Creation and validation of a GAN-based approach to minimize the cost and radiation burden of PET radiopharmaceuticals

This project will facilitate the development and validation of a novel AI-based model that can improve the quality of PET images such that diagnostic-quality images can be reconstructed from administering a smaller radiopharmaceutical dose to the patient. The two main advantages of this are decreased radiation dose to the patient which decreases the chance of developing a radiation-induced cancer, and also decreased cost to the healthcare system because less radiopharmaceutical is required to produce a diagnostic-quality study.

Multiparametric Analysis of Brain & Lung Imaging from COVID-19 Patients

The intern will participate in NEUROCOVID19, a project studying how the COVID-19 virus can potentially infect and damage the brain. The intern will develop methods for analyzing magnetic resonance imaging (MRI) of the brain and of the lung, as acquired from people who are no longer infected, and people who were never infected. The intern will also develop new MRI methods for enhanced imaging of brain areas that are damaged by COVID-19 infection.

Siemens next generation land-based gas turbine engine combustors: Characterization and development of a newly-designed injection system

Siemens Canada develops land-based gas turbine engines that are used for power generation. These engines burn natural gas and produce combustion pollutants such as carbon monoxide and nitrogen oxides. Reducing emission of these pollutants addition of low carbon fuel, such as hydrogen, to natural gas. However, addition of hydrogen leads to occurrence of combustion instabilities which are of safety concern for gas turbine operation.

“Advanced Manufacturing Automation, Digitization and Optimization - AMADO”

The 4th Industrial Revolution "Industry 4.0" (Germany, 2011), with its automation and digitization technologies will change the landscape of the industry. It is characterized by optimization and management of assets, sharing and security of big data, tracking parts from cradle to grave: the "Digital Thread," data analytics and Artificial Intelligence (AI).

A Finite Element Framework for Non-linear Material Constitutive Modelling of Superalloy Additive Manufactured Parts

Due to its versatility, time and cost saving, additive manufacturing (AM) technology, and more specifically selective laser melting process (SLM), is replacing conventional manufacturing processes, particularly for producing complex geometry components. In this technology, the near net shape parts are incrementally built by fusing layers of powder material using an intensive heating source/ Structural stress analysis and lifing assessment via finite element (FE) analysis are well-accepted modern engineering practices within product development procedures.

A digital technology platform for supply chain: Development of scenario planning and forecasting models and tool for the engine build program to assess the impact of variations in sales forecast on inventory planning

Industry 4.0 is the digitization of a company’s physical assets and the company’s integration into digital ecosystems with its value chain partners, from suppliers to customers. It uses smart technology and the use of real-time data to increase flexibility, customization, efficiency and productivity, and to reduce time, costs and innovation cycles. This project will focus on adapting novel concepts to an enterprise to meet these challenges, with an emphasis on smart processes as a means to achieve the transition to Industry 4.0.

Fugitive Emissions in Liquefied Natural Gas Transmission, Storage, and Distribution: Canadian Solutions for Transportation and Remote Power

The proposed research will be focused on eliminating fugitive emissions from liquefied natural gas (LNG) transmission, storage, and distribution operations. LNG can be used as fuel for transportation, and for combined heat and power generation in remote locations. We will study transmission, storage, and distribution operations by developing quasi-steady-state and time-dependent thermodynamic models. These models will be validated using data from instrumented equipment at our industrial partners’ sites (a small consortium has been created specifically to support the proposed research).

SIEMENS Perceived Value of Energy Consumers Project: Research and Development for Modeling the Perceived Value of Energy Consumers

Research and Development for Modeling the Perceived Value of Energy Consumers is a novel area of study which should help to better understand the energy consumers and the perceived value they place on different services and products. By identifying, understanding potential new products and services the related business models can be developed.

Next Generation Engine

The next generation of engines will needs to comply with increasing stringent pollutant emissions legislation. These engines will also have to be able to accept a wide range of gaseous fuel composition and have the capability to operate on liquid fuel either for emergency backup or for full baseload operation. Additionally, they will need to able to burn alternative fuels, both gaseous and liquid, and either as blends or as pure fuel. These requirements impose significant technical and modeling challenges.

Selective Laser Melting Process Simulation of a Nickel-Based Superalloy Gas Turbine Component

Selective laser meting (SLM) is a promising additive manufacturing process that can be effectively utilized to manufacture structural components with complex geometries. Instead of removing the material, SLM adds the material selectively layer after layer using high power laser beam to form near net shape parts. Due to high cost associated with experimental development of the technology, the need for an accurate model to simulate the process is inevitable.