In this study, we rely on a Bayesian approach to estimate the seismic velocity from first arrival travel times. The advantage of the Bayesian approach compared to linearized ones is its ability to properly quantify the uncertainties associated with the solution. However, this approach remains fairly expensive, and the Markov chain-Monte Carlo algorithms that are used to sample the posterior distribution are efficient only when the number of parameters remains within reason. Therefore, a first step toward an efficient implementation of the Bayesian approach is to properly parameterize the model to reduce its dimensionality. In this article, we introduce new parsimonious parameterizations which enable us to accurately reproduce the wave velocity field and the associated uncertainties. The first parametric model that we propose uses a random Johnson-Mehl tessellation, a generalization of the Voronoi tessellation. The main difference of the Johnson-Mehl model when compared to the Voronoi model is that the shapes of the generated cells are much more general. The cells of a Voronoi tessellation are indeed convex polytopes, while the Johnson-Mehl tessellation model yields cells whose boundaries are portions of hyperboles and which are not necessarily convex, hence allowing for a greater variety of shapes. We demonstrate the gain in efficiency and the better convergence when compared to the Voronoi model. The second parameterization uses Gaussian kernels as basis functions. Its purpose is to provide a way to reproduce localized variations in the seismic velocity field. We first illustrate the tomography results with a synthetic velocity model which contains two small anomalies. We then apply our methodology to a more advanced and realistic synthetic model that serves as a benchmark in the oil industry. We finally present an example where Gaussian

Cold spray is a promising process to coat polymers and carbon fiber-reinforced polymer (CFRP). The choice of the metal-polymer couple of materials, however, has a strong influence on coating build-up and properties. In the present work, we show that spraying mixtures of copper and polymer particles lead to composite coating. We observe that the polymer promotes coating build-up onto CFRP to the expense of the electrical conductivity of the coating as a result of its insulating properties. The present work investigates the influence of the coating microstructure on electrical conductivity. Various copper powders, with different morphologies, particle sizes and oxygen contents were mixed with a PEEK (Polyaryl-Ether-Ether-Ketone) powder. Cold spray of these powders resulted into composite coatings and we study the microstructures and electrical properties of such coatings as a function of powder characteristics and spraying parameters. A morphological model of the coating microstructure was developed to reproduce numerically microstructures in 3D. The conductivity of the coatings was measured experimentally for various copper powders. Careful selection of blends of copper and PEEK powders coupled with optimized spraying parameters led to metal-polymer coatings onto CFRP with a fairly high electrical conductivity.

This paper describes the development of a numerical homogenization tool adapted to TATB-based pressed explosives. This is done by combining virtual microstructure modeling and Fourier-based computations. The polycrystalline microstructure is represented by a Johnson-Mehl tessellation model with Poisson random nucleation and anisotropic growth of grains. Several calculations are performed with several sets of available data for the thermoelastic behavior of TATB. Good agreement is found between numerical predictions and experimental data regarding the overall thermal expansion coefficient. The results are shown to comply with available bounds on polycrystalline anisotropic thermoelasticity. Finally, the size of the representative volume element is derived for the bulk, shear and volumetric thermal expansion moduli.