Projets novateurs réalisés

Health and Climate Benefits of Energy Intervention Programs in Rural China: Ensuring Data Quality and Evaluating Technology Adoption

Household air pollution from combustion of solid fuels (e.g .. biomass and coal) for cooking and heating is the fourth leading contributor to the global burden of disease (Lim et al” 2012), and significantly contributes to regional climate warming. Evidence has pointed to the joint health and environmental benefits of energy intervention programs; however. little about the magnitude of these benefits is known. A joint collaboration led by six principal investigators from four different countries seeks to measure the air quality, climate and cardiovascular benefits of an existing energy intervention program in the Tibetan plateau that replaces traditional biomass cookstoves with improved technologies. The student will ensure data quality during the start of this large-scale study through quality control and assurance procedures. Additionally, the student will

conduct an ancillary study on the factors affecting energy use behavior to evaluate the ability of the improved stove technologies to successfully integrate into rural communities,

Characterization of high-amylose durum wheat starch and flour to promote industrial applications

This project aims to investigate the structural features, functional properties, and nutritional quality of durum wheat flours and respective isolated starches of five different lines varying in amylose contents. Flours of two selected durum wheat lines (one normal and one high-amylose) will also be used to produce high-quality and nutritious pasta products and evaluated to compare their performance. The proposed research will enable our industry collaborator and the agri-food industry in Canada to better utilize high-amylose durum wheat to produce different food products, which will not only generate significant economic returns but also promote the health of consumers in the long term.

Combination of different types of gravity observation for precise geoid determination

The Global Positioning System (GPS) has become an indispensable component of both civil and Geomatics practices. However, the GPS observations provide heights that refer to a reference ellipsoid and are not directly suitable for practical purposes. To transform GPS heights into physical (orthometric) heights referenced to an equipotential surface representing mean sea level on land, a conversion model known as the geoid is essential. With the sub-centimetre accuracy of GPS observations, an accurate geoid model is also required to achieve the same accuracy for the orthometric heights. Geoid models are computed using different resources of the Earth’s gravity field observations including land or airborne gravity surveys and satellite gravity missions. These resources vary in their spatial and spectral resolutions due to their respective distances from the Earth’s surface and the sensors they employ for measurements.
Achieving the desired level of sub-centimetre accuracy in geoid modelling necessitates a combination of a uniform, high-resolution, and accurate gravity dataset. Consequently, determining the optimal combination of gravity observables from each resource is a complex and challenging task.

Application de composés organométalliques pour la production de nanoparticules par décharges plasma à une interface liquide-liquide

La production de nanoparticules métalliques par décharge plasma à une interface liquide-liquide est un procédé présentant à la foi des perspectives de dépollution de milieux naturels et de production de nanomatériaux.
L’objectif du stage sera d’abord de caractériser l’effet de cette décharge sur les propriétés et surtout la composition des milieux liquides sera aussi étudiée par plusieurs techniques spectroscopiques et diagnostics électriques.
Par la suite, la synthèse de nanoparticules à partir de sels métalliques déjà décrite dans la littérature sera reproduite. L’influence de l’introduction de composés organométalliques aux différents liquides sera ensuite étudiée. En effet, ces précurseurs chimiques pourraient intervenir dans la synthèse des nanoparticules et ainsi permettre le contrôle de leurs caractéristiques de composition et de taille. Enfin, des méthodes permettant l’extraction et la purification des nanoparticules via des procédés chimiques et physiques seront étudiées.

Emerging Concepts and Technologies (ECT) Research Program – Reducing Greenhouse Gases and Improving the Carbon Sequestration Potential of Nova Scotia’s Wild Blueberry Industry

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Neutronics simulation on the McMaster Nuclear Reactor (MNR)

This research will develop and test new software tools in reactor physics (openMC) for neutronic calculations. It involves development/extension of the existing McMaster Nuclear Reactor (MNR) core physics models in the openMC code and benchmarking against existing results obtained with other tools. Once benchmarked the student will perform a serries of simulations to mimic the recent MNR Feedback Coefficient Tests where the moderator temperature was physically changed and the impact on core reactivity was measured by observing the impact on control rod position. The student will parametrically simulate the MNR core for a wide range of control rod positions and moderator temperatures and compare the results to the transients measured during the MNR experiments. The student will document the model and results in an end of term report.

Parameter Optimization for Additive Manufacturing using Machine Learning

Additive manufacturing (AM), also known as 3D printing, is a process of building products with the material layer by layer. It can produce the products with complex geometries in a simple setup. But it is a challenge in selecting right printing parameters to build a quality part. This project proposes using machine learning techniques for selection of process parameters to improve the AM efficiency and product quality and reduce costs. The expected solution of this project has significant potential for an efficient and sustainable AM processing tool used by manufacturing industries to improve the AM efficiency and product quality while simultaneously reducing printing time and cost.

Facteurs explicatifs de la diversité végétative sur les 36 iles du Lac Hébécourt, QC, Canada

Les écosystèmes boréaux englobent une multitude de lacs de morphologies variées, dont certains abritent des îles. Les îles boisées constituent des interfaces forêts-lacs, présentant des dynamiques végétales et de perturbation, distinctives du continent. Néanmoins, l’influence de la structure forestière et des dynamiques de perturbation sur la biodiversité des environnements insulaires a été insuffisamment explorée, ce qui pose des défis pour les efforts de conservation. Ce projet vise à déchiffrer les principaux moteurs de la diversité biologique des 36 îles du lac Hébécourt, au sud-ouest du Québec, en caractérisant la composition et la structure actuelles des arbres, et en évaluant la diversité et l’abondance des communautés d’insectes et de champignons. La richesse et l’abondance des espèces entre les îles seront mis en relation avec divers paramètres géographiques et les conditions du sol des horizons minéraux-organiques. En utilisant une approche multiproxy impliquant la détermination des dates d’établissement des arbres, l’identification des cicatrices de feu, et la datation radiocarbone du charbon du sol, nous établirons une chronologie du temps écoulé depuis le dernier feu pour chaque île. Les résultats visent à améliorer notre compréhension de la succession forestière post-perturbation dans les écosystèmes insulaires lacustres, et à mieux comprendre les peuplements forestiers anciens.

Favorable propagation studies for massive MIMO systems

Favorable propagation studies for massive MIMO systems: mMIMO is a key technology for 5G and beyond, due to its ability to deliver high spectral efficiency/rate in multi-user environments using simplified (linear) processing. Its advantages can be fully exploited under certain conditions known compactly as “favorable propagation” (FP). Unlike the known methods, which rely on sophisticated beamforming algorithms (e.g. successive interference cancellation, maximum SNR or minimum mean square error) and thus are difficult to implement when the number of antennas is large, our approach targets reducing drastically implementation’s complexity and improving its robustness by exploiting the FP property. The known FP studies were carried out in simplified environments, and under a number of simplified assumptions. It remains unclear whether FP will hold in realistic 5G environments under practical constraints. This project will address these issues by studying the FP in realistic propagation environments (multipath propagation, correlated fading), for moderate to large number of antennas various user configurations and array geometries, with implementation errors included. The results of performance analysis will be used to optimize system design and performance and to develop processing algorithms under various practical constraints.

Magnetic field assisted DED towards the development of functionally graded materials

Metal additive manufacturing (AM), or metal 3D printing, enables “unique structures printed for unique functions” and “the right materials printed in the right place”. Therefore, it is the preferred fabrication method for creating functionally graded materials (FGMs), whose material properties varies over its volume to meet industrial needs. In this project, I leverage external magnetic fields during the AM process to locally tailor the microstructures of the materials being printed, consequently creating controlled graded mechanical properties. Using in-situ high-speed synchrotron X-Ray imaging combined with analytical, simulation, and machine learning techniques, I hope to establish a quantitative relation between the magnetic field parameters and the resultant microstructure and mechanical properties and use it to guide the development of metallic FGMs. This research project aligns with the research interests for both the host (UCL) and home (UofT) universities. The successful completion of this project will add valuable data and experiences to UCL’s ongoing research in the metal additive manufacturing process. For UofT, success of this proposed project will enable the design and manufacturing of high performing metallic FGMs revolutionary to Canada’s aerospace, automotive, and energy sectors.

Entrepreneurial Reconciliation

This research utilizes the community as the learning environment and embeds the strengths and gifts of its members to foster entrepreneurial growth. The research aims to show that learning is everyone’s responsibility and when done from as asset-based approach can benefit entire communities. Students work with community members to bring ideas and business to communities through an 8-week internship paired with academic credit. The goal is to record success and growth on communities in profit and not for profit business.

Intelligent cyber-threat awareness system for 5G networks

5G is the latest and fastest wireless internet technology that is not only being used by phone companies, but also in businesses, factories, and the military. As 5G is being used in more places and for different purposes, it gets more complicated and faces more security risks. Imagine many different gadgets, from phones to cars to tiny sensors, all connecting to this internet. The more gadgets, the more chances for bad actors to attack. Our research project wants to use a special method, called the “FiGHT framework”, to spot and understand these potential threats. We will gather data, like logs and security checks, to map out the risks. With the help of artificial intelligence, we want to create a smart system that can tell us where the dangers might come from and how to protect against them. This will be helpful for experts who try to find these threats and those who work to stop them, making 5G safer for everyone.