A Review of Trends for Corrosion Loss and Pit Depth in Longer-Term Exposures
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corrosion and materials degradation Review A Review of Trends for Corrosion Loss and Pit Depth in Longer-Term Exposures Robert E. Melchers Centre for Infrastructure Performance and Reliability, The University of Newcastle, Newcastle, NSW 2308, Australia; [email protected] Received: 19 June 2018; Accepted: 17 July 2018; Published: 18 July 2018 Abstract: For infrastructure applications in marine environments, the eventual initiation of corrosion (and pitting) of steels (and other metals and alloys) often is assumed an inescapable fact, and practical interest then centres on the rate at which corrosion damage is likely to occur in the future. This demands models with a reasonable degree of accuracy, preferably anchored in corrosion theory and calibrated to actual observations under realistic exposure conditions. Recent developments in the understanding of the development of corrosion loss and of maximum pit depth in particular are reviewed in light of modern techniques that permit much closer examination of pitted and corroded surfaces. From these observations, and from sometimes forgotten or ignored observations in the literature, it is proposed that pitting (and crevice corrosion) plays an important role in the overall corrosion process, but that longer term pitting behaviour is considerably more complex than usually considered. In turn, this explains much of the, often high, variability in maximum depths of pits observed at any point in time. The practical implications are outlined. Keywords: metal corrosion; uniform; pitting; anoxic; bi-modal; long term 1. Introduction Despite their proneness to corrosion, particularly if not well-protected, mild and low alloy structural steels remain important economic construction materials in marine offshore and marine coastal industrial environments. In part, this is because of their relative ease of use in construction in hostile environments, their high strength to weight ratio and also their relatively lower down-time costs for maintenance. Particularly in the offshore environment, for shipping, naval defence facilities and structures in coastal industrial environments, corrosion is an important aspect of overall facility management [1–3] and estimates of the likely rate or development of future corrosion of considerable practical interest [4]. For these applications, it is recognized that corrosion tests under laboratory conditions, such as short-term exposures in artificial seawaters, electrochemical tests or accelerated tests are of limited value, mainly because the results from such tests are difficult to relate to longer term corrosion behaviour. Because of these practical demands, renewed efforts have been made in the last two decades to develop more realistic models for corrosion. For estimating the likely structural strength and structural reliability of structures, models for predicting the development of general corrosion loss are critical [5]. Pitting is of interest more for liquid retaining or exclusion systems such as pipelines, tanks and cofferdams or sheet piling, but for these, too, prediction of pit depth development is of much interest [3]. Careful examination of data and consideration of the corrosion mechanisms that may be involved have shown that for the general or ‘uniform’ corrosion, or corrosion as measured by mass loss as a function of exposure time, the bi-modal characteristic (Figure1) is more appropriate than earlier models [6]. This model has been shown to be suitable for the longer term exposure of steels, cast irons and aluminium and copper alloys to a wide variety of natural exposure environments, including Corros. Mater. Degrad. 2018, 1, 42–58; doi:10.3390/cmd1010004 www.mdpi.com/journal/cmd Corros.Corros. Mater. Mater. Degrad.Degrad. 2018, 1, x FOR PEER REVIEW 2 of 17 43 including immersion (fresh and marine), tidal and various atmospheric exposure zones [7–9], as well immersionas for ferrous (fresh metals and in marine),soils [10]. tidal and various atmospheric exposure zones [7–9], as well as for ferrous metals in soils [10]. Figure 1. Schematic bi-modal model for long-term corrosion loss (from mass loss) showing the two Figure 1. Schematic bi‐modal model for long‐term corrosion loss (from mass loss) showing the two modes, the various phases and some of the model parameters. In the idealized case, the change modes, the various phases and some of the model parameters. In the idealized case, the change from from predominantly oxic Mode 1 to predominantly anoxic Mode 2 occurs at ta with a change of the predominantly oxic Mode 1 to predominantly anoxic Mode 2 occurs at ta with a change of the instantaneous corrosion rate from r to ra. instantaneous corrosion rate from rb bto ra. WhileWhile thethe actual mass loss loss values values and and the the timing timing of of the the phases phases shown shown in Figure in Figure 1 depend1 depend very very muchmuch onon the metal or or alloy alloy and and on on the the environment, environment, the theoverall overall characteristic characteristic is pervasive. is pervasive. It is at It is atodds odds with with the the conventional conventional power power law law function function that that has has its roots its roots in the in theoriginal original derivation derivation by by TammannTammann inin 19231923 [[11]11] and refined refined and and examined examined by by many many authors authors [12–14]. [12–14 The]. The power power law lawhas has mathematicalmathematical deficienciesdeficiencies in meeting meeting the the conditions conditions for for the the corrosion corrosion process process at t at = t0,= as 0, well as well as other as other issuesissues [ [14],14], althoughalthough it could could be be taken taken as as an an approximation approximation to tothe the bi‐ bi-modalmodal model model for forthe theearly early period period of exposure, that is from Time 0–ta (Figure 1), during which time, the corrosion process is assumed to of exposure, that is from Time 0–ta (Figure1), during which time, the corrosion process is assumed to be predominantly under oxygen diffusion control. This is shown as Mode 1. Subsequently, in Mode be predominantly under oxygen diffusion control. This is shown as Mode 1. Subsequently, in Mode 2, 2, the corrosion processes according to the bi‐modal model are predominantly under anoxic the corrosion processes according to the bi-modal model are predominantly under anoxic conditions. conditions. In each of these two modes, the model has a number of different phases, each of which In each of these two modes, the model has a number of different phases, each of which idealize the idealize the dominant corrosion process at that time. The details have been given many times (e.g., dominant corrosion process at that time. The details have been given many times (e.g., [7]) and need [7]) and need not be rehearsed here. not be rehearsed here. The critical issue for an explanation of the bi‐modal behaviour is the change at ta from a very low t instantaneousThe critical corrosion issue for rate an explanationrb before ta is of reached the bi-modal to ra almost behaviour immediately is the change after it, at althougha from ain very lowpractice, instantaneous there is some corrosion short transition rate rb before period t(Figurea is reached 1). Given to r thata almost the bi immediately‐modal characteristic after it, is although seen infor practice, a range of there aluminium is some alloys short and transition for many period copper (Figure alloys1 ).(but Given not for that ‘pure’ the bi-modal copper in characteristic ‘pure’ water is seen[15]), for as a well range as offor aluminium many different alloys steels and including for many high copper chromium alloys (but steels, not it for follows ‘pure’ that copper it is in not ‘pure’ waterprimarily [15]), a asfunction well as of for the many type of different material. steels Moreover, including it has high been chromiumshown that steels,small changes it follows in steel that it is notcomposition primarily (including a function carbon of the typecontent) of material. have little Moreover, effect on it the has mass been losses shown or that on smallthe bi‐ changesmodal in steelcharacteristic composition [16,17]. (including Similarly, carbon the bi‐ content)modal characteristic have little effect is seen on for the the mass outer losses skin orof cast on the irons, bi-modal as characteristicwell as for machined [16,17]. cast Similarly, irons in thethe bi-modalsame environment characteristic [18]. In is this seen context, for the it outeris interesting skin of that cast for irons, assteels well (and as for cast machined irons) buried cast irons in soils, in the the same degree environment of compaction, [18]. Inand this by context,implication it is the interesting air‐void that forcontent steels next (and to cast the irons)metal surface, buried inhas soils, a major the degreeinfluence of on compaction, the amount and of corrosion, by implication but not the on air-void the contentgeneral nextform to (topology) the metal of surface, the bi‐modal has a characteristic major influence [10]. on the amount of corrosion, but not on the generalUsing form the (topology) limited data of the that bi-modal were available characteristic at the [10 time,]. it was proposed that the bi‐modal characteristicUsing the also limited applies data thatto longer were availableterm trends at the for time, maximum it was proposed and average that pit the depths bi-modal [19]. characteristic Some relationship between them might be expected particularly for metals that corrode mainly by pitting also applies to longer term trends for maximum and average pit depths [19]. Some relationship between since mass loss caused by pitting is included automatically and predominantly in the overall mass them might be expected particularly for metals that corrode mainly by pitting since mass loss caused by loss. In other cases, such as for steels, pit depth usually is corrected by increasing it by the corrosion pitting is included automatically and predominantly in the overall mass loss.