C alloys • Para-equilibrium between the carbide phase and the alloy
matrix• Growth of the carbide phase as a function of time and
time evolution of the Cr-depleted zone
• Relating the model predictions to corrosion phenomena• Intergranular corrosion• Change in the repassivation potential
Sensitization model:Fundamentals
• At any time, total accumulation of Cr in the carbide is equal to total Cr depletion in the matrix
• Cr concentration at the phase boundary is defined by paraequilibrium
• Cr concentration profile results from diffusion from the grain
• Cr concentration far from the boundary remains essentially identical to bulk concentration (due to large excess of Cr relative to C)
Cr concentration
Distance from grain boundary
r – carbide dimension
r
CCr(z)
z
CCr
Cr
Cr
C
C
0
C
Cr
Calculating Cr depletion profile:Alloy 600
• Cr depletion results from M7C3 precipitation
• At a fixed temperature, the width of depletion zone increases with time; then, self-healing follows
• The model is in good agreement with experiment
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0 200 400 600 800 1000
Distance from grain boundary, nm
x(C
r)
T=973 K, t=1h
T=973 K, t=1h, cal
T=973 K, t=10h
T=973 K, t=10h, cal
T=973 K, t=30h
T=973 K, t=30h, cal
T=973 K, t=100h
T=973 K, t=100h, cal
T=873 K, t=250h
T=873 K, t=250h, cal
T=1073 K, t=0.42h
T=1073 K, t=0.42h, cal
Data: Was and Kruger (1985)
Predicting intergranular corrosion
• Depletion parameter: proportional to the area of depletion profile below a certain Cr concentration
• It is calculated directly from the sensitization model
• Rate of intergranular corrosion correlates with the depletion parameter for x(Cr)*=0.120
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
0 20 40 60 80 100Aging time, h
rate
, mp
y
Streicher test
Huey test
0
24
68
10
1214
16
0 20 40 60 80 100
Aging time, h
De
ple
tio
n p
ara
me
ter
x(Cr)* = 0.12
x(Cr)* = 0.11
x(Cr)* = 0.1
x(Cr)* = 0.09
Standard intergranularcorrosion tests
Alloy 600 heat-treatedat 700 C:Depletion parametersfor various Cr levels
Predicting the repassivation potential: Heat-treated Alloy 825
• The measured Erp is assumed to primarily reflect the localized corrosion of the depleted regions (a pit is more likely to stabilize in an area that is more susceptible to localized corrosion)
• The measurable Erp can be obtained by integration over the depleted zone
• The prediction agrees with the data within experimental uncertainty
95 C0.00266 m Cl-
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
850 900 950 1000 1050 1100
Sensitization temperature
Erp
- E
rp(a
nn
eale
d), V t=15 h, exp
t=100h, exp
t=15h, cal
t=100 h, cal
Predicting Erp for welded alloy 22
• Solidification of welds may lead to segregation patterns of Ni depletion and solute enrichment in interdendritic volumes
• Dendrite cores are then depleted in Cr, Mo and W
• Direct prediction of Erp for annealed and welded samples using the generalized correlation for Erp as a function of alloy composition
95 C
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1E-04 0.001 0.01 0.1 1 10 100
M Cl-
Erp
(S
HE
) Mill annealed, exp
Bulk alloy, predicted
As welded, exp
Welded, predicted
Modeling the propagation of localized corrosion
• Deterministic Extreme Value Statistics • Combining the deterministic and statistical
view of localized corrosion• Prediction of long-term time evolution of localized
corrosion using short-term data• Implemented in Corrosion Analyzer v. 3.0
• New development: Monte Carlo simulation of corrosion damage
Difference between Damage Function Analysis (DFA) and Monte Carlo Simulation of
Corrosion Damage
The main idea of DFA is to regard each corrosion defect (pit, crack) as a “particle” that moves into the metal. Accordingly, the definition of damage function (number of defects for a given penetration) reduces to the solution of a system of balance equations in discontinuous media.
The main idea of the Monte Carlo method is to keep track of each stable pit (or crack) that nucleates, propagates and repassivates on the metal surface. • to effectively describe the progression of damage when
only several pits, or even a single pit, are alive and propagating; all other pits having repassivated.
• to take into account the interaction between a particular individual pit (crack) and the remaining (living) pits (cracks) on the surface in an explicit manner.
Advantages: The method allows us
Disadvantage: The Monte Carlo Method is relatively slow
Algorithm for Monte Carlo Simulation of Corrosion Damage
• Determine the location of the newly born active stable pits (randomly)
• Calculate new dimensions of active pits• Check if any active pit becomes passive due to
repassivation or due to overlapping with other pits• Check if any pit transitions into a crack• Calculate the new dimensions of each crack
In each time step, we need to
These calculations are repeated for every given time until all necessary statistical values are established.
We need models for each stage of damage propagation
Application of Monte Carlo SimulationMean depth of the deepest pit as a function
of time
Time, day
0 100 200 300 400
Max
imum
Pit
Dep
th, m
cm
0
200
400
600
800
1000
Alcan Alloy 2S-O in Kingston Tap Water
Xav -average depth of the largest pit
- standard deviation
Xav
Xav
Xav
Experimental data are taken from P.M. Aziz, Corrosion, 12, 495 (1956).
Experiment
Application of Monte Carlo Simulation:
Corrosion Fatigue
Oxygen Concentration, ppm
0 2 4 6 8 10
Pro
babi
lity
of F
ailu
re
0.0001
0.001
0.01
0.1
1
Lcr = 0.5 cm
Service Life = 15 years
[Cl-] = 350 ppm
Corrosion Fatigue
[Cl-] = 35 ppm
[Cl-] = 3500 ppm
Failure probability for low pressure steam turbine blades as a function of O2 concentration for different Cl- concentrations in electrolyte film during shutdown
Corrosion Analyzer:Underlying Technology at Present
• Thermodynamics of corrosion• Real-solution stability diagrams for alloys can be generated
using both the aqueous and MSE models
• Electrochemistry of corrosion• Computation of corrosion rate, corrosion potential and
repassivation potential• Calculated using the aqueous model for thermophysical
properties• Parameters available for carbon steel, aluminum, stainless
steels (13Cr, 304, 316 and 254SMO) and nickel-base alloys (22, 276, 625, 825, 600, 690, and Ni)
• Propagation of localized corrosion• Deterministic extreme value statistics (in Analyzer 3.0)
Development plans
• Corrosion Analyzer 3.0:• Deterministic extreme value statistics (already
implemented)• Module to predict the effect of heat treatment (to be
implemented)• Monte Carlo simulation of localized corrosion (to be
implemented)
• New technology• Development of electrochemical model parameter for
Cu and Cu-Ni alloys• Extending the electrochemical models to mixed-solvent
systems and coupling them with the thermophysical MSE models
top related