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This thesis presents research into the micromechanical modelling of composite materials using numerical techniques. Composite materials are generally examined from two points of view: macromechanics and micromechanics, owing to their inherent heterogeneous nature. In this research, the material behaviour is examined on a microscopic scale, as the properties of interest, i.e. strength and toughness, are dependent on local phenomena. In general, the strength and toughness of composite materials are not as well understood as the simpler elastic properties, because in many cases the modes of failure under a given system of external load are not predictable in advance. Previous research in this field has typically involved specially designed experiments, theoretical/statistical studies, or the use of numerical models. In this study, advanced implementations of numerical methods in continuum mechanics, i.e. the boundary element and the finite element methods are employed to gain a greater understanding of composite behaviour. The advantage of using numerical methods, as opposed to experimental studies, is that the geometric and material characteristics can be investigated parametrically, in addition to the reduced time and expense involved. However, to model the complete behaviour of real composites is still not possible, due to the degree of complexity and uncertainty involved in modelling the various mechanisms of damage and failure, etc. and also due to the immense computational cost. Therefore, simplified models must be employed which are limited by their assumptions. For the preliminary studies within this thesis, geometrically simplified models are presented to provide an understanding of the influence of embedding second phase inclusions on the local stress fields, and also to validate the numerical techniques with readily available analytical solutions. These models are then extended to accommodate additional phenomena, such as inclusion interaction, spatial inclusion arrangement, material formulation, i.e. consisting of two- and three-phases of various material properties. The influence of such factors on the local stress concentrations, which play an important role in determining the strength of the composite, is analysed through a series of parametric studies. The localised toughening of composites is also considered through novel investigations into the interaction between a propagating crack with inclusions and microcracks. Through the development of the numerical models a more realistic representation of composite behaviour is achieved, which in tum, provides an improved knowledge of the factors that control strength and toughness. Such information is invaluable to composite material designers, who presently rely heavily on experimental studies to develop composite materials.

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Computing marginal likelihoods to perform Bayesian model selection is a challenging task, particularly when the models considered involve a large number of parameters. In this thesis, we propose the use of an adaptive quadrature algorithm to automate the selection of the grid in path sampling, an integration technique recognized as one of the most powerful Monte Carlo integration statistical methods for marginal likelihood estimation. We begin by examining the impact of two tuning parameters of path sampling, the choice of the importance density and the specification of the grid, which are both shown to be potentially very influential. We then present, in detail, the Grid Selection by Adaptive Quadrature (GSAQ) algorithm for selecting the grid. We perform a comparison between the GSAQ and standard grid implementation of path sampling using two well-studied data sets; the GSAQ approach is found to yield superior results. GSAQ is then successfully applied to a longitudinal hierarchical regression model selection problem in Multiple Sclerosis research. / Using an identity arising in path sampling, we then derive general expressions for the Kullback-Leibler (KL) and Jeffrey (J) divergences between two distributions with common support but from possibly different parametric families. These expressions naturally stem from path sampling when the popular geometric path is used to link the extreme densities. Expressions for the KL and J-divergences are also given for any two intermediate densities lying on the path. Estimates for the KL divergence (up to a constant) and for the J-divergence, between a posterior distribution and a selected importance density, can be obtained directly, prior to path sampling implementation. The J-divergence is shown to be helpful for choosing importance densities that minimize the error of the path sampling estimates. / Finally we present the results of a simulation study devised to investigate whether improvement in performance can be achieved by using the KL and J-divergences to select sequences of distributions in parallel (population-based) simulations, such as in the Sequential Monte Carlo Sampling and the Annealed Importance Sampling algorithms. We compare these choices of sequences to more conventional choices in the context of a mixture example. Unexpected results are obtained, and those for the KL and J-divergences are mixed. More fundamentally, we uncover the need to select the sequence of tempered distributions in accordance with the resampling scheme.

Thesis (Ph. D.)--University of Washington, 1988. / Vita. Includes bibliographical references.