Cohesive Elements for Shells

A cohesive element for shell analysis is presented. The element can be used to simulate the initiation and growth of delaminations between stacked, non-coincident layers of shell elements. The procedure to construct the element accounts for the thickness offset by applying the kinematic relations of shell deformation to transform the stiffness and internal force of a zero-thickness cohesive element such that interfacial continuity between the layers is enforced. The procedure is demonstrated by simulating the response and failure of the Mixed Mode Bending test and a skin-stiffener debond specimen. In addition, it is shown that stacks of shell elements can be used to create effective models to predict the inplane and delamination failure modes of thick components. The results indicate that simple shell models can retain many of the necessary predictive attributes of much more complex 3D models while providing the computational efficiency that is necessary for design

Bowing the violin : a case study for auditory-motor pattern modelling in the context of music performance

This project addresses methodological and technological challenges in the development of multi-modal data acquisition and analysis methods for the representation of instrumental playing technique in music performance through auditory-motor patterning models. The case study is violin playing: a multi-modal database of violin performances has been constructed by recording different musicians while playing short exercises on different violins. The exercise set and recording protocol have been designed to sample the space defined by dynamics (from piano to forte) and tone (from sul tasto to sul ponticello), for each bow stroke type being played on each of the four strings (three different pitches per string) at two different tempi. The data, containing audio, video, and motion capture streams, has been processed and segmented to facilitate upcoming analyses. From the acquired motion data, the positions of the instrument string ends and the bow hair ribbon ends are tracked and processed to obtain a number of bowing descriptors suited for a detailed description and analysis of the bow motion patterns taking place during performance. Likewise, a number of sound perceptual attributes are computed from the audio streams. Besides the methodology and the implementation of a number of data acquisition tools, this project introduces preliminary results from analyzing bowing technique on a multi-modal violin performance database that is unique in its class. A further contribution of this project is the data itself, which will be made available to the scientific community through the repovizz platform.