No / A buried or dumped body may be accompanied by a range of materials, including clothing and other textiles, metals such as tools and weapons, as well as plastics and paper products. This chapter concentrates on clothing and metal fastenings associated with clothing. Bodies that have been subject to clandestine disposal may be clothed, semiclothed, or naked. Reconstructing the nature and position of this clothing is critical to understanding the circumstance of disposal as well as perhaps to assisting in establishing motive and offender behavior. In addition, clothing and personal effects may provide assistance in establishing identity, although this will need confirmation by dental records or DNA. Modern clothing, footwear, and accessories are made from a range of materials: natural and synthetic textiles, leather, plastic, and metal. Along with the body they may be subject to a range of depositional environment, including surface disposal and burial in a range of soil types and microclimates. These materials will respond and degrade at different rates often leading to differential preservation.

This thesis uses Proper Orthogonal Decomposition to model parametric and coupled systems. First, Proper Orthogonal Decomposition and its properties are introduced as well as how to numerically compute the decomposition. Next, a test case was used to show how well POD can be used to simulate and control a system. Finally, techniques for modeling a parametric system over a given range and a coupled system split into subdomains were explored, as well as numerical results. / Master of Science

Proper Orthogonal Decomposition (POD), combined with the Method of Snapshots and Galerkin projection, is a popular method for the model order reduction of nonlinear PDEs. The POD requires the left singular vectors from the singular value decomposition (SVD) of an n-by-m "snapshot matrix" S, each column of which represents the computed state of the system at a given time. However, the direct computation of this decomposition can be computationally expensive, particularly for snapshot matrices that are too large to fit in memory. Hierarchical Approximate POD (HAPOD) (Himpe 2016) is a recent method for the approximate truncated SVD that requires only a single pass over S, is easily parallelizable, and can be computationally cheaper than direct SVD, all while guaranteeing the requested accuracy for the resulting basis. This method processes the columns of S in blocks based on a predefined rooted tree of processors, concatenating the outputs from each stage to form the inputs for the next. However, depending on the selected parameter values and the properties of S, the performance of HAPOD may be no better than that of direct SVD. In this work, we numerically explore the parameter values and snapshot matrix properties for which HAPOD is computationally advantageous over the full SVD and compare its performance to that of a parallelized incremental SVD method (Brand 2002, Brand 2003, and Arrighi2015). In particular, in addition to the two major processor tree structures detailed in the initial publication of HAPOD (Himpe2016), we explore the viability of a new structure designed with an MPI implementation in mind. / Master of Science / Singular Value Decomposition (SVD) provides a way to represent numeric data that breaks the data up into its most important components, as well as measuring how significant each part is. This decomposition is widely used to assist in finding patterns in data and making decisions accordingly, or to obtain simple, yet accurate, representations of complex physical processes. Examples of useful data to decompose include the velocity of water flowing past an obstacle in a river, a large collection of images, or user ratings for a large number of movies. However, computing the SVD directly can be computationally expensive, and usually requires repeated access to the entire dataset. As these data sets can be very large, up to hundreds of gigabytes or even several terabytes, storing all of the data in memory at once may be infeasible. Thus, repeated access to the entire dataset requires that the files be read repeatedly from the hard disk, which can make the required computations exceptionally slow. Fortunately, for many applications, only the most important parts of the data are needed, and the rest can be discarded. As a result, several methods have surfaced that can pick out the most important parts of the data while accessing the original data only once, piece by piece, and can be much faster than computing the SVD directly. In addition, the recent bottleneck in individual computer processor speeds has motivated a need for methods that can efficiently run on a large number of processors in parallel. Hierarchical Approximate POD (HAPOD) [1] is a recently-developed method that can efficiently pick out the most important parts of the data while only accessing the original data once, and which is very easy to run in parallel. However, depending on a user-defined algorithm parameter (weight), HAPOD may return more information than is needed to satisfy the requested accuracy, which determines how much data can be discarded. It turns out that the input weights that result in less extra data also result in slower computations and the eventual need for more data to be stored in memory at once. This thesis explores how to choose this input weight to best balance the amount of extra information used with the speed of the method, and also explores how the properties of the data, such as the size of the data or the distribution of levels of significance of each part, impact the effectiveness of HAPOD.

Orientador: João Antonio Pereira / Banca: Luiz de Paula do Nascimento / Banca: Mário Francisco Mucheroni / Resumo: Este trabalho apresenta o estudo, a implementação e a aplicação de duas técnicas de identificação de parâmetros modais utilizando apenas as respostas da estrutura, denominadas: Identificação Estocástica de Subespaço (IES) e Decomposição no Domínio da Freqüência (DDF). A IES é baseada na Decomposição em Valores Singulares (DVS) da projeção ortogonal do espaço das linhas das saídas futuras no espaço das linhas das saídas passadas. Uma vez realizada a DVS da projeção ortogonal é possível obter o modelo de espaço de estado da estrutura e os parâmetros modais são estimados diretamente através da decomposição em autovalores e autovetores da matriz dinâmica. A DDF é baseada na DVS da matriz de densidade espectral de potência de saída nas linhas de freqüências correspondentes a região em torno de um modo. O primeiro vetor singular obtido para cada linha de freqüência contém as respectivas informações daquele modo e os correspondentes valores singulares levam a função densidade espectral de um sistema equivalente de um grau de liberdade (1GL), permitindo a obtenção dos parâmetros do respectivo modo. Os métodos são avaliados utilizando dados simulados e experimentais. Os resultados mostram que as técnicas implementadas são capazes de estimar os parâmetros modais de estruturas utilizando apenas as respostas. / Abstract: This work presents the study, implementation and application of the two techniques for the modal parameters identification using only response data: Stochastic Subspace Identification (SSI) and Frequency Domain Decomposition (FDD). The SSI is based on Singular Value Decomposition (SVD) of the orthogonal projection of the future output row space in the past output row space. After the completion of the SVD of the orthogonal projection, is possible to get the state space model of the structure and the modal parameters are estimated directly through the eigenvalues and eigenvectors decomposition of the dynamic matrix. The FDD is based on the SVD of the output power spectral density matrix in the frequencies lines around a mode. The first singular vector obtained for each frequency line contains the respective information about this mode and the corresponding spectral density function leads to an equivalent system of one degree of freedom (1 DOF), allowing the calculation of the parameters of the mode. The methods are evaluated using simulated and experimental data. The results show that the techniques implemented are capable to estimate the modal parameters of structures using only response data. / Mestre

Modern big data often emerge as tensors. Standard statistical methods are inadequate to deal with datasets of large volume, high dimensionality, and complex structure. Therefore, it is important to develop algorithms such as low-rank tensor decomposition for data compression, dimensionality reduction, and approximation. With the advancement in technology, high-dimensional images are becoming ubiquitous in the medical field. In lung radiation therapy, the respiratory motion of the lung introduces variabilities during treatment as the tumor inside the lung is moving, which brings challenges to the precise delivery of radiation to the tumor. Several approaches to quantifying this uncertainty propose using a model to formulate the motion through a mathematical function over time. [Li et al., 2011] uses principal component analysis (PCA) to propose one such model using each image as a long vector. However, the images come in a multidimensional arrays, and vectorization breaks the spatial structure. Driven by the needs to develop low-rank tensor decomposition and provided the 4DCT and Displacement Vector Field (DVF), we introduce two tensor decompositions, Population Value Decomposition (PVD) and Population Tucker Decomposition (PTD), to estimate the respiratory lung motion with high levels of accuracy and data compression. The first algorithm is a generalization of PVD [Crainiceanu et al., 2011] to higher order tensor. The second algorithm generalizes the concept of PVD using Tucker decomposition. Both algorithms are tested on clinical and phantom DVFs. New metrics for measuring the model performance are developed in our research. Results of the two new algorithms are compared to the result of the PCA algorithm.

Combustion instability due to feedback coupling between unsteady heat release and natural acoustic modes can cause catastrophic failure in liquid rocket engines and to predict and prevent these instabilities the mechanisms that drive them must be further elucidated. With this goal in mind, the objective of this thesis was to develop techniques that improve the understanding of the specific underlying physical processes involved in these driving mechanisms. In particular, this work sought to develop a small-scale, optically accessible liquid rocket engine simulator and to apply modern, high-speed diagnostic techniques to characterize the reacting flow and acoustic field within the simulator. Specifically, high-speed (10 kHz), simultaneous data were acquired while the simulator was experiencing a 170 Hz combustion instability using particle image velocimetry, OH planar laser induced fluorescence, CH* chemiluminescence, and dynamic pressure measurements. In addition, this work sought to develop approaches to reduce the large quantities of data acquired, extracting key physical phenomena involved in the driving mechanisms. The initial data reduction approach was chosen based on the fact that the combustion instability problem is often simplified to the point that it can be characterized by an approximately linear constant coefficient system of equations. Consistent with this simplification, the experimental data were analyzed by the dynamic mode decomposition method. The developed approach to apply the dynamic mode decomposition to simultaneously acquired data located a coupled hydrodynamic/combustion/acoustic mode at 1017 Hz. On the other hand, the dynamic mode decomposition's assumed constant operator approach failed to locate any modes of interest near 170 Hz. This led to the development of two new data analysis techniques based on the dynamic mode decomposition and Floquet theory that assume that the experiment is governed by a linear, periodic system of equations. The new periodic-operator data analysis techniques, the Floquet decomposition and the ensemble Floquet decomposition, approximate, from experimental data, the largest moduli Floquet multipliers, which determine the stability of the periodic solution trajectory of the system. The unstable experiment dataset was analyzed with these techniques and the ensemble Floquet decomposition analysis found a large modulus Floquet multiplier and associated mode with a frequency of 169.6 Hz. Furthermore, the approximate Rayleigh criterion indicated that this mode was unstable with respect to combustion instability. Overall, based on the positive finding that the ensemble Floquet decomposition was able to locate an unstable combustion mode at 170 Hz when the operator's time period was set to 1 ms, suggests that the dynamic mode decomposition based 1017 Hz mode parametrically forces the 170 Hz mode, resulting in what could be characterized as a parametric combustion instability.

Made available in DSpace on 2014-06-11T19:27:14Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-07-30Bitstream added on 2014-06-13T19:55:34Z : No. of bitstreams: 1 freitas_tc_me_ilha.pdf: 1484818 bytes, checksum: 9f0ca1d5825d93918e44fc9b31aae513 (MD5) / Agência Nacional de Energia Elétrica (ANEEL) / Este trabalho apresenta o estudo, a implementação e a aplicação de duas técnicas de identificação de parâmetros modais utilizando apenas as respostas da estrutura, denominadas: Identificação Estocástica de Subespaço (IES) e Decomposição no Domínio da Freqüência (DDF). A IES é baseada na Decomposição em Valores Singulares (DVS) da projeção ortogonal do espaço das linhas das saídas futuras no espaço das linhas das saídas passadas. Uma vez realizada a DVS da projeção ortogonal é possível obter o modelo de espaço de estado da estrutura e os parâmetros modais são estimados diretamente através da decomposição em autovalores e autovetores da matriz dinâmica. A DDF é baseada na DVS da matriz de densidade espectral de potência de saída nas linhas de freqüências correspondentes a região em torno de um modo. O primeiro vetor singular obtido para cada linha de freqüência contém as respectivas informações daquele modo e os correspondentes valores singulares levam a função densidade espectral de um sistema equivalente de um grau de liberdade (1GL), permitindo a obtenção dos parâmetros do respectivo modo. Os métodos são avaliados utilizando dados simulados e experimentais. Os resultados mostram que as técnicas implementadas são capazes de estimar os parâmetros modais de estruturas utilizando apenas as respostas. / This work presents the study, implementation and application of the two techniques for the modal parameters identification using only response data: Stochastic Subspace Identification (SSI) and Frequency Domain Decomposition (FDD). The SSI is based on Singular Value Decomposition (SVD) of the orthogonal projection of the future output row space in the past output row space. After the completion of the SVD of the orthogonal projection, is possible to get the state space model of the structure and the modal parameters are estimated directly through the eigenvalues and eigenvectors decomposition of the dynamic matrix. The FDD is based on the SVD of the output power spectral density matrix in the frequencies lines around a mode. The first singular vector obtained for each frequency line contains the respective information about this mode and the corresponding spectral density function leads to an equivalent system of one degree of freedom (1 DOF), allowing the calculation of the parameters of the mode. The methods are evaluated using simulated and experimental data. The results show that the techniques implemented are capable to estimate the modal parameters of structures using only response data.

The disproportionation of H₂O₂ to H₂O and molecular O₂ catalyzed by platinum nanocatalysts is technologically very important in several energy conversion technologies, such as steam propellant thrust applications and hydrogen fuel cells. However, the mechanism of H₂O₂ decomposition on platinum has been unresolved for more than 100 years and the kinetics of this reaction were poorly understood. Our goal was to quantify the effect of reaction conditions and catalyst properties on the decomposition of H₂O₂ by platinum nanocatalysts and determine the mechanism and rate-limiting step of the reaction. To this end, we have characterized two commercial platinum nanocatalysts, known as platinum black and platinum nanopowder, and studied the effect of different reaction conditions on their rates of H₂O₂ decomposition. These samples have different particle size and surface chemisorbed oxygen abundance, which were varied further by pretreating both samples at variable conditions. The rate of H₂O₂ decomposition was studied systematically as a function of H₂O₂ concentration, pH, temperature, particle size and surface chemisorbed oxygen abundance. The mechanism of H₂O₂ decomposition on platinum proceeds via two cyclic oxidation-reduction steps. Step 1 is the rate limiting step of the reaction. Step 1: Pt + H₂O₂ → H₂O + Pt(O). Step 2: Pt(O) + H₂O₂ → Pt + O₂ + H₂O. Overall: 2 H₂O₂ → O₂ + 2 H₂O. The decomposition of H₂O₂ on platinum follows 1st order kinetics in terms of H₂O₂ concentration. The effect of pH is small, yet statistically significant. The rate constant of step 2 is 13 times higher than that of step 1. Incorporation of chemisorbed oxygen at the nanocatalyst surface resulted in higher initial rate of H₂O₂ decomposition because more sites initiate their cyclic process in the faster step of the reaction. Particle size does not affect the kinetics of the reaction. This new molecular-scale understanding of the decomposition of H₂O₂ by platinum is expected to help advance many energy technologies that depend on the rate of H₂O₂ decomposition on nanocatalysts of platinum and other metals. / Ph. D. / Platinum nanomaterials are indispensable to catalyze a variety of industrial and technological processes ranging from catalytic conversion of carbon monoxide (CO), hydrocarbons, and nitrogen oxides (NO_x) in modern automobiles to energy production by hydrogen fuel cells and thrust generation in steam propellers. These technological innovations have a tremendous impact in modern society, including the areas of transportation, energy supply, soil and water quality, environmental remediation and global climate change. The decomposition of hydrogen peroxide (H₂O₂) to water (H₂O) and oxygen (O₂) on platinum nanomaterials is of particular importance because it affects the efficacy of many technological applications, such as hydrogen peroxide steam propellers and hydrogen fuel cells. However, the reaction pathway and kinetics of H₂O₂ decomposition on platinum were only partly understood. My goal was to understand how the reaction conditions and the nanocatalyst properties control the mechanism and kinetics of platinum-catalyzed hydrogen peroxide decomposition. To do that we characterized the atomic scale structural and chemical properties of two different platinum nanocatalysts, known as platinum black and platinum nanopowder and evaluated the effect of their properties in their catalytic activity. Our characterization studies were used to understand the reactivity of these two platinum nanocatalysts in the decomposition of H₂O₂, which we evaluated separately in laboratory studies. Establishing relationships between the catalyst properties and their activity, as we have done in this work for platinum nanocatalysts in the decomposition of hydrogen peroxide, has the potential to improve nanocatalyst design and performance for those applications.

No description available.

In this study the performance of pure, very fine iron (III) oxide powder was investigated as catalyst for the decomposition of sulphur trioxide into sulphur dioxide and oxygen. This highly endothermic reaction requires a catalyst to lower the reaction temperature. This reaction forms part of the HyS (Hybrid Sulphur) cycle, a proposed thermochemical process for the industrial scale production of hydrogen and oxygen from water. The study aimed at obtaining reaction kinetics for this reaction employing pure, unsupported iron (III) oxide as catalyst as a cheaper alternative compared to supported iron catalysts. It was found that the SO3 conversion was carried out in the absence of diffusion limitations and that the reverse reaction did not play a significant role. By assuming plug flow conditions in the reactor and 1st order kinetics, the kinetic parameters of the reaction were obtained. These parameters that form part of the Arrhenius law in describing the reaction rate constant, were determined to be 118(±23) kj / mol for the activation energy ( Ea ), and a value of 3(±0.5) x 108hr-1 was obtained for the Arrhenius frequency factor ( A ). Both values correspond to literature, although in general larger activation energies were published for iron (III) oxide derived supported catalysts. A comparison of the performance of the pure, unsupported iron (III) oxide catalyst with other iron (III) oxide derived supported catalysts (or pellets) has shown that the pure iron (III) oxide catalyst exhibit similar activities. Avoiding expensive catalyst preparation will be an initial step in the direction of developing a cost effective catalyst for the decomposition of sulphur trioxide. It is, however, recommended to investigate different particle sizes as well as purity levels of the unsupported iron (III) oxide to find an optimum cost to performance ratio, as the degree of fineness and the degree of purity will largely influence the final catalyst cost. A qualitative investigation with various reaction product species as well as water in the reactor feed was conducted to assess the influence of these species on the reaction rate. The addition of these species seems to have a larger influence on the reaction rate at low reaction temperatures around 700°C than at higher reaction temperatures (i.e. 750°C and 825°C). This can be attributed to adsorption rates of such species that reduce at higher temperatures. Observations at higher reaction temperatures also suggest that the reaction is of a first-order nature. / MIng (Chemical Engineering), North-West University, Potchefstroom Campus, 2014