APPLICATION OF GALVANIC CORROSION MODELS. The University of Akron Akron, OH ABSTRACT

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1 2013 Virtual Department of Defense Corrosion Conference September 16-17, 2013 NACE International APPLICATION OF GALVANIC CORROSION MODELS J. H. Payer 1, R.S. Lillard 2, G.W. Young 3 1 Corrosion and Reliability Engineering Program 2 Dept. of Chemical and Biomolecular Engingeering 3 Dept. of Applied Mathematics The University of Akron Akron, OH44325 JPayer@uakron.edu ABSTRACT Models for corrosion degradation processes and the corrosion damage evolution are effective tools to link corrosion expertise with those carrying out design and implementation of corrosion management strategies. This pertains to several stages: design and materials selection; fabrication, operation, maintenance and rehabilitation; life prediction and performance assessment; and corrosion mitigation and control. An overall objective is to extend the practical use of corrosion degradation models for design, materials selection, corrosion mitigation and forecasting damage accumulation. There is great need and high pay-off for the application of advanced models of corrosion degradation processes in the design stage for durable equipment and structures and to forecast damage accumulation for life prediction. Several cases are described. The first two examine detrimental effects of galvanic action and the third examines a beneficial application of galvanic action. Key Words: Corrosion models, galvanic, crevice corrosion, design, life prediction 1 INTRODUCTION Better linkage is needed between corrosion knowledge and design, manufacture and operation of corrodible equipment and structures. Models for corrosion degradation processes and the corrosion damage evolution are effective tools to link corrosion expertise with those carrying out design and implementation of corrosion management strategies. This pertains to several stages: Design and materials selection Fabrication, operation, maintenance and rehabilitation Life prediction and performance assessment

2 Corrosion mitigation and control The benefits are clear. Make corrosion knowledge more accessible to engineers and decision makers. Present outputs in readily understood visualizations. Realize more effective corrosion management and mitigation programs. Reduce time and cost of corrosion. Validation of models is essential aspect for reliability and confidence Reliable data for model inputs Validation of model outputs The validation is best as a continuous learning and improvement process that includes feed back from in-service applications, extension of reliable data bases for model inputs and enhancements of the model itself. A thrust of the National Corrosion Center (NCERCAMP) at UAkron is to extend the practical use of corrosion degradation models for design, materials selection, corrosion mitigation and forecasting damage accumulation. The work is the collaborative effort of an interdisciplinary team with subject matter experts from corrosion, chemical engineering and applied mathematics. This combines expertise in modeling of complex systems with knowledge of corrosion, electrochemistry and materials science. Presently used treatments for galvanic corrosion and crevice corrosion are often limited and difficult to extend to more realistic conditions. There is great need and high pay-off for the application of advanced models of corrosion degradation processes in the design stage for durable equipment and structures and to forecast damage accumulation for life prediction. The work spans the range of modeling from development of first principles models to application of commercial software packages. The findings inform both development and practical application through benchmarking, validation and demonstration. This is accomplished through controlled laboratory exposures and measurements on basic configurations and more complex components with multiple materials, complex shapes and a range of environmental exposures. An important aspect is the consideration of coupled and sequential degradation modes, e.g. galvanic corrosion leading to crevice corrosion leading to corrosion fatigue, environmental cracking and fracture. Three cases are described in order to demonstrate the capabilities and some directions for advanced model development: Early Stages of Corrosion from steel Fasteners in Aluminum Formulation and solution of damage evolution in galvanic systems Sacrificial Cathodic Protection of Subsea Structures The first two examine detrimental effects of galvanic action and the third examines a beneficial application of galvanic action. 2 EARLY STAGES OF CORROSION FROM STEEL FASTENERS IN ALUMINUM Fasteners in aluminum are a particular corrosion challenge and increase the corrosion risks for aircraft and aerospace applications. The objective of this work is to examine the early stages of atmospheric corrosion from fasteners in aluminum. Aluminum coupons were examined with fasteners of stainless steel; cadmium-plated steel; and cadmium-plated steel with Cd partially removed by grinding. Modeling of galvanic action using a commercial software product complemented marine atmospheric corrosion exposures, laboratory measurements of galvanic action. Aluminum [Al 2024 T3] coupons with fasteners were examined after marine atmospheric exposure at Daytona Beach. The fastener materials were stainless steel; cadmium-plated steel; and steel with cadmium-plate partially removed. The detrimental effect of galvanic coupling of aluminum to fasteners of more noble metals was examined. The test assemblies comprised an aluminum plate with four fasteners. Figure 1 is a schematic of the coupon and fastener assembly depicted as the anode and cathode, respectively. Figure 2 (a)

3 and (b) present a 3-D surface image and schematic cross section of the fastener/coupon, respectively. The 3-D reconstruction [Alicona-Infinite Focus] shows a trench formed on the coupon around the fastener, and this area will collect and retain moisture. There is also a vertical area of the fastener perpendicular the face that is exposed in the trench. FIGURE 1 Schematic of aluminum/fastener assembly with expected galvanic action Fasten Aluminum (a) (b) FIGURE 2- (a) 3-D image of fastener in aluminum coupon and (b) schematic cross section of fastener in aluminum Model Methodology for Fasteners Insights to the galvanic corrosion behavior were gained by modeling the fasteners in aluminum exposed to a marine atmospheric corrosion environment. GalvanicMaster, a commercial software product, predicts galvanic action of complex geometries and was utilized to model thin-film atmospheric corrosion of the aluminum/fastener assembly. Figure 3 depicts the computer-aided design (CAD) constructed using BS Solidworks with separate components for each fastener head. 3

4 FIGURE 3- Geometry of fastener-plate CAD. Model inputs include polarization data for each material in the simulation. Additionally, molecular weight, material density, valence state, and electrical resistivity of each material were specified. To approximate atmospheric conditions, a film thickness of 100 µm and electrolyte conductivity of 1.76 x 10 4 µs/cm were specified. Simulation results are displayed as 3-D models identical to input CAD, along with color-coding based on severity of predicted galvanic action. From these images, values for potential, current density, and corrosion rate can be identified across the assembly. Comparison of Cadmium and Stainless Steel Fasteners To model the galvanic action, thin-film simulations were executed for cadmium-aluminum and stainless steel-aluminum couples. Results are shown in Figure 4 where a much larger degree of polarization is observed with the stainless-aluminum combination due to the more noble polarization behavior of stainless steel. Under the simulated conditions, stainless steel fasteners resulted in severe galvanic attack of the aluminum, whereas cadmium fasteners had little detrimental effect. A current density difference of approximately 3 orders of magnitude was seen between the two materials. Anodic Cathodic (a) FIGURE 4- Charts of (a) Potential distribution and (b) corrosion rates from aluminum outer edge to fastener center, showing severe aluminum corrosion with stainless. Galvanic Action with Steel Fasteners (b) 4

5 To study the effect of steel in galvanic action, the Cd plating was ground off of the outer circumference of the fastener. The face of the fastener was defined as an inner (Cd-plated) area and an outer (ground steel) area. The ground steel surface was modeled as bare steel and as rusty steel. Results for the rusty steel fastener are presented in Figure 5. For bare steel, as expected since steel is more noble than aluminum, the simulation predicted increased galvanic activity of the Cd/Steel fastener coupled with aluminum. The corrosion rate for Al at the fastener interface was 42 um/yr and decreased to 18 um/yr about 9 mm from the interface. The steel surface was cathodic, and there was little effect on the Cd-plated inner area of the fastener. For rusty steel, the outer fastener zone is even more noble than aluminum, and the simulation predicted much greater galvanic corrosion than for bare steel. The corrosion rate for Al at the fastener interface was 200 um/yr and decreased to 96 um/yr about 9 mm from the interface. The rusty steel surface was cathodic. FIGURE 5- Corrosion rates across the aluminum and fastener assembly along with a 3-D color coded image for visualizing corrosion damage. There is a synergism among field exposures, laboratory testing, and analytical modeling to better understand and predict galvanic corrosion damage. Results are useful to guide design and materials selection; corrosion mitigation strategies; and life prediction and performance assessment. The field exposures determine corrosion under realistic conditions. The laboratory experiments measure corrosion under controlled conditions and generate supplemental data for corrosion and electrochemical processes. The model findings provide a tool to analyze a wider range of galvanic corrosion scenarios. A particular benefit of the combined experimental and analytical approach results from the opportunity for verification and validation of the model. DAMAGE EVOLUTION IN GALVANIC SYSTEMS The goal is to formulate, analyze and solve mathematical models for galvanic corrosion damage evolution. The knowledge gained through mathematical modeling and simulation coupled with an experimental plan of investigation provides insight into the fundamental mechanisms underlying galvanic corrosion and means to prevent it, and improve our ability to predict performance assessment of metal components and their life. The models receive input from and validation by laboratory experiments. Experiments provide direct measurement of corrosion rates and cumulative damage, characterization of interface processes, and electrochemical tests for dissolution evolution. In turn the models identify material parameters, environmental and system variables that need to be measured. 5

6 Nearly all models for galvanic corrosion determine the initial condition for potential distribution, galvanic currents and corrosion rates. A major thrust of the UA work is to extend the modeling to determine damage evolution through iterative processes that extend beyond the initial conditions. This galvanic corrosion modeling extends the UA theoretical and computational framework developed for crevice corrosion to damage evolution. Scenarios are also analyzed using GalvanicMaster, a commercial software program by Elsyca, Inc. The combination of modeling methods along with experimental results is useful for comparison, verification and validation. Mild Steel and Magnesium Alloy: Galvanic Corrosion Damage Evolution The steel is more noble than magnesium, and galvanic action results in accelerated corrosion of magnesium. A schematic of model for a two-metal couple and a time slice of model output are shown in Figure 6.!! Figure 6- Galvanic model schematic for two-metal couple and time slice of model output. The numerical model calculates the evolution of the surface geometry with time during galvanic corrosion. The UA model was benchmarked with data from the literature for the Mg/Fe galvanic system (K. Deshpande, Corrosion Science, 52 (2010) ). Figure 7 shows a comparison between the UA dynamic model and the Deshpande model as well as experimental data for this system (scanning vibrating electrode). Results from a commercial software model are included as well. We see excellent agreement between dynamic models (UA and Deshpande) and static (GalvanicMaster) models. 6

7 Figure 7- Comparison of predicted and experimental damage profiles for magnesium (0 10 mm domain)/mild steel (10 20 mm domain) galvanic couple Aluminum and Copper: Galvanic Corrosion Damage Evolution Copper is more noble than aluminum, and galvanic action results in accelerated corrosion of aluminum. A schematic of model for the aluminum-copper couple comprised of two cylindrical sections is shown in Figure 8. The model inputs include geometry of the metals (radii and length), thickness of the electrolyte, electrolyte conductivity, and the potential-current polarization behavior (E vs I) for each metal. As the radii of metals increases and conductivity of the electrolyte decreases, the corrosion damage is localized and more severe for aluminum at the Al/Cu boundary. For the converse, the galvanic action is spread over a larger area of aluminum (extending farther from the boundary), the depth of penetration is more uniform and the damage at the boundary is less. The benefit of the model is that quantitative results are determined for these well-known, qualitative behaviors. 7

8 Figure 8- Galvanic model schematic for aluminum-copper couple. The results using the commercial software model for corrosion rates on a more complex configuration of an aluminum-copper galvanic couple are shown in Figure 9. The scenario was run for exposure in a thin layer of electrolyte. The concentration of corrosion damage near the joining boundary between copper and aluminum is apparent. Benefits of the commercial software model are the capability to analyze complex shapes and to present results in effective visualizations of the findings. Figure 9-Corrosion rates determined by a commercial software model for more complex shape of an aluminum-copper galvanic couple. SACRIFICIAL CATHODIC PROTECTION OF SUBSEA STRUCTURES Sacrificial cathodic protection (CP) is employed within the oil and gas industry to protect subsea pipeline end terminals (PLETs), subsea manifolds, subsea pipelines, keel joints, and many other structures. Sacrificial CP systems use beneficial galvanic action, i.e. more active aluminum anodes attached to the steel structure to be protected. The potential difference between the steel structure and aluminum anodes is the driving force for protective currents. Current industry recommended practices for CP design allow for estimation of the required amount of anodic material for a given structure based on suggested current densities for the environment. However, the recommended practices do not account for attenuation of the protective currents as the distance increases from the anode and shielding effects for complex structures. Computer software packages have the capability to model the CP system based on the geometry of the system, environment, and electrical connections. By incorporating computer simulation, CP systems for complex structures can be evaluated to identify regions of over or under protection. The computer simulations can also guide design parameters such as anode geometry, size, number of anodes, and placement. Further, by adjusting input parameters, such as aging coating properties and degradation of anode performance, scenarios allow the determination of future performance. Model Methodology The simulations described below were determined using Elsyca CPMaster software. Inputs to the model describe the geometry, materials of construction, material properties and environmental properties. Material parameters include molecular weight, density, valence electrons, and resistivity. Electrolyte parameters include the resistivity of the solution along with physical description of the 8

9 conductive media. The polarization behaviors (current vs. potential) of the material/environment combinations are crucial input. Simulation results include on-potential across the metal surfaces, off-potential (potential corrected for ohmic drop) and current density across the metal surface. A major benefit is the visualization of these properties over the components. Individual components, faces, bodies, and selections can be hidden from view to allow visualization of desired areas. Model Output and Findings A sacrificial CP system was designed based on industry recommended practices for a subsea, pipeline end terminals (PLET). The required anodic material and anode geometries were estimated based on current industry recommended practices for CP design. A scenario was based upon initial (asconstructed) conditions, and a second scenario was based upon conditions expected after 20 years service. For this case study, the aged cathodic protection system was characterized to have degraded coating (more exposed metal) and degraded anodes (less anode area due to metal loss). For each scenario, the CAD/CAM drawings of the CP sacrificial anodes and PLET were created within SolidWorks. Figure 10 presents the initial scenario. The PLET is approximately 6.4 meter by 9.7 meter by 5.6 meter high. Each anode is approximately 0.46 meters long by 0.20 meters wide. Figure 10- PLET and initial anode geometry Electrolyte resistance of seawater was ohm-m. The PLET was partially submerged in sediment. Polarization data of steel in aerated seawater was used for the seawater-exposed portion, and polarization data of steel in deaerated seawater was used for sediment-covered portion. For the anodes, polarization data for a relevant aluminum alloy in 3.5% NaCl was used. The IR free potential distributions for the initial and 20-yr conditions are shown in Figure 11 and Figure 12, respectively. Based on a potential at or more negative than -0.8 V Ag/AgCl being protected, it is evident that the number and distribution of anodes was not great enough to protect the entire structure for initial conditions and even more inadequate for after 20-years. While the total weight of anodes was sufficient based on CP guidelines, the number of anodes was too few to provide proper current distribution across the structure. With the model for sacrificial cathodic protection, scenarios with additional anodes and various anode placements can readily be analyzed. Further, the behavior with anodes of different size and shape can be analyzed. 9

10 Figure 11- PLET initial condition: IR Free Potential (V Ag/AgCl ) distribution Figure 12- PLET after 20-years condition: IR Free Potential (V Ag/AgCl ) distribution In addition to insights from the model output for potential distribution, insights into performance are gained from the current distribution output. Current density on the anode surface is related to anode corrosion rate, anode performance as size and shape change and anode useful life. A major benefit of the CP models is the ability to examine a number of scenarios to assist design materials selection, performance assessment and life prediction. For instance, 10

11 Design-- anode number and placement, effects of shielding Material selection-- anode polarization behavior Life predictions-- aging coatings, anode performance, steel corrosion at lower or no protection SUMMARY The practical use of corrosion degradation models for design, materials selection, corrosion mitigation and forecasting damage accumulation was demonstrated. Models for corrosion degradation processes and the corrosion damage evolution are effective tools to enhance Design and materials selection Life prediction and performance assessment Corrosion mitigation and control The benefits of reliable corrosion degradation models are clear: Make corrosion knowledge more accessible to engineers and decision makers Present outputs in readily understood visualizations More effective corrosion management and mitigation programs Reduce time and cost However, validation of the models is essential for reliability and confidence Reliable data for model inputs Validation of model outputs A continuous learning function is built into several model systems, so as more data and service performance information is available, the model advances. An overall objective was to extend the practical use of corrosion degradation models for design, materials selection, corrosion mitigation and forecasting damage accumulation. Three cases were described to demonstrate the capabilities and some directions of advanced model development: Early Stages of Corrosion from steel Fasteners in Aluminum Formulation and solution of damage evolution in galvanic systems Sacrificial Cathodic Protection of Subsea Structures There is a crucial need for models to include the evolution of corrosion damage and evolution of the environment. The extent and distribution of metal loss is crucial input to determination of remaining strength and depth of penetration. Environmental conditions are seldom constant but rather vary over the service life. These changes in turn affect primary factors for corrosion behavior, e.g. polarization behavior, forms of corrosion, degradation processes. ACKNOWLEDGMENTS This work is associated with the National Corrosion Center (NCERCAMP) at The University of Akron and the DoD Technical Corrosion Collaboration (TCC) supported by the U.S. Department of Defense Office of Corrosion Policy and Oversight. The TTC research is administered by the US Air Force Academy under agreement number FA Use of GalvanicMaster and CPMaster software was through Alan Rose, Elsyca, Inc. Bill Abbott, Battelle, provided samples for the Al-fastener work. We acknowledge our colleagues in Applied Mathematics: C.B. Clemons, D. Golovaty, K.L. Kreider, N. Mimoto, P. Wilber, and J. Wilder along with Huang Lin, Corrosion and Reliability Engineering. A number of students participated and all are affiliated with the Corrosion Squad, a multidisciplinary student organization at The University of Akron. In particular, we acknowledge the contributions of Corrosion Engineering: MS-P. Young; BS-N. Sutton, Z. Lerch Chemical and Biomolecular Engr: BS (2013)-P. Young Mathematics: PhD-Aaron Stenta; MS-M. Brackman, S. Basco, A. Stenta, A. Smith 11