Narrow Results

The main studies on pitting consist in proposing Markovian stochastic models, based on the statistics of extreme values and focused on growing the depth of wells, especially the deepest one. We show that a non-Markovian model, described by a nonlinear Fokker–Planck (nFP) equation, properly depicts the time evolution of a distribution of depth values of pits that were experimentally obtained. The solution of this equation in a steady-state regime is a q-Gaussian distribution, i.e. a long-tail probability distribution that is the main characteristic of a nonextensive statistical mechanics. The proposed model, that is applied to data from four inspections conducted on a section of a line of regular water service in power water reactor (PWR) nuclear power plants, is in agreement with experimental results.

In this paper, a high-throughput droplet method is presented for screening corrosion inhibitors, particularly for those metals that are subjected to pitting. To this objective, AA5083 was used as a case study as it is subject to pitting corrosion in saline solution containing different corrosion inhibitors. This paper outlines how critical parameters are measured and calculated (average pit depth and maximum pit depth) and the errors and consistency of the method, and the definition of corrosion inhibition via the method. The results from this method were then compared to the results of inhibition efficiency derived from potentiodynamic polarization scans for inhibitors with a range of performance. The method was conducted at temperature $T=22\pm 2^{\circ }$C inside a humidity chamber, and concentration 10${}^{-3}$ M of seven chemical compounds that have similarities in their structures. The discrepancies were profound for the cases where the inhibitor was of low efficiency, which is ascribed to the fact that these pits’ growth is dominated by that of a few large pits.