3D geometrical characterization of populations of particles by stochastic geometry and image analysis: application to two-phase flows

This thesis aims at developing a new approach for geometric modeling of two-phase flows, from 2D images of orthogonal projections, with the objective of extracting 3D morphological characteristics of the particles. The study mainly addresses the case of droplets and bubbles of spherical and ellipsoidal shape. Among the existing methods to deal with 2D images resulting from the projection of a 3D particles system, the pattern recognition and segmentation ones are the most common. However, they present major limitations. To overcome these problems, a 3D stochastic geometrical model (a marked point process) is proposed. The model was fitted to the observed data thanks to a numerical optimization process. The method's performance was evaluated on numerical simulations of 2D images resulting from projections of spherical and ellipsoidal particles of known geometry. The accuracy of the model to retrieve the 3D size and shape distribution of the particles was highlighted, even from high density images. Experimental validation was also performed based on fully characterized suspensions of PMMA balls, of different sizes. Finally, in order to characterize typical systems encountered in multiphase flow processes, the proposed approach was applied to a bubbly flow with different gas flow rates (i.e. for several sizes and densities of bubbles). This PhD work illustrates the relevance of stochastic geometrical modeling for the characterization of two-phase flows. It opens up wide perspectives, as e.g. the implementation of more flexible models to better describe the possible interactions (attractions/repulsions) between particles. (author)