APPROXIMATE GAUSS-MARKOV REALISATION OF MULTIVARIATE STOCHASTIC PROCESSES.

Given a spectral density matrix or, equivalently, a real autocovariance sequence, the author seeks to determine a finite-dimensional linear time-invariant system which, when driven by white noise, will produce an output whose spectral density is approximately PHI ( omega ), and an approximate spectral factor of PHI ( omega ). The author employs the Anderson-Faurre theory in his analysis.

Stable multivariate rational interpolation for parameter-dependent aerospace models

A multivariate, robust, rational interpolation method for propagating uncertainties in several dimensions is presented. The algorithm for selecting numerator and denominator polynomial orders is based on recent work that uses a singular value decomposition approach. In this paper we extend this algorithm to higher dimensions and demonstrate its efficacy in terms of convergence and accuracy, both as a method for response suface generation and interpolation. To obtain stable approximants for continuous functions, we use an L2 error norm indicator to rank optimal numerator and denominator solutions. For discontinous functions, a second criterion setting an upper limit on the approximant value is employed. Analytical examples demonstrate that, for the same stencil, rational methods can yield more rapid convergence compared to pseudospectral or collocation approaches for certain problems. © 2012 AIAA.

A microstructural investigation of deformation in fine SiC particulate- and whisker-reinforced aluminium-matrix composites

Aluminium-based composites, reinforced with low volume fractions of whiskers and small particles, have been formed by a powder route. The materials have been tested in tension, and the microstructures examined using transmission electron microscopy. The whisker composites showed an improvement in flow stress over the particulate composites, and this was linked to an initially enhanced work-hardening rate in the whisker composites. The overall dislocation densities were estimated to be somewhat higher in the whisker composites than the particulate composites, but in the early stages of deformation the distribution was rather different, with deformation in the whisker material being far more localized and inhomogeneous. This factor, together with differences in the internal stress distribution in the materials, is used to explain the difference in mechanical properties.

Microstructure, strain fields and flow stress in deformed metal matrix composites

An experimental study of local orientations around whiskers in deformed metal matrix composites has been used to determine the strain gradients existing in the material following tensile deformation. These strain fields have been represented as arrays of geometrically necessary dislocations, and the material flow stress predicted using a standard dislocation hardening model. Whilst the correlation between this and the measured flow stress is reasonable, the experimentally determined strain gradients are lower by a factor of 5-10 than values obtained in previous estimates made using continuum plasticity finite element models. The local orientations around the whiskers contain a large amount of detailed information about the strain patterns in the material, and a novel approach is made to representing some of this information and to correlating it with microstructural observations. © 1998 Acta Metallurgica Inc. Published by Elsevier Science Ltd. All rights reserved.

Mechanical and magnetic properties of metal fibre networks, with and without a polymeric matrix

Bonded networks of metal fibres are highly porous, permeable materials, which often exhibit relatively high strength. Material of this type has been produced, using melt-extracted ferritic stainless steel fibres, and characterised in terms of fibre volume fraction, fibre segment (joint-to-joint) length and fibre orientation distribution. Young's moduli and yield stresses have been measured. The behaviour when subjected to a magnetic field has also been investigated. This causes macroscopic straining, as the individual fibres become magnetised and tend to align with the applied field. The modeling approach of Markaki and Clyne, recently developed for prediction of the mechanical and magneto-mechanical properties of such materials, is briefly summarised and comparisons are made with experimental data. The effects of filling the inter-fibre void with compliant (polymeric) matrices have also been explored. In general the modeling approach gives reliable predictions, particularly when the network architecture has been characterised using X-ray tomography. © 2005 Published by Elsevier Ltd.

Dispersion rheology of carbon nanotubes in a polymer matrix

We report on rheological properties of a dispersion of multiwalled carbon nanotubes in a viscous polymer matrix. Particular attention is paid to the process of nanotubes mixing and dispersion, which we monitor by the rheological signature of the composite. The response of the composite as a function of the dispersion mixing time and conditions indicates that a critical mixing time t* needs to be exceeded to achieve satisfactory dispersion of aggregates, this time being a function of nanotube concentration and the mixing shear stress. At shorter times of shear mixing t< t*, we find a number of nonequilibrium features characteristic of colloidal glass and jamming of clusters. A thoroughly dispersed nanocomposite, at t> t*, has several universal rheological features; at nanotube concentration above a characteristic value nc ∼2-3 wt. % the effective elastic gel network is formed, while the low-concentration composite remains a viscous liquid. We use this rheological approach to determine the effects of aging and reaggregation. © 2006 The American Physical Society.

Sequential Monte Carlo computation of the score and observed information matrix in state-space models with application to parameter estimation

Sequential Monte Carlo (SMC) methods are popular computational tools for Bayesian inference in non-linear non-Gaussian state-space models. For this class of models, we propose SMC algorithms to compute the score vector and observed information matrix recursively in time. We propose two different SMC implementations, one with computational complexity $\mathcal{O}(N)$ and the other with complexity $\mathcal{O}(N^{2})$ where $N$ is the number of importance sampling draws. Although cheaper, the performance of the $\mathcal{O}(N)$ method degrades quickly in time as it inherently relies on the SMC approximation of a sequence of probability distributions whose dimension is increasing linearly with time. In particular, even under strong \textit{mixing} assumptions, the variance of the estimates computed with the $\mathcal{O}(N)$ method increases at least quadratically in time. The $\mathcal{O}(N^{2})$ is a non-standard SMC implementation that does not suffer from this rapid degrade. We then show how both methods can be used to perform batch and recursive parameter estimation.