Techn. Hochsch., Diss., 2002--Aachen.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 43-45). Also available in electronic version.

Techn. University, Diss., 2004--Berlin.

Molecular dynamics (MD) simulations have been applied to study the interactions between different carbohydrates and graphene. In cellulose-graphene complexes, the behaviour of hydrophobic and hydrophilic faces of cellulose chains on a single layer of graphene in aqueous solvent have been investigated. The hydrophobic cellulose face forms a stable complex with graphene and the interface remains solvent-excluded over the course of the simulation. Cellulose chains contacting graphene preserved their intra- and inter-chain hydrogen bonds and maintaining a tg orientation of its hydroxymethyl groups that is similar to that found for the sugar in a vacuum environment. The solvent-exposed cellulose chains of the complex showed more flexibility. By contrast, over the course of the 300 ns MD simulation, the hydrophilic face of cellulose exhibits progressive rearrangement as it seeks to present its hydrophobic face, with disrupted intra- and inter-chain hydrogen bonding; sequential residue twisting to form CH-pie interactions with graphene; and permeation then expulsion of interstitial water. This transition is also accompanied by a more favourable cellulose-graphene adhesion energy as predicted at the PM6-DH2 level of theory. The stability of the cellulose-graphene hydrophobic interface in water reflects the amphiphilicity of cellulose and provides insight into favoured interactions within graphene-cellulose nanocomposites. Furthermore, water is observed to permeate cellulose during rearrangement of the hydrophilic face which may have application in addressing cellulose recalcitrance. In addition, the interaction of six different types of monosaccharide (β/alpha-D-Glc, β/alpha-D-Gal and β/alpha-D-Man) on the surface of graphene has been studied, using PM6-DH2 and PMF calculations in both gas phase and explicit water. The parameters studied included anomer, epimer, saccharide face, hydroxymethyl orientation and solvation. Binding of graphene to monosaccharide is more preferred in vacuum than in water; solvation of the complexes leads to reduction in the number of pie-interactions formed with graphene. In almost all studied complexes, β-anomers bind stronger to graphene compared to alpha-anomers in gas phase and water. Each monosaccharide has two unique faces parallel to the plane of the pyranose ring and these surfaces determine the interaction formed with graphene and water. Binding of graphene with different faces significantly influences the value of the computed interaction and binding free energy. We also find that the interactions between graphene and saccharide are mainly controlled by the number of CH-pie and OH-pie interactions formed between saccharides and graphene. The interaction energy and binding energy values suggest that the a-face of β-D-Glc is the most preferred to bind on graphene in vacuum while the b-face of β-D-Glc is preferred in the aqueous phase.

This study is concerned with the effect that mean annual precipitation (P) has on the relationship between bankfull channel width (Wbf) and drainage area (Ada). Several other studies have been conducted in which relationships were developed for predicting Wbf as a function of Ada and P. In most cases, however, the relationships were developed for specific regions, e.g., physiographic regions. This study is unusual in that it evaluates the relationship between Wbf, Ada, and P over a broad area (i.e., across a range of geologic, terrestrial, and climatic environments). In one study, where a broad area was considered, the relationship between Wbf, Ada, and P was found to be linear. The dataset for this study was compiled from data in U.S. Geological Survey flood-flow-frequency reports, regional curve studies (i.e., studies in which Wbf vs. Ada relationships are developed) and other sources. A total of 435 sites that span across 12 states of the continental U.S. are represented in the dataset. Streams represented in the dataset are alluvial and have widths from 1 to 110 m, drainage areas from 0.50 to 22,000 km2, and mean annual precipitation depths ranging from 22 to 277 cm/yr. Data from the U.S. Environmental Protection Agency's Wadeable Streams Assessment study were employed in validating the results of this study. An analysis of covariance (ANCOVA) model was developed and it was determined that the intercept coefficient for the relationship between Wbf and Ada varies as follows: for P < 50 cm/yr the intercept coefficient (α) is constant; for 50 cm/yr ¡Ü P ¡Ü 100 cm/yr, α increases with P, and for P ¡Ý100 cm/yr, α is again constant. Across all values of P, the slope coefficient is constant (90% Confidence level). Changes in the relationship between Wbfand Ada are attributed to vegetation by noting that biome types changes from shrubland to forest as P increases from 50 to 100 cm/yr. These findings can be incorporated in regional curve studies and landscape evolution models (i.e., models which aim to integrate hydrology, land use history, geomorphology and climate change with models of vegetation succession).

Field Programmable Gate Arrays (FPGAs) são componentes reconfiguráveis de hardware que encontraram grande sucesso comercial ao longo dos últimos anos em uma grande variedade de nichos de aplicação. Alta vazão de processamento, flexibilidade e tempo de projeto reduzido estão entre os principais atrativos desses dispositivos, e são essenciais para o seu sucesso comercial. Essas propriedades também são valiosas para sistemas críticos, que frequentemente enfrentam restrições severas de desempenho. Além disso, a possibilidade de reprogramação após implantação é relevante, uma vez que permite a adição de novas funcionalidades ou a correção de erros de projeto, estendendo a vida útil do sistema. Tais dispositivos, entretanto, dependem de grandes memórias para armazenar o bitstream de configuração, responsável por definir a função presente do FPGA. Assim, falhas afetando esta configuração são capazes de causar defeitos funcionais, sendo uma grande ameaça à confiabilidade. A forma mais tradicional de remover tais erros, isto é, scrubbing de configuração, consiste em periodicamente sobrescrever a memória com o seu conteúdo desejado. Entretanto, devido ao seu tamanho significativo e à banda de acesso limitada, scrubbing sofre de um longo tempo médio de reparo, e que está aumentando à medida que FPGAs ficam maiores e mais complexos a cada geração. Partições reconfiguráveis são úteis para reduzir este tempo, já que permitem a execução de um procedimento local de reparo na partição afetada. Para este propósito, mecanismos rápidos de detecção de erros são necessários para rapidamente disparar este scrubbing localizado e reduzir a latência de erro. Além disso, diagnóstico preciso é necessário para identificar a localização do erro dentro do espaço de endereçamento da configuração. Técnicas de redundância de grão fino têm o potencial de prover ambos, mas normalmente introduzem custos significativos devido à necessidade de numerosos verificadores de redundância. Neste trabalho, propomos uma técnica de detecção de erros de grão fino que utiliza recursos abundantes e subutilizados encontrados em FPGAs do estado da arte, especificamente as cadeias de propagação de vai-um. Assim, a técnica provê os principais benefícios da redundância de grão fino enquanto minimiza sua principal desvantagem. Reduções bastante significativas na latência de erro são atingíveis com a técnica proposta. Também é proposto um mecanismo heurístico para explorar o diagnóstico provido por técnicas desta natureza. Este mecanismo tem por objetivo identificar as localizações mais prováveis do erro na memória de configuração, baseado no diagnóstico de grão fino, e fazer uso dessa informação de forma a minimizar o tempo de reparo. / Field Programmable Gate Arrays (FPGAs) are reconfigurable hardware components that have found great commercial success over the past years in a wide variety of application niches. High processing throughput, flexibility and reduced design time are among the main assets of such devices, and are essential to their commercial success. These features are also valuable for critical systems that often face stringent performance constraints. Furthermore, the possibility to perform post-deployment reprogramming is relevant, as it allows adding new functionalities or correcting design mistakes, extending the system lifetime. Such devices, however, rely on large memories to store the configuration bitstream, responsible for defining the current FPGA function. Thus, faults affecting this configuration are able to cause functional failures, posing a major dependability threat. The most traditional means to remove such errors, i.e., configuration scrubbing, consists in periodically overwriting the memory with its desired contents. However, due to its significant size and limited access bandwidth, scrubbing suffers from a long mean time to repair, and which is increasing as FPGAs get larger and more complex after each generation. Reconfigurable partitions are useful to reduce this time, as they allow performing a local repair procedure on the affected partition. For that purpose, fast error detection mechanisms are required, in order to quickly trigger this localized scrubbing and reduce error latency. Moreover, precise diagnosis is necessary to identify the error location within the configuration addressing space. Fine-grained redundancy techniques have the potential to provide both, but usually introduce significant costs due to the need of numerous redundancy checkers. In this work we propose a fine-grained error detection technique that makes use of abundant and underused resources found in state-of-the-art FPGAs, namely the carry propagation chains. Thereby, the technique provides the main benefits of fine-grained redundancy while minimizing its main drawback. Very significant reductions in error latency are attainable with the proposed approach. A heuristic mechanism to explore the diagnosis provided by techniques of this nature is also proposed. This mechanism aims at identifying the most likely error locations in the configuration memory, based on the fine-grained diagnosis, and to make use of this information in order to minimize the repair time of scrubbing.

The magnetic behaviour of thin films of (111) FCC structures and (0001) corundum structured materials were studied by the mean field analysis and some Monte Carlo simulation. These models were conditioned on a mapping from first principles calculations to the Ising model. The effect of the suggested octopolar reconstruction for the polar (111) surfaces of FCC was also examined.

The teachers at Linköpings University are currently using an emailclient to handle the sharing of information between teachers involved ina course. This is inefficient due to the difficulty of sharing existinginformation with new course memebers, sharing new information with existingcourse members and finding specific information is an issue because of thelack of course separation. Some features, such as handing over a courseto a new leading teacher, are also lacking which makes course managementdifficult. To solve this issue, we have developed a web application whichfocuses on its usability and efficiency to make it easier for coursestaff to share course information. By performing usability and efficiencytests where test subjects used the new application as well as the oldemail system we established that the newly developed application with itsadditional features became an improvement over the old email system.

Field Programmable Gate Arrays (FPGAs) são componentes reconfiguráveis de hardware que encontraram grande sucesso comercial ao longo dos últimos anos em uma grande variedade de nichos de aplicação. Alta vazão de processamento, flexibilidade e tempo de projeto reduzido estão entre os principais atrativos desses dispositivos, e são essenciais para o seu sucesso comercial. Essas propriedades também são valiosas para sistemas críticos, que frequentemente enfrentam restrições severas de desempenho. Além disso, a possibilidade de reprogramação após implantação é relevante, uma vez que permite a adição de novas funcionalidades ou a correção de erros de projeto, estendendo a vida útil do sistema. Tais dispositivos, entretanto, dependem de grandes memórias para armazenar o bitstream de configuração, responsável por definir a função presente do FPGA. Assim, falhas afetando esta configuração são capazes de causar defeitos funcionais, sendo uma grande ameaça à confiabilidade. A forma mais tradicional de remover tais erros, isto é, scrubbing de configuração, consiste em periodicamente sobrescrever a memória com o seu conteúdo desejado. Entretanto, devido ao seu tamanho significativo e à banda de acesso limitada, scrubbing sofre de um longo tempo médio de reparo, e que está aumentando à medida que FPGAs ficam maiores e mais complexos a cada geração. Partições reconfiguráveis são úteis para reduzir este tempo, já que permitem a execução de um procedimento local de reparo na partição afetada. Para este propósito, mecanismos rápidos de detecção de erros são necessários para rapidamente disparar este scrubbing localizado e reduzir a latência de erro. Além disso, diagnóstico preciso é necessário para identificar a localização do erro dentro do espaço de endereçamento da configuração. Técnicas de redundância de grão fino têm o potencial de prover ambos, mas normalmente introduzem custos significativos devido à necessidade de numerosos verificadores de redundância. Neste trabalho, propomos uma técnica de detecção de erros de grão fino que utiliza recursos abundantes e subutilizados encontrados em FPGAs do estado da arte, especificamente as cadeias de propagação de vai-um. Assim, a técnica provê os principais benefícios da redundância de grão fino enquanto minimiza sua principal desvantagem. Reduções bastante significativas na latência de erro são atingíveis com a técnica proposta. Também é proposto um mecanismo heurístico para explorar o diagnóstico provido por técnicas desta natureza. Este mecanismo tem por objetivo identificar as localizações mais prováveis do erro na memória de configuração, baseado no diagnóstico de grão fino, e fazer uso dessa informação de forma a minimizar o tempo de reparo. / Field Programmable Gate Arrays (FPGAs) are reconfigurable hardware components that have found great commercial success over the past years in a wide variety of application niches. High processing throughput, flexibility and reduced design time are among the main assets of such devices, and are essential to their commercial success. These features are also valuable for critical systems that often face stringent performance constraints. Furthermore, the possibility to perform post-deployment reprogramming is relevant, as it allows adding new functionalities or correcting design mistakes, extending the system lifetime. Such devices, however, rely on large memories to store the configuration bitstream, responsible for defining the current FPGA function. Thus, faults affecting this configuration are able to cause functional failures, posing a major dependability threat. The most traditional means to remove such errors, i.e., configuration scrubbing, consists in periodically overwriting the memory with its desired contents. However, due to its significant size and limited access bandwidth, scrubbing suffers from a long mean time to repair, and which is increasing as FPGAs get larger and more complex after each generation. Reconfigurable partitions are useful to reduce this time, as they allow performing a local repair procedure on the affected partition. For that purpose, fast error detection mechanisms are required, in order to quickly trigger this localized scrubbing and reduce error latency. Moreover, precise diagnosis is necessary to identify the error location within the configuration addressing space. Fine-grained redundancy techniques have the potential to provide both, but usually introduce significant costs due to the need of numerous redundancy checkers. In this work we propose a fine-grained error detection technique that makes use of abundant and underused resources found in state-of-the-art FPGAs, namely the carry propagation chains. Thereby, the technique provides the main benefits of fine-grained redundancy while minimizing its main drawback. Very significant reductions in error latency are attainable with the proposed approach. A heuristic mechanism to explore the diagnosis provided by techniques of this nature is also proposed. This mechanism aims at identifying the most likely error locations in the configuration memory, based on the fine-grained diagnosis, and to make use of this information in order to minimize the repair time of scrubbing.

abstract: A new theoretical model was developed utilizing energy conservation methods in order to determine the fully-atomized cross-sectional Sauter mean diameters of pressure-swirl atomizers. A detailed boundary-layer assessment led to the development of a new viscous dissipation model for droplets in the spray. Integral momentum methods were also used to determine the complete velocity history of the droplets and entrained gas in the spray. The model was extensively validated through comparison with experiment and it was found that the model could predict the correct droplet size with high accuracy for a wide range of operating conditions. Based on detailed analysis, it was found that the energy model has a tendency to overestimate the droplet diameters for very low injection velocities, Weber numbers, and cone angles. A full parametric study was also performed in order to unveil some underlying behavior of pressure-swirl atomizers. It was found that at high injection velocities, the kinetic energy in the spray is significantly larger than the surface tension energy, therefore, efforts into improving atomization quality by changing the liquid's surface tension may not be the most productive. From the parametric studies it was also shown how the Sauter mean diameter and entrained velocities vary with increasing ambient gas density. Overall, the present energy model has the potential to provide quick and reasonably accurate solutions for a wide range of operating conditions enabling the user to determine how different injection parameters affect the spray quality. / Dissertation/Thesis / M.S. Aerospace Engineering 2013