Engineering Commons^™

Near-Ir Laser Ablation Of Simulated Radiologically Contaminated Oxides On Carbon Steel Feeder Pipes, Thao Viet Do

Electronic Thesis and Dissertation Repository

As nuclear power plants age and retire from service, many countries face significant challenges concerning the safe long-term storage and disposal of large volumes of low and intermediate level radioactive wastes (L&ILW). The volumes of metallic waste are of particular concern, as when metal corrodes it produces hydrogen that could lead to pressure build-up in interim storage and disposal. In Canada, a significant fraction of the metallic wastes for Canada Deuterium Uranium (CANDU) nuclear reactors are out-of-core reactor components, such as carbon steel (CS) feeder pipes. The radioactive contamination is expected to be largely confined to the surface oxide layers …

Machine Learning With Big Data For Electrical Load Forecasting, Alexandra L'Heureux

Electronic Thesis and Dissertation Repository

Today, the amount of data collected is exploding at an unprecedented rate due to developments in Web technologies, social media, mobile and sensing devices and the internet of things (IoT). Data is gathered in every aspect of our lives: from financial information to smart home devices and everything in between. The driving force behind these extensive data collections is the promise of increased knowledge. Therefore, the potential of Big Data relies on our ability to extract value from these massive data sets. Machine learning is central to this quest because of its ability to learn from data and provide data-driven …

Deep Learning For Load Forecasting With Smart Meter Data: Online And Federated Learning, Mohammad Navid Fekri

Electronic Thesis and Dissertation Repository

Electricity load forecasting has been attracting increasing attention because of its importance for energy management, infrastructure planning, and budgeting. In recent years, the proliferation of smart meters has created new opportunities for forecasting on the building and even individual household levels. Machine learning (ML) has achieved great successes in this domain; however, conventional ML techniques require data transfer to a centralized location for model training, therefore, increasing network traffic and exposing data to privacy and security risks. Also, traditional approaches employ offline learning, which means that they are only trained once and miss out on the possibility to learn from …

Multi-Device Data Analysis For Fault Localization In Electrical Distribution Grids, Jacob D L Hunte

Electronic Thesis and Dissertation Repository

The work presented in this dissertation represents work which addresses some of the main challenges of fault localization methods in electrical distribution grids. The methods developed largely assume access to sophisticated data sources that may not be available and that any data sets recorded by devices are synchronized. These issues have created a barrier to the adoption of many solutions by industry. The goal of the research presented in this dissertation is to address these challenges through the development of three elements. These elements are a synchronization protocol, a fault localization technique, and a sensor placement algorithm.

The synchronization protocol …

The Role Of Transient Vibration Of The Skull On Concussion, Rodrigo Dalvit Carvalho Da Silva

Electronic Thesis and Dissertation Repository

Concussion is a traumatic brain injury usually caused by a direct or indirect blow to the head that affects brain function. The maximum mechanical impedance of the brain tissue occurs at 450±50 Hz and may be affected by the skull resonant frequencies. After an impact to the head, vibration resonance of the skull damages the underlying cortex. The skull deforms and vibrates, like a bell for 3 to 5 milliseconds, bruising the cortex. Furthermore, the deceleration forces the frontal and temporal cortex against the skull, eliminating a layer of cerebrospinal fluid. When the skull vibrates, the force spreads directly to …

Physical Investigation Of Downburst Winds And Applicability To Full Scale Events, Federico Canepa

Electronic Thesis and Dissertation Repository

Thunderstorm winds, i.e. downbursts, are cold descending currents originating from cumulonimbus clouds which, upon the impingement on the ground, spread radially with high intensities. The downdraft phase of the storm and the subsequent radial outflow that is formed can cause major issues for aviation and immense damages to ground-mounted structures. Thunderstorm winds present characteristics completely different from the stationary Gaussian synoptic winds, which largely affect the mid-latitude areas of the globe in the form of extra-tropical cyclones. Downbursts are very localized winds in both space and time. It follows that their statistical investigation, by means of classical full scale anemometric …