Because of its attractive material properties like high hardness, high toughness, and excellent high temperature strength, materials like silicon nitride are becoming more common for use in high performance applications. However, there have been limited studies of the fatigue behavior of small cracks in silicon nitride and other materials toughened by grain bridging mechanisms. This study explores using micro Raman spectroscopy, fatigue crack growth data and results from static fracture experiments to determine a bridging stress profile for silicon nitride doped with MgO and Y₂O₃ as sintering additives. These bridging stress profiles allow for the creation of a geometry specific fatigue threshold R-curve which can be used to develop a fatigue endurance strength prediction tool to aid in the design of products using the material. Cyclical fatigue experiments conducted on bend beams with induced semi-elliptical surface cracks were conducted to verify the prediction tool. The results show that no bend beams with this crack geometry failed below the predicted endurance level. It is expected that this method can be extended to create fatigue endurance strength predictions for other materials similarly toughened by grain bridging and other mechanisms. / Graduation date: 2013

Reprint from: Allergology International 55(1), 2006, p.59-65

An approach for robust design based on stochastic expansions is investigated. The research consists of two parts : 1) stochastic expansions for uncertainty propagation and 2) adaptive sampling for Pareto front approximation. For the first part, a strategy based on the generalized polynomial chaos (gPC) expansion method is developed. Second, in order to alleviate the computational cost of approximating the Pareto front, two strategies based on adaptive sampling for multi-objective problems are presented. The first one is based on the two aforementioned methods, whereas the second one considers, in addition, two levels of fidelity of the uncertainty propagation method.

No description available.

Steam generators in nuclear power plants experienced varying degrees of under-deposit pitting corrosion. A probabilistic model to accurately predict pitting corrosion is necessary for effective life-cycle management of steam generators. This thesis presents an advanced probabilistic model of pitting corrosion characterizing the inherent randomness of the pitting process and measurement uncertainties of the in-service inspection (ISI) data obtained from eddy current (EC) inspections. A Bayesian method is developed for estimating the model parameters. The proposed model is able to estimate the number of actual pits, the actual pit depth as well as the maximum pit depth, which is the main interest of the pitting corrosion model. A MATLAB program of the Markov chain Monte Carlo technique is developed to perform the Bayesian estimations. Simulation experiments are performed to check the behavior of the Bayesian method. Results show that the MCMC algorithm is an effective way to estimate the model parameters. Also, the effectiveness and efficiency of Bayesian modeling are validated. A comprehensive case study is also presented on the in-service inspection data of pitting corrosion in a steam generator unit. The Weibull distribution is found to be an appropriate probability distribution for modeling the actual pit depth in steam generators.

Weathering steel is a high-strength, low-alloy steel which has been proven to provide a significantly higher corrosion resistance than regular carbon steel. This corrosion resistance is a product of the small amounts of alloying elements added to the steel, which enable it to form a protective oxide layer when exposed to the environment. The main advantage of its use in bridges is that, under normal conditions, it may be left unpainted, leading to significantly reduced maintenance and environmental costs. Weathering steel has been a material of choice for highway structures for almost half a century, and a very large number of structures have been constructed with it. Although its use has for the most part been successful, it has also become evident that, in circumstances where there is the presence of salt and sulphur oxides, its performance is deficient. In these situations the corrosion penetration rate is much higher than expected, and the oxide layer forms in thick layers. This presents an added risk, since these layers flake off and fall onto the roadway. The degree of corrosion on structures can be very different, even if the structural type, location, and climate are similar. Therefore the focus of the thesis is on the lifespan of weathering steel highway structures. Primarily this research is concerned with the effect of corrosion on the integrity of these structures, as well as ways of quantifying corrosion loss and protecting the structure from further corrosion. In order to determine the lifespan of weathering steel highway structures subject to different rates of corrosion, a probabilistic structural analysis program has been developed to assess the time-dependent reliability of the structure. This program used iterative Monte Carlo simulation and a series of statistical variables relating to the material, loading, and corrosion properties of the structure. A corrosion penetration equation is used to estimate thickness loss at a selected interval, and the structural properties of the bridge are modified accordingly. The ultimate limit states of shear, moment, and bearing, and the fatigue limit state of web breathing, are taken into account. Three types of structures are examined: simply-supported box and I-girder composite bridges, and a two-span box girder bridge. Based on the structural analysis of the corroding bridge structures presented herein, it can be seen that corrosion to the weathering steel girders can cause reduced service lives of the structures. I-girder bridges are shown to be more susceptible to corrosion than box girder bridges, with continuous box girder bridges showing the best performance. The amount of truck traffic does not affect the reliability of the bridge. The short-span and high strength steel bridges are more susceptible to corrosion loss, primarily because their girders have thinner sections. A two-lane bridge also has better performance than the wider bridges because the weight of the barriers and sidewalks is carried by fewer girders, so these girders are stockier. The web breathing limit state is less significant than the combined ultimate limit states. Lastly, and most importantly, inspection data from a highway bridge is used to demonstrate the benefit that can be derived from using field data to update the time-dependent reliability. The ultrasonic thickness gauge (UTG) is a common tool for thickness measurement of steel sections. When used to measure weathering steel, this instrument provides accurate data about the depth of corrosion pits, but not their lateral dimensions. The measurement does not include the corrosion layer on the opposite side of the plate from the one being measured; however, if the corrosion layer is on the measured face, a disproportionate increase in the measured thickness can be seen. In order to prevent or minimize corrosion loss, the steel is currently painted, a process with several environmental and financial disadvantages. Therefore, three novel protection methods have been assessed in a cyclic corrosion test: a zinc metallizing, an aluminum-zinc-indium alloy metallizing, and a zinc tape with a PVC topcoat. All these coatings are designed to act not just as barriers, but also as sacrificial anodes. The test was run for 212 24-hr cycles, over the course of which the all the coatings were proven effective at protecting the steel substrate, regardless of steel type and surface roughness and pretreatment. In conclusion, the threat to all types of weathering steel highway structures by contaminant-induced corrosion is significant, but inspection data permits a more accurate prediction of time-dependent reliability for a structure, and protective coatings are a promising method of slowing the advance of corrosion.

With power system restructuring, continuous growth of demand, and deregulation, small, scattered generators referred to as Distributed Generation (DG) are predicted to play a key role in the power distribution system. Moreover, among the different types of DG units, it is widely accepted that renewable DG units are the key to a sustainable energy supply infrastructure, since they are both inexhaustible and non-polluting. However the intermittent nature and the uncertainties associated with the renewable resources create special technical and economical challenges that have to be comprehensively investigated in order to facilitate the deployment of these DG units in the distribution system. The objective of the work proposed in this thesis is to tackle some of the challenges associated with the increased penetration of renewable DG units into existing distribution systems. This includes the study of the impact of different renewable DG units on the supply adequacy of the distribution system, and the development of planning technique that optimally allocate renewable DG units into the distribution system. Furthermore, a methodology is proposed to check the feasibility of implementing energy storage system (ESS) into the distribution system to mitigate the problems associated with the high penetration of renewable DG units. These problems include the maximum reverse power flow limit, the equipment rating limit, and the voltage limit on each bus. The first step toward the accomplishment of this work is to model the random behaviour of the renewable resources (i.e. wind speed and solar irradiance). Here, different approaches are proposed to model the random behaviour of both wind speed and solar irradiance, either chronologically or probabilistically. Among those approaches are a novel technique of annual wind speed estimation based on a constrained Grey predictor, and a new implementation of the probability density function (pdf) of the clearness index so as to model solar irradiance using Monte Carlo Simulation (MCS). Supply adequacy of distribution systems is assessed based on well-being criteria during different modes of operation (i.e. grid-connected mode and islanding mode), using analytical and (MCS) techniques. During the grid-connected mode, from the load perspective, the substation transformers act as generating units. Therefore, supply adequacy of distribution systems is assessed by considering that the generating units of the distribution system are the substation transformers and the DG units. During the islanding mode of operation, the island is acting as a small autonomous power system (SAPS) and the most important issue during this mode of operation is to determine the probability of the island to be successful (the DG power output within the island matches the load) or a failure (there is a deficit in power generation). The focus of the model developed to optimally allocate the renewable DG units in existing distribution systems is to minimize annual energy losses and at the same time, avoid any violation of the system constraints under any operating condition. The methodology is based on generating a probabilistic generation-load model that combines all possible operating conditions of the renewable DG units with their probabilities, hence accommodating this model in a deterministic planning problem. The objective function of the planning formulation is to minimize annual energy losses; whereas the constraints include the voltage limits, the feeders’ capacity, the maximum penetration limit, and the discrete size of the available DG units. The objective of the methodology proposed for allocating an ESS into distribution systems with high penetration (greater than 20% of the feeder capacity) of renewable energy is to maximize the benefits for both the DG owner and the utility. This is done by sizing the ESS to accommodate the entire surplus of renewable energy, and then allocating it within the system in order to minimize the annual cost of the electricity.

The main goal in this thesis is to select and test Forward Error Correction (FEC) schemes suitable for network video transmission over RTP/UDP. There is a general concern in communication networks which is to achieve a tradeoff between reliable transmission and the delay that it takes. Our purpose is to look for techniques that improve the reliability while the realtime delay constraints are fulfilled. In order to achieve it, the FEC techniques focus on recovering the packet losses that come up along any transmission. The FEC schemes that we have selected are Parity Check algorithm, ReedSolomon (RS) codes and a Convolutional code. Simulations are performed to test the different schemes. The results obtained show that the RS codes are the more powerful schemes in terms of recovery capabilities. However they can not be deployed for every configuration since they go beyond the delay threshold. On the other hand, despite of the Parity Check codes being the less efficient in terms of error recovery, they show a reasonable low delay. Therefore, depending on the packet loss probability that we are working with, we may chose one or other of the different schemes. To summarize, this thesis includes a theoretical background, a thorough analysis of the FEC schemes chosen, simulation results, conclusions and proposed future work.

Radiation-hardened processors are designed to be resilient against soft errorsbut such processors are slower than Commercial Off-The-Shelf (COTS)processors as well significantly costlier. In order to mitigate the high costs,software techniques such as task re-executions must be deployed together withadequately hardened processors to provide reliability. This leads to a huge designspace comprising of the hardening level of the processors and the numberof re-executions of each task in the system. Each configuration in this designspace represents a tradeoff between processor load, reliability and costs. The reliability comes at the price of higher costs due to higher levels of hardeningand performance degradation due to hardening or due to re-executions.Thus, the tradeoffs between performance, reliability and costs must be carefullystudied. Pertinent questions that arise in such a design scenario are — (i)how many times a task must be re-executed and (ii) what should be hardeninglevel? — such that the system reliability is satisfied. In order to evaluate such tradeoffs efficiently, in this thesis, we proposenovel framework that harnesses the computational power of Graphics ProcessingUnits (GPUs). Our framework is based on a system failure probabilityanalysis that connects the probability of failure of tasks to the overall systemreliability. Based on characteristics of this probabilistic analysis as well asreal-time deadlines, we derive bounds on the design space to prune infeasiblesolutions. Finally, we illustrate the benefits of our proposed framework withseveral experiments

Today rise in oil and gas demand, energy crisis, issues concerning energy security and increase in oil prices in the world provoke further exploration and production of oil and gas. The Arctic Sea is the last frontier of abundant hydrocarbon reserves. Having effective regulations, innovative technologies and adequate safety norms, the world has still seen some major accidents such as Gulf of Mexico accident. Knowing that offshore petroleum industry is moving further north in Arctic poses additional challenges due to harsh climatic conditions and remoteness from existing oil and gas infrastructure. The objective of this thesis is to provide an overview of offshore petroleum activity in arctic and sub-arctic areas as well as the accidents which took place in these areas. Furthermore, the accidents are analyzed with respect to the emergency preparedness handling of accidents using the NORSOK Z-013 standard as one basis. The thesis also discusses the anticipated emergency preparedness challenges for arctic and sub-arctic areas. The Arctic Sea is the final destination in the north having enormous amount of hydrocarbons. The harsh weather conditions of the Arctic Sea characterized by polar lows, long nights, extreme fog and sub-zero temperatures have not prevented the countries bordering the Arctic Sea in exploiting the oil and gas resources. Russia, having extended pipeline infrastructure, has the most active part in the Arctic region followed by the US and Norway. The offshore exploration and production activities in the Canadian and Greenland Arctic Sea are gradually progressing. Up to date, there have been very few accidents in the Arctic Sea thereby providing a limited knowledge base for emergency response in the Arctic Sea. The accidents which we have been able to account for are mainly related to blowout, pipeline leak, ship collision and capsize accidents. The accidents have occurred in the Russian Arctic Sea and the Alaskan Arctic Sea.The NORSOK Z-013 standard refers to alert, danger limitation, rescue, evacuation, and normalization as the five emergency preparedness phases and their detailed description is available in the Activities Regulation by the Petroleum Safety Authority in Norway (PSAN). It is stated in the NORSOK Z-013 standard that a set of Defined Situation of Hazard and Accident (DSHA) needs to be defined as part of the risk and emergency preparedness analysis. The set of DSHAs provided in the “Trends in risk level” project is used in this thesis for the classification and analysis of the accidents in the Arctic Sea.The analysis of the accidents in the Arctic Sea shows that all the emergency operations went through the phases of alert and normalization while the oil spill related accidents did not pass through escape and evacuate operations as there were no personnel involved in these accidents. All the emergency operations were affected by bad and tough weather conditions. Advanced emergency preparedness tools, equipment and technology are needed for effective emergency operations under such conditions. Due to scarcity of accidents in the Arctic Sea, only four DSHAs (out of 12) have been experienced, or at least reported. It may be that some of the remaining DSHAs are not reported due to minor consequences. In addition to the lessons learned from the emergency response operations for the accidents experienced in the Arctic Sea, there are also some emergency preparedness challenges which can be anticipated. The challenging weather conditions due to sudden polar lows, strong winds, spray icing, snowstorms and severe fog can hamper the emergency operations. Furthermore, long distances and lack of infrastructure can create communication and logistic problems and can result in delay of rescue and evacuation operations. Robust and reliable ice and weather data is a challenge due to global warming and may become a limiting factor for the adequate design of offshore equipment. The use and maintenance of emergency response equipment is also a challenge in sometimes dark, snowy and foggy areas of the Arctic Sea and the Barents Sea.