The GHM Method provides viscoelastic finite elements derived from the commonly used elastic finite elements. Moreover, these GHM elements are used directly and conveniently in second-order structural models jut like their elastic counterparts. The forms of the GHM element matrices preserve the definiteness properties usually associated with finite element matrices—namely, the mass matrix is positive definite, the stiffness matrix is nonnegative definite, and the damping matrix is positive semi-definite. In the Laplace domain, material properties are modeled phenomenologically as a sum of second-order rational functions dubbed mini-oscillator terms. Developed originally as a tool for the analysis of damping in large flexible space structures, the GHM method is applicable to any structure which incorporates viscoelastic materials.

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The procedure outlined in this paper extends the finite element method to viscoelastic space structures; predictions of mode shapes, frequencies, and damping factors can be made based on a new modeling approach that uses specific measured material data. Briefly, the procedure is as follows: 1) the modulus function of the material is measured from free-decay tests of a uniform cantilever beam of the subject material, and 2) these material data are then inserted into the new damped-structure modeling technique. These two steps are illustrated and validated using a strawman space structure fabricated entirely from a single epoxy material. Predicted damping characteristics based on material data and the new modeling technique are compared with modal data obtained for the test structure. Using commercially available software, the modal test data are processed to obtain mode shapes, frequencies, and damping factors. A comparison between predicted eigenvalues and experimentally determined modal frequencies and damping factors indicates that the proposed new technique is a valuable tool in structural analysis.

Surface registration involving the estimation of a rigid transformation (pose) which aligns a model provided as a triangulated mesh with a set of discrete points (range data) sampled from the actual object is a core task in computer vision. This paper refines and explores the previously introduced notion of Continuum Shape Constraint Analysis (CSCA) which allows the assessment of object shape towards predicting the performance of surface registration algorithms. Conceived for computer-vision assisted spacecraft rendezvous analysis, the approach was developed for blanket or localized scanning by LIDAR or similar range-finding scanner that samples non-specific points from the object across an area. Based on the use of Iterative Closest-Point Algorithm (ICP) for pose estimation, CSCA is applied to a surface-based self-registration cost function which takes into account the direction from which the surface is scanned. The continuum nature of the CSCA formulation generates a registration cost matrix and any derived metrics as pure shape properties of the object. For the context of directional scanning as considered in the paper, these properties also become functions of viewing direction and is directly applicable to the best view problem for LIDAR/ICP pose estimation. This paper introduces the Expectivity Index and uses it to illustrate the ability of the CSCA approach to identify productive views via the expected stability of the global minimum solution. Also demonstrated through the examples, CSCA can be used to produce visual maps of geometric constraint that facilitate human interpretation of the information about the shape. Like the ICP algorithm it supports, the CSCA approach processes shape information without the need for specific feature identification and is applicable to any type of object. Keywords: Shape analysis; pose estimation; best view; computer vision. 45 Int. J. Shape Model. 2009.15:45-76. Downloaded from www.worldscientific.com by UTRECHT UNIVERSITY on 03/15/15. For personal use only.

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