Abstract · Web viewThis has the effect of quenching stress corrosion cracking (SCC). The technology to manufacture these primers is quite advanced with regards to particulate size,

THREE-DIMENSIONAL ELECTROCHEMICAL MODELING OF ANODIC ALUMINUM RICH PRIMER

Dr. Siva Palani, Corrdesa LLC

Dr. Alan Rose, Corrdesa LLC

Dr. Keith Legg, Corrdesa LLC

Dr. Julio Mendez, Corrdesa LLC

Keywords: aluminum rich primer, cathodic protection, corrosion modeling, aluminum alloy

ABSTRACT

Metal-rich primers containing anodic materials are gaining an increasing share of the aerospace and defense market, in large part because environmental restrictions both in the US and particularly in Europe are driving the aerospace and defense industry away from the use of legacy chromated primers.

In particular, the aluminum rich (Al-Rich) primer developed by NAVAIR has shown some very promising results, and important advantages that they not only behave anodic to steels, but also anodic to common aerospace alu-minum alloys.

The technology to manufacture these primers is quite advanced with regards to control of particulate size, loading and a number of other parameters that can be manipulated, but the number of variables make them hard to opti -mize. However, development and optimization of these primer systems is inhibited by a lack of understanding of how the entire system behaves and protects coated components, and assemblies in which they are used.

To support this process, Corrdesa has developed a novel computational CAE approach to create a 3-D model of the metal-filled primer, incorporating both its physical structure and its electrochemical properties that can be used to determine how it will interact electrochemically with the substrate and adjacent materials. Consequently, these models are aimed to help to identify and understand the key parameters and their sensitivities for primer develop-ment, such as particle loading, particle size, shape and treatments, resin/media properties, etc..

This paper will present initial corrosion models of an Al-rich primer applied on an aluminum substrate. Using case studies, we will demonstrate how it is now possible to illustrate sacrificial anode-based cathodic protection mecha-nism capabilities of the anodic particles spatially distributed in a 3-D primer microstructure in terms of potential and current density distribution. We will also provide some insight on the depletion of the pigment particles as a function of their electrochemical properties, primer electrolyte conductivities, and their location within the primer layer.

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Paper No. 2019-0000

2019 Department of De-fense – Allied Nations

Technical Corrosion Con-

INTRODUCTION

Anticorrosive coatings are generally classified in accordance with the mechanisms by which they protect a metal against corrosion, namely barrier protection, passivation/ inhibition and sacrificial protection. Legacy primer sys-tems used by the Aerospace and Defense industry to enhance the corrosion resistance and paint adhesion per -formance of air vehicle structures typically contain hexavalent chromium (Cr6+) compounds in the corrosion-pre-vention scheme. However, environmental regulations such as REACH and RoHS are forcing OEMs to replace legacy corrosion control coatings and pretreatments containing Cr6+ with a variety of alternative materials and coatings that may not work as well or as reliably.

One of few viable primers without hexavalent chromium is metal-rich primer containing anodic materials. These coatings rely on the principle of galvanic corrosion for the protection of metals against corrosion. This means that the substrate is protected by a pigment alloy that is electrochemically more active than the material to be pro -tected. Since the performance of metal-rich coatings is based on transfer of the galvanic current, there must be metallic contact between the individual particles of the sacrificial metal. Hence, sacrificial coatings usually are very highly pigmented, typically just below the critical pigment volume concentration.

Metal-rich primers such as zinc-rich primers (ZRP) have a long history for corrosion prevention of metals in oil and gas, infrastructure and transportation industries. Meanwhile magnesium-rich primers have been developed for aerospace use and are widely available commercially [1][2]. These primers have been extensively tested both in the laboratory and in beachfront atmospheric testing. Although metal-rich primers have existed for some time, the US Navy reports that there were underlying problems. First, zinc coatings are never used on aircraft due to em-brittlement concerns, and traditional zinc-rich coatings are too heavy in any case. Second, other metal-rich coat -ings do not have the longevity of performance in harsh Navy environments [3].

Recently, NAVAIR has developed aluminum-rich primers (AlRP) as a better alternative to address the more diffi-cult corrosion issues on Naval aircraft [4]. The NAVAIR AlRP primers have been shown to have some very impor-tant advantages. Firstly, they can be engineered with different alloys to provide a range of open circuit potentials (OCPs) that can make them not only anodic to steels, but also anodic to aerospace aluminum alloys. They also provide sacrificial corrosion protection for aluminum alloys, as they drive the potential of the underlying aluminum alloy below the pitting potential region and well within the oxygen reduction regime. This has the effect of quench -ing stress corrosion cracking (SCC).

The technology to manufacture these primers is quite advanced with regards to particulate size, loading and a number of other parameters that can be manipulated. However, development and optimization of these new primer systems is inhibited by a lack of understanding of how the entire system behaves and protects coated air-craft components and assemblies in which they are used. Optimization would be much easier if they could be modeled reliably.

One established method for analyzing such systems is computational modeling using numerical methods. Quanti-tative prediction of electric current and potential distributions in conductive media has existed for at least five decades [5][6] with some background mathematics evident over a century ago [7]. Recently, corrosion models have become increasingly relevant for simulation, lifetime prediction, and optimization of corrosion prevention measures. Previous studies have shown good agreement between modeling and experiments, which shows that numerical simulation is an appropriate method for the evaluation of real galvanic systems [8-15].

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The objective of this work is to develop a computational CAE workflow in order to create a 3-D model of an AlRP containing Al-Zn-In particles, incorporating both its physical structure and its electrochemical properties that can be used to determine how it will interact electrochemically with the Trivalent Chrome Passivated (TCP) Al 2024-T351 substrate and adjacent materials. Results will discuss the model prediction of AlRP coating system as a function of coating parameters, physical conditions, as well as environment.

MODEL DESCRIPTION

Primer Microstructure Generation

Figure 1. SEM image of an as-sprayed AlRP primer cross-section [16].

Figure 1 shows a Scanning Electron Microscope (SEM) cross-section image of a fully cured, intact AlRP with high particle loading of various sizes, spray applied onto the substrate. Its complexity demonstrates the need for a methodology for generating this spatially-resolved complex 3-dimensional (3D) primer microstructure explicitly capturing all the main physical attributes. For this we developed an automated and fully numerical workflow based on Siemens finite volume code STAR-CCM+ multi-physics software. We employed the discrete element method (DEM) which is an engineering numerical method to simulate motion of many interacting discrete objects that are typically solid particles first introduced by Cundall and Strack [17]. In DEM, the particles are typically injected into the domain and Newton’s law of motion is solved for each particle with consideration of all relevant forces acting on the particles. The DEM model is an extension of the Lagrangian modeling methodology to include dense parti -cle flows.

In this work, particles were introduced into the defined computational domain representative of primer layer di -mensions through particle injectors made available by STAR-CCM+ at one or more discrete locations. An injector defines the size and the velocity vector distribution of the particles, and for unsteady simulations, the frequency, which is the case here. The sizes of the particles being introduced into the domain are represented statistically by a particle size distribution. Here, size means diameter or mass, depending on the particle size specification. The resulting primer microstructure at the end of the particle injection process is captured to create a 3D computa-tional mesh of the matrix between the particles. The detail is shown in the result section.

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Galvanic Corrosion Model

The mathematical formulation for the corrosion modeling aspect is based on the potential model. Ohm’s law de -scribes the current density proportional to the electric field, E:

(1)

: Electric field [V/m] : Conductivity [Ω-1m-1] : Potential [V]

with the proportionality factor being the electrical conductivity. Expressing current conservation in local form yields the Laplace-equation which is a second order partial differential equation [18].

(2)

At this juncture, we treat the electrolyte or in this case the polymer/resin matrix as a homogeneous ohmic conduc-tor with no ion diffusion or convection effects. f (U) is the imposed polarization curve which serves as the bound-ary condition. The polymer matrix domain is discretized and Eq. (2) is solved numerically to obtain potential and current density at all computational cells on all of the materials.

Electrochemical Data

The aim of this work is to understand the protection and corrosion mechanism of AlRP on an AA2024-T3 sub-strate and to evaluate the effects of TCP treatment on the Al pigment particles using computational models. The model requires the polarization behavior of the materials involved, along with the electrical conductivity of the primer/resin matrix. The metallic particles of AlRP are composed of an Al-Zn-In alloy and the substrate is TCP-treated Al 2024-T3 alloy. Potentiodynamic polarization measurement was performed on a bulk Al-Zn-In alloy and AA2024-T3 using a three-electrode cell setup. The detail on the polarization measurement of the involved materi-als can be found in the work reported by Wang et al [19].

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UE

E

U

)()( UfU

Figure 2. Polarization curves of AA2024-T3 and Al-Zn-In alloys [19].

Figure 2 shows the anodic and cathodic polarization curves behavior of Al-Zn-In bulk alloy and TCP treated Al 2024-T3 respectively in 0.1 M NaCl electrolyte. It is clear from the figure that the particle alloy is more anodic when compared to Al 2024-T3 substrate, with an open circuit potential (OCP) lying between -1.1 and -1.2 V vs SCE. These polarization curves are used as boundary conditions for the corrosion model. As for the conductivity of the primer matrix, we know in reality Al-Zn-In particle encapsulated by the resin will experience large ionic re-sistance. Electrochemical Impedance Spectroscopy (EIS) measurements were conducted on actual AlRP sam-ples in full immersion conditions in order to estimate the nominal resistivity and eventually the conductivity of the resin.

RESULTS

3D Microstructure

A final particle-resolved AlRP model generated using the DEM method is shown in . It essentially represents a mi-croscopic slice of intact highly loaded AlRP on a substrate with a dimension of 30 x 30 x 37.5 µm. The surface (blue) underneath the packed particles represents the substrate that is to be protected.

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Figure 3. 3D Computational model and mesh of polydisperse primer microstructure

A Log-Normal size distribution was followed in order to achieve this particular particle size distribution with mini -mum particle size, maximum particle size, mean particle size and standard deviation defined as 2 µm, 10 µm, 4 µm and 1 respectively.

Galvanic Throwing Power

Current Density Distribution

The most important experimental result that the model must initially predicts is the effectiveness of the primer as a function of its chemistry and morphology, since this is what defines how the primer protects the substrate and in -teracts with adjacent materials. In this study, we considered the impact of a range of resin electrical conductivities: 1E-2 S/m to 1E-8 S/m. This value of conductivity was applied to the whole matrix phase separating particles in the model. The EIS measurements made on real as-sprayed AlRP under well-soaked conditions resulted in conduc -tivities in the range of 1E-5 S/m and 1E-6 S/m. The polarization curves shown in Figure 2 were prescribed as boundary conditions accordingly on the substrate and the particles, along with the appropriate electrical conduc-tivity of the polymer matrix for performing the galvanic corrosion modeling using the potential model.

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a) Bare Particle (1E-6 S/m) b) TCP Particle (1E-6 S/m)

Figure 4. Current density distribution of highly loaded AlRP with 1E-6 S/m polymer conductivity

Figure 4 presents the predicted current density mapping on the particles as a result of being galvanically coupled with the Al 2024-T3 TCP substrate (hidden in this case for clarity) as a function of particle surface treatment in a polymer with 1E-6 S/m conductivity. Figure 4 a), for example, shows a high current density concentration very close to the substrate owing to the high resin resistivity. As a result, most of the particles are galvanically inactive. When the particles were TCP treated as shown in Figure 4 b), more particles became galvanically active. But on the other hand, the highest current density was reduced to about 0.084 A/m2 (8.4 µA/cm2) from 12.5 µA/cm2. How-ever, the lowest current density for particles furthest from the substrate has increased to 1.75E-6 A/m2 from 4.93E-15 A/m2 (for the untreated condition - Figure 4 a)) which is an increase of about 9 orders of magnitude. This suggests that TCP treatment of the particles not only helped to reduce their galvanic corrosion rate, but their pas-sive anodic behavior also helped to sustain the current longer, even with the large ohmic potential drop due to the rather resistive polymer matrix. As a result, more particles were still actively providing cathodic protection to the substrate.

He

Figure 5. Current density distribution of highly loaded AlRP with 1E-4 S/m polymer conductivity

When the conductivity of the polymer increased to 1E-4 S/m, as expected many more particles became galvani -cally active (Figure 5 a) and b)). The increase in conductivity helped to expand the current to more particles, lead -ing to a much more uniform current density distribution in both untreated and TCP treated particle conditions, which therefore resulted in much lower maximum current densities near the substrate. The advantage of particle TCP treatment predicted here qualitatively agrees with the experimental findings from previously published inves-tigations, where the performance of a similar AlRP on Al 2024-T3 TCP panels in a GMW14872 accelerated expo-sure test were analyzed [19].

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Galvanic Potential Distribution

Figure 6. Potential distribution on Al 2024 substrate underneath AlRP

Figure 6 and Figure 7, on the other hand, show the predicted potential distribution on the Al 2024 TCP substrate (top view with underlying particles hidden) corresponding to the conditions shown in Figure 4 and Figure 5 re-spectively. The circular spots seen on these figures denote the regions where the particles were either in close proximity or in contact with the substrate. It is evident that the substrate potential distribution is very localized in nature in Figure 7. Potential distribution on Al 2024 substrate underneath AlRP a) when reacting with bare parti-cles. However, TCP treated particles created a more uniform protection potential on the substrate.

Figure 7. Potential distribution on Al 2024 substrate underneath AlRP

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Figure 8. Potential distribution within the polymer and the substrate for conductivity a) 1E-6 S/m, b) 1E-4 S/m

The potential variation, ΔU within the substrate is around 86 mV and 53 mV for a polymer conductivity of 1E-6 S/m with non-treated and TCP treated particles respectively. Whereas, for the same microstructure, when the polymer conductivity increased to 1E-4 S/m (Figure 7. Potential distribution on Al 2024 substrate underneath AlRP) the substrate ΔU fell to 10 mV and 14 mV respectively. This demonstrates the importance of the polymer con-ductivity i.e. the ionic resistance, which dominates the galvanic current passing between anodes and cathodes and determines the extent of the sacrificial galvanic protection afforded by the Al particles to the Al 2024-T3 sub -strate. Figure 8 shows the central cross section of the primer layer, revealing the ohmic potential drop within the polymer layer (with TCP treated particles) and the substrate potential for a polymer conductivity of 1E-6 S/m and 1E-4 S/m respectively. The impact of polymer conductivity is clearly evident from this figure, where a conductivity of 1E-6 S/m resulted in a 204 mV potential drop within a 37.5 µm thick primer, while a polymer conductivity of 1E-

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a) TCP Particle (1E-6 S/m)

Substrate (Al 2024 TCP)

Polymer

ΔU = 204 mV ΔU = 58 mV

b) TCP Particle (1E-4 S/m)

4 S/m reduced it to 58 mV. This complements the predicted current density distribution (Figure 4 and Figure 5) shown earlier.

Figure 9. Particle and substrate average potential as a function of primer conductivity

Additional simulations were conducted to evaluate the impact of the polymer electrical conductivity on the galvanic couple. Figure 9 depicts the average potential evolution for both substrate and bare particles as a function of poly-mer conductivities. The figure also shows 3 regions (color coded) representing the primary electrochemical activ -ity of the Al 2024 TCP treated substrate, based on the polarization curve shown in Figure 1. It can be clearly seen that on average these particles are keeping the substrate in the safe cathodic potential region over a wide range of polymer conductivity, on the one hand avoiding anodic pitting corrosion and on the other avoiding cathodic cor -rosion and subsequent coating blistering due to hydrogen evolution. This phenomenon is more effective for higher polymer conductivities i.e. >1E-6 S/m where both substrate and particle potentials merge towards the “mixed po-tential”. As the polymer conductivity becomes very low e.g. <1E-7 S/m, the substrate potential moves towards its open circuit potential, which is undesirable as Al 2024-T3 is prone to pitting corrosion close to its open circuit po-tential (OCP) conditions. This will be detrimental for a 7000-series aluminum alloy, for example, as its OCP is more negative than Al 2024.

CONCLUSION

The present study describes a novel computational methodology to create, analyze and optimize a 3-D metal-filled primer system, incorporating both its physical structure and its electrochemical properties. The method em-ploys a Lagrangian modeling methodology to realistically constitute a highly loaded metal filled-primer surrogate model comprising spherical particles and a CFD code to fully the solve conservation equations of mass, momen-tum, and energy, and finally solving the Laplacian potential equation to accurately resolve the galvanic protection efficiency of the AlRP system as a function of coating parameters and physical conditions.

We showed the cathodic protection capabilities afforded to Al 2024-T351 by Al-Zn-In anodic particles spatially dis-tributed in a 3-D primer microstructure, as a function of surface treatment and polymer conductivities. In general, the model using the experimental electrochemical data as boundary conditions successfully predicted the trends, and is consistent with findings from previously conducted experimental work to assess AlRP performance in ac-celerated exposure tests [19]. Furthermore, the model has revealed that in a metal-filled primer system, the elec-trical and ionic resistances of any pretreatment layers, primer formulations, or topcoat systems would be an im-portant factor governing the galvanic protection provided by the AlRP resulting from its throwing power and self-corrosion of its particles.

The model clearly shows that AlRP is capable of keeping an Al 2024-T3 substrate in the safe cathodic potential region at a wide range of polymer conductivity, avoiding the substrate’s critical anodic pitting potential. However, it is important to note that as the polymer conductivity falls, the galvanic interaction tends to localize at regions very close to the substrate due to the large potential drop within the polymer layer. Therefore, those particles close to the substrate interface will be consumed more rapidly, while more distant particles will not contribute, and will tend to undergo self-corrosion.

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TCP surface treatment of particles helps to reduce the self-corrosion current and the galvanic coupling current in comparison to untreated particles, which could be an important factor when considering the coating lifetime. Addi -tionally, due to its rather passive anodic behavior it also helps to spread out the current, activating more particles further from the substrate. This minimizes the local current density concentration effect, which in turn reduces un-even particle corrosion rates.

As next steps, there are improvements which could be made to increase the fidelity of the model and improve the correlation to real environmental exposure. For example, knowing the polarization behavior of the particles and the substrate in the actual electrolyte chemistry within the polymer is extremely valuable for quantitative analysis. With this knowledge, modeling will be a powerful tool for guiding the choice of primer/particle parameters for man-ufacture, and an upfront prediction of how painted components will behave in galvanic assemblies.

ACKNOWLEDGEMENT

This work was sponsored by Office of Naval Research (ONR), William Nickerson. The views and conclusions con-tained herein are those of the authors and should not be interpreted as necessarily representing the official poli-cies or endorsements either expressed or implied, ONR or the U.S. Government. Special thanks to Xi Wang and Prof. G.S. Frankel, Ohio State University for providing the electrochemical data and for the useful discussion.

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Documents

Abstract · Web viewThis has the effect of quenching stress corrosion cracking (SCC). The technology to manufacture these primers is quite advanced with regards to particulate size,