Structural Properties Measurement: A Morphological Analysis Tool for Transport Properties Determination Jérome Vicente, Frédéric Topin, Lounès Tadrist Ecole Polytechnique Universitaire de Marseille - Laboratoire I.U.S.T.I - CNRS- UMR 6595 Université de Provence Technopôle de Château-Gombert - 5, Rue Enrico Fermi 13453 Marseille Cedex 13 – France The aim of this work is to develop morphology analysis tools to study the impact of foams structure on physical transport properties. The reconstruction of the solid-pore interface allows the visualization of the 3D data and determination of specific surface and porosity. We present an original method to measure the geometrical tortuosity of a porous media for the two phases (solid and pore). This technique is based on numerical fast marching implementation and calculates the geodesics in the medium. A centerline extraction method structures allows us to model the solid matrix as a network of linear connected segments. Results obtained on a set of nickel foams covering a wide range of pore size are discussed. Keywords: metallic foam, morphology, transport properties 1 Introduction The control of the texture of porous materials used for the optimization of compact and multipurpose heat exchangers (boiler, vapo-reformer...) represents a significant technological stake. Indeed, the choice of foam optimized for a given application requires correlating the microscopic structure to the transport properties. A first study showed up the feasibility of the 3D reconstruction and basics measurements on a X-ray tomography of a 10 PPI copper foam 1) . To analyze geometry of foams different methods of visualization, segmentation and morphometry are needed. We develop tools to characterize both pore space and solid matrix as these two phases may have different geometric characteristics that impact on various properties (e.g. heat conductivity is linked mainly to matrix structure, flow laws are governed by pore shape). It provides geometrical measurements (e.g. specific area) and segmentation as an idealized network which gives access to structural properties. Segmentation of pores in individualized cells gives access to both porosimetry and morphometry. A centerline extraction method allows us to model the solid matrix as a network of linear connected segments. As physical transport phenomena are directly linked to the path line notion, we calculate geodesics in the medium using a technique based on numerical fast marching implementation to calculate geodesics in the medium. We then determine geometrical tortuosity of each phase. This approach will enable us to proceed to the morphology analysis in correlation with the physical transport properties obtained via numerical simulations or on experimental data using the tomographied samples. We discuss here the used methods and present several results obtained on a set of Nickel foams (Table 1) 2 Polygonal model Two options are usually available for viewing the scalar volume datasets, direct volume rendering 2,3) and volume segmentation combined with conventional surface rendering 4) . The direct volume rendering only supply images of the data whiles the volume segmentation open access to measurements. Fig. 1 3D rendering: Solid matrix and segmented pores (Sample Ni27-33). We use the classic "Marching cubes" algorithm for extracting interface between the phases 5) . This technique creates a polygonal model that approximates the iso-surface embedded in a scalar volume dataset for a particular iso- value. The surface represents all the points within the volume that have the same scalar value. The reconstruction of the dividing surface between solid and pore allows the visualization of the 3D data (Fig.1). The polygonal surface is created by examining each cube of eight voxels and defining a set of triangles that approximates the piece of the iso-surface within the space bounded by the eight points. The efficiency of the algorithm is due to the limited number of cases (256) for which a surface cuts a cube. This allows their tabulation and reduces greatly the calculations. Due to the variation of level variations of X-Ray reconstructed images, an optimal threshold (iso-density) based on the density histogram was calculated for each images.