Bayesian inversion algorithm for estimating local variations in permeability and porosity of reinforcements using experimental data
M.Y. Matveev, A. Endruweit, A.C. Long, Marco Iglesias, M. V. Tretyakov
Abstract
A novel Regularising Ensemble Kalman filter Algorithm based on the Bayesian paradigm was applied to RTM processes to estimate local porosity and permeability of fibrous reinforcements using measured values of local resin pressure and flow front positions during resin injection. The algorithm allows to detect locations of defects in the preform. It was tested in virtual experiments with two geometries, a two-dimensional rectangular preform and a more complex 3D shape, as well as in laboratory experiments. In both the virtual and laboratory experiments, it was demonstrated that the proposed methodology is able to successfully discover defects and estimate local porosity and permeability with good accuracy. The algorithm also provides confidence intervals for the predictions and estimations of defect probabilities, which are valuable for analysis of the process.