CENTRE FOR INFRASTRUCTURE

PERFORMANCE AND RELIABILITY

RESEARCH REPORT

Modelling The Long Term Atmospheric Corrosion Of

Aluminium Alloys

Robert E. Melchers

Research Report No. 278.04.2010

ISBN No. 9780 9807 6180 1

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Modelling the long-term atmospheric corrosion of aluminium alloys Robert E Melchers Centre for Infrastructure Performance and Reliability The University of Newcastle, Australia. Abstract: It is common practice in atmospheric corrosion to fit the power-law function as a 'model' to help predict longer term corrosion loss or maximum pit depth. This function is also known as the 'log-log model' since it plots as a straight line on log-log paper. It used extensively including for aluminium alloys. A review of the relatively few data sets for long-term corrosion of aluminium shows systematic variation from the log-log power law model. Instead, in many cases the data can be interpreted as consistent with the multi-phase model previously proposed for steel corrosion in marine exposure conditions. Consideration is given to why the multi-phase model appears to be applicable also to long-term corrosion of aluminium alloys. This includes the important effect of corrosion products on oxygen diffusion, even though corrosion products of aluminium tend to occur in localized areas, that almost always involves pitting rather than general or uniform corrosion. It is proposed that the build-up of corrosion products forces a change to the chemical reactions responsible for aluminium corrosion, specifically forcing a change from cathodic oxidation and polarization in the shorter term to cathodic hydrogen reduction and polarization in the longer-term. The greater possible rate of outward diffusion of hydrogen compared to inward diffusion of oxygen is primarily responsible for the bi-modal character of corrosion loss or maximum pit depth as a function of exposure time. Keywords: Aluminium, corrosion, long-term, trends, models. Introduction Aluminium and aluminium alloys generally display very favourable corrosion resistance, with low general corrosion and, often, limited pitting even in marine conditions (Schumacher 1979, Kreysa and Eckermann 1992, Vargel 2004). Aluminium and its alloys are of particular interest in aerospace, defence and marine applications. Increasingly they are finding application in structural and other commercial infrastructure. Particularly for the latter longer-term durability is an important performance requirement. In turn this makes it desirable to be able to predict long-term corrosion loss, material strength loss or maximum pitting depth as a function of exposure time. The design of most infrastructure is based on mathematically-based engineering models derived from experience, observation and data. Evidently, for long-term prediction of corrosion this implies the availability of suitable models for long-term corrosion. Ideally such models are based on reasonably accurate representations of field data and on adequate understanding of the trends in that data. As will become evident, the currently available models have some deficiencies. Because of the good resistance of aluminium and aluminium alloys, the usual measures used for corrosion studies, such as mass loss and maximum pit depth tend to have very low values and also to show considerable variability. For this reason, measures such as average mass loss, average pit depth or average loss of tensile strength have been employed in many studies. These as well as maximum pit depth, will be used herein, as dictated by the source of the data. As will be seen, this does not affect the overall conclusions. While there are a number of studies of the short-term corrosion of aluminium and aluminium alloys with time (Szklarska-Smialowska 1999), there are relatively few data sets for long-term exposures. These include the 20 year data for a variety of environments including coastal marine in the USA (Walton and King 1955), the 16 year study of aluminium pitting and mass loss in marine

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environments in the Panama Canal Zone reported by Southwell et al. (1965), the 7 year atmospheric corrosion observations reported by Ailor (1968). the 10 year observations by Mattson and Lindgren (1968) and the 30 year data reported by Sowinski and Sprowls (1982) for mainland USA. There is also data reported for a 10 year study on the Spanish Mediterranean coast that included a number of aluminium alloys (Otero et al. 1978). This data appeared to show an approximately linear function between mass-loss and exposure time but not for maximum pit depth and for the average of the 15 deepest pits. For these an empirical semi-log format was proposed: c(t) = a + b log t (1) where c(t) is the pit depth, t is exposure time and where a and b are constants obtained from fitting the function to the data. The most widely-applied relationship, both in laboratory studies and in empirical field studies, however, is that the so-called power-law given by c(t) = AtB (2) with c and t as before and with A and B constants obtained from fitting the function to data. Expression (2) was derived originally for the atmospheric corrosion of copper by Tammann (1923). It was based on the mathematics of the diffusion of oxygen through the increasing thickness of the corrosion product formed on the surface of a metal as it corrodes. It has since been applied also to other metals and according to Pourbaix (1982) equation (2) was applied first, entirely empirically, and apparently without reference to the original theory, by Passano in 1934. The theory for the original derivation was refined somewhat by Booth (1948) and by Evans (1960). However, only recently it was pointed out that (2) has the unfortunate requirement that at t = 0 it has an infinite corrosion rate, which is at odds with reasonable expectations. Moreover, there were several severe approximations in the derivation and these could be removed, provided the model (2) is taken as inappropriate for very early corrosion, since at that time kinetics and diffusion rather than mainly diffusion considerations are likely to be governing. With this interpretation (2) can be fitted reasonably well to actual shorter-term corrosion loss data (Melchers 2003a). The classical function (2) has been claimed to be appropriate to represent data trends found in many corrosion studies. For short-term laboratory studies of maximum pit depth values of B between 0.33 and 0.5 were found (Aziz and Godard 1952, Godard 1960). Equation (2) was considered to be appropriate by Sowinski and Sprowls (1982) for various grades aluminium and aluminium alloys exposed to inland US conditions up to 30 years, even though B varied from 0.33 to more than 1 while 'eye-balled' curves could be considered a better fit. Similarly, Morcillo et al. (1995) considered (2) to be an appropriate representation for corrosion loss for exposures up to 16 years at a number of Spanish atmospheric sites. Similarly, de la Fuenta et al. (2007) in a recent review of all available data, including that for short exposure periods, concluded that (2) provides the most appropriate representation for all longer-term exposure data. And, in a recently published study of 10 and 20 year observations for mass loss obtained in mainland China, Sun et al. (2009) claimed (2) correlated well with their data. Usually the claim that (2) provides a good fit to is based on plotting the data on a log-log plot. It is not mentioned that deviations from (2) become less noticeable on a log-log plot. In fact, impartial close examination of most of the longer-term (10+ years) exposure data sets does not show a particularly good fit. It also shows that the variations of the data about (2) tend to be systematic rather than random, as would be expected if (2) were indeed a sound model. The empirical evidence

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for this will be given in the next section, using data available in the open literature. It will then be proposed that much of the long-term data is more properly consistent with the multi-phase (bi-modal) model (Figure 1) previously advanced for steel corrosion in marine exposure conditions (Melchers 2003b, Melchers and Jeffrey 2008) provided allowance is made for the generally slower and less severe average rate of corrosion of aluminium compared with steel. One important difference, however, is that unlike steel, aluminium exposed to fresh, seawater or marine atmospheric environments is known not to be prone to significant microbiologically influenced corrosion (MIC) caused by bacteria. It is unlikely that its proneness to fungal attack in fuel tank systems is relevant to marine environments (Vargel 2004).

Figure 1. Schematic of the bi-modal model for long-term corrosion loss and pit depth for steel in marine environments After the review of a number of empirical observations, consideration will be given to the question why the multi-phase model might be applicable to the long-term corrosion of aluminium alloys. It will be proposed that it is the result of the effect of the build-up of corrosion products in reducing oxygen diffusion (and that of pollutants) both to the corroding surface and to localized areas of corrosion (pitting). It will be proposed that his process is likely to influence the reactions involved in subsequent, long-term, corrosion and be responsible for generating the second mode in the bi-modal behaviour seen in various data sets. Data and interpreted data trends As is well known, when exposed at ambient temperatures to environments containing oxygen aluminium develops a thin protective oxide film. The associated mass-loss usually is very small and any corrosion loss tends to be non-uniform over the metal surface. However, under low oxygen conditions (Schumacher 1979, Kreysa and Eckermann 1992) pitting tends to be more severe and deaerated conditions are known to be particularly harmful (Reinhart and Jenkins 1972). Thus 'general' corrosion as often derived from mass-loss has little meaning. For this reason the most common measures for aluminium corrosion are average mass-loss, maximum pit depth, average pit depth and loss of tensile strength. These measures are used also in this section, in each case as dictated by the original source data. An early set of 20-year exposure test data for loss of tensile strength and for a variety of sites in the USA, was reported by Walton and King (1955). Many of the data sets show inconsistencies such as isolated later observations showing lower losses than earlier observations. This appears to be more prevalent for data sets with low overall levels of corrosion and presumably reflects both the natural variability in micro-environments for highly localised exposure conditions and the usual variability expected in corrosion data. However, the data sets for coastal sites show generally higher corrosion losses and less inconsistency, as illustrated in Figure 2.

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Figure 2. Loss of tensile strength trends for several grades of aluminium under coastal marine atmospheric exposure conditions at La Jolla, California. Data from Walton and King (1955). The bold trends shown were obtained by the best-fit curve-fitting routine supplied with Kaleidagraph. Where both light and bold lines are shown for the same data points the light lines are best-fits and the bold lines are subjective fits based on a bi-modal trend curve. In Figure 2 it is evident that the data has a distinct bi-modal character even though there are some inconsistencies. One of these is marked with an arrow. While it is now impossible to trace the data and its reporting, the reported data point is completely out of character and it may be speculated that the most likely possibility is that there was a transcription error. Nevertheless, all data sets show the essential bi-modal characteristic. There is a very extensive set of data for corrosion loss and for pit depth for a large variety of metals and non-metals exposed in the Panama Canal Zone. Data sets for aluminium Al 1100 and Al 6061-T exposed for 16 years to fresh water immersion and to seawater immersion and mid-tide conditions are shown in Figures 3 and 4, based on data reported by Southwell et al. (1965). In all cases it is seen that there is an increase in long-term average mass loss, inconsistent with the power law model (2). In contrast, little or no interpretation of the data is required to draw bi-modal trend curves.

Figure 3. Data and interpreted trends for Al 1100 exposed to marine immersion, marine tidal and freshwater immersion conditions. Data from Southwell et al. (1965). The lines (light and bold) have the same meaning as in Figure 2.

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Figure 4. Data and interpreted trends for Al 6061T exposed to marine immersion, marine tidal and freshwater immersion conditions. Data from Southwell et al. (1965). Some average corrosion loss data obtained for much shorter exposure periods (up to 5 years) but at higher atmospheric temperatures are shown in Figure 5 for coastal atmospheric marine conditions at a number of sites in India. Figure 6 shows two sets of data for industrial atmospheres (Natesan et al. 2006). In all cases the data is either clearly bi-modal or can be interpreted as such, despite the short overall exposure period. Both Figures 5 and 6 show some data that is more clearly consistent with a delayed start to exposure, as is typical for situations where there is low moisture and / or low temperatures after first exposure. Similarly, for somewhat longer exposure periods but for much colder conditions, much of the bi-modal trend is evident for several different aluminium alloys in the 10-year data shown in Figures 7 and 8 for industrial and coastal atmospheric conditions in Sweden. Another example is shown in Figure 9 for 4 year data for marine environments (Vilche et al. 1995). Figure 10 shows data for marine-urban exposures and industrial-urban exposures at some sites in Russia over a 10 year period (Mikhailovzki and Strekalov 1982) and in both cases the bi-modal characteristic is evident in the data, without interpretation.

Figure 5. Data and trends (interpreted where shown) for unspecified aluminium exposed to marine atmospheric conditions at several sites in India. Data from Natesan et al. (2006).

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Figure 6. Data and trends (interpreted where shown) for unspecified aluminium exposed to industrial atmospheric conditions at two sites in India. Data from Natesan et al. (2006).

Figure 7. Data and best fit trends (one with a slight interpretation) for aluminium alloys exposed to industrial atmospheric conditions in Sweden. Data from Mattson and Lindgren (1968).

Figure 8. Data and best fit trends for aluminium alloys exposed to coastal marine atmospheres in Sweden. Data from Mattson and Lindgren (1968).

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Figure 9. Data and best fit trends as obtained from a plotting routine, for aluminium alloys exposed to coastal marine atmospheres in Argentina. Data from Vilche et al. (1995). In the case of Jobany the data point at 3 years exposure was quite inconsistent both with the earlier and later data but also compared to a parallel study for zinc. The point shown is estimated based on the consistent trend in the zinc data. In all cases the original data had been converted to annual corrosion rates as reported in the published paper and this may have been a factor (such through an error in conversion).

Figure 10. Data and piece-wise linear trends for 99.5% pure aluminium exposed to industrial-urban and marine-urban atmospheres in Russia. Data from Mikhailovzki and Strekalov (1982). Recently new data have been reported for some other aluminium alloys (Sun et al. 2009). Although again not all of this can be considered for long-term trends owing to inconsistencies in the data, for the data with higher overall corrosion Figures 11 and 12 show data and trends (one interpreted, as shown) for industrial, marine coastal and urban exposures for Al 2024 and Al 7075 respectively. Some of the data trends show the bi-modal characteristic clearly, while some others (Figure 12) show the early part of such a trend, without interpretation.

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Figure 11. Data and trends (interpreted where shown) for Al 2024 for industrial, marine coastal and urban exposures. Data from Sun et al. (2009).

Figure 12. Data and trends, without interpretation, as obtained from a best fit plotting routine, for Al 7075 for industrial, marine coastal and urban exposures. Data from Sun et al. (2009). Finally, Figures 13 and 14 show data over a 16 year period for 99% pure aluminium exposed to severe marine, mild marine, industrial, rural and urban atmospheres in Spain (de la Fuente et al. 2007). In all cases the curve-fitting routine produced the bi-modal curve-fits shown. Interestingly, in a parallel study for the exposure of steel at the same sites Morcillo et al. (1995) earlier had noted that the data for steel, when plotted on a log-log plot, showed two trends, contrary to the single trend expected if the data was consistent with the conventional power-law model (2). These authors did not explore the issue in any detail but suggested the dual trend behaviour might be the result of low aggressiveness of the rural and urban environments, and short wet-dry cycles, as well as protective corrosion products. They also suggested that the power-law model could be modified, purely empirically, to c(t) = A t1B-b t b where t1 is the first period, with short-term exponent B and b the long-term exponent. Similar dual trending behaviour can be seen also for aluminium when the data in Figure 14 is replotted on a log-log plot, simply by changing the axes to lognormal (Figure 15). It shows two trend lines for each data sets (in bold) as against single trend lines such as AB (shown with broken lines). This indicates, again, that (2) is not the most appropriate model for this data.

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Figure 13. Data and trends, without interpretation, as obtained from a best fit plotting routine, for 99% pure aluminium exposed to severe marine, mild marine and industrial atmospheres in Spain. Data from de la Fuente et al. (2007).

Figure 14. Data and trends, without interpretation, obtained from a best fit plotting routine, for 99% pure aluminium in rural and urban atmospheres in Spain. Data from de la Fuente et al. (2007).

Figure 15. The same data as in Figure 14 plotted on a log-log plot and showing for each data set dual trend behaviour, consistent with the bi-model model.

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In summary, all of the above selected examples for corrosion loss, pitting depth or loss of tensile strength as a function of time show evidence of what has been termed the 'bi-modal' model (Figure 1). Evidently, the data are from a wide variety of independent sources, collected in individual test programs, mostly many years apart and from quite different locations. The trends shown are for data obtained where higher levels of corrosion loss or pit depth were evident. Data that exhibited only low levels of corrosion typically showed considerable variability. In most cases this rendered the data unsuitable for distinguishing trends consistent with the minimum expectation that corrosion increased with time. The examples show clearly that the power-law function (2), which is a strictly concave function with a steadily declining instantaneous rate of corrosion (slope dc/dt at any time t > 0), is seen repeatedly to be inappropriate, except perhaps as a gross approximation that ignores the actual trends. In contrast, the data was seen repeatedly to have a continuous monotonic bi-modal functional relationship, similar to that shown in idealized form in Figure 1. In the next section consideration will be given why the model shown in Figure 1 is a more appropriate representation. Corrosion under changing local environments In the following attention will be limited, as in the examples above, to 'wet' environments, such as immersion, tidal and coastal atmospheric environments and others with significant rainfall or other wetting processes. It is noted that the corrosion of aluminium under these conditions is known to occur mainly in localized areas as pitting, including undercutting and not as uniform corrosion, although layers of corrosion products that are dense and of low permeability usually occur also (Szklarska-Smialowska 1999, de la Fuente 2007). Hydrogen has been observed to evolve from corrosion pits. It is proposed that these observations provide important pointers to the reasons for the observed bi-modal behaviour. Hypotheses 1. It is proposed that as corrosion and pitting progress, the metal surface becomes highly non-

uniform under the layers of exterior corrosion product even when such layers are relatively thin. 2. The inward diffusion of oxygen from the external environment (water, air) becomes increasingly

more difficult as the corrosion products, both on the exterior surfaces and over pits, build-up. 3. Within the more severely (deeper) corroded parts of the surface anoxic conditions may develop.

This will permit cathodic hydrogen reduction to become thermodynamically possible. 4. When cathodic hydrogen reduction takes over, the rate of corrosion is governed by the rate of

outward gaseous hydrogen diffusion through the exterior corrosion product layers and the corrosion products over the pits.

5. Hydrogen molecules are much smaller than those of oxygen and thus, since the amount and presumably the diffusion permeability, of the corrosion product remains essentially the same during this take-over period, the localized switch to hydrogen diffusion is likely to produce a faster, local, instantaneous rate of corrosion. When this occurs over a sufficient number of areas, a considerable increase in the rate of corrosion is produced resulting in the inflexion in the bi-modal characteristic.

6. With time the corrosion products build-up further and, together with cathodic hydrogen polarization, produce a long-term decline in the rate of corrosion.

In general terms, the build-up of corrosion product initially is responsible for cathodic oxygen polarization and this could be represented, approximately at least, by the classical power-law relationship (2). This is consistent with the early part of the bi-modal model (Figure 16). However, when the change from cathodic oxygen to cathodic hydrogen reduction occurs, step 5 implies a 'sudden' increase in the instantaneous rate of oxidation and hence of corrosion, and a rapid departure

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from (2). With time and further build-up of corrosion products further cathodic polarization will occur but now for hydrogen reduction, consistent with the slow decline in instantaneous corrosion rate for longer term exposures (Figure 16). In any actual situation it is likely that both processes proceed, with hydrogen reduction applicable in the deeper, more protected parts of the corroded surface, consistent with the observation of gaseous hydrogen evolution.

Figure 16. Schematic model showing, at the red circle, the change in corrosion rate (green), controlled by oxygen diffusion to the higher corrosion rate (green arrow) controlled predominantly by hydrogen diffusion. The polarization of each of these processes is shown also. The above set of hypotheses will now be outlined for two cases one for pure aluminium in pure water and one for pure aluminium in seawater. Case 1 - Wet unpolluted environments Because aluminium has an affinity for oxygen, including at ambient temperatures, in air it very quickly develops a thin, compact and highly corrosion resistant oxide (passive) film by direct oxidation of aluminium metal to -alumina according to 4Al + 3O2 2Al2O3 (3) In the presence of water or moisture the g-alumina quickly hydrolizes, first to a thin (2-3nm) layer of boehmite [-AlOOH]: 2Al2O3 + 2H2O 4AlOOH (4) and subsequently to bayerite [Al(OH)3 or Al2O3 . 3H2O] that, in mildly corrosive environments, can have thicknesses around 20-40 microns and more (70 microns) in industrial atmospheres (de la Fuente et al. 2007) AlOOH + H2O Al(OH)3 (5) Evidently, reactions (4) and (5) are not directly involved in the oxidation of Al but only in phase changes. Reaction (3), and its variants (4) and (5), involves a change of free energy of -394 kJ/mol of Al and is exothermic. As the corrosion products build-up, either on the surface of the metal or over the pits, localized anoxic environments may form. These are regions, such as within pits, to which oxygen cannot diffuse sufficiently fast to sustain oxygen as an electron acceptor. Under these conditions the dissolution of water may permit cathodic hydrogen reduction 2H2O + e- 2OH- + H2 (gaseous). The overall oxidation reaction is then

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2Al + 3H2O Al2O3 + 3H2 (gaseous) (6) which has a free energy change of -433 kJ/mol of Al and is exothermic. This is thermodynamically favoured over (3-5) and will therefore be the more likely reaction provided the local conditions have become sufficiently anoxic to permit it to occur. In this case there is no oxygen diffusion involved as the rate limiting step. Instead, the rate limiting step is the outward diffusion of hydrogen gas through the protective corrosion product layers. This reaction can continue to occur provided the local pH remains sufficiently low. This is the usual case for corrosion within pits (Szklarska-Smialowska 1986) and likely to remain so owing to the high concentration of H+ ions while in the process of H2 (gas) formation. With the availability of sufficient moisture, the Al2O3 in (6) may hydrolyse to Al(OH)3 similar to the hydrolysis from (3) to (5). Within pits and without transport of dissolved species this may produce a small increase in local pH owing to the slight solubility of Al(OH)3 (Alavi and Cottis 1987). Case 2 - Wet chloride environments Reaction (3) remains valid even in the presence of chlorides, such as sodium chloride present in seawater or marine aerosols. For seawaters account should be taken also of other constituents. Seawater is supersaturated with calcium carbonate CaCO3 and under the usual conditions with seawater pH at around 8.2 calcium bicarbonate Ca(HCO3)2 is the dominant form of CaCO3 present in seawater (Brown and Lemay 1981). The initial oxidation reaction is 4Al + {NaCl}+{Ca(HCO3)2} + {H2O} + 3O2 2Al2O3 + {NaCl} + {Ca(HCO3)2} + {H2O} (7) which is effectively the same as for (3) since both NaCl and Ca(HCO3)2 are here seen as acting, chemically at least, as spectators, shown here with { }. This is despite NaCl widely being held responsible for aggressive corrosion conditions. Also, in general the possibility of calcium bicarbonate entering into reactions for seawater corrosion must be considered. As before in (4) and (5), the product Al2O3 may be hydrolized. Since (7) is essentially identical to (3) it has the same free energy and is exothermic. In marine or industrial environments the aggressive ions typically present will cause the base material to experience localized attack, usually in the form of pitting. For seawater the corrosion products commonly associated with chloride-induced pitting and in particular the corrosion products within pits are AlCl3, Al(OH)Cl2 and Al(OH)2Cl (Foley and Nguyan 1982, Szklarska-Smialowska 1999). These are all highly acidic, with pit solution pH in the range 1-3. It can be shown that with Ca(HCO3)2 included in the chemical reactions the free energy changes are greater than without. This consistent with laboratory observations for iron (Davies and Burstein 1980). Therefore only these reactions are considered here, as follows: 4Al + 12NaCl + 6Ca(HCO3)2 + 6H2O + 3O2 4AlCl3 + 12NaHCO3 + 6Ca(OH)2 (8) which has a net free energy of -507 kJ/mol Al and is exothermic. 4Al + 8NaCl + 4Ca(HCO3)2 + 6H2O + 3O2 4Al(OH)Cl2 + 8NaHCO3 + 4Ca(OH)2 (9) with a free energy change of -650 kJ/mol Al. This reaction is exothermic. 4Al + 4NaCl +2Ca(HCO3)2 + 6H2O + 3O2 4Al(OH)2Cl + 4NaHCO3 + 2Ca(OH)2 (10)

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for which the free energy change is -732 kJ/mol Al. This reaction also is exothermic. Evidently, of the last three reactions, (10) has the greatest net free energy change and is thus the reaction most likely to occur, eventually, in preference to (7) provided the local conditions are sufficiently anoxic to permit it to occur. Although Al(OH)Cl2 is thermodynamically favoured over either Al(OH)2Cl or AlCl3, definitive information about the aluminium complexes actually observed in and around pits after long-term corrosion in chloride environments is scarce (Foley and Nguyen 1982, Graedel 1989), despite detailed analysis of the corrosion products. All three products appear to have been observed (de la Fuente et al 2007, Sun et al. 2009). Each of reactions (7-10) have oxygen as the sole electron acceptor. In each case the rate-limiting step is the diffusion of oxygen through the corrosion products. As before, it may be hypothesised that with time the rate of oxygen diffusion to the corroding surfaces will be insufficient and lead to anoxic conditions. This will then permit corrosion to occur according to (6). In all of the above expressions the theoretically required corrections for the concentration of oxygen and NaCl in seawater and for the concentration NaHCO3 in seawater can be shown to be quite small and negligible compared to the free energy values given above. The other reactants and products are either solids or liquids. Discussion The above analysis is based on the hypothesis that as the corrosion products build-up on the metal surface, the transportation of oxygen, as the key electron acceptor, declines until eventually (localized) anoxic environments are created that permit reaction (6) to occur. Aluminium corrosion is known to be non-uniform and to have a high degree of pitting. This means that the corroding metal surface should be considered as highly non-uniform and non-homogenous, the topography of which changes slowly and continuously, and non-uniformly, as corrosion progresses. Similarly, the thickness of the corrosion product layer usually is non-uniform and non-homogenous and also changes and usually increases slowly in thickness as corrosion progresses. The irregularities of these topographies and properties immediately implies differentiation in the local environmental conditions on the corroding surface at any point in time. For pits such differentiation is well-known (Szklarska-Smialowska 1986). This means that the more severely local corroded regions (including pits) or those with greater thickness of local corrosion product, or both, are likely to reach an anoxic state ahead of other regions. Thus the commencement of corrosion according to (6) will be non-uniform over the metal surface. As exposure and thus corrosion continues, there will be a gradual increase in the number of pits or severely corroded regions, within which conditions have become anoxic. Thus there will be a gradual transition from corrosion with oxygen reduction as the primary cathodic reaction to hydrogen reduction as the primary cathodic reaction. Obviously these two processes cannot co-exist at any particular location. For this reason the concept of a non-homogeneous topography for the corroded surface is crucial. Topographical locations on the surface of the metal that permit a sufficient rate of availability of oxygen will have oxygen reduction as the cathodic reaction, while the generally deeper parts of the topography will be favoured by reaction (6). And this balance will change, gradually, with time as the corrosion products build-up, most likely non-uniformly, thereby changing the local availability of oxygen and forcing greater overall participation of (6). As noted, for given corrosion product layers, hydrogen diffusion outward can occur at a much faster rate than oxygen diffusion inward. This is considered to account for the change in the local rate of corrosion at the inflexion point in Figure 16. The actual rates of corrosion, directly resulting from the rate limiting steps depend on whatever local conditions govern the diffusion of oxygen and hydrogen. Direct testing of the hypotheses above for the mechanism of long-term corrosion of aluminium is

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theoretically possible but is likely to require long time frames longer than may be considered acceptable for hypothesis testing. Instead it is proposed that the extensive examples given in Figures 2-14 'illustrate' the point, and allow the inference to be made that the hypotheses are likely to be correct. Apart from the data sets shown in Figures 2-14, the corrosion literature contains a number of other data sets that, although insufficient in time to display the full bi-model characteristic, are nevertheless not inconsistent with it, usually through showing a longer-term data point that is higher than expected (Walton et al. 1953, Schumacher 1979). There are other data sets that have corrosion observations spaced too widely in time to make any conclusion about the nature of the corrosion loss or pit depth as a function of exposure period. In most cases it was assumed that the data can fit the power-law (2) but it is now evident that a different interpretation, namely that of the bi-modal function, should be tried instead for these data sets. There are a number of practical observations about the corrosion of aluminium that support aspects of the bi-modal model. For example, it is well-known that deaerated waters are harmful to aluminium (Kreysa and Eckermann 1992). Evidently, corrosion in such waters essentially would by-pass all the reactions that involve oxygen as the electron acceptor and only (6) would be able to occur. With only the relatively thin passive film formed under atmospheric conditions by reactions (3-5), hydrogen diffusion is likely to be limited by other criteria, such as kinetic rates of hydrogen formation and pH constraints. Similarly, in aerated waters, once pitting had occurred the increasing difficulty of oxygen diffusion into the pits is likely to lead to reaction (6) being the governing corrosion process, liberating hydrogen. Indeed, this phenomenon has been widely reported for laboratory tests (Szklarska-Smialowska 1986). For field observations hydrogen evolution is not likely to be practically observable. In marine exposure conditions the overall corrosion of aluminium tends to be higher in the mean tide zone compared with that in the splash zone but less than in the immersion zone (Figures 3 and 4). However, pitting tends to be slower to initiate in the splash and tidal zones, presumably owing to the greater availability of oxygen to help keep the oxide film intact, but once corrosion pitting initiates it progresses quickly and after some time pit depths are similar for all exposure zones (Schumacher 1979). These observation are consistent with the progressively lower availability of oxygen from the splash to the immersion zone, when this is interpreted as a gradual increase in the amount of metal surface for which anoxic conditions can exist as a result of the undulating surface topography. However, for the pits anoxic conditions are likely to be similar for each of the three exposure zones. It also is consistent with the observation of high levels of pitting for one year exposures at depth (and thus for low levels of dissolved oxygen) off the California coast (Reinhart and Jenkins 1972, Schumacher 1979). Pitting of aluminium can be severe under deposits of corrosion product, compared to elsewhere (Kreysa and Eckermann 1992). This is consistent with the concept of localized anoxic conditions permitting reaction (6). It is also consistent with the observed non-homogeneity of corroded and uncorroded regions on the metal surface and with the large variability observed in the actual depth of pits (Schumacher 1979). This would explain the inconsistencies found for some experimental results for maximum pit depth, as these were measured on different (but nominally identical) coupons exposed for different lengths of time. Unlike steel exposed to marine environments for which bacterial activity has been implicated in corrosion loss and pitting, both for short-term and for long-term corrosion, there is little evidence to date that bacterial activity is involved in the corrosion of aluminium or its alloys in marine exposure conditions (Vargel 2004). In fact, aluminium ions can be detrimental to bacterial metabolism (Amonette et al. 2003). It follows that, for aluminium at least, the bi-modal characteristic behaviour is due entirely to the chemical changes, most likely those outlined above.

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The present paper has focussed on the progression of corrosion mass-loss with time. It has dealt only indirectly with the development of maximum pit depth, although through the mechanisms proposed above, mass-loss and pitting are intimately related in bringing about the change in corrosion behaviour. Data for maximum pit depth for aluminium is much less easily available and shows, typically, high variability for maximum pit depth measurements on different coupons exposed for differing lengths of time. Maximum pit depth measurements on multiple coupons extracted at each time point would be required to obtain sufficiently large samples for good estimations to be obtained of maximum pit depths. To a lesser extent that is the case also for steel, for which a specially designed testing program was required to elicit the necessary data (Melchers 2003c). Unfortunately no such data appear to be available at this time for aluminium. The theoretical discussion given above should provide a sufficiently direct basis for developing the mathematics for the parts of the model of Figure 1 governed by diffusion processes, similar to what has been done for steel (Melchers 2003a). However, at this time it would appear that insufficient is known about the protectiveness and diffusion properties of the aluminium corrosion product layers and in particular their properties over and near pits. Evidently, the development of the parameters of the model is highly dependent on such properties. Until such time as this information becomes available the most appropriate course of action appears to be semi-empirical, with the model calibrated to actual observations and tested against other data (Turnbull 1994). This semi-empirical approach also has been that used to date for steel (Melchers 2003a,b). The final matter of interest is why, apart from observation of a change in behaviour reported by Morcillo et al. (1995), the bi-modal behaviour apparently has not been observed in previous studies. One reason appears to be preconceptions of what ought to be expected rather than detached interpretation. For example, Sowinski and Sprowls (1982) reported extensive data sets for 30 years of exposure. They gave some 'eye-balled' trends (Figure 17) that clearly are not consistent with the power law c(t) = AtB but discarded these in favour of the power law because as they clearly note, that was the expectation for the corrosion loss function. Similarly, there are many other references in the literature to data, not entirely consistent with the power law, being dismissed as caused by experimental error or natural variability or being 'outliers'. This is related to the second reason - the power law has a long history of apparently 'successful' application to empirical data. The third reason follows directly - experiments tend to be planned with the power law in mind and thus crucial changes in trend may be missed by the increasingly wider spacing of observations in time. A final reason could be that the power-law function is sometimes seen as a 'natural' law, having being derived from oxidation considerations. However, as is now apparent, this view overlooks the possibility of alternative electron acceptors.

Figure 17. Data for loss of tensile strength for 3 aluminium alloys showing eye-balled trends (bold) and the power-law (light) fitted to the same data.

16

Conclusions A considerable body of empirical data for long-term corrosion of aluminium, when interpreted without preconception, is consistent with a bi-modal function rather than the conventional power law function to represent the evolution with increased exposure period of corrosion loss, maximum pit depth or loss of tensile strength. It is proposed that the bi-modal function is the result of the build-up of corrosion products and a resulting change in the corrosion process. Initially the corrosion process is rate-controlled by the diffusion of oxygen to the corroding surfaces but as this becomes more difficult localized anoxic conditions develop on the corroding surface. Under these conditions corrosion through cathodic hydrogen reduction becomes thermodynamically possible. This reaction gradually takes over, rate-controlled by the rate of outward diffusion of hydrogen through the corrosion products. With time and the further build-up of corrosion products this reaction slows (polarizes) to a steady-state. The proposed mechanism is consistent with various observations of the longer-term corrosion of aluminium alloys. These indicate that the proposed bi-modal function applies to the corrosion of aluminium alloys in fresh waters, seawaters and in various atmospheric exposure conditions. The present results have implications for the design of long-term corrosion experiments since the use of the conventional power law to determine data collection intervals may mean that crucial changes in corrosion behaviour will be missed. Acknowledgement The financial support of the Australian Research Council is gratefully acknowledged. References Ailor WH (1968) Performance of aluminum alloys at other test sites, (In) Metal Corrosion in the

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