Innovative Projects Realized

Heat Transfer Analysis of an Electric Baseboard Heater using Computational Fluid Dynamics

This research is an attempt to understand a heat transfer challenge encountered in the Glen Dimplex electric baseboard heaters in details and provide recommendations to resolve that. This baseboard heater uses electricity to heat up an internal element which is designed to transfer the heat to the room’s air via a series of thin aluminum plates called fins. The poor heat transfer from heater fins to air results in low efficiency of the heater as well as the unwanted physical deformation of fins by thermal expansion. Using computer modeling, the heat transfer mechanisms inside the heater will be analyzed, the possible reasons for the heater’s low heat transfer performance will be identified, and recommendations to improve its performance will be given. Using these recommendations, Glen Dimplex would be able to redesign and improve its heaters consuming less electricity while providing higher heat output.

Managing Shared State for Video Games in a Networked Multi-core Environment

Video Games require a vast array of different computations to present the desired experience. These

computations must be completed consistently to make the software responsive to the user. The

industry trend towards many separate processors (multi-core) in the same physical device and the

emergence of network based ‘cloud’ computing have created many opportunities, but also many

challenges for the game industry.

The goal of this project is to create efficient techniques to organize and schedule the computations to

take advantage of all the processors available. These techniques must ensure that the results

produced correctly and are obtained quickly enough to satisfy responsiveness. Each of these

techniques will have a corresponding aspect that allows them to be used by programmers who are not

experts in utilizing multiple processors.

While this project focuses on games, the benefits will be applicable to many domains, especially in the

emerging field of mobile consumer-oriented applications.

Atmospheric water generation with nanostructured material

Atmospheric water harvesting allows arid regions with low rainfall or unsustainable/expensive groundwater resources to collect fresh water from the air to meet their needs. Typical water harvesting methods rely on a simple principle: humid air is forced through a mesh net, where water condenses and droplets drain away through gravity. However, these traditional approaches face limitations with respect to the rate at which water can condense and drain away from the surface. AWN Nanotech Inc. has developed an alternative configuration for water harvesting, relying on a nanomaterial-enhanced, functionalized porous substrate that
will promote water nucleation and actively “pump” the condensed water away from the surface for collection through capillary action. AWN requires the expertise developed at Polytechnique Montreal and McGill University in surface engineering and nanomaterial synthesis to refine design parameters for their new configuration and construct testable systems. This project will allow the development of a new, Canadian water harvesting system capable of revolutionizing how fresh water is supplied. With a worldwide market estimated at nearly 1 billion USD by 2020, not to mention the decreasing levels of Canadian fresh water sources, the successful outcome of this project will yield substantial benefits to the Canadian economy.

Décharges pulsées dans les liquides : synthèse de nanoparticules

“Les plasmas dans les milieux liquides” est un projet de recherche très original, en termes des applications dans différents domaines et en termes de physique fondamentale. Mon projet consiste à étudier les plasmas générés dans l’eau en utilisant une décharge électrique impulsionnelle. Pratiquement, nous allons étudier en premier temps les caractéristiques électriques de la décharge en déterminant son circuit électrique équivalent afin de remonter aux propriétés physiques de la décharge, telle que la densité électronique.
D’autre part, nous allons caractériser les nanoparticules synthétisées par cette décharge. En premier temps, nous utiliserons des électrodes en nickel et cobalt pour produire des nanoparticules d’oxyde de nickel et de cobalt. Ensuite, des nanoparticules contenant simultanément du cobalt et du nickel (i.e., nanoalliage) seront synthétisées. Nous étudierons l’effet de la tension appliquée, surtout la polarité (positive ou négative) et la largeur de la pulse (c’est à dire la durée de vie de plasma) sur les caractéristiques de la décharge et des nanoparticules produites. Le développement des procédés écologique permettant la production efficace des nanoparticules est un besoin dans les applications technologiques, surtout dans le domaine de stockage des données (e.g., disques durs) et de l’énergie (e.g., batteries).

Application of Different Machine Learning and Data Mining Algorithms in the Detection of Financial Fraud

Detection of financial fraud is a priority for financial institutions. There are a variety of techniques and models that can be used to address the problem of financial fraud. However, as fraudsters are becoming more inventive and adaptive, they have been able to penetrate the conventional protective methods. This is one of the main reasons for the growth in financial fraud activity, regardless of the efforts of financial institutions and government and law enforcement agencies. This project investigates the use of artificial intelligence and machine learning algorithms to detect financial fraud.

Detecting Credit Transaction Fraudulent Behavior Using Recurrent Neural Networks

Fraudulent activities are hard to detect, but they cost financial institutions millions of dollars in monetary losses and legal costs every year. Millions of dollars are being lost in credit transactions as criminals are finding new, more sophisticated ways to conduct financial crime. This research project examines novel ways of detecting fraudulent behavior using powerful tools such as Recurrent Neural Networks, a type of machine learning model that is well suited for sequence or historical data.

Geosynthetic drainage for improved stability of fine-grained materials in slopes & embankments

Innovative geosynthetic drainage products have been developed that have the potential to significantly benefit the stability of constructed embankments or reconstructed slopes especially where these are constructed from soil (or soil-like materials such as tailings) that are finer and less permeable (and thus weaker) then free-draining coarse-grained granular fills. Applications include reconstruction and stabilisation of natural slopes, embankments or dams constructed of (or at least partly from) mine tailings or other finer-grained materials. The project will consist of testing the DrainTube® material to determine its ability to convey water in saturated and unsaturated conditions. A computer numerical model will be developed using the properties of the DrainTube®, and a small physical model will be constructed to verify the numerical model. Parameters including spacing of tubes, slope of the ground, soil permeability, infiltration, and anticipated rainfall will be analyzed. The numerical simulation will then be optimized based on these parameters, and design guidelines will be recommended. The project partners will benefit from results that allow the DrainTube® product to be used confidently in engineering design.

An Analysis of the Cambridge Self-Help Food Bank Network and its Potential for Development

The project looks at the Cambridge Self-Help Food Bank to consider how the redevelopment of the existing network can promote opportunities for more integrated community development and cohabitation, while also improving access to food for vulnerable populations. The proposed method is to study the operations of the food banks by analyzing existing and new data, using geographic and statistical mappings, conducting interviews and undertaking comparative analyses with relevant case studies. In light of the organization’s impending relocation, the intention is to consider the operations holistically – across procurement through donation or cultivation, storage, distribution, kitchens, cooking workshops and other affiliated programs – so as to critically address the opportunities and potential limitations inherent to the spatial deployment of the organization in a single new site or across a number of different sites.
Ultimately, the ambition of the research is to think through the specific case of the Cambridge Self-Help Food Bank to spatially address the critical question of food security in mid-size cities.

Bacterial ATP synthase inhibitors as novel antibiotics for Gram positive and Gram negative bacterial infections.

The goal of this research project is to exploit a recent discovery made at Université de Sherbrooke and develop novel antibacterial agents for difficult to treat bacterial infections. The project will target both Gram positive and Gram negative bacteria, using a combination of structural biology, molecular modeling medicinal chemistry and microbiology.

Towards species specific management of invasive hawkweeds in British Columbia:Quantifying distributions, modeling potential invasion extent, and investigating genetic-morphometriccongruence

The species of European hawkweeds present in British Columbia are aggressive and ecologically

detrimental invaders of meadows, parks, agricultural lands and rangelands. Left unmanaged, these

species could cost the province of British Columbia upwards of $60 million in economic losses by

2020. The Ministry of Forests, Lands and Natural Resource Operations (MoFLNRO) is responsible

for addressing invasive plant species on Crown land. This collaborative project between the

MoFLNRO and UBC Okanagan will clarify the distributions and invasion extent of each hawkweed

species present in east-central British Columbia in order to inform management of these species. We

will use these data to predict, using Ecological Niche Models, areas of the province vulnerable to

invasion by these species. We also will determine if the current morphology-based identification

system is effective for on-site differentiation of these species. This research project will directly

inform and improve the effectiveness of managing these invasive weed species

High Performance Computing (HPC) of Full Waveform Inversion and Reverse Time Migration (FWI/RTM)

We will develop advanced software toolkits for seismic inversion and imaging. These method are called Full Waveform Inversion and Reverse Time Migration (FWIIRTM). The FWIIRTM will be used to obtain accurate 30 images and elastic properties of subsurface complex structures.

Determination the effectiveness of drought resilient bioretention bed in stormwater management

The greatest river in Calgary, Bow River, is an important source of water for industrial, agricultural and living activities for city and people of Calgary. With the incredibly fast of urbanization in Calgary, the natural landscapes are gradually replaced by impervious areas, such as urban streetscapes, houses and buildings. While the amount of water that cannot penetrate through those impervious areas during rainfall, known as stormwater run-off, will discharge into the Bow River and consequently increase pressure to water treatment systems, there are other extended droughts period that need to take potable water for irrigation activities. Therefore, this project focuses on analyzing the amount of stormwater run-off retained and treated using a bioretention system, and evaluates the possibility of using retained stormwater for irrigation. This project contributes to protecting the water of Bow River, which is one of the mandates of my partner organization Bow River Basin Council.