Probabilistic model for pitting of copper canisters. Issue 1 (29th June 2020)

• Record Type: Journal Article
• Authors: Briggs, Scott; Lilja, Christina; King, Fraser
• Is Part Of: Materials and corrosion. Volume 72:Issue 1/2(2021)
• Page Start: 308
• Page End: 316
• Publication Date: 2020-06-29
• Subjects: copper corrosion -- deep geological repository -- pitting -- probabilistic modelling; Materials -- Periodicals; Metals -- Periodicals; Corrosion and anti-corrosives -- Periodicals; 620.1122305
• DOI: 10.1002/maco.202011784
• Languages: English
• ISSNs: 0947-5117

Abstract

Abstract: Copper canisters for the final disposal of spent nuclear fuel are expected to be subject to general (uniform) corrosion and a limited degree of localised damage. Although the canister is expected to be exposed to predominantly active conditions, it is nevertheless prudent to assess the probability of pitting corrosion in passive conditions. A probabilistic pitting model is developed for aerobic conditions in water‐saturated bentonite. While aerobic, saturated conditions may be unlikely to occur if saturation is slow or O2 consumption is fast, these conditions have been assumed as a starting point because of the availability of data. The model samples from environmental conditions use a Monte Carlo approach to predict active or passive conditions. Under passive conditions, the model compares corrosion potentials to breakdown and repassivation potentials to determine pit initiations and deaths, respectively. Pit depth distributions can then be calculated. For the assumed conditions, the model predicts that active conditions dominate 90% of the time while the remaining passive conditions can lead to pitting during the first few years primarily controlled by the availability of oxygen. Abstract : A methodology has been developed for the probabilistic assessment of pitting of copper canisters in environments expected for the final disposal of spent nuclear fuel. Based on the experimental data sets, a Monte Carlo‐based model was developed to predict active/passive behaviour, as well as pit initiation and repassivation resulting in likely pit distributions under aerated and saturated conditions.