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Opening new doors with Chemistry

Andre Anderko George Engelhardt Margaret Lencka. Advances in Corrosion Simulation Technology. 24 th Conference October 23-24, 2007. THINK SIMULATION!. Opening new doors with Chemistry. Scope. Structure of corrosion simulation technology General corrosion model

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Opening new doors with Chemistry

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

  2. 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

  3. Hierarchy of models for simulating aqueous corrosion

  4. 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

  5. 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

  6. 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

  7. 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

  8. Corrosion potential and depassivation pH pH=0.8 • Corrosion potential of 304L SS in aerated solutions • Predicted polarization curves include active-passive transition and partial processes of O2, H+ and H2O reduction pH=1.8

  9. 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

  10. 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

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

  12. 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 316L in Cl- + OH- 316L in Cl- + NO3- Erp values above ~0.7 V indicate lack of localized corrosion

  13. Effect of molybdates on Erp of various alloys:Similar patterns 316L 254SMO 600 2205 690

  14. 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-, MoO42-, VO3-, NO3-, SO42-) T = 368 K

  15. 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

  16. 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

  17. 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 Data: Was and Kruger (1985)

  18. 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 Alloy 600 heat-treated at 700 C: Depletion parameters for various Cr levels Standard intergranular corrosion tests

  19. 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 C 0.00266 m Cl-

  20. 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

  21. 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

  22. 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. Advantages: The method allows us • 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. Disadvantage: The Monte Carlo Method is relatively slow

  23. Algorithm for Monte Carlo Simulation of Corrosion Damage In each time step, we need to • 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 These calculations are repeated for every given time until all necessary statistical values are established. We need models for each stage of damage propagation

  24. Application of Monte Carlo SimulationMean depth of the deepest pit as a function of time

  25. Application of Monte Carlo Simulation:Corrosion Fatigue Failure probability for low pressure steam turbine blades as a function of O2 concentration for different Cl- concentrations in electrolyte film during shutdown

  26. 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)

  27. 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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