Inter-relationship between physical-chemical processes and extreme value modelling Melchers, Robert E.
Of considerable interest in various industries such as aerospace is the longer term safety of aluminium alloy structures and the effect of deterioration. Corrosion of aluminium alloys occurs mainly as pitting for which uncertainty and variability issues usually have led to maximum pit depth being considered a random variable that is also a function of time. An extreme value distribution is fitted to the statistical data obtained from multiple observations. Usually, the selection of the most appropriate model is based on the claim that one or other distribution is a ‘better fit’ to the data. This classical approach takes no account of prior knowledge of the underlying physicochemical process(es) that drive pitting behaviour. A more sophisticated approach uses such prior understanding. Recently it was shown that the linear model implied by the ‘pitting rate’ is too simplistic. Instead, a bi-modal model better represents both mass-loss as a function of exposure period and the evolution of maximum pit depth with time. This leads directly the possibility that one distribution may not be suitable for the whole range of pit depth data. These concepts are illustrated with examples.
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