Engineers that design and enhance buildings widely rely on computer simulations to understand wind flows. Wind flows around buildings can affect human safety, comfort, structural safety, and the surrounding environment.
Therefore, accurate and fast simulation of urban wind flows is critical to ensure safe, comfortable, and sustainable designs. If architectural engineers have access to a fast and accurate tool for simulating urban window flows, they can rapidly iterate and design the most energy efficient and sustainable buildings.

SFU will be collaborating with partners of a supercluster on the Continuous Connected Patient Care (CCPC) Platform, which is being developed to provide safe, responsive, and high-quality outpatient care at home. We will be working directly with our partner, Medtronic Canada. SFU’s will focus on the research, development, and testing of medical grade wearable sensor systems for untethered and continuous monitoring of vital patient parameters including SpO2, pulse rate, respiration rate, activity, and blood pressure.

In the proposed project, a series of computer-aided simulations will be conducted to determine the thermal behaviour of engine components in the presence of a fire inside the engine. The goal is to predict the heat transfer rate into the components, and the behaviour of the oil found inside those components.

Recently, the interest in the design and development of electric vertical take-off and landing (eVTOL) aerial vehicles has increased to shape a new flying revolution such as urban air-taxi mobility to combine the bene?ts of both conventional aircraft and helicopters. In this case, the risk of aircraft system faults has greatly increased owing to the tough working circumstances in high altitudes and orientations resulting from tilting motion and the large number of engines.

The proposed research project involves developing machine learning models to predict the mechanical properties of polymer composites. The interns will collect and preprocess data from various sources including open-source databases and conducting extensive experimental tests, build artificial neural network (ANN) models using advanced algorithms, and validate the accuracy of these models using test data.

In the era of the industrial revolution, the availability of vast amounts of data necessitates the development of software applications with online dynamic reliability prediction capabilities. Such software should enable a data-driven analysis of equipment life by incorporating various operational and environmental covariates that impact equipment failure, thereby mitigating production losses.

Our proposed research project aims to develop and optimize innovative 3D-Hybrid Composites (3DHC) and 3D-Fiber Metal Laminates (3D-FML) using a new development called PlastiCore. These materials offer exceptional mechanical properties, such as specific stiffness, strength, fatigue and impact response, and damage tolerance. PlastiCore simplifies the fabrication process and enhances material consolidation, providing a cost-effective solution for lightweight structures.

Our research project aims to develop an advanced coating solution for the tire molding industry to address the problem of rubber sticking to mold and vent surfaces during the tire curing process. Currently available solutions are costly, have a limited lifespan, and cause interruptions in the manufacturing process. We propose to use an electroless composite coating composed of three components, which offers excellent resistance to galling, corrosion, and wear. This coating can be applied to different surfaces with minimal preparation and ensures uniform coverage, preventing rubber adhesion.

Anomaly detection of machinery is a crucial aspect of predictive maintenance. It involves monitoring specific parameters related to the machinery's condition to identify significant changes that indicate a potential fault. Bearings, which are important components in rotary machinery, are responsible for over 40 percent of rotary machinery failures. Consequently, researchers have been focusing on detecting anomalies in bearings. While there are various methods available for bearing anomaly detection, most of them require high engineering expertise to detect faults.

Different industries have widely employed condition monitoring to evaluate their system’s health. For gas turbines, current methods have limitations related to certain operating and environmental conditions and are mainly based on supervised approaches requiring annotated data that is hard to acquire. This project will focus on developing an unsupervised anomaly detection mechanism that alarms users about a potential fault in the machine. The false alarm in typical models is big concern and, therefore will be addressed by a hybrid approach.