The long-term purpose of corrosion modeling is to predict corrosion in complex assemblies with variable corrosivity experienced by DoW assets in service around the world, and interface with structural lifetime modeling to accurately capture corrosion effects on lifetime reduction. But such models far exceed current capabilities, and complex computational modeling can be used only by specialists, using data that would be far more extensive than is currently available.
Until recently, predicting corrosion of DoW weapons systems has been based on subjective (or collective) field experience. Determining actual corrosion has always required extensive corrosion testing, usually involving ASTM B117 salt fog testing as well as long-term atmospheric exposure testing, in high-corrosivity coastal locations, followed by final testing on actual assets. This approach is time-consuming and expensive, and does not provide useful predictions of corrosion behavior under the wide range of conditions experienced by weapons systems in service.
The uncertainty inherent in adopting novel technologies is magnified by the lack of a clear standard for evaluating alternative technologies or selecting the best predictive methods for assessing them.
Over the past few years various approaches to modeling corrosion have been developed, based on electrochemical mechanisms. The simplest of these approaches is the electrochemical polarization curve-crossing method introduced in MIL-STD-889D. This method is usable by any engineer, provided the electrochemical data are available, but it is limited to evaluating galvanic corrosion between two materials, and is not suitable for complex assemblies. Far more detailed and complex electrochemical computational methods attempt to take into account the varied materials, geometries, corrosion environments and corrosion mechanisms experienced by weapons systems in service. Yet, these models are typically usable only by experienced computational experts, and often require input data that is not readily available. Additionally, there are still significant gaps in the technology as even the most complex of these models rarely incorporate more than one of the various corrosion mechanisms, they do not include passive and active corrosion mitigation from the organic coating stackups, sealants, etc. used to combat corrosion in actual weapons systems, and they do not link corrosion with structural lifing modeling to capture the effect of corrosion on lifetime reduction. As such, there are significant opportunities to develop these models to be more realistic, easier to use, and enable broader impacts for DoW acquisition and maintenance communities.
