Innovative Projects Realized

Aeration of hydraulic turbines for increased dissolved oxygen

In warm climates, warm temperatures cause thermal stratification in hydropower reservoirs inhibiting mixing and leading to deoxygenation of waters at depth (hypolimnium). Turbines withdrawing water at depth result in low dissolved oxygen (DO) in the downstream flow having a large negative impact on the downstream riverine ecosystem. Legislation in the USA and elsewhere now requires hydropower operators to guarantee meeting minimum DO limits in downstream flows. Andritz Hydro Canada has initiated this project to optimize the elbow deflectors used in draft tube aeration, which is a technological retrofit approach not excessively impacting operation schedules. The main deliverables will be the optimization of the elbow deflectors, through a parametric study of the design parameters involved in maximizing bubble surface area and bubble concentration to result in an increase in dissolved oxygen concentration, and a set of data for validation of Andritz’s Computational Fluid Dynamics model.

An Efficient Data Analysis Pipeline

The proposed research project targets computational performance improvements of an data analysis pipeline. The project has a duration of four months and aims to achieve two objectives: (1) to properly characterize the performance of individual stages of the existing data analysis pipeline in terms of execution time, memory, and I/O, and (2) to improve the performance of individual stages where possible. The intern will use methods learnt and developed during the masters research and apply them to a real-world system at Acerta Analytics Solutions. The expected benefit to the partner organization, Acerta, is that the outcomes of the project will improve the performance of the existing data analysis pipeline.

Anomaly Detection using GAN

The proposed research project targets anomaly detection of event data. The project has a duration of four months and aims to achieve two objectives: (1) to evaluate the effectiveness of a novel approach on GAN for real-world data, and (2) compare it to alternative methods. The intern will use existing research resources, and will apply them to real-world data provided by the partner, Acerta Analytics Solutions, Inc. to evaluate the different methods. The expected benefit to the partner organization, Acerta, is that the outcomes of the project will improve the existing a software platform to detect failures in automotive vehicles, and eventually to predict them before they happen.

Develop integrated management of bacterial canker disease for greenhouse tomato –Phase II

Tomato is one of the most important greenhouse crops. The high nutritive quality, huge consumption, high yield potential and marketing value make tomato a popular household food and ideal commercial crop. However, frequent outbreaks of tomato canker disease resulting in devastating economic loss place it as a big threat to greenhouse tomato production. Tomato canker disease is caused by Clavibacter michiganensis subsp. michiganensis (Cmm), a Gram-positive bacterium. It is very difficult to eradicate the Cmm pathogen once it has been introduced into a greenhouse.
Dr. Yuan’s lab recently identified several bacterial agents capable of suppressing/killing the pathogen that cause the tomato canker disease, including the Gram-positive bacterium Paenibacillus polymyxa CR1 and Bacillus velezensis 9D-6. In addition, Dr. Yuan found that plant natural chemical salicylic acid suppressed the growth of Cmm pathogen. TO BE CONT’D

Dam Seepage Monitoring using Distributed Optical Fiber Sensing

The safe operation of a dam, such as Mactaquac, necessitates regular integrity monitoring over the structure lifespan. Optical fiber temperature sensing can provide seepage monitoring throughout a dam structure providing the operator with location specific seepage rates. Since the monitoring will be continuous over time and potentially operate over the lifespan of the dam operators can identify trends and evaluate repair effectiveness. The intern will be upgrading an existing laboratory instrument suite which will include calibration of the temperature measurements followed by installation of the sensor system at the dam site. Adapting the instrument suite for remote control through the internet as well as collecting temperature data from previously installed optical fiber sensing cables will be conducted. With several months of data collected, particularly including seasonal temperature changes, the intern will evaluate the data for seasonal dependent changes.

Characterization and Design of Additively Manufactured Components for Predictability and Materials Integrity

Rapid prototyping, or 3D printing, has inspired the imagination of the general public, from simple build-it-yourself “hobby” machines using polymer-based binder material with inkjet functionality, to portable printers that can fashion components in zero gravity on the International Space Station. The functionality is user-friendly, in that printed material is dropped onto a substrate in viscous plastic form, which solidifies to take on the designed shape. The resulting piece is a plastic prototype that may be used as-is, for some applications, or as scaled models to assist the product development process. This work focuses on 3D metal printing, specifically, direct metal laser sintering (DMLS), to build three-dimensional, complex parts using metallic powders. We integrate materials science, design of experiments, and engineering design for the purpose of manufacturing components with complex geometries and lightweight, high-strength metallic-alloy properties for aircraft applications. TO BE CONT’D

National Smart Vehicle Demonstration Project

Autonomous vehicle technologies and associated “smart” infrastructures are innovative technologies that can provide many benefits to transportation such as reducing traffic congestion and collisions, improving ridership experience by operating in a more on-demand application providing real-time updates to the rider, and reducing GHG emissions through integration of electric propulsion and route optimization technologies. This project will develop the technological certification standards and specification of the National Smart Vehicle Demonstration projects, in partnership with CUTRIC, which aims to test smart vehicles and smart infrastructure technologies within five to seven municipal jurisdictions across Canada in a closed or restricted laneway using low-speed shuttles. Additionally, this project aims to integrate some form of autonomous vehicle into First Nations communities across Canada where a lack of transportation services often leads to safety concerns for youth and women in particular. TO BE CONT’D

Evaluation of strategies in decreasing energy consumption at Irving Paper byfractionation

The thermo mechanical pulping (TMP) process uses large amounts of electrical Energy to

turn wood chips into separated pulp fibers suitable for papermaking Hecent advances being

developed by supplier-mill partnerships employ fiber fractionation processes between refining stages to achieve significant reductions in total energy consumption During this

internship these new processes will be investigated to understand how the discrete process

stages impact the resultant fiber fractions and overall pulp properties, and its implication to

[he operation at irving Paper These will be done by conducting bench and pilot-scale

fractionation and refining equipment. Pulp and fiber properties will be determined, thus

identifying the process configuration with the best potential for decreasing the energy

consumption while maintaining pulp properties

Modelling partial mortality wildfire dynamics in boreal and mountain landscapes

An emerging strategy for managing natural resources such as Canada’s forests more sustainably and responsibly is to use knowledge of how Mother Nature has done it to help guide our hand. This so-called ‘ecosystem-based” approach has gained favour with provincial and federal governments, as well as national and international certification agencies. One of the foundations of such an approach is a fundamental understanding of how natural forest ecosystems have worked for millennia over time and space: How has Mother Nature provided the rich array of goods and services such as timber, clean water, recreation, fishing, hunting, and critical species habitat that we enjoy today? Unfortunately, it is not possible to define historic reference conditions using actual data. The next best solution is to develop simulation models that can capture what we understand of landscape dynamics over time and space to re-create such reference points. TO BE CONT’D

Influence of base rigidity on the load-carrying capacity of loadbearing masonry walls

Design of loadbearing, out-of-plane (OOP), tall masonry walls tends to have stringent limits related to their buckling stability and the scarcity of research on their structural reliability. This currently puts the masonry industry at a disadvantage as a construction alternative compared to other structural options. The proposed research investigates the strength of tall masonry walls against lateral loads, considering the influence of base rigidity. Current design practice does not recognize the influence of actual support conditions in estimating the load capacity of slender masonry walls. Neglecting the rigidity of common foundation systems leads to an underestimation in load capacity that can be uneconomical. This indicate the need to determine the structural response of walls with realistic boundary conditions at the base, in terms of strength against lateral loads, and to develop numerical models that allow for the development of new design methods that account for base rigidity.

Machine Learning for the Telecommunication Industry – Year two

Ericsson is an industry leader in offering telecommunication solutions and products. As an important step on the path towards the automatic and autonomous management of next generation networks, Ericsson is developing technology in machine learning and artificial intelligence that will benefit operators around the world, including in Canada where Ericsson supplies technology to most of the major telecommunication network operators. In the proposed project, through the exploration of concrete telecommunication industry use cases, the Ericsson researchers in Canada and their academic partners will evolve the start-of-the-art in machine learning and artificial intelligence for the analysis of telecommunication data and operation of telecommunication networks. This will allow Ericsson to develop new products and services, which will allow Canadian network operators to offer improved communication services to Canadian customers. The proposed project will lead to new methodologies for processing complex communication network data, addressing significant imbalances in data sets, and performing anomaly detection.

Machine Learning for the Telecommunication Industry

Ericsson is an industry leader in offering telecommunication solutions and products. As an important step on the path towards the automatic and autonomous management of next generation networks, Ericsson is developing technology in machine learning and artificial intelligence that will benefit operators around the world, including in Canada where Ericsson supplies technology to most of the major telecommunication network operators. In the proposed project, through the exploration of concrete telecommunication industry use cases, the postdoctoral researcher will collaborate with the Ericsson researchers in Canada and the academic supervisor to evolve the start-of-the-art in machine learning and artificial intelligence for the analysis of telecommunication data and operation of telecommunication networks. This will allow Ericsson to develop new products and services, which will allow Canadian network operators to offer improved communication services to Canadian customers. TO BE CONT’D