The early detection of damage of in-service structural or mechanical systems is of vital importance. With early detection, the damage may be repaired before the integrity of the system is jeopardized, avoiding possible monetary losses, environmental impacts, injury and death. With this goal in mind, many structural health monitoring techniques have been developed which use a combination of sensors and algorithms to collect, process and interpret data to detect damage in a structure. This thesis presents work completed in support of the experimental validation of a novel structural health monitoring technique developed with the aim of providing improved qualitative results compared to those methods currently available.

Modern superscalar pipelines have tremendous capacity to consume the instruction stream. This has been possible owing to improvements in process technology, technology scaling and microarchitectural design improvements that allow programs to speculate past control and data dependencies in the superscalar architecture. However, the speed of the memory subsystem lags behind due to physical constraints in bringing in huge amounts of data to the processor core. Cache hierarchies have subdued the impact of this speed gap; however, there is much that can be still done in improving microarchitecture. Data prefetching techniques bring in memory content significantly before the instruction stream actually witnesses demand misses. However, a majority of the techniques proposed so far depend upon an initial demand miss that initiates a stream of previously identified prefetches. In this thesis, we propose a novel prefetching algorithm, which leverages branch prediction to facilitate deep memory system speculation. The branch predictor directed lookahead mechanism builds a speculative control flow path for the instruction stream about to be fetched by the main superscalar pipeline. Prefetches are generated along this speculative path from a condensed representation of the memory instructions, leveraging register index based correlation. The technique integrates eloquently with the main pipeline's branch predictor to filter out prefetches along invalid speculative paths. Impact of the prefetching scheme is analyzed using out- of-order model of the Gem5 cycle accurate simulator. Evaluation shows that on a set of 13 memory intensive SPEC CPU2006 benchmarks, our prefetching technique improves performance by an average of 5.6% over the baseline out-of-order processor.

A study of gouges and dents in the context of pipelines has been completed, using the non-destructive evaluation (NDE) techniques of magnetic flux leakage (MFL) and magnetic Barkhausen noise (MBN). The research is part of an ongoing effort by the Applied Magnetics Group (AMG) at Queen's University to improve the interpretation of the MFL signal, which is used extensively by industry for defect detection and evaluation. The gouges were found to have distinctive MFL signatures depending on their orientation relative to the magnetization axis. Features in the MFL signal were identified as superpositions of geometry-related effects and strain or work hardening of the surface material. A qualitative magnetic permeability distribution in the material near a gouge has been proposed. The distribution is expected to vary in magnitude and extent according to the defect severity. The MFL results of the dent studies, on samples made available by Gaz de France (GdF), largely agreed qualitatively with previous research of dents. However, the differences pointed to the need for study of more varied dent shapes; new signal features were observed that suggested tensile residual strain in the dent rim is more prominent than earlier studies and modeling have predicted. Additionally, upgrades made to the MFL scanning system used by the AMG and a novel approach for building computer models are detailed. / Thesis (Master, Physics, Engineering Physics and Astronomy) -- Queen's University, 2009-09-24 17:13:12.775

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Carbon Capture and Storage (CCS) is recognised as having a significant role to play in tackling climate change and reducing carbon dioxide (CO2) emissions. In CCS schemes, CO2 is captured from anthropogenic sources, and transported to suitable sites either for EOR (Enhanced Oil Recovery) or storage. The transport of such huge amount of CO2 causes new challenges. The main concern is in the difference between natural gas and CO2 transportation pipelines. CO2 phase behaviour during decompression, existence of different impurities and very high operating pressure are some of the new challenges for pipeline designer and operators. This PhD study has taken a systematic approach to understand the mechanics involved in the fracture of pipes containing high pressure flue-gas CO2. The work involved the development of a novel weight function stress intensity factor solution that can be used with complex stress fields induced by residual and/or thermal stresses in addition to applied pressure. In addition, the thesis reports a substantial experimented test programme which involved low temperature fracture toughness tests linked to a detailed finite element based stress analysis. Overall, the thesis presents an integrated engineering criticality means to assess the suitability or otherwise of a pipeline system to transport high pressure flue-gas CO2.