A numerical and statistical approach to estimate state variables in flow-accelerated corrosion problems - Moura, Bruno Furtado de (2014)
Sequential Monte Carlo (SMC) or Particle Filter Methods became very popular in the last few years in the statistical and engineering communities. Such methods have been widely used to deal with sequential Bayesian inference problems in several fields. SMC Methods are an approximation of sequences of probability distributions of interest, using a large set of random samples, named particles there are propagated along time with a simple Sampling Importance distribution, SI, and re-sampling techniques as well. In this work we applied two Bayesian filters to a state estimation problem involving the corrosion amount-time in a contraction-expansion geometry and compared with Computational Fluid Dynamics (CFD) results. The model adopted to estimate the mass losses is based on a double resistance due to the oxygen diffusion toward the wall through the hydrodynamic boundary layer and the oxide layer. Mass loss data over time is obtained from the literature as well. The main objective of this work is to discuss and compare the performance of the following filters: Sampling Importance Re-sampling Filter (SIR Filter) and Auxiliary Sampling Importance Re-sampling Filter (ASIR Filter) in estimation of the mass losses. Also, it is discussed the influence of the corrosion products. Best results in corrosion damage estimation were obtained using the ASIR Filter.