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Opening new doors with Chemistry THINK SIMULATION! Advances in Corrosion Simulation Technology 24 th Conference October 23-24, 2007 Andre Anderko George Engelhardt Margaret Lencka

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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

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Hierarchy of models for simulating aqueous corrosion

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OLIs 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

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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

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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

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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 Corrosion potential Corrosion rate

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Corrosion potential and depassivation pH Corrosion potential of 304L SS in aerated solutions Predicted polarization curves include active-passive transition and partial processes of O 2, H + and H 2 O reduction pH=0.8 pH=1.8

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Corrosion potential as a function of dissolved O 2 Transition between controlling cathodic processes (H 2 O and O 2 reduction) explains the dependence of corrosion potential on dissolved O 2 pH=0.013 ppm pH=0.096 ppm

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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

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Repassivation potential model: Alloys 22, 825, and 316L The slope changes as a function of chloride activity 316L 825 22

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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 E rp values above ~0.7 V indicate lack of localized corrosion 316L in Cl - + OH - 316L in Cl - + NO 3 -

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Effect of molybdates on E rp of various alloys: Similar patterns 316L 600 690 254SMO 2205

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Generalized correlation for predicting E rp 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 -, MoO 4 2-, VO 3 -, NO 3 -, SO 4 2- ) T = 368 K

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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 (M 7 C 3 or M 23 C 6 ) 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

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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

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Calculating Cr depletion profile: Alloy 600 Cr depletion results from M 7 C 3 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 Data: Was and Kruger (1985)

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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)*=0.12 Standard intergranular corrosion tests Alloy 600 heat-treated at 700 C: Depletion parameters for various Cr levels

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Predicting the repassivation potential: Heat-treated Alloy 825 The measured E rp 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 E rp can be obtained by integration over the depleted zone The prediction agrees with the data within experimental uncertainty 95 C 0.00266 m Cl -

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Predicting E rp 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 E rp for annealed and welded samples using the generalized correlation for E rp as a function of alloy composition 95 C

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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

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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

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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

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Application of Monte Carlo Simulation Mean depth of the deepest pit as a function of time

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Application of Monte Carlo Simulation: Corrosion Fatigue Failure probability for low pressure steam turbine blades as a function of O 2 concentration for different Cl - concentrations in electrolyte film during shutdown

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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)

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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

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Chapter 23 Corrosion.

Chapter 23 Corrosion.

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