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.
Highlights
Resin Transfer Moulding (RTM) is a cost-effective and versatile process for the manufacture of components from composite materials
This paper presents a methodology to predict local porosity and permeability of a preform based on Bayesian inference
A Bayesian inversion algorithm starts with an initial guess, a prior distribution in the Bayesian Statistics terminology, of local porosity and permeability
Summary
Bayesian inversion algorithms in the context of RTM are based on measurement of fluid (resin) pressure at several locations in the preform and/or of flow front positions at several moments in time during the process. A Bayesian inversion algorithm starts with an initial guess, a prior distribution in the Bayesian Statistics terminology, of local porosity and permeability This distribution is characterised by an ensemble of samples of these material properties weighted with their probabilities. Once an initial ensemble is generated, the RTM process is simulated for every sample of porosity and permeability fields to obtain pressure values and flow front positions at specified locations and moments in time using a mathematical/computational model,. Based on these simulation results, the Bayesian inversion algorithm iteratively evolves the samples, updating the distribution of local porosity and permeability, so that the calculated pressure values and flow front positions become consistent with the measurements.
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