Abstract · Web viewOn the cathodic acting areas there will be no appreciable loss of material...

COMPUTATIONAL CORROSION ANALYSIS GUIDES MATERIAL CHOICEFROM DESIGN THROUGH SUSTAINMENT

Dr. Alan Rose, Corrdesa LLC ([email protected]; 770-683-3960)

Dr. Siva Palani, Dr. Keith Legg, Dr Julio Mendez, Corrdesa LLC

Keywords: computational corrosion analysis, galvanic corrosion prediction, FEA, corrosion prediction, polarization data, missed potential model, fluid shell elements, CFD, Corrosion Djinn

ABSTRACT

At more than $20 Billion annually, corrosion is a major cost of maintaining defense equipment and facilities. But more than that, corrosion, and in particular galvanic corrosion between dissimilar materials, is a primary cause of structural failure that puts aircraft and other equipment out of service.

The present approach for considering galvanic incompatibilities is simplistic. It is simplistic in that the only thing it takes into account is the galvanic potential difference between two adjacent materials. An estimate of the galvanic corrosion severity is usually based on some form of galvanic potential table, as outlined in many military, profes-sional institution and commercial guidelines. However, in mixed material assemblies it is actually the electrochem-ical kinetics of the materials that determines the severity of. A more accurate way of predicting galvanic corrosion is the use of the mixed potential approach. Although this approach has been around for decades, up to this point in time there has never been accessible, robust and consistent data to use it. These aspects have been recog-nized and consequently NAWCADa intend to issue a revision of the military standard MIL-STD-889Cb which will rely on calculation of corrosion current as a metric rather than galvanic potential difference.

This paper demonstrates how the mixed potential theory combined with qualified electrochemical data (polariza-tion curves) is leveraged in Corrosion Djinn™, an easy to use software tool to accurately calculate galvanic corro-sion risk. Thinner electrolyte coverage can lead to higher corrosion rates due to higher access and availability of oxygen for the cathodic oxygen reduction reaction – this too can be calculated by using the appropriate polariza -tion curve. The paper also discusses results from 3D analyses of more complex components, using the same electrochemical database and approach.

a NAWCAD Naval Air Warfare Center Aircraft Division,b MIL-STD-889C, Dissimilar Metals, revision C (NAWCAD, 22 August 2016), Code 4.1.2, Mail Stop 120-3,Route 547, Joint Base-MDL, NJ 08733-5100, or emailed to [email protected].

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Paper No. 0218_0314_000072

2019 Department of De-fense – Allied Nations

Technical Corrosion Con-

mailto:[email protected]

INTRODUCTION

Corrosion prevention and control is a major cost driver [1] for military assets. In particular, galvanic corrosion be-tween dissimilar materials is a primary cause of structural failure that puts aircraft and other equipment out of ser -vice. Because so many disparate materials are used, driven by structural performance needs, galvanic corrosion is the most serious corrosion mechanism in aircraft. In addition, the combined effects of environment and loading experienced by aircraft may also accelerate protection system breakdown and support the transition of localized corrosion damage to environmentally-assisted cracking and corrosion-fatigue. A major long-term concern is degradation due to localized corrosion and stress corrosion cracking, especially on high strength aluminum alloys. A U.S. Air Force study concluded that 80% of structural failures originated from corrosion pits and cracks com-monly initiated from galvanically driven corrosion pits around fastener holes [2].

Meanwhile, environmental regulations such as European Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) and Restriction of Hazardous Substances (RoHS) are forcing OEMs to replace legacy cor-rosion control coatings and pretreatments containing for example, hexavalent chromium (Cr6+) and cadmium (Cd) with a variety of alternative materials and coatings that may not work as well or as reliably.

To minimize galvanic corrosion, designers always turn to standards or OEM design specifications that rely on the well-known galvanic series. As long-standing theory and recent work has shown, galvanic tables are poor predic-tors of galvanic corrosion severity because they are mainly based on differences in galvanic potential between materials. However, severity does not only depend on galvanic potential difference, but on galvanic current across an interface. For this reason, NAVAIR, the custodian of MIL-STD-889 is beginning the task of adopting this funda -mental science-based approach i.e. to base it on galvanic current instead of potential difference [3].

Currently there exist established computational methods for performing galvanic corrosion analysis using 1-di-mensional (1D) and more complicated 3-dimensional (3D) approaches. Complex 3D problems are usually solved using finite volume, element or boundary element methods. Regardless of the method used, the key to successful and accurate computational analysis is of course the quality and validity of the input data, which essentially de -fines the model prediction accuracy and relevance to reality that it represents. So, there is an instant need for rel -evant and robust electrochemical data for assuring accurate prediction.

GALVANIC CORROSION

Electrochemical corrosion is, in principle, characterized by localization of the anodic and cathodic processes in dif-ferent regions. These different regions can be found on the same metal surface between different microscopic phases, as well as between two dissimilar materials electrically in contact with each other in the presence of a conductive electrolyte. The direction of the electrochemical process between two dissimilar metals or phases de-pends on the difference between the two Gibbs free energies, ∆G [4].

The tendency for a bimetallic couple to corrode can be expressed in terms of a thermodynamic parameter, the electromotive force (EMF) or the static corrosion potential of the corrosion cell [5]. The polarity and direction of galvanic current flow between two connected bare materials is determined by the thermodynamic reversible po-tentials of the materials. The material which has a higher reversible potential in the (EMF) series or galvanic se -ries is the cathode (noble material) in the galvanic couple. The relation between ∆G and EMF is defined by:

∆ (∆G )=−∆E .n . F (1)

where

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n = Number of electrons taking place in the reaction

∆E = EMF (difference between the EMFs of the two members of the corrosion couple

F = Faraday’s constant

In a galvanic system, current flow between anode and cathode either on a single material or between two different materials will take place by the movement of electrons from anodic to cathodic regions and in the solution by movement of cations from the anodic to the cathodic regions and movement of anions in the opposite direction. The material effect of galvanic corrosion will therefore appear principally on the anodes. On the cathodic acting ar-eas there will be no appreciable loss of material since it is being galvanically protected by anode.

GALVANIC CORROSION RESISTANT DESIGN TODAY

Today, the susceptibility of materials to galvanic corrosion is usually ranked according to the galvanic potential se-ries and this ranking method has been widely used in engineering [6][7]. Other than MIL-STD-889C, the primary specifications governing galvanic corrosion prevention in defense systems are MIL-DTL-14072[9] for ground sup-port equipment, MIL-STD-1250A [10] for electronics. These standards, in common with all other galvanic design guides such as ASTM G82-98 [11], are based on the galvanic potential difference (ΔE) between two materials. They mandate design based on galvanic potential tables, most of the underlying data for which is half a century old [12]. Figure 1 shows a typical galvanic series and table extracted from standards being used today.

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Figure 1 Example of galvanic series and table based on potential difference [11]

This type of classification of metals and alloys, although provides quite useful indication of general trends in gal -vanic corrosion, however does not help much to understand the actual process of galvanic corrosion when dissim-ilar metals are coupled since the electrochemical potential series is obtained for individual metals under “uncou -pled” condition [13][14][15]. The electrode potential on these galvanic series is a measure, under certain fixed conditions, of the thermodynamic tendency of a metal to undergo a corrosion reaction or lose an electron, provid -ing no information concerning the kinetics of the corrosion reaction. So, (ΔE) based on thermodynamics can indi-cate corrosion is possible, i.e., ΔG<0, but thermodynamics cannot predict the corrosion rate. The magnitude of galvanic corrosion depends not only on the potential difference between dissimilar metals but also on the electro-chemical kinetic parameters on each material, electrolyte composition and temperature when they are coupled to-gether. In order to capture the kinetics, one would need the polarization behavior, which is also called the polar-ization curve of each material involved. This is explained in the following sections.

NEW SCIENCE-BASED CORROSION DESIGN – MIXED POTENTIAL THEORY

The equilibrium potential given in a galvanic series is also referred to as the mixed-potential of the particular mate-rial when its cathodic and anodic currents are equal in that specific environment. The same applies to galvanic corrosion between two different materials. When two different materials are electrically coupled in the same elec-trolyte, both materials are polarized to a new potential lying in between the two uncoupled corrosion potentials, which can be explained by mixed potential theory. The application of mixed potential theory was first described by Wagner and Traud based on two simple hypotheses [16]. First, any electrochemical reaction can be divided into two or more oxidation or reduction reactions, and secondly, there can be no net accumulation of electrical charge during an electrochemical reaction.

This can be illustrated by plotting the current produced as a function of applied potential, called a polarization curve. The magnitude of current flow and the increase in corrosion rate due to this coupling are determined by the tendency of each of the materials in the couple to deliver current when its potential is forced to change, called po -larization. If a material delivers little current for a large change in potential, it is said to have a large polarization re -sistance, while if it can deliver a large current for a small enforced potential change, it is said to have a small po -larization resistance. The latter is often the case with aerospace aluminum alloys when they are anodically polar-ized.

For galvanic corrosion, the curve-crossing or intersection of the anodic curve of the less noble metal of the couple with the cathodic curve of the more noble metal gives the galvanic current and potential of the couple for equal surface areas. This is depicted in Figure 2 showing anodic and cathodic polarization curves which are used to vi-sualize the polarization within a galvanic couple. In this case iron is the anode and copper is the cathode in the couple. The solid lines represent experimental polarization curves of copper and iron. The dashed lines represent the polarization curves for the half cell reactions defining the uncoupled corrosion potentials, E corr for anode and cathode, respectively. The couple potential, Ecouple is established where anode and cathode are polarized to poten-tials by the same galvanic current, Icouple.

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Figure 2 Polarization curve-crossing between active material (anode) and less active material (cathode) [17].

Reduction reactions on the anode metal are assumed to be very small compared to such reactions on the cath-ode, shown in Figure 2. That is, Icouple >> Icorr,A. The galvanic current (Icouple) which essentially translates to the rate of galvanic attack on the anodic metal is a more accurate way to assess galvanic corrosion susceptibility of a bimetallic couple since it is a measure of corrosion kinetics. One key assumption of this approach is that there is no ohmic resistance between anode and cathode. Such a condition may occur when e.g. two dissimilar materials are in direct/electrical contact immersed in a very conductive electrolyte e.g. seawater with negligible ohmic drop in the electrolyte.

Galvanic coupling current divided by the anode area gives the average anode current density which is propor-tional to average corrosion or penetration rate. Some knowledge of current distribution on the anode is vital to pre-dict the local rate of galvanic corrosion at any given point on the anode. Here, 3-D modelling, which is essentially also based on mixed potential theory, is vital for prediction of current distribution and local corrosion rate, espe-cially for more complicated geometries.

COMPUTATIONAL METHODS FOR CORROSION PREDICTION

Recent developments in numerical computational methods and hardware have enabled computational techniques to become faster and more efficient, leading to the ability to incorporate greater detail into models and hence to make more precise prediction, provided that the theoretical basis remains valid.

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Modern aircraft design practices mandate sophisticated computational evaluation of stress, heat flow, fluid flow, and even alloy design. In the corrosion world, quantitative prediction of electric current and potential distributions in conductive media has existed for at least five decades [18][19] with some background mathematics evident over a century ago [20]. But corrosion design today still depends on fast-disappearing tribal knowledge, rules of thumb which are often less relevant for new materials, and outdated specifications that sometimes exacerbate corrosion problems rather than solve them.

Attempts to quantify these predictions for the specific discipline of electrochemistry are traceable nearly as far [21], the motivation being applications in corrosion prediction and protection, electrodeposition, and battery tech-nology. The history of this problem depicts a slow development via analytical methods before more rapid ad-vancement using numerical methods began in the late 1970s. For macroscopic galvanic corrosion with different materials, there have been numerous publications laying some good mathematical foundations. Waber et al., for example. made an analytical discussion on the effect of geometrical, polarization and electrolyte parameters on the potential and current density distribution on simple one dimensional electrodes [22-25].

More recent, numerical methods have been used to model and compute the galvanic current and potential distri -butions: (1) finite difference method (FDM) [26], (2) finite element method (FEM), and (3) boundary element method (BEM) [27].

In the present numerical methods, partial differential equations (PDE) are solved based on discretization tech-niques by which the domain of the problem is divided into a number of finite regions called elements, intercon-nected at joints or nodes. The solution for the PDE is then approximated within each element and expressed in terms of nodal values. The problem is then solved by an appropriate procedure for minimizing the overall error caused by the approximation. The degree of accuracy of the result is directly dependent on the refinement of the computational mesh and the order of approximation used.

However, these numerical methods are highly dependent on the quality and relevance of the imposed boundary condition i.e. the polarization curve operating under the given environmental conditions. It is experimentally chal-lenging to obtain the curve, since in many real situations, polarization curves vary as a function of time, potential and to some extent the method used to generate them. So, the relevance and the limits of polarization data must be well understood before using it for modeling galvanic corrosion.

The Office of Naval Research (ONR) set up the Sea-Based Aviation (SBA) program to remedy these design shortcomings, introduce 21st century design practices into corrosion design, and ultimately combine computa-tional methods in both stress and corrosion field to solve the problem of stress corrosion cracking (SCC) that has long been an issue on weapon systems.

1D computational galvanic corrosion predictor

In order to design durable aircraft, i.e. aircraft that are designed with effective protection systems that minimize corrosion and other degradation mechanisms, it is necessary to evaluate correctly the electrochemical interac-tions between materials. Nevertheless, many engineers involved in design activities lack understanding of the is-sues associated with corrosion.

In order to make the approach of assessing galvanic corrosion based on corrosion current accessible to more en-gineers who are not Computer Aided Engineering (CAE) experts, a software tool called Corrosion Djinn™ [28] was developed as one of the outcomes of the ONR SBA program. The aim was to offer an easy-to-use curve-crossing application based on the established principle of mixed-potential theory that calculates the galvanic cur-

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rent and corrosion rate between dissimilar materials, coatings and treatments in common geometries, using a new curated, consistent Electrochemical Database of qualified material polarization curves which are generated using SBA-developed best practices. The software additionally calculates the potential difference between the material being assessed, making it compatible with the old approach.

Corrosion Djinn™ provides design and sustainment engineers with a user-friendly means of predicting the gal -vanic corrosion rate between dissimilar materials. While galvanic tables contain only generic materials, the Djinn™ Electrochemical Database provides data from specific modern alloys, coatings, and surface treatments, and can be readily updated with any specific material or coating that users need, an example of the range of ma -terials is shown in Table 1.

Table 1 Example materials in DjinnTM electrochemical database

Al Alloys1 2024-T3, 2219-T81, 6061-T6, 7050-T7451, 7075-T6Carbon Fiber Composites2 BMS8-212, BMS8-276Copper Alloys Copper, Al-BronzeHigh strength steels 4130, 4340Stainless Steels 15-5 PH, 304, 316, 321, PH 13-8 MoTitanium Alloys Ti6Al4V, Ti3Al2.5VCoatings3 IVD Al, Cd, Cd LHE, nCo-P, ZnNi, ZnNi LHE, SAA, BSAATreatments Seal Cr6, TEA etch, Alodine 600 & 1200, Chromate,

TCP, Iridite 14-2, Cr3 passivate, annealedNotes:1 - Bare and anodized2 - Surface, cross-section, sanded and unsanded3 - Trademark coatings to be added, eg SIFCO, Coventya, Dalic, Corrdesa can acquire materials and coating data if provided with samples

Both CAE and curve-crossing depend on the electrochemical polarization curves of dissimilar materials in an as-sembly. CAE uses Finite Element Analysis (FEA) for the CAD model of the assembly of components, with their materials and coatings wrapped in a layer of electrolyte. This approach maps the current density/corrosion rate onto the surface of the CAD model. By contrast, the 1D curve-crossing approach uses the well-known fact that at equilibrium the anodic and cathodic currents across any galvanic interface must be equal and opposite, see Fig-ure 3. It calculates the curve-crossing current and mixed potential at the interface where the potentials and abso-lute current values are identical.

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15-5PH/Al crossingTi6Al4V/Al crossing

Figure 3 Example of polarization curve crossing of aerospace alloys

Figure 4 shows on the vertical axis both ΔE and curve-crossing galvanic corrosion rate for corrosion of bare and anodized 2024 aluminum coupled with several more cathodic behaving materials. It is immediately apparent that there is no correlation (at least among these common aircraft materials) between corrosion rate and ΔE. In partic-ular, Ti-6Al-4V corrodes Al 2024 almost two orders of magnitude slower than stainless steel does, even though its ΔE is almost identical – a fact that has often been observed by engineers, but could not be accounted for and in-cluded in specifications when relying solely on potential difference as the metric.

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vs Al 2024-T3 bare vs Al 2024-T3 anodized, sealed

Figure 4 Comparison of ΔE and Corrosion Djinn™ galvanic current analysis for predicting galvanic corrosion of Al 2024. The OCP of aluminum is shown in gray

Figure 3 and Figure 4 also show that the curves cross at almost the same current for any aluminum treatment. Thus, despite our best efforts, conversion coatings or anodized layers do very little to protect aluminum against galvanic corrosion in particular, although they work well to protect it from self-corrosion. The standard approach of cadmium plating the cathode (e.g. a bushing) works for a while by eliminating the galvanic driving force, but corrosion initiates as soon as that sacrificial layer is damaged or consumed. To minimize galvanic corrosion, the cathodic reaction on the cathodic behaving material in the couple needs to be suppressed, not necessarily the anodic aluminum. So, in this type of system more cost-benefit may be gained by changing the cathodic material by passivating it or insulating it with a protective layer in order to suppress oxygen reduction reaction.

ELECTROCHEMICAL DATABASE

Whether using simple analysis or complex 3-D computational methods, a reliable database is critical. Stress engi-neers use modern computational analysis with design allowables drawn usually from the Metallic Materials Prop-erties Development and Standardization (MMPDS) database formerly known as MIL-HDBK-5 [29][30]. However, when it comes to corrosion modeling, there are no accessible, yet reliable electrochemical database with polariza-tion curves for accurate galvanic corrosion modeling.

Updating specifications

Recognizing the inaccuracies of the galvanic series approach, MIL-STD-889C is being revised in 2019 [3], so as to base it correctly on galvanic current between dissimilar materials rather than galvanic potential difference. This revision is being managed by NAWCAD. In order to be effective, there must also be available qualified electro-chemical data, so that users can execute their own curve-crossing analyses (or CAE) to predict corrosion current

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and hence corrosion rates. An outcome of the earlier mentioned ONR-SBA project was a measurement protocol that would ensure consistent robust acquisition of the necessary polarization data [31]. This protocol underwent a successful round robin test across DoD, academic, and commercial laboratories and will now become part of the revised MIL-STD-889.

Impact of environment on galvanic corrosion

As discussed earlier, in a galvanic situation, the cathodic reactions on the cathode material, predominantly the oxygen reduction reaction, will govern the galvanic corrosion kinetics and the resulting corrosion rate on the an-ode. The cathodic oxygen reduction reaction rate is mainly affected by the mass transport from and to the elec-trode e.g. due to oxygen transport. Oxygen diffuses much faster through a thin electrolyte film, keeping the sur-face supplied with oxygen ions, which is not the case with bulk electrolyte where oxygen concentration is defined by the bulk solution concentration. Thinner films therefore increase the galvanic current significantly and therefore the corrosion rate.

This increase in cathodic kinetics due to decrease in electrolyte film thickness can be effectively captured in a po-larization curve acquired using the appropriate experimental techniques. Using Corrosion Djinn™ we can quickly conduct a curve-crossing analysis by pulling the appropriate polarization curves from the electrochemical data-base. Figure 5, for example, depicts the impact of film thickness for Al 7075-T6 and cathodic carbon fiber compos-ite (CFC) material couples under bulk conditions (Group 1) and thin film conditions of 20 µm (Group 2). From the graph we can see that the potential difference ΔE (ordinate) between the Al 7075-T6 and the CFC, is more or less the same whether we consider thin film or bulk. However, the corrosion rate, which is derived from the corrosion current (abscissa) at the respective crossing points of the curves (anodic Al 7075-T6, with cathodic CFC curves) is substantially different. From the summary output provided in Table 2 the corrosion rate for bulk (Group 1) is 221 µm/year compared to 2920 µm/year, a factor of 13x, despite the similar potential difference!

To help a designer to quickly assess such scenarios, Corrosion Djinn defines a Galvanic Acceleration Factor (GAF). This is the ratio of the corrosion rate resulting from the galvanic coupling compared to (divided by) the self-corrosion rate of the anodic material when in isolation. In these scenarios, galvanic coupling increases the corro -sion beyond the self-corrosion of the isolated Al 7075-T6. For bulk electrolyte conditions the GAF is 12, that is to say, that when the Al 7075-T6 is electrically connected to the CFC and is submerged under a large thickness (bulk) of electrolyte, the corrosion of the alloy will be accelerated 12x more than the self-corrosion rate. However, if the electrolyte was a thin 20 µm film, the corrosion will be accelerated by 159x.

So, by adopting a curve-crossing galvanic current approach, if one has the data, it is possible to also capture the kinetics change and its consequences on galvanic corrosion due to changes in electrolyte thickness that may re -sult from changing environmental conditions, such as thin film atmospheric corrosion or bulk flow situations that might be the case in accelerated chamber tests, effects that are simply not possible to address with the existing ΔE approach.

COMPUTATIONAL ANALYSIS EXAMPLES

This section presents some examples of the issues involved with common galvanic interfaces in service today, and some demonstrations on how new computational tools are being employed to aid galvanic corrosion assess-ment activity and enable exploration of better solutions to minimize corrosion.

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Bushings and bushing repairs

The result of basing galvanic corrosion decisions on the old ΔE and MIL-STD-889 is clearly seen in every F-18 wing that has seen a few years’ service, as shown in Figure 6. During inspection almost every fastener hole shows traces of galvanic corrosion because of galvanic interaction between the fastener, carbon fiber composite skin and the aluminum airframe. Depot repair is based on MIL-STD-889; the corroded aluminum is ground away, the fastener hole drilled out, and a stainless steel bushing inserted. This makes the corrosion on aluminum even worse around the newly inserted stainless steel bushing, and during the next inspection, again following MIL-STD-889, the bushing is removed, the corrosion once again ground out, and a new, larger bushing inserted. However, from Figure 3 and Figure 4 using mixed potential theory it clearly shows that a far better solution would be to use Ti bushings, even though they are harder to machine.

Figure 5 Assessing impact of film thickness on galvanic corrosion using Corrosion Djinn™

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CFC under 20µm thin film

CFC under bulk

Al 7075-T6

Icouple,bulk

Icouple,thin

film

Current density (A/m2)

Potential (VSCE)

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Table 2 Summary Corrosion Djinn results

Figure 6 Galvanic corrosion inside F-18 wing (NAVAIR Public Release SPR-2012-982)

Rotary Gear Actuators

Rotary gear transmissions are used for the Leading Edge Flap and Wing Fold Actuator Systems (LEFAS and WFAS) on F-18’s and other high performance Naval aircraft. This type of actuator is a complex mixed material problem, with high strength steel gears, high nickel alloy pins, and stainless steel or copper beryllium (Cu-Be) bushings in aluminum or titanium lugs, all unpainted and in close proximity on an exposed area of the aircraft. For the F-18 the result has been a very high SCC failure rate for WFAS lugs (similar to Figure 7 (a)).

As we would expect from Figure 3, wherever a cathodic (noble) bushing is inserted in an anodic aluminum com-ponent corrosion is expected on the aluminum around the periphery of the bushing, no matter how well protected it is (Figure 7 (b)). CAE analysis of the galvanic corrosion rate around the small bushing (left bushing in Figure7(c)) versus the (larger replacement bushing on the right) shows that the galvanic corrosion rate of the aluminum is proportional to the area of the bushing. Since the supply of galvanic current from aluminum to the bushing is de-pendent on the size of the bushing used; carrying out the standard bushing repair of replacing a smaller bushing with a larger one as corrosion proceeds simply accelerates the galvanic corrosion.

Figure 7 (a) F-18 LEFAS, leading edge flap removed, (b) typical corrosion around a bushing, (c) model of Al galvanic corrosion rate around CuBe bushing (blue) in Al alloy LEFAS lug. (NAVAIR Public Release SPR-2012-982)

How, therefore, can we minimize common problems of this type? The answer is a careful CAE analysis of the whole assembly, but a major step would be to make the bushing out of a material such as titanium rather than

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stainless steel. This could have been easily identified using the curve-crossing approach, but is the opposite of what the present MIL-STD-889C prescribes. This is why it is so important to update MIL-STD-889 to base it scien-tifically on corrosion current, and to provide the Electrochemical Database that engineers need to change the standard way of making repairs.

Electrical Connectors

The majority of military or aerospace connectors are cadmium plated and finished with a chromate conversion treatment. The reason for this is that high strength steel and aluminum connector shells must be protected against corrosion, and chromate converted cadmium has been the material of choice for decades. However, with REACH and an increasing emphasis on protecting workers from toxic materials, everyone is looking hard at alter-natives. But, replacing a tried-and-true coating with a new material is a very high risk proposition – how do we know whether the alternative is going to work? Typically, extensive tests are carried out over a period of months or years, but is there a faster, more scientific way to choose an alternative? This was the question that the French connector manufacturer, Radiall, was attempting to solve. Trivalent chrome-passivated zinc nickel (Zn14Ni) had begun to replace chromated cadmium (Cd) on aircraft landing gear, but it was difficult to meet the contact resis -tance requirements for electrical connectors. Other manufacturers were moving towards PTFE-filled electroless nickel on aluminum connectors because that solved the contact resistance problem, but the concern with that ap-proach is that Ni vs Al is a well-known galvanic couple.

Figure 8 CAE analysis of corrosion by EN-PTFE. (a) Cd-plated connector mated to EN. (b) EN-PTFE bolted to Al 6061 electrical box. (Courtesy Radiall, France)

Computational analysis was carried out on male and female aluminum electrical connector shells to determine how different coatings would work when mated to existing Cd-plated electronics or aluminum electrical boxes. The results of curve-crossing and CAE analysis (Figure 8), validated by laboratory testing, were that an EN-PTFE plated connector would corrode the Cd off a mated Cd plated connector, and that bolting an EN-PTFE connector to an Al 6061 electrical box could be a problem depending on how that aluminum is processed. Some Al 6061components have a passivation region that minimizes galvanic corrosion by Ni, and some do not, depending on the heat treat and fabrication method. Additionally, US Navy testing has shown (and corrosion analysis con-firms) that wherever the EN-PTFE coating on an aluminum connector is incomplete or damaged, there will be se -vere galvanic corrosion of the aluminum by the nickel [33].

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Based on these computational analyses and laboratory testing, it was decided that the risk of using EN-PTFE was too high to adopt the EN-PTFE technology.

CONCLUSION

Present methods and standards for assessing galvanic corrosion issues depend simply on the potential difference between the coupled materials by referring to a published galvanic table. This approach may only highlight a pos-sible issue but does not help the user to quantify the corrosion rate, which depends primarily on the galvanic current, not only the potential. Today, science-based corrosion analysis is now possible and finding its way into the mainstream as the modeling techniques are validated and the necessary reliable electrochemical database is developed. Traction in the use of these methods is expected to grow later in 2019, particularly when the revision of MIL-STD-889 dictates that corrosion current is to be used in place of the galvanic potential.

This revised approach for assessing galvanic corrosion risk meets the Navy’s vision of Durable Aircraft, with cor -rosion prevention built into the design and retained in maintenance. With the constant pressure to eliminate toxic materials that we currently use to keep corrosion at bay, corrosion modeling provides a way to minimize the time, cost and risk of adopting new materials and coatings in the face of ever-stricter regulation.

Several in-service scenarios and simulations were presented where the relevance and advantages of this new ap-proach were evident in comparison to the existing method and standard.

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.

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