A statistical approach to estimate state variables in flow-accelerated corrosion problems

Abstract

Sequential Monte Carlo or Particle Filter Methods have been widely used to deal with sequential Bayesian inference problems in several fields of knowledge. This technique involves approximation of probability sequences distributions of interest, by means of a large set of random samples, i.e. particles that are propagated along time with a simple Sampling Importance distribution, SI. A re-sampling technique is also used to improve the predictive probability. In this study, a methodology is proposed: apply the Bayesian filters to a state estimation problem involving the corrosion amount-time in a contraction–expansion geometry with the aid of Computational Fluid Dynamics to improve the accuracy of the results. The following filters were applied and compared: Sampling Importance Re-sampling filter (SIR filter) and Auxiliary Sampling Importance Re-sampling filter (ASIR filter). The corrosion model adopted is based on a double resistance due to the oxygen diffusion towards the wall through the hydrodynamic boundary layer and the oxide layer. Mass loss data over time are obtained from the literature to compare corrosion rates. Also, the influence of the corrosion products in rates of corrosion is discussed . Best results in corrosion damage estimation were obtained using the ASIR filter.