MEng Projects

Not all projects available are listed, it may be a good idea to review the faculty list and contact professors whose research area matches your interest and inquire if projects are available.

For more information on how to enrol in a MEng Project, please review Enrolling in a MEng Project here.


  • Optimization of a catheter tip design to maximize visibility in the aorta
    Faculty advisor: Prof. Edgar Acosta

    A novel catheter-based imaging device has been developed to visualize the interior surface of blood vessels and to guide endovascular therapies. This device cannot see through blood and currently uses saline ejected from the catheter tip to displace the blood and increase the field of view of the probe.

    The objective of this project is to design and optimize the catheter tip and saline delivery system to maximize the field of field and allow the probe to better visualize the vessel walls. This project will be carried out using computational fluid dynamics (CFD) models, benchtop flow loop testing, and ultimately in-vivo studies. The goal of this project is for the student to develop the CFD models for catheter tip design and optimization to maximize performance before prototypes are constructed and for iterative performance testing.

    Prerequisites: a) Previous course(s) in fluid mechanics, b) experience using ANSYS Fluent or related CFD package, c) proficiency in computer programming using MatLab and C/C++, and d) basic knowledge of vascular anatomy and physiology (optional).

    Research area: Computational Fluid Dynamics

  • Evaluating the impact of workflow communication tools on the Gamma Knife workflow at the Odette Cancer Centre (*New – Fall 2018*)
    Faculty advisor: Prof. Dionne Aleman

    Gamma Knife radiosurgery (RS) uses hundreds of narrow radiation beams to treat abnormalities in the brain with sub-millimeter accuracy. At Sunnybrook Health Sciences Centre, Odette Cancer Centre, a multi-disciplinary team consisting of radiation oncologists, therapists, nurses and physicists is involved in patient care and treatment plan generation, which is expected to have short turnover of 2-3 days to treatment. Currently, a combination of paper lists and electronic patient record ticketing are used to coordinate Gamma Knife task assignments. The reliance on manual documentation and uncoordinated records has led to inefficiencies such as miscommunications in task hand-off, uneven workload distribution and rushed assignments, which could lead to suboptimal plan quality, patient treatment delays or overall lower patient throughput. To address these issues, a real-time electronic dashboard and emailing system was implemented that retrieves electronic tickets from the record-and-verify system (Mosaiq) and consolidates the RS workflow from all scheduled patient plans into a single, intuitive real-time display. The dashboard software also initiates immediate email reminders to downstream staff when a task is due for completion. The goal of this work is study the impact of the dashboard using both human factors and operations research techniques to establish baseline usability and process performance metrics. It is also envisioned that long-term process improvements and promising additional ancillary technologies (e.g., task assignment apps) will be identified.
  •  Model Refinement and Validation in Simulation-based Design Optimization
    Faculty advisor: Prof. Cristina Amon

    The optimal design of complex systems in engineering requires the availability of mathematical models of system behavior as a function of a set of design variables; such models allow the designer to find the best solution to the design problem. However, system models (e.g. CFD analysis, physical prototypes) are usually time-consuming and expensive to evaluate, and thus unsuited for systematic use during design. Approximate models of system behavior based on a limited set of data allow significant savings by reducing the resources devoted to modeling during the design process.

    This project is part of our research into methods to support engineering design based on computer simulation models, in which we use model approximation and optimization techniques to assist decision making during the design process. The goal of this specific project is to develop strategies for the sequential exploration of multi-dimensional design spaces with approximation models (a.k.a. metamodels). These strategies have the potential to reduce the number of model analysis that are required to reach an optimal solution to the design problem, thus resulting in significant savings in both time and cost.

    Pre-requisites: (a) previous courses on numerical methods, statistics/design of experiments, optimization (optional); (b) Proficiency in computer programming (Matlab or C/C++).

    If you are interested in this project, consider taking the course MIE1299H “Special Topics in Fluid Mechanics – Methodological Tools for Simulation-based Design Optimization”.

  • Simulation of Wind Turbine Wakes using CFD Techniques
    Faculty advisor: Prof. Cristina Amon

    In this project, we model air flow in a wind farm to study wake behavior using CFD methods as implemented in the OpenFOAM code base. We will use the actuator disk model to represent the turbines, as we are interested more in the mid- to far-wake regions than in the detailed flow around the turbine blades. Based on the results of the simulations, we aim to formulate simplified yet accurate wake models to support optimization efforts.

    Pre-requisites: (a) Previous courses in fluid dynamics, thermodynamics, numerical methods. (b) Experience using ANSYS/Fluent/CFX software, either in fluids, thermal or solid analysis. (c) Proficiency in computer programming (Matlab or C/C++).

    If you are interested in this project, consider taking the course “MIE1240H: Wind Power”.

  • Transistor-Level Transient Modelling of Thermal Transport in Electronic Devices
    Faculty advisor: Prof. Cristina Amon

    Proper thermal transport modelling in an electronic device requires developing a hierarchical multi-scale model. The hierarchical methodology incorporates physics-based models at different length scales ranging from nanometers to a few millimetres. Appropriate modelling techniques for each level of the hierarchy are developed. Different levels of the hierarchy include atomistic-level (a few tens of nanometres), to transistor and logic-gate level (a few microns), to functional blocks level (a few hundreds of micrometres), and finally to the package level (a few millimetres). Simulations start at the smallest length scale (atomistic level), and information transfer to the next higher length level is through the definition of a compact model or an effective physical property.

    The first step of this hierarchical modelling is to simulate thermal transport in thin films and nanowires using atomistic-level techniques. Our group has done extensive work on this part of project and have developed atomistic-level techniques for thermal transport modelling in such systems. The results of the atomistic-level simulations are then transferred to the next level in the hierarchy of length scales (i.e., transistor and logic gate level) in the form of effective thermal conductivity and thermal diffusivity for different parts of the transistor.

    The MEng student will work on the transistor and logic-gate level modelling, continuing our group’s work in 3D models of different basic logic gates based on FinFET and MOSFET technologies, performing transient simulations. A parametric study of the effect of geometrical parameters of the FinFET and MOSFET technologies on the predicted equivalent thermal conductivity will also be performed.

    Research area: Nano-Heat Transfer

  • A Machine Learning System to Optimize the Performance of Spray Nozzles (*New – Fall 2018*)
    Faculty advisor: Prof. Nasser Ashgriz

    Contact: Prof. Nasser Ashgriz ashgriz@mie.utoronto.ca
    Research Area: Fluid Thermal Science, Multiphase flows
  • Characterizing the Breakup of a Liquid Droplet (*New – Fall 2018*)
    Faculty advisor: Prof. Nasser Ashgriz

    Contact: Prof. Nasser Ashgriz ashgriz@mie.utoronto.ca
    Research Area: Fluid Thermal Science, Multiphase flows
  • Development of Pharmaceutical Aerosols (*New – Fall 2018*)
    Faculty advisor: Prof. Nasser Ashgriz

    Contact: Prof. Nasser Ashgriz ashgriz@mie.utoronto.ca
    Research Area: Fluid Thermal Science, Multiphase flows
  • Job Shop Scheduling Problems and Mixed Integer Programming Pre-solvers
    Faculty advisor: Prof. Christopher Beck

    In this project, the classical job shop scheduling problem will be studied. The student will work on a reformulation of a well known mixed integer programming model. The changes in the model will allow for development of a pre-solving algorithm which generates cuts to make finding optimal solutions easier. The student will have a chance to work on modelling of a scheduling problem as well as algorithmic development for optimization software solvers.

    Recommended pre-requisites: (a) Previous courses and/or experiences in scheduling, linear programming, constraint programming. (b) Proficiency in C/C++ and IBM ILOG CPLEX Optimizer.

  • Scheduling in Queueing Network Environments with Flexible Servers and Setup Times
    Faculty advisor: Prof. Christopher Beck

    In this project, we will create hybrid queueing theory and scheduling models to solve combinatorically complex problems in dynamic environments. This project is part of our research in combining the two research areas which have mostly been developed independently. By using tools provided by both research areas, we attempt to more accurately represent a queueing network with flexible servers. The goal will be to develop scheduling models which make use of queueing models which gather information from online realizations to guide future scheduling decisions.

    Recommended pre-requisites: (a) Previous courses and/or experiences in discrete event simulation, linear programming, stochastic modelling, and queueing theory. (b) Proficiency in C/C++ and IBM ILOG CPLEX Optimizer.

  • Robotic Vision, Mobile Robotics, 5-axis Milling Machine Design
    Faculty advisor: Prof. Beno Benhabib

  • Operations Research, Optimization, Radiation Therapy, Healthcare Operations
    Faculty advisor: Prof. Timothy Chan

  • Monte Carlo Simulation of the Impact of Distributional Properties on the Effectiveness of Cluster Boosted Regression
    Faculty advisor: Prof. Mark Chignell

    Clustering into patient types is a way of generating clinical predictions based on non-confidential summarized patient data (Chignell et al., 2013). Predictions made based on segmented patient types using Cluster-boosted regression can improve on predictions made using confidential raw patient data, with studies reported by Rouzbahman et al. (2017) showing around a 2 percent predication in the case of predicting length of stay and death status in an intensive care unit, and in predicting the likelihood of a visit to an emergency department within one month of assessment for late stage cancer patients.

    The purpose of this project is to use Monte Carlo Simulation experiments to determine which distributional properties of multivariate data influence the magnitude of the boosting effect in cluster boosted regression. It is anticipated that this research should lead to a scientific paper that provides key insights into why cluster boosting is beneficial as well as providing criteria that can be used to determine which types of data set will stand to benefit more from the cluster boosting approach.

    Required Skill: To carry out this project you should have some experience with statistical analysis and regression analysis in particular, and should be familiar with the R programming language and associated statistical and machine learning packages.

    References

    Rouzbahman, M., Jovicic, A., and Chignell, M. (2017). Can Cluster-Boosted Regression Improve Prediction?: Death and Length of Stay in the ICU. IEEE Journal of Biomedical and Health Informatics, 21(3), 851-858.

    Chignell, M., Rouzbahman, M., Kealey, M.R., Yu, E., Samavi, R. and Sieminowski, T. Development of Non-Confidential Patient Types for Use in Emergency Medicine Clinical Decision Support. (2013). IEEE Security & Privacy, November/December, 2-8.

    Contact: Mark Chignell chignel@mie.utoronto.ca

    Research Area: Clinical Decision Support, Health Data Mining

  • Data Management and the Web, Analytics, Information Retrieval and Visualization, Data Modeling and Business Process Engineering, Healthcare Data Management
    Faculty advisor: Prof. Mariano Consens

  • Large Scale Data Analytics and Visualization
    Faculty advisor: Prof. Mariano Consens

    The project objectives are to apply data analytics tools and techniques to large datasets to provide useful insights and to support decision making. Several projects are available in different application areas (in particular, social services, smart cities, mining industry). The sources of data include open data as well as data provide by collaborating organizations.
    The project activities include the extraction, combination, and cleaning of multiple data sources (leveraging a scalable data management environment such as Hadoop and Spark), followed by the application of data mining and machine learning analysis techniques, and culminated by the preparation of visualizations of the results obtained (using tools such as Tableau, and/or interactive notebook-based visualizations).
    Knowledge of SQL, as well as an interest in programming/scripting in notebook environments (e.g., Jupyter, Zeppelin), are required.
    Several projects are available.
  • Fabrication and Testing of Medical Microrobots (*New – Fall 2018*)
    Faculty advisor: Prof. Eric Diller

    The Microrobotics Laboratory is developing a new class of millimeter-size robotic devices powered by magnetic field for use inside the human body for remote surgery, diagnosis and therapy. Using new fabrication techniques, we are developing magnetically-driven mechanisms which are strong, fast, and dexterous. This project will focus on refining fabrication techniques, characterizing the device mechanical properties and performance under conditions seen in actual operation.
    For more details, see our lab website at http://microrobotics.mie.utoronto.ca

    Contact: ediller@mie.utoronto.ca

    Research Area: Robotics and Mechatronics, Biomedical Engineering

  • Survey to investigate voice-controlled system usage patterns (*New – Fall 2018*)
    Faculty advisor: Prof. Birsen Donmez

    Voice-controlled systems (VCS) have the potential to reduce driver distraction by offering eyes-free and hands-free interactions, leading to an improvement in safety and the overall driving experience. In order to design effective VCS, it is important to identify and understand the factors that encourage and discourage the use and adoption of these systems. This project will dig deeper into the findings of a previously held focus group study to explore how attitudes towards technology, use of technology (including other voice UIs) and other factors influence the perception and acceptance of VCS. It will entail the design and implementation of a large survey followed by statistical analysis. The MEng student will work closely with a MASc student and will ideally have an understanding of statistical methodologies and tools (R, Python or SAS).

    Contact: donmez@mie.utoronto.ca

  • Micro-mechanical characterization of solid 3rd bodies created in dry lubricated contact within space mechanisms
    Faculty advisor: Prof. Tobin Filleter

    Lubricating space mechanisms is a great challenge as lubrication must be sustained in several environments during mechanism life (up to 15 years once in space). Lubricants must face humid air, dry nitrogen and simulated vacuum environments as well as gravity on Earth; high stress mechanical environment during launching; vacuum, radiations, weightlessness in space, etc. Dry lubricants are often used over oil or grease lubricants as they offer a wider range of temperature working conditions and a lower risk of contaminating surrounding instruments (especially optics). Furthermore they can be used at very low speed and are more flexible in term of accelerated testing. However, until now, no model can predict their behaviour reliably.

    Previous studies focusing on the dry lubrication efficiency of coatings and composite materials used for space applications showed that low friction and long wear life is reached only when a 3rd body layer is formed between the two bodies in contact. Elements comprising this 3rd body come from the material initially in contact (via detachments of particles mainly) and the surrounding environment. A complex physico-chemical rearrangement is then mechanically induced under friction to create the 3rd body layer with a specific rheology and with the ability to slide inside the contact. The rheology provides cohesion and ductility which enables plastic flow inside the contact to help the accommodation of relative velocities. The quantification of the rheology of the 3rd body and of the interaction between materials is a key to quantitative prediction.

    The project aims to develop experimental tools to measure the mechanical properties of the 3rd body to characterise its rheology and inform a numerical model developed in parallel. The MEng student will take part in the definition and testing of an experimental protocol to measure the interaction between the different bodies used and created inside a contact representative of the real application. The work will notably focus on:

    – defining a reliable protocol to measure those interaction in controlled environments,
    – perform the measurements and post treat the data to discuss them regarding the application and the history of the contact, and regarding the numerical model.

    Contact: filleter@mie.utoronto.ca

    Research area: Mechanics and Materials

  • Ontologies for Representing and Measuring City Performance (multiple projects) (*New – Fall 2018*)
    Faculty advisor: Prof. Mark Fox

    Cities use a variety of metrics to evaluate themselves. With the introduction of ISO 37120, which contains over 100 indicators for measuring a city’s quality of life and sustainability, it is now possible to consistently measure and compare cities, assuming they adhere to the standard. The goal of this PolisGnosis Project is to automate the longitudinal analysis (i.e., how and why a city’s indicators change over time) and transversal analysis (i.e., how and why cities differ from each other at the same time), in order to discover the root causes of differences. But for PolisGnosis to analyse a city’s indicator, it first needs to understand the definition of the indicator. Hence we need to translate the ISO definition from English into a computer understandable representation – this requires an ontology. Second, the engine needs to understand a city’s specific indicator value and the data used to derive it. This information may be available in PDF files or spreadsheets but needs to be translated into a computer understandable representation – this too requires an ontology. Third, the engine needs to understand a certain amount of city “common sense” knowledge in order to analyse the data properly – this too requires an ontology. The project focuses on developing an ontology to represent an indicator theme knowledge, and the definitions of the theme’s indicators. There are several projects available. One for each of the following ISO 37120 themes: Urban Planning, Governance, Waste Water, Solid Waste, Water & Sanitation, and Economy.

  • An Ontology for Building Management and Evacuation (*New – Fall 2018*)
    Faculty advisor: Prof. Mark Fox

    The focus of this project is the design of smarter and greener environments, in particular buildings, using a sensor data driven approach. We bring a multi-disciplinary approach to addressing problems in data collection, representation, dissemination, monitoring and demand-response, with a common theme being a data-driven methodology – we seek to use data obtained from measurements to drive actuation/control, optimization or resource management to address problems for smart environments. At the core of this research is an ontology for representing various aspects of a building, including its logical built structure, sensors, controls, environment, people, etc. The MEng project will focus on the development of rules of deduction for the existing ontology.

  • Smart Cities: Intelligent Agents for the Urban Operating System (multiple projects) (*New – Fall 2018*)
    Faculty advisor: Prof. Mark Fox

    The Urban Operating System for future smart cities will not be a rigidly engineered, monolithic software architecture, or will it be just a sea of services interacting via APIs as specified by rigid business processes, instead it will be a network of Intelligent Agents dynamically interacting to flexibly and contextually achieve the goals of the city. The goal of this project will be to construct a generic intelligent agent shell for the future Urban Operating System. Projects will focus on: Role-based cybersecurity within an intelligent agent framework; Methods of coordination, including bid/propose, negotiation, constraint revelation; Explanation and accountability of agent decisions and actions.
    The software that plans, manages and controls the operations of the future smart city will be composed of intelligent agents that cooperate in making decisions and coordinate their actions.

  • Handheld bioprinter design for deposition of multilayered engineered skin grafts (*New – Fall 2018*)
    Faculty advisor: Prof. Axel Guenther

    At the Guenther laboratory, we have designed a handheld microfluidic cartridge-based 3D bioprinter that allows the formation of cell-embedded, multi-layered gels in a single, continuous process directly on a patient’s burn wound to facilitate wound healing [1]. This project will be focused on further developing the handheld bioprinter to include functions such as temperature control to enable a wider range of compatible bioinks, printhead designs to allow printing on non-flat surfaces, and wheel designs to permit printing on fragile wound surfaces. We are seeking two highly motivated MEng candidates with experience in product design and prototyping who are interested in designing a medical device in a collaborative setting, working with molecular biologists, surgeons, and other technical staff.

    Recommended prerequisites
    – Previous product design and prototyping experience, with interest in engineering design
    – Proficiency in design software, for example Solidworks, AutoCAD, or Labview
    – Strong communication and teamwork skills

    References
    [1] Hakimi N, Cheng R, Leng L, Sotoudehfar M, Ba P, Bakhtyar, N, Amini-Nik S, Jeschke M, and Gunther, A. Handheld skin printer: in situ formation of planar biomaterials and tissues. Lab on a Chip. doi: 10.1039/c7lc01236e.

    Contact
    Please contact Richard Cheng at richard.cheng@mail.utoronto.ca

    Research area: Product design, prototyping, development, and manufacturing. Biological materials, biomedical engineering, tissue engineering, regenerative medicine, medical devices

  • Experimental and Numerical Studies of Fluid Properties and Measurements
    Faculty advisor: Prof. David James

    The following projects are available to interested M.Eng. students:

    Measuring the relaxation time of viscoelastic liquid, using a variety of techniques and instruments.

    Preparing and characterizing a water-based ideally elastic liquid having a high viscosity.

    Numerical simulation of a sphere in an unbounded creeping flow of a Newtonian fluid, to find the drag.

    Numerical simulation of an isolated circular cylinder in a creeping flow of a Newtonian fluid, to find the drag.

    Design of a miniature flow cell which simultaneously measures the density, viscosity and surface tension of a liquid sample.

  • Finding Yourself in Virtual Reality Navigation Task (*New – Fall 2018*)
    Faculty advisor: Prof. Greg A. Jamieson

    Understanding your current position and immediate surroundings is a crucial task in navigation. Automatically displaying and updating the user’s current position as they move through the environment is a relatively recent feature in the long history of map development. Further advances in display technology will directly present users with even greater information about their surroundings in real time, though may introduce new challenges about self positioning. This project will investigate the presentation of a user’s current position and immediate surroundings in a novel display format. Experiments will be conducted in a virtual environment and presented using a virtual reality headset. The project will entail working with a PhD student and industry partner on the design and execution of an experiment with human participants to test different navigation display presentations, followed by statistical analysis of the collected data.

  • Materials for Energy Storage and Conversion (*New – Fall 2018*)
    Faculty advisor: Prof. Olivera Kesler

    Various projects dealing with materials-processing-microstructure-property relationships for component designs to be used in clean energy applications.
  • Solid oxide fuel cells, high-efficiency electrolysis, oxygen separation membranes, clean energy
    Faculty advisor: Prof. Olivera Kesler

  • Machine learning to classify underpayment claims (*New – Winter 2019*)
    Faculty advisor: Prof. Chi-Guhn Lee

    A Toronto-based food manufacturer has been struggling with underpayment claims submitted by major retailer clients. The reasons for underpayment are diverse from late delivery, insufficient fulfilment and even damaged products. Upon submission of claim the manufacturer starts investigation to disprove the claim by tracking history of customer order and follow up actions. While a significant portion of such claims can be disproved for full payment, increasing the profit, investigation of such claims is very time consuming. Therefore, the manufacturer would like to categorize the claims so that they can pay different levels of attention considering the likelihood of disproval as well as revenue consequence. This project is to develop machine learning algorithms to classify the claims given historical data. The student needs to have good background in machine learning, optimization, and python programming.

    Contact: Prof. Lee, cglee@mie.utoronto.ca

  • Reinforcement learning for optimal inventory rationing (*New – Winter 2019*)
    Faculty advisor: Prof. Chi-Guhn Lee

    A Toronto-based food manufacturer has experienced fluctuating market demand. When the demand exceeds the available inventory, the company will have to fulfill demand only partially, resulting in penalty according to contract with major clients. Therefore, the manufacturer wants to optimize the allocation of insufficient inventory considering terms and conditions in contracts with major clients. The objective of such inventory rationing problem is to minimize the long-term consequences. This project is to develop Monte Carlo simulation-based dynamic optimization algorithm to recommend how to allocate insufficient inventory. The student will have to analyze business contracts, develop a Monte Carlo simulation system, design an optimization algorithm on top of the simulation model, and produce solutions for the manufacturer. The student needs to have good understanding on Markov decision process, Monte Carlo simulation, and reinforcement learning.
    Contact: Prof. Lee, cglee@mie.utoronto.ca

  • Optimal waiting time quotation (*New – Winter 2019*)
    Faculty advisor: Prof. Chi-Guhn Lee

    A Toronto-based pharmacy faces a challenging problem of estimating waiting time for a customer who just drops a prescription. If the quoted waiting time is too long, customer is likely disappointed and may not return for future service. If too short, the pharmacy will risk at not meeting the expectation when the customer comes back for a pickup. Therefore, the objective of the project is to recommend an optimal quotation for customer return time considering the number and the type of prescriptions in the queue, the number of employees working at the moment ,etc. The student will be given a large data set to implement a machine learning algorithm. The student needs good understanding of major machine learning algorithms, queueing theory, optimization with uncertainty.
    Contact: Prof. Lee, cglee@mie.utoronto.ca

  • Matching shippers and carriers with constraints
    Faculty advisor: Prof. Chi-Guhn Lee

    Heuristic algorithm is to be developed to optimally match shippers and carriers over time considering service requirements.
    Contact: Prof. Lee, cglee@mie.utoronto.ca

  • Resource sharing in healthcare system
    Faculty advisor: Prof. Chi-Guhn Lee

    Quantitative analysis of the benefits from shared resources in healthcare system when the demands are uncertain.
  • Nonparametric methods for reinforcement learning 
    Faculty advisor: Prof. Chi-Guhn Lee

    In the reinforcement learning and Markov decision process, the value function approximation is a very important question. This approximation problem becomes much more complicated when dealing with a high-dimensional scenario. This project focus on developing and implementing nonparametric methods for reinforcement learning, especially on the value function approximation.

    Contact: Peng Liu pliu@mie.utoronto.ca, Chi-Guhn Lee cglee@mie.utoronto.ca

    Research area: Mathematical finance in high dimensional setting; Copula methods in financial econometrics; Dynamic correlations in pricing and hedging; Information asymmetry; Machine learning in finance.

  • Dynamic correlations in bond portfolios 
    Faculty advisor: Prof. Chi-Guhn Lee

    Bond portfolios are widely used in finance industry. However, a nicely behaved dynamic correlations structure are usually difficult to be obtained due to the term structure, the type of bonds, noise and etc. This project focus on developing empirical methods to model the dynamic structure of correlation for bond portfolios.

    Contact: Peng Liu pliu@mie.utoronto.ca, Chi-Guhn Lee cglee@mie.utoronto.ca

    Research area: Mathematical finance in high dimensional setting; Copula methods in financial econometrics; Dynamic correlations in pricing and hedging; Information asymmetry; Machine learning in finance.

  • Artificial Neural networks for Israeli option valuation 
    Faculty advisor: Prof. Chi-Guhn Lee

    Explore deep learning techniques for the valuation of Game contingent claims.

    Contact: Aloagbaye Momodu aimomodu@mie.utoronto.ca, Chi-Guhn Lee cglee@mie.utoronto.ca

    Research area: Machine learning, finance

  • Hedging Israeli options 
    Faculty advisor: Prof. Chi-Guhn Lee

    Analyse the greeks (Delta, Gamma, Vega) of Israeli options

    Contact: Aloagbaye Momodu aimomodu@mie.utoronto.ca, Chi-Guhn Lee cglee@mie.utoronto.ca

    Research area: Finance

  • Polymer/Composite Coextrusion Foaming (*New – Fall 2018*)
    Faculty advisor: Prof. Patrick Lee

    Most new polymeric products contain two or more polymers and functional additives resulting in desired properties contributed from each component. The multilayer coextrusion process is a single-step process starting with two or more polymeric and hybrid materials simultaneously extruded and shaped in a single nozzle to form a multilayer structure. Recently, micro-/nano-layer (MNL) coextrusion has been used to manufacture unique optical, mechanical, and gas barrier films, such as brightness-enhancement filters for electronic screens, ultra-strong safety and security window films, and elastomeric barrier films for cushioning bladders in athletic shoes, consisting of hundreds of layers each less than 100-nm thick. Foams can be prepared from any type of plastic by introducing a gas or SCF within the polymer matrix. The applications of microcellular plastics containing billions of tiny bubbles less than 10 microns in size have broadened due to the lightweight characteristics, excellent strength-to-weight ratios, superior insulating abilities, energy absorbing performances, and the comfort features associated with plastic foams, as well as their cost-effectiveness and cost-to-performance ratios. This project will involve developing a novel MNL coextrusion foam manufacturing system for fabricating multiphase lightweight composites.

    Contact:
    Prof. Patrick Lee, patricklee@mie.utoronto.ca

  • Visualization of Plastic Crystallization and Foaming Behaviors under Stress (*New – Fall 2018*)
    Faculty advisor: Prof. Patrick Lee

    High-performance composite foams with well-engineered crystal microstructures and foam morphologies (i.e., cell population density, foam density, and porosity) are essential to tune the final material properties, such as the barrier, thermal, acoustic, and mechanical performances, and can have diverse applications in the automotive, aerospace, biomedical, and food and electronics packaging industries. In this context, the objective of this research is to achieve a thorough understanding on cell and crystal nucleation, growth, and deterioration phenomena that determine cell and crystal structures in plastic foaming processes. The core research strategy of this research is to develop and utilize innovative visualization systems to capture and study these phenomena. To be specific, three visualization systems have been developed to investigate foaming under both static and dynamic conditions. The dynamic systems are capable induce controllable extensional and shear strain to study the effects of stresses in plastic foaming to simulate conditions in industrial foaming processes, while the static system is key to establish baseline knowledge and to study critical processing parameters in an isolated manner. The wide range of future studies made possible by the visualization systems will be valuable to the development of innovative foaming technologies and foams.

    Contact:
    Prof. Patrick Lee, patricklee@mie.utoronto.ca

  • Nanofibers Enhanced Strain Hardening of Linear Polymer (*New – Fall 2018*)
    Faculty advisor: Prof. Patrick Lee

    Strain hardening has important roles in understanding material structures and polymer processing methods, such as foaming, film forming, and fiber extruding. A common method to improve strain hardening behavior is to chemically branch polymer structures, which is costly, thus preventing the users from controlling the degree of behavior. A smart nanofiber blending technology, however, would allow cost-efficient tuning of the degree of strain hardening. In our previous study, we hypothesized and proved that compounding polymers with heat-shrinking fibers enhances the strain hardening of a polymer. In this study, we want to explore nanofiber enhanced structures for various applications.

    Contact:
    Prof. Patrick Lee, patricklee@mie.utoronto.ca

  • Fabrication and Testing of Stretchable Conductive Electrodes for Soft Robotics (*New – Winter 2019*)
    Faculty advisor: Prof. Xinyu Liu

    This project aims to fabricate highly-conductive electrodes for constructing stretchable electronics and sensors on soft-bodied robots. We are developing new types of stretchable materials integrating electronic components for constructing wearable electronic devices with multiple sensing modalities. A critical component of this types of devices is conductive electrodes that can seamlessly integrated onto the stretchable substrates and maintain its high conductivity under high strain (>100%). The candidate will evaluate the existing designs of stretchable electrodes in the literature and propose the most suitable solution for our devices. The fabrication of the stretchable electrodes will involve elastomer synthesis, material doping, electrode patterning, and stretchable device integration. The candidate will test the electronic properties of the electrodes under different working conditions, and finally generate a project report including all the experimental procedures and results.

    Laboratory: Microfluidics and BioMEMS Laboratory
    Contact: Prof. Liu, xyliu@mie.utoronto.ca
    Research Areas:flexible and stretchable electronics; wearable devices; physical sensors; advanced materials

  • Design and Control of A Soft Wall-Climbing Robot (*New – Winter 2019*)
    Faculty advisor: Prof. Xinyu Liu

    Soft robotics is a newly emerging research direction in the field of robotics, and have found many exciting applications such as wearable medical devices and untethered field navigation. The objective of this project is to design and control a pneumatically actuated soft robot capable climbing vertical walls for field navigation and inspection applications. Through proper structure design of the pneumatic actuators of the robot, the robot will be able to locomote on flat and vertical surfaces along arbitrary in-plane directions, and multiple adhesion regulation mechanisms will be investigated for effectively holding the robot body on a vertical surface. The research tasks include robot design and fabrication, position sensor integration, control sequence programming, and prototype testing.

    Laboratory: Microfluidics and BioMEMS Laboratory
    Contact: Prof. Liu, xyliu@mie.utoronto.ca
    Research Areas:soft robotics; mechanical design and simulation; feedback position control

  • Design and Testing of a Microfluidic Device for Cancer Cell Studies (*New – Winter 2019*)
    Faculty advisor: Prof. Xinyu Liu

    In this project, a microfluidic device for dynamic cancer cell culture will be developed, which can culture cells on chip for days and apply combined mechanical and chemical stimulations to them. A multilayer microfluidic device will be designed and fabricated, and cell culture experiments will be performed for proof of demonstration. The candidate will also collaborate with a cancer cell biology group for studying specific biomechanical pathways regulating the cancer cell apoptosis.

    Laboratory: Microfluidics and BioMEMS Laboratory
    Contact: Prof. Liu, xyliu@mie.utoronto.ca
    Research Areas:microfluidics; laboratory automation; bioengineering; cancer cell biology

  • Paper-Based Microfluidic Biosensors (*New – Winter 2019*)
    Faculty advisor: Prof. Xinyu Liu

    Paper-based microfluidics, the technology of manipulating small amounts of fluids in patterned channels in a single- or multi-layer paper device, has emerged as a simple yet powerful tool for bioanalysis. We are focused on developing paper-based biosensors for a wide range of applications, such as point-of-care diagnosis, environmental sampling testing, and large-scale drug screening. In this project, the student will fabricate new paper substrates integrating novel functional nanomaterials, and fabricate paper-based microfluidic devices with unique surface chemistries and sensing capabilities. The fluid-transport dynamics in these microfluidic devices will also be investigated. Proof-of-concept experiments will be conducted for disease marker detection.

    Laboratory: Microfluidics and BioMEMS Laboratory
    Contact: Prof. Liu, xyliu@mie.utoronto.ca
    Research Areas:microfluidics; laboratory automation; bioengineering; cancer cell biology

  • Multiproject job scheduling 
    Faculty advisor: Prof. Viliam Makis

    The objective is to develop an operations research model and a computational algorithm for a multi-project job scheduling considering given arrival times, due dates, available resources and project job requirements , including job precedence, parallel processing, and various resource requirements. A feasible schedule should be found minimizing a makespan, first for a given, finite horizon, then with a re-scheduling upon new project arrival and moving time horizon.

    Note: In addition to the listed topics, topics in the area of process/quality improvement, maintenance, reliability, production and inventory control are possible, interested students should contact Prof. Makis, e-mail: makis@mie.utoronto.ca.

  • Implementation of SPC and DE Techniques in Automotive Industry 
    Faculty advisor: Prof. Viliam Makis

    The objective is to study to what extent the SPC techniques have been applied in the automotive industry for both the statistical process control as well as for the process improvement.
    In the first phase of the MEng project development, a thorough literature review will be done focusing mainly on the case studies dealing with the SPC implementation in the automotive industry.
    Two interesting case studies will be selected for a detailed study and analysis. Finally, selected SPC and DE tools will be applied to real data, focusing on achieving process stability and improving process capability.Note: In addition to the listed topics, topics in the area of process/quality improvement, maintenance, reliability, production and inventory control are possible, interested students should contact Prof. Makis, e-mail: makis@mie.utoronto.ca.
  • SPC and DE for Process Improvement with a Practical Application 
    Faculty advisor: Prof. Viliam Makis

    SPC and DE methods are widely used to improve stability, capability, and reduce variability of industrial processes in all industrial sectors. The objective is to apply SPC and DE techniques to improve real manufacturing processes. Real data will be analyzed, root causes of defects will be investigated and control charts and DE will be applied to improve process stability and capability by determining and removing the main causes of process variation.

    Note: In addition to the listed topics, topics in the area of process/quality improvement, maintenance, reliability, production and inventory control are possible, interested students should contact Prof. Makis, e-mail: makis@mie.utoronto.ca.

  • Quality Control and Improvement Using Lean Six Sigma Approach 
    Faculty advisor: Prof. Viliam Makis

    The objective is to study in depth lean six sigma approaches and methodologies for quality improvement in organizations. This would include the study from the books, analysis of two published case studies focusing on this area, which will include critical, detailed review of the published case studies, coding of examples whose results are summarized in the published papers, perform more numerical analysis including sensitivity analysis and provide detailed comments. It is expected that the required numerical work will be done using Matlab.

    Note: In addition to the listed topics, topics in the area of process/quality improvement, maintenance, reliability, production and inventory control are possible, interested students should contact Prof. Makis, e-mail: makis@mie.utoronto.ca.

  • Transmission Oil Data Modeling for Condition-Based Maintenance Decision-Making
    Faculty advisor: Prof. Viliam Makis

    Description:
    Real oil data obtained in the healthy state of a heavy-hauler truck transmission will be pre-processed and modeled using time series methodology. Residuals will be obtained using complete data histories. Several fault detection schemes will be designed, tested, and compared for fault detection using residuals. Matlab and MINITAB software will be used as well as C programming.Note: In addition to the listed topics, topics in the area of process/quality improvement, maintenance, reliability, production and inventory control are possible, interested students should contact Prof. Makis, e-mail: makis@mie.utoronto.ca.
  • Development of models for bus fleet maintenance and replacement
    Faculty advisor: Prof. Viliam Makis

    The objective is to develop stochastic models for group and opportunistic maintenance as well as replacement of a fleet of buses in large organizations such as TTC, considering service requirements, budgeting constraints and other regulations. Several case studies will be analyzed and an extensive sensitivity analysis will be performed to study the impact of various parameters on replacement decisions.

    Note: In addition to the listed topics, topics in the area of process/quality improvement, maintenance, reliability, production and inventory control are possible, interested students should contact Prof. Makis, e-mail: makis@mie.utoronto.ca.

  • Development of a Stochastic Dynamic Model for a Make-to-Order Production System
    Faculty advisor: Prof. Viliam Makis

    The problem is described as follows. A limited number of expensive, high quality parts is required in a given time period with a strict deadline. No rework of a nonconforming part is possible. To meet the demand, and to avoid the penalty, batch production is considered. Batch sizes as well as the maximum number of batches which can be produced are limited. Examples include make-to-order military and aerospace industry contracts as well as just-in-time manufacturing orders. The problem will be formulated and solved using stochastic dynamic programming. The optimal production policy will be found and a numerical analysis will be performed to get insight into the structure of the optimal policy.

    Note: In addition to the listed topics, topics in the area of process/quality improvement, maintenance, reliability, production and inventory control are possible, interested students should contact Prof. Makis, e-mail: makis@mie.utoronto.ca.

  • Optimal scheduling of repairs for several production facilities 
    Faculty advisor: Prof. Viliam Makis

    The following problem will be considered. There are several production facilities at different locations and a single repair crew located at a repair depot. When the first failure occurs, the repair crew is sent to that facility to fix the problem. When the repair is completed, the repair requests from other facilities are updated and it is necessary to decide to which facility the repair crew should be sent. If there are no failures upon a repair completion, the repair crew travels back to the repair depot awaiting further requests. It is assumed that the times to failure as well as the repair times are random variables with given distributions. The costs include the travel costs (dependent on the distance travelled), repair costs (both fixed costs and cost rates per unit time), and the facility downtime costs due to lost production. The objective is to find the optimal repair schedule minimizing the total expected cost over a planning horizon.

    Note: In addition to the listed topics, topics in the area of process/quality improvement, maintenance, reliability, production and inventory control are possible, interested students should contact Prof. Makis, e-mail: makis@mie.utoronto.ca.

  • Non-Destructive Diffusion-wave Techniques and Imaging for Solar Cells and Clean Energy Conversion Optoelectronic Devices 
    Faculty advisor: Prof. Andreas Mandelis (with Dr. Alexander Melnikov)

    Development of novel diffusion-wave imaging and other diagnostic techniques for industrial quality control of optoelectronic materials and devices, primarily silicon, thin-film and quantum dot solar cells. For more information, consult https://cadipt.mie.utoronto.ca

    Contact: Prof Mandelis (mandelis@mie.utoronto.ca), Dr Melnikov (melnikov@mie.utoronto.ca)

  • Dynamic Imaging of Solar Cell Optoelectronic Quality using a Near-Infrared Camera
    Faculty advisor: Prof. Andreas Mandelis

    We have developed a non-destructive imaging technique (solar cell carrierography) which monitors the optoelectronic quality of industrial silicon-based photovoltaic solar cells and aims to correlate the images with the electrical output and overall performance efficiency of the solar cell. A MEng student will be required to work with the research team in generating and analyzing carrieorgraphic images in order to build the statistics of these optical-electrical correlations and relate images to quantitative measurements of the parameters responsible for the solar cell efficiency. For more information, consult https://cadipt.mie.utoronto.ca
  • Non-Destructive Diffusion-wave Techniques and Imaging for Solar Cells and Clean Energy Conversion Optoelectronic Devices
    Faculty advisor: Prof. Andreas Mandelis

    Development of novel diffusion-wave imaging and other diagnostic techniques for industrial quality control of optoelectronic materials and devices, primarily silicon, thin-film and quantum dot solar cells. For more information, consult https://cadipt.mie.utoronto.ca
  • Wearable Technologies for Digital Eye Glass (computerized seeing and memory aid) with health monitor.
    Faculty advisor: Prof. Steve Mann

    Wearable Technology is set to become the next multi-billion dollar industry, but more importantly, has tremendous potential to help people live longer and more healthy lives. S. Mann, widely regarded as the founder of this field (http://wearcam.org/nn.htm), defines Wearable Technologies as embodying Humanistic Intelligence (http://wearcam.org/hi.htm), capturing, processing, and presenting sensory data from the body of the wearer as well as from the surroundings. We’re building not just the next eyeglasses (soon all eyeglasses will be “Digital Eye Glass”) but a complete situational awareness system that provides health, wellness, safety, and longevity: http://www.eyetap.org

    The ideal student for this project is one who is imaginative and creative at making things and passionate about making and building electrical, computational, and mechanical devices. If you’re a “renaissance person” who have been building things for many years, you’ll fit right in with the rest of us here who are just like you. To demonstrate your aptitude at “making”, please bring something you’ve built on your own, outside of classroom or lab requirements.

    Research area: Wearable Computing, Human-Computer Interaction; fluid dynamics (e.g. hydraulophonics); energy systems and action/actergy systems; engineering design and education; veillance (surveillance and sousveillance); priveillance (privacy and veillance); health

  • Birdstrike and Novel Design of Fan Blades
    Faculty advisor: Prof. Shaker Meguid

    One major problem in aircraft safety is bird strike. The bird is digested in the engine during take-off or landing. This results in local and global damage to the aircraft and could lead to fatalities. The objective of this research is not only to evaluate that damage using finite element but also improve the fan design to accommodate birdstrikes. We have published numerous papers on this topic and we are interested in improving the material model for the striking birds as well as focus our attention to specific birds.
  • Design, Analysis and Development of Micro Gas Turbine for propulsion of Micro UAVs
    Faculty advisor: Prof. Shaker Meguid

    Micro gas turbines or simply micro turbines are very promising technology for propelling micro unmanned aerial vehicles. These micro turbines vary in size and power. They can be hand held producing a fraction of wattage to large ones producing 100s of kilowatts. Because of their numerous advantages over internal combustion engines with their higher power to weight ratio and low emissions, and reduced size and number of moving parts, they have replaced internal combustion engines in Rover electric motor car. They have also been used to generate electricity in commercial power grid. The objective of this project is to design, analyse and develop a model of micro turbine for the propulsion of micro UAV. Two cases may be considered for the flapping wing micro turbine: should the design employ micro gas turbine or use the micro turbine to power an electric motor to propel the UAV.
  • Design, Analysis and Optimisation of Novel Morphing Chevron Nozzle in Gas Turbine Engines
    Faculty advisor: Prof. Shaker Meguid

    This study is motivated by the need to advance the concept of using mixing enhancement devices, such as chevron, to reduce jet noise. In spite of its current use in some engines, the impact of the concept of mixing enhancement to reduce jet noise remains unclear. For example, it is not clear how these devices impact jet noise and aero-performance. Furthermore, what is the effect of the number of chevrons along the jet axis, their length and angle for a given nozzle diameter and flow characteristics, upon vortex strength and ultimately noise reduction. Three aspects of the work are accordingly examined: (i) design and develop novel modular morphing chevrons using SMAs not only to reduce noise but also heat signature, (ii) develop a unified physics-based aero-thermo-acoustic prediction model that takes into account the morphed chevron geometric parameters, flow and thermal characteristics, and far-field noise, and (iii) test and develop functional prototypes capable of demonstrating the proof of concept, its strength, challenges and the associated costs.
  • Morphing Wing Design for UAV
    Faculty advisor: Prof. Shaker Meguid

    The field of shape morphing aircraft has attracted the attention of hundreds of research groups during the past century. Although many interesting concepts have been synthesized, only a handful of such reconfigurable planes have been ever produced (all of them supersonic and consisted of pivoting wings). In the range of low speed, small aircraft no commercial product exists to our knowledge. Although several conceptual designs of small or low speed aircraft has made it to the wind tunnel testing stage, only very limited number of such shape morphing prototypes have ever been fabricated or flight tested.In this project, we are trying to copy birds in the design of unmanned aerial vehicles. This project involves design, prototyping using intelligent materials such as shape memory alloys, and proof on concept via testing.
  • Multifunctional Nanocomposites for Self Health Monitoring
    Faculty advisor: Prof. Shaker Meguid

    The objective of this research is to provide greater understanding of the complex phenomena that take place at the nanoscale level in multifunctional nano-tailored composites. Specifically, attention will be given to the research activities and achievements in my laboratory in developing multifunctional nano-tailored adhesive bonds for aerospace applications. In particular, we introduce this multifunctionality, and a certain level of intelligence, by homogeneously dispersing carbon nanotubes, and other nanofillers, into high strength thermoset epoxy adhesives. Application of molecular dynamics and atomistic based continuum techniques to treat this class of intelligent multifunctional materials will be discussed and their viability for in-situ diagnostics examined.
  • Smart Materials Based Environmental Sensing (*New – Fall 2018*)
    Faculty advisor: Prof. Hani Naguib

    Smart materials based sensors exists in research with the capability of detecting environmental changes locally in a relatively short period of time. However, utilizing these sensors over large areas poses challenges in electrical design. This project aims to develop an electrical sensor system that will be deployable within large areas such as plants with hydrocarbon response times of under an hour. Design for environmental conditions and abnormalities will be important in creating a system with high efficacy and robustness. Current designs feature the use of time domain reflectometry to detect and pinpoint damage points over the span of the sensor network. Our goal is to improve this reflectometry design and implement creative additions to improve efficacy and scalability. There is much room for innovation in this project, and the student will be working in a team to combine material science and electrical principles to construct a medium-scale prototype of the sensor system.
  • Investigation of the Processing Parameters of Functionally Graded Closed-Cell Bio-Compatible Cellular Materials
    Faculty advisor: Prof. Hani Naguib

    The project is focused on fabricating and SEM (Scanning ElectronMicroscope) imaging of functionally graded closed-cell cellular materials. Polylactic acid (PLA) which is a bio-polymeric material will be fabricated in plate-like structures. The solid PLA sample is placed in a pressure vessel (PV) with high pressure supercritical (Sc)
    CO2 at room temperature. The time duration should be enough for ScCO2 to diffuse and achieve a uniform concentration throughout the PLA sample. The ScCO2 tends to diffuse out of the PLA sample due to concentration gradient compared to the atmosphere (adsorption). The desorption time (td) decides the thickness of the skin layer where no
    ScCO2 left compared to the internal layers. The PLA sample is then placed between two platens of different temperatures at which the minimum is above glass transition temperature (Tg). Cells’ sizes will be grading smaller towards the lower temperature. The parameters to be optimized are the saturation pressure, td, and the annealing duration as they are the ones mostly affecting the resulted microstructure. The project is self-motivating once the SEM micrographs start linking process-parameters to the resulted microstructure guiding to a definite vision for macro-manufacturing scale.
  • Project 4: Investigation of the Processing Parameters of Biocomposites Materials
    Faculty advisor: Prof. Hani Naguib

    The proposed research aims to bridge sciences to technology by investigating the processing-structure-property relationships of multifunctional biobased polymeric composites. It aims to design and fabricate smart “green” materials that possess tailored multifunctional properties. In this context, the short-term objectives of this research project are two-folded: (i) designing novel processing and fabrication strategies to tailor the micro-and-nano-structures of biobased polymeric composites; and (ii) characterizing the structures and multifunctional properties of these composites.
    This research will improve the fundamental understanding of using various processing strategies and material sciences to control the dispersion and network formation of functional fillers in new biobased matrices. Hence, it will offer new insights to fabricate these composites with tailored multifunctional properties. This will result in the development of an innovative and new class of green electronic composites that can be used in lightweight functional materials with high performance.
  • Functionalized Porous-Carbon Composite with Attached Metal Nanoparticles for CO2/CO Capture and Utilization (*New – Fall 2018*)
    Faculty advisor: Prof. Hani Naguib

    CO2 is one of the greenhouse gases with high impact in the global warming. Although a natural cycle regulates CO2 concentration by photosynthetic organisms, the large-scale burning of fossil fuels has increased considerably the amount of CO2 in the atmosphere. The project here described presents a novel solution for CO2 sequestration from the atmosphere in ambient conditions and low CO2 concentrations. The double functionalized open-cell structure made of a polymer/activated carbon composite with metal oxide nanoparticles as a catalyst also presents high cyclability for regeneration. As a second stage, the CO2 is transformed into a reusable source of energy through electrochemical reduction. Therefore, a complete sustainable carbon cycle can be achieved. Furthermore, the manufacturing techniques and design of the system facilitate the scalability and adaptability of the project, which can be used in both conditions, flue gases produced by industry and direct CO2 sequestration from the air.
  • Development of thermoplastic composites for high pressure and temperature applications 
    Faculty advisor: Prof. Hani Naguib

    This project involves the development and characterization of fiber reinforced thermoplastic composites targeting high strength and resistance to extreme operating conditions including high temperature and pressure. The grad student will be involved with the manufacturing and characterization of various composite systems including mechanical and thermal properties as well as analysis of the embedded fibers by scanning electron microscopy.

    Hani Naguib: naguib@mie.utoronto.ca

    Project duration 2 to 3 terms and it involves an industrial partner

    Research area: Composite materials development

  • Multifunctional Nano Porous Organic Aerogels with Enhanced Mechanical & Physical Properties
    Faculty advisor: Prof. Hani Naguib

    Increasing demand of technology for novel metamaterials with unique properties such as high service temperature, super insulation and ultra-lightweight flexible structure motivated the development of new porous materials to be used in many fields such as aerospace, apparel, naval, transportation and construction. In this context aerogels with super thermal and acoustic insulating properties along with ultra-light weight are presenting very high potential to be developed as the next generation of novel insulation materials. As 85% to 99% of aerogel volume is consist of gas, they can present very exceptional properties such as high porosity and extremely low density comparable to air, with reported density as low as 160 g/m³ of graphene aerogels. On the other hand porous structure of aerogel gives them the possibility to present very high thermal insulation properties, with reported thermal conductivity as low as 0.004 W/mK. Acoustic properties of aerogels are also highly dependent to their porous structure design as well as their material. Therefore by tailoring aerogel porous structure it is expected to highly enhance mechanical, thermal and acoustic properties of aerogels.

    Hani Naguib: naguib@mie.utoronto.ca

    Project duration 2 to 3 terms

  • Robots learning their behaviors
    Faculty advisor: Prof. Goldie Nejat

    In order for service robots to be used for numerous healthcare applications, they need to be able to learn their appropriate behaviors for each particular application and facility. In this project we will develop a learning system for a robot to learn behaviors for healthcare application scenarios. This system will improve the acceptance, adaptability, and ease of use of such systems for healthcare professionals and their patients. Students involved in this project will gain experience in applying machine learning algorithms and sensor-fusion techniques to robotic problems as well as learn to design for human-robot interaction scenarios.

    Recommended background: C++ programming

  • Service and Assistive Robotics, Social and Personal Robots, Robot Sensory Systems, AI and Control, and Human-Robot Interaction
    Faculty advisor: Prof. Goldie Nejat

  • CAD models and design reviews: survey data analysis (*New – Fall 2018*)
    Faculty advisor: Prof. Alison Olechowski

    In the product development process, the Design Review is an activity of intense collaboration. Here, not only are designers involved, but also project managers, quality engineers, manufacturing engineers, marketers, managers, and even customers. With design teams being more global, and an increase in cloud-based software solutions, we see the likelihood for change in the traditions of the Design Review. In theory, the CAD platform affects how remote teams share the models, how non-designers can visualize the design, and how new versions of files are disseminated.
    We surveyed over a hundred engineers in industry about their design review experience. The student working on this project will conduct data analysis, visualization and communicate the results back to our industry partners.Recommended background: Statistics and data processing. Strong communication skills.
  • Injection Foam Molding of Polymer Nanocomposites
    Faculty advisor: Prof. Chul Park

    Injection Moulding of polymers is one of the most interesting production methods which is capable to produce polymeric parts with complicated geometries. Nowadays, mechanical properties of polymers have been improved by adding nano particles; for instance, nano-clay, nano-crystalline cellulose, carbon nano-tube. Moreover, by the advent of microcellular polymers, so many defects of the final thermoplastic products such as warpage and sink marks have been disappeared. In addition, this method has decreased the amount of consuming materials significantly. This project focuses on the production of microcellular nanocomposites by injection moulding methods with higher cell density, higher expansion ratio, uniform cell distribution, and smaller cell size.
    Supervisor: Prof. Park park@mie.utoronto.ca
  • Visualization of Plastic Foaming Process in Injection Foam Molding
    Faculty advisor: Prof. Chul Park

    Cell nucleation and bubble growth are the most important steps governing the ultimate morphology and properties of foamed plastics. The goal of this research is to investigate fundamental mechanisms of cell nucleation and bubbles’ dynamics in foam injection molding by means of in situ visualization methods. Furthermore, foam microstructure formation and evolution will be mathematically modeled to simulate the phenomena during the mold filling stage in different foam injection molding techniques. A better understanding of aforementioned mechanisms will significantly help in determining the optimum processing conditions which will lead to the most appropriate microstructure for desired applications.
    Contact: Dr. Vahid Shaayegan vahidsh@mie.utoronto.ca
  • Polymer based silica aerogel production and optimization for thermal insulation applications 
    Faculty advisor: Prof. Chul Park

    This research proposes to develop innovative solutions for manufacturing lightweight thermally insulative polymer-based silica aerogel with the presence of carbon body filler with improved mechanical properties such as toughness and stiffness. This study will scientifically investigate micro- and nano-scale-tailoring of carbon body influence on the material properties, optimizing their nano- micro-scale structure design (cross-link density, type, and location), and modeling/analysis of structure-property relationships of the final roduct. Successful completion of the proposed research program will give our industry partner preliminary guidelines by which to manufacture advanced and functional polymer/carbon-based silica aerogel. With their exceptional thermal insulation, and mechanical properties, these aerogels will broaden the spectrum for insulation material usage in numerous and varied industries.

    Contact: solmazkk@mie.utoronto.ca

    Research Area: Hybrid polymer based aerogel

  • Fundamental studies on cellular structure development in foam injection molding process 
    Faculty advisor: Prof. Chul Park

    The main purpose of this project is to identify mechanisms contributing in formation and development of the cellular structure in foam injection molding process (Opportunities: manufacturing engineering, materials processing, materials science, and materials characterization)

    Contact: Dr. Vahid Shaayegan vahidsh@mie.utoronto.ca

  • Low-density extrusion foaming of engineering polymers
    Faculty advisor: Prof. Chul Park

    Project description: Development of extrusion foaming technology for manufacturing of low-density foams for high temperature, high performance engineering application (e.g., aerospace industries etc)

    Supervisor: Dr. Park park@mie.utoronto.ca

    Research Area: Manufacturing Engineering, Foaming Process

  • Hybrid nanofibrillar polymeric composites with improved physical-mechanical properties  (*New – Winter 2019*)
    Faculty advisor: Prof. Chul Park

    Light-weight hybrid nanofibrillar polymeric composites with superior physical-mechanical properties are good potentials for advanced automotive, aerospace, and medical applications. This research involves the processing of these hybrid nanofibrillated composites through extrusion blending various polymers followed by spinning them into the-island-in-the-sea nanofiber in microfiber morphologies via different methods of meltspinning and meltblowing. The next step includes the physical and mechanical characterizations of those novel composites along with investigating their micro and nanostructures, which would result in such superior properties.

    Supervisor: Professor Chul Park

    Contact: Dr. Iman Soltani isoltani@mie.utoronto.ca
    Research Area: Manufacturing Engineering, Foaming Process

  • Production of nanocomposite with tailored properties (*New – Winter 2019*)
    Faculty advisor: Prof. Chul Park

    The advanced composite market is predicted to reach $23.52 billion by 2020, while it was valued at $16.67 billion in 2015. The growing global interest in advanced composites signifies a high demand for these materials which can be used in various applications from packaging to aerospace. Among all materials for production of advanced polymeric composites, boron nitride nanotube (BNNT) has attracted great attentions due to its special characteristics, such as mechanical strength, thermal conductivity, electrical resistivity, thermal stability, optical clarity, piezoelectric characteristics and radiation shielding. To take advantage of these properties of BNNTs as fillers in polymeric matrices, it is essential to achieve homogenous distribution and dispersion of BNNT. Well dispersion and distribution of BNNTs into polymeric matrices remain very challenging due to their low dispersibility in polymer matrices as well as lack of experimental studies due to difficulty in mass production of good quality BNNTs. In this project, dispersion and distribution of BNNTs throughout polycarbonate (PC) matrix, using different mixing conditions have been studied, characterized, and analyzed. The ultimate goal of this project is to develop thermally conductive yet electrically insulating polymeric nanocomposites suitable for applications such as microelectronic packaging, thermal management, and power generation.

    Supervisor: Professor Chul Park
    Contact: Azadeh Zandieh azandieh@mie.utoronto.ca

  • Functional Nanofiber-Reinforced Nanocomposites Foams for Wearable Electronics (*New – Winter 2019*)
    Faculty advisor: Prof. Chul Park

    Brief description of the project: Rapid developments of the nanotechnology, conventional material couldn’t satisfy the human needs due to their limited material properties. By that reason, composites, which are composed of two or more materials, have been actively studied to overcome material’s limit. There are many filler materials to enhance properties such as carbon blacks (zero-dimensional, 0-D), carbon nanotubes (one-dimensional, 1-D), and graphene (two-dimensional, 2-D). Among them, the 1-D nanomaterial is easy to make a percolation network that the fillers are fully connected in the matrix. In this project, we will demonstrate the system which can demonstrate the functional nanofiber (1-D), which have sub-micron diameter with a high-aspect ratio, networks. Also, we will demonstrate the functional nanofiber-reinforced nanocomposites and their foam structures using supercritical fluids. You will learn from design the fabrication system to the realization of functional composites and measurement of their properties.

    Supervisor: Professor Chul Park
    Contact: Dr. Byung Gwan Hyun bghyun@mie.utoronto.ca

  • The development of high performance EMI shielding materials for next generation wearable electronics (*New – Winter 2019*)
    Faculty advisor: Prof. Chul Park

    1. Background
    Today’s leading-edge electronics made of densely integrated circuits produce severely high levels of electromagnetic radiation, which causes adverse effects on sensitive precision electronic equipment and safety of human beings. To address this, the development of high-performance electromagnetic interference (EMI) shielding materials that are lightweight and flexible has become a crucial technical prerequisite for next-generation wearable electronics. Conventionally, metals and metallic composites have been employed as EMI shielding materials, but they suffer from severe oxidation/corrosion, and have poor chemical resistance, unfavourably high density and weight, and are difficult to process. Alternatively, electrically conductive polymer composites integrated with carbon-based fillers can potentially replace conventional metallic composites because of their resistance to chemical oxidation/corrosion, great processibility, lightweight, flexibility and low cost.
    2. Objective and Approach
    The EMI shielding performance of polymer composites is highly dependent on the intrinsic electrical conductivity, dielectric constant, magnetic permeability, aspect ratio, and content of conductive fillers. It is also very crucial to create a robust inter-connectivity between fillers so that these properties can be realized across the entire composite. Hence, the objective of the proposed research is to take advantage of nanotechnology by developing a three-dimensionally uniformly dispersed array of nano-fillers that can favourably incorporate into polymers. Specifically, the fillers will be nano-engineered into novel nanostructured carbon materials such as graphene, carbon nanotubes, nanowires, nanofibers and nanoribbons. These unique nanomaterials is expected to exhibit superior properties only attainable at nano-scale morphologies (as opposed to bulk) such as extremely high electrical conductivity which in turn will greatly improve EMI shielding efficiency.

    Supervisor: Professor Chul Park
    Contact: Dr. Yun-seok Jun ysjun@mie.utoronto.ca

  • Production of flexible thin film aerogel (*New – Winter 2019*)
    Faculty advisor: Prof. Chul Park

    The study on how the gelation reaction can participate in the GnP’s inclusion in the aerogel backbone during the sol-gel process to strengthen the gel’s body by investigation of the effect of the spinodal decomposition process in creating a co-continuous morphology in the GnP’s orientation and dispersion.

    Supervisor: Professor Chul Park
    Contact: Solmaz Karamikamkar solmazkk@mie.utoronto.ca

  • Applying lead-user methods to identify and overcome obstacles to environmentally significant behavior.
    Faculty advisor: Prof. Li Shu

    Please visit: https://www.mie.utoronto.ca/labs/bidlab/publications.htm#environ
  • Nuclear power plant design and operations, materials evaluation, nondestructive testing, signal processing
    Faculty advisor: Prof. Anthony Sinclair

  • Artificial photosynthesis: Design materials to convert CO2 into hydrocarbons
    Faculty advisor: Prof. Chandra Singh

    There is a great research interest in developing technologies that can replicate plant lead and convert CO2 into useful hydrocarbon fuels. In this multidisciplinary, multi-group project we will design novel materials that can help in improving efficiency of this process using a computational materials modeling techniques. The student will be trained in state-of-art techniques to simulate these processes. The developed models will be compared against experimental data obtained from collaborating researchers at UofT. Basic physics and engineering background is required for the project.
  • Damage and failure analysis of wind turbine composite blades
    Faculty advisor: Prof. Chandra Singh

    Due to their lightweight, composites are widely used to manufacture wind turbine blades. However, accurately predicting progressive failure in composite materials under multiaxial and fatigue conditions has been a difficult task. In this project, the student will improve so called synergistic damage mechanics methodology, implement in commercial finite element codes, and apply to the case of wind turbine structures. The module developed from the project will be highly valuable in design of safe and long-serving wind turbines.
  • Nanomechanics of graphene-polymer nanocomposites
    Faculty advisor: Prof. Chandra Singh

    Graphene nanocomposites show hold great promise as materials for superior energy storage, e.g. batteries; for building strong and light-weight structures, e.g. wind turbine blades and for biomedical applications. However, their thermomechanical properties have not been understood well. In this project we will investigate these material properties through computational materials engineering methodology. Towards this end, large scale 3D molecular dynamics (MD) simulations will be conducted in order to investigate the fundamental failure mechanisms in these systems. Our particular attention will be the polymer/graphene interaface properties. The student should have background in solid mechanics; they will be trained in molecular dynamics simulations.
  • Ultrastrong, ultralight nanocrystalline hybrid materials for future aerospace technologies
    Faculty advisor: Prof. Chandra Singh

    While nanocrystalline metals and alloys have shown substantial enhancements in strength and hardness, improvements in ductility have been rather disappointing. Recently, Integran Technologies has developed novel nanolaminated materials with significantly improved strength and elongation to failure while maintaining light-weight advantage. However, to realize the full potential of the proposed material systems, their failure characteristics need to be properly established. The long-term goal of this project is to develop a fundamental understanding of failure mechanisms at the atomic-scale using molecular dynamics. Large-scale atomistic simulations will be conducted to evaluate material properties inaccessible to experiments and to derive cohesive laws that describe load-deformation characteristics of these nanomaterials.
  • Comparing flexible designs for the process of boarding patients from the emergency department to inpatient wards
    Faculty advisor: Prof. Vahid Sarhangian

    In this project we will build stochastic models to answer certain design and control issues that arise for elevator systems in residential (or commercial) high rise buildings. The goal is to reduce waiting times, especially during the before/after work rush hours. The project involves building and analyzing stochastic models, as well as developing simulation models of the system dynamics. Candidates are expected to be familiar with stochastic modeling, and be able to implement simulation models in a high-level programming language (e.g., R, Matlab or Python).

    Contact: Vahid Sarhangian (sarhangian@mie.utoronto.ca)

    Research Area: Operations Research (Stochastic Models)

  • Congestion control of elevator systems 
    Faculty advisor: Prof. Vahid Sarhangian

    In this project we will build stochastic simulation models to compare the performance of various flexible designs of a certain queueing network. The problem is motivated by the process of boarding patients from the emergency department of a hospital to the inpatient wards and flexibility corresponds to admitting a patient to a non-specialized ward. Candidates are expected to be familiar with stochastic modeling and discrete-event simulation and be proficient in Python.

    Contact: Vahid Sarhangian (sarhangian@mie.utoronto.ca)

    Research Area: Operations Research (Stochastic Models)

  • Silicon on Insulator (SOI) for high temperature pressure measurements (*New – Fall 2018*)
    Faculty advisor: Prof. Pierre Sullivan

    There exists a need for pressure sensors that operate in high temperature environments such as melt plastics. In partnership with a local company, there is an interest in developing a pressure sensor that meets EU requirements for liquid free operation and can be used in environments up to 600oC. The work will combine computational modeling and initial experimental work. Experience with COMSOL is a benefit.

    Contact:
    Prof. Pierre Sullivan, sullivan@mie.utoronto.ca

  • Comparison of image processing options for microarrays (*New – Fall 2018*)
    Faculty advisor: Prof. Pierre Sullivan

    Microarrays require a number of steps including printing of biological and chemical materials on an optically-transparent substrate (this require dispensing, curing, putting down a protective coating that is then dried to create a plate). Tests are then run by loading a sample, onto the plate, performing mechanical rotation of the plate, washing and then drying the final plate. This is then followed by analysis using proprietary software analysis tools. If a new microarray is to be developed, design and construction of custom arrays requires a rigorous understanding of the printing, chemistry and physics. Printing requires the generation of a GAL (GenePix Associated List) file with spot coordinates and lampposts. Every new microarray layout requires
    coordination of all steps. To circumvent these issues, I am interested in benchmarking an open-source microarray suite that combines all of the steps from printing to analysis.

    It is useful to have a background in ImageJ and Python

    Contact:
    Prof. Pierre Sullivan, sullivan@mie.utoronto.ca

  • GPU based particle image velocimetry (*New – Fall 2018*)
    Faculty advisor: Prof. Pierre Sullivan

    Particle Image Velocimetry (PIV) is a powerful and widely used tool to for studying a multitude of fluid flows. However, despite the many advantages of PIV, the algorithm used is computationally expensive, often limiting the possible size of datasets. This can call into question the validity of the statistical convergence of the data, which is particularly important for resolving higher order statistics needed in turbulent flows. In prominent PIV studies, generally small datasets, or larger datasets with small fields of view are used. PIV has been shown to be massively accelerated buy GPU computing. However, some drawbacks still exist. Most of the software is not open source, therefore to use a GPU accelerated algorithm means either developing in house code, or purchasing a commercial license. Commercial software has the added drawback that the details of the algorithm are unknown to the user, making it impossible to know exactly how the data is being processed. Additionally, most PIV software is platform dependent, generally running only on Windows, which excludes the possibility of running high performance systems such as supercomputing clusters.
    To fully utilize the power of GPU acceleration, an open-source, cross-platform, GPU-accelerated PIV algorithm is needed. As a basis for development, OpenPIV is a popular, open-source PIV software package written in Python. Since it is written in Python, OpenPIV can run on essentially any platform and operating system, from small embedded systems using to large supercomputing clusters, and will likely be supported for the foreseeable future, making OpenPIV an excellent option to develop. This aim of this project is to extend the OpenPIV algorithm to utilized GPU acceleration, enabling the realistic collection of larger PIV datasets, and ultimately increasing the statistical accuracy of the measurements. The algorithm will be rigorously validated with standard methods using synthetically generated images as well as experimental data. The tools developed in this project will be included with the OpenPIV distribution, and freely available for anyone to use.

    It is useful to have a background in Multithreading and Python and benefit from a current code that is already implemented on the SOSCIP GPU cluster.

    Contact:
    Prof. Pierre Sullivan, sullivan@mie.utoronto.ca

  • Design and construction of a medical device (urethro-vesical stapler) 
    Faculty advisor: Prof. Yu Sun

    Prostate cancer is the most common cancer in males after skin cancer (Globocan). The radical prostatectomy is the gold standard of treatment and grossly it consists on taking the prostate and the seminal vesicles out, to do an anastomosis of the bladder directly to the urethra. Only in the U.S.A., more than 90 thousand of this procedure are carried out every year. The anastomosis takes 30-60 minutes depending on the technique and experience of the surgeon. If the anastomosis is not done correctly (the space or depth of the suture is inadequate, or not correctly tied) the patient can suffer from incontinence, bladder outlet obstruction, urinomas or fistulas. This project proposes the development of a mechanical device that can do the anastomosis safely and faster, and still respect the surgical principles that have been described for this procedure. The design will involve the use of malleable materials, development of mechanical components, analysis through finite element simulation, and consideration of factors such as organic tissues.

    Contact:
    Prof. Yu Sun (MIE), sun@mie.utoronto.ca
    Dr. Jaime Omar Herrera-Caceres (Princess Margaret Hospital), jaime.herreracaceres@uhn.ca

    Research area: Applied Mechanics and Design; Biomedical Engineering

  • Solar Chimney with Wind Catcher in High-Rise Multi-Unit Residential Buildings (*New – Winter 2019* Immediate start)
    Faculty advisor: Prof. Marianne Touchie

    Using natural ventilation techniques to improve occupant comfort in the built environment is increasingly important as GHG emissions are becoming more problematic. Further intensifying this issue is the movement of societies into dense living environments, such as high-rise condominiums, which do not have the same cross-ventilation opportunities as freestanding homes. One method to improve natural ventilation in buildings involves using a wind catcher, which uses an air intake to re-direct outdoor airflow into the interior space of a building. A second method involves using a solar chimney, which utilizes a solar collector to heat air that then rises due to buoyancy effects, and pulls air out from interior spaces. However, each of these systems have drawbacks when either wind or solar availability is limited. This project investigates combining these two systems, in the context high-rise residential buildings, to mitigate these availability issues and further improve the comfort of occupants within the building.

    This project will focus on model development and a parametric analysis of the combined system. First, the fundamental equations for both solar chimneys and wind catchers will be combined to determine overall system performance. Next, a software tool will be developed to carry out a parametric analysis of the system as a function of different wind speeds, wind directions, building heights, duct dimensions, and dwelling locations. These results will then be used to create basic design guidelines for the combined system, which will also be used to guide further research. Following the completion of these analyses, it is also anticipated that a journal article will be written to publish the findings.

    Required Skills: Ability to use MATLAB (or similar) to create analysis algorithms. Background in basic fluid mechanics. Understanding of HVAC fundamentals.

    Contact: Prof. Marianne Touchie touchie@mie.utoronto.ca

    Research area: Building Energy and Environmental Engineering

  • Bio-oil use in burners and engines
    Faculty advisor: Prof. Murray Thomson

    We are working with companies in Canada, Finland and Brazil to develop burners and engines that can operate on a bio-oil, a biofuel made from wood waste. The student will work closely with postdocs and graduate students to conduct experimental research.

    Murray Thomson murray.thomson@utoronto.ca

    Research area: Energy

  • Investigating First-year Engineering Student Resilience 
    Faculty advisor: Prof. Chirag Variawa

    In this study, we seek to systematically optimize the transition process for prospective first-year undergraduate students, easing their integration into the university educational system by concentrating on scaffolding resilience and intellectual grit-enhancing strategies, and measuring the effectiveness and persistence in practice as appropriate.

    Some commentators have described “learning shock” in shifting from a knowledge- and application-based learning paradigm to independent assessment and evaluation as the primary reason why so many promising students do not pursue engineering careers and subsequent advancement.

    We need to understand what resilience and grit means, their attributes from theory/practice, and how this understanding influences transition to and from an undergraduate program of technical instruction, specifically engineering education. Additional analyses can be performed on how the first-year undergraduate environment and program account for this; and how this transition needs to be managed. It is recognised that there is a balance to be struck between anxiety and effective student development, but unclear what that balance should be at each stage of the transition. Though engineering students may not experience physical stressors directly, the impact of intellectual and other stressful environments may play a role in performance and mental/physical health. Research would include working with Outreach and Recruitment, the First-year Office, and with stakeholders in the undergraduate engineering program at Faculty of Applied Science and Engineering, University of Toronto.

    Contact: Prof Variawa chirag.variawa@utoronto.ca

    Research Area: Engineering Education

  • Data Analysis and Strategic Development: Engineering Student Workload 
    Faculty advisor: Prof. Chirag Variawa

    The Faculty of Applied Science and Engineering at the University of Toronto takes student workload concerns very seriously. Understanding what assignments students are working on and when enables the Director of First Year Curriculum (the PI for this project) to better relay course assignment deadlines to all first-year engineering instructors. Furthermore, it enables more effective and efficient integration of campus resources (such as, but not limited to, tutorials, recitations, etc.) so that they can be deployed when students need them most.

    The first-year workload survey is an instrument deployed via an authenticated portal already used by the Office of Student Life, University of Toronto. Access to this system, called Campus Labs: Baseline, was granted to the First Year Office at the Faculty of Applied Science and Engineering by the Office of Student Life so that the workload survey could more safely, securely, and reliably handle the large volume of responses (900 students) that we have in our program here in engineering. Each week, only 25 students from each first-year engineering program will be asked to complete the survey. Every week, there will be a new batch of 25 students from each program completing the survey; the goal is to have each student in first-year engineering respond to at least one weekly workload survey.

    The research questions that this workload survey investigate include:
    1) What course-related activities are the students working on each week in first-year engineering?
    2) How much time are students spending on each of these assignments?
    3) How difficult are these assignments?
    4) How much of these assignments are review material?

    These research questions help frame a more broader study of workload in first year engineering as they are used in addition to the two surveys already deployed:
    1) All incoming first-year students were asked how much time they thought they’d be spending per first-year engineering course before they arrived to their first class.
    2) All Course Coordinators of first-year courses were asked to provide a list of all assignments they use in their class, this includes information about what those assignments were worth (%-weight) and how much time those instructors think students ought to be spending on each of those assignments.

    We hope to use the triangulation of data from instructor expectations // student expectations // student actual workload data (quant and qual) to investigate and mitigate barriers to learning in engineering education.

    Contact: Prof Variawa chirag.variawa@utoronto.ca

    Research Area: Engineering Education, Quantitative + Qualitative Data analysis

  • Design and Fabrication of a Microfluidic Device for Tissue Engineering
    Faculty advisor: Prof. Lidan You

    (co-supervised by Hani Naguib) The goal of this research project is to design and fabricate a microfluidic device to study the effect of fluid flow on the osteogenic differentiation of human mesenchymal stromal cell (hMSCs). Microfluidics devices allow fluids to be handled and analyzed at the micrometer scale. It has found many applications in biology in the fields of macromolecular analysis and cellular analysis. Multipotent mesenchymal stromal cells (MSCs) are a population of multipotent stem cells primarily isolated from the bone marrow. They are also found in other organs such as adipose tissue, muscle, liver, and umbilical cord blood. MSCs is a popular candidate for bone tissue engineering due to their multilineage differentiation potential and immunomodulatory properties. The design entails to design and fabricate a microfluidic device that is suitable for investigating the effect of fluid flow on hMSCs.
  • Design and modelling of a lung-on-a-chip airflow system for studying particle inhalation and deposition in airways (*New – Winter 2019*)
    Faculty advisor: Prof. Edmond Young

    Chronic lung diseases (CLDs) such as asthma and chronic obstructive pulmonary disease (COPD) are major global health problems, and are made significantly worse by environmental factors such as air pollution. To study the effects of air pollution on the biological response of lung airway cells and tissues, the Young Lab has recently developed an airway-on-a-chip device that accurately models the structure and physiology of airway tissue. To further advance this device for air pollution studies, a custom airflow system is required to deliver air pollutants and other particles into the airway-on-a-chip device in a physiological manner. The objective of this project is to design and model a suitable airflow system that can be integrated with the airway-on-a-chip platform. The project will involve designing the system, sourcing the parts, and modelling the airflow generated in the airway-on-a-chip, with potential to work in collaboration with members of the Young Lab to test and implement the airflow system.

    Contact: Prof. Young eyoung@mie.utoronto.ca

    Research Area: Microfluidics; biofluid mechanics; microscale cell-based systems; cellular microenvironments; microfabrication; cell biology; cell imaging and microscopy; biomedical engineering; and cancer.

  • Design and fabrication of 3D-printed interconnects and manifolds for open microfluidic devices (*New – Winter 2019*)
    Faculty advisor: Prof. Edmond Young

    Microfluidic devices are increasing being developed for biomedical applications, and are frequently designed with open access ports in order to allow simple delivery of fluid volumes via common instrumentation found in biology labs, including micropipettes and automated liquid handlers. Such “open” microfluidic devices, however, are limited in their ability to allow steady continuous fluid flow that is often necessary for modelling perfusion. Thus, a method is needed to enable the rapid application of steady continuous fluid flow on these “open” microfluidic devices. The objective of this project is to design and fabricate – using 3D printing – different interconnects and manifolds that can be interfaced with open microfluidic devices. The project will involve surveying the literature on current interconnects and accessories for applying fluid flow, and then designing and fabricating new interconnects to be used in conjunction with existing open microfluidic devices in the Young Lab.

    Contact: Prof. Young eyoung@mie.utoronto.ca

    Research Area: Microfluidics; biofluid mechanics; microscale cell-based systems; cellular microenvironments; microfabrication; cell biology; cell imaging and microscopy; biomedical engineering; and cancer.

  • Advanced manufacturing and novel materials for biomicrofluidic and organ-on-a-chip systems (*New – Winter 2019*)
    Faculty advisor: Prof. Edmond Young

    Organ-on-a-chip (OOC) systems are microfabricated devices that combine living cells, biomaterials, and other biological elements together with microscale feature geometries and microfluidics to mimic the structure, function, and physiology of real tissues and organs. OOCs are quickly emerging as a powerful technology with significant advantages over traditional experimental models. To date, however, the majority of OOCs have been fabricated from only a few popular materials such as PDMS and thermoplastics. The objective of this project is to investigate and review advanced manufacturing and novel materials that can be used to develop next-generation organ-on-a-chip systems. The ideal material will have properties that include optical transparency, cell compatibility, scalability (for mass manufacturing), and amenability to achieving high spatial resolutions.

    Contact: Prof. Young eyoung@mie.utoronto.ca

    Research Area: Microfluidics; biofluid mechanics; microscale cell-based systems; cellular microenvironments; microfabrication; cell biology; cell imaging and microscopy; biomedical engineering; and cancer.

  • Design considerations for multi-organ microphysiological systems (*New – Winter 2019*)
    Faculty advisor: Prof. Edmond Young

    Organ-on-a-chip (OOC) systems are microfabricated devices that combine living cells, biomaterials, and other biological elements together with microscale feature geometries and microfluidics to mimic the structure, function, and physiology of real tissues and organs. OOCs are quickly emerging as a powerful technology with significant advantages over traditional experimental models, with researchers actively studying ways to further integrate and connect multiple OOCs to create multi-organ microphysiological systems (MPSs). The objective of this project is to apply engineering principles and scaling laws to develop design considerations and guidelines for developing such multi-organ MPSs.

    Contact: Prof. Young eyoung@mie.utoronto.ca

    Research Area: Microfluidics; biofluid mechanics; microscale cell-based systems; cellular microenvironments; microfabrication; cell biology; cell imaging and microscopy; biomedical engineering; and cancer.

  • Design and development of a portable organ-on-a-chip incubator (*New – Winter 2019*)
    Faculty advisor: Prof. Edmond Young

    Organ-on-a-chip (OOC) systems are microfabricated devices that combine living cells, biomaterials, and other biological elements together with microscale feature geometries and microfluidics to mimic the structure, function, and physiology of real tissues and organs. OOCs are quickly emerging as a powerful technology with significant advantages over traditional experimental models. A major challenge, however, is maintaining cell viability with OOCs, particularly when OOCs are transported between labs. Thus, there is a need to design and develop a low-cost and portable incubation system with active control of temperature, humidity, and CO2 concentration. The goal of this project is to design a hand-held, battery-operated version of a laboratory-scale incubator, with the ability to maintain living cells in culture for up to a day without loss of cell viability or function. The student will be involved with developing the concepts related to thermodynamic and environmental control within this portable incubator, with potential to expand the project to include on-board imaging capabilities.

    Contact: Prof. Young eyoung@mie.utoronto.ca

    Research Area: Microfluidics; biofluid mechanics; microscale cell-based systems; cellular microenvironments; microfabrication; cell biology; cell imaging and microscopy; biomedical engineering; and cancer.

  • Studying Wetting Properties of Different Coatings
    Faculty advisor: Prof. Chandra & Prof. Nejad

    Wetting is the first step in defining adhesion of coating to the substrate. To have a good adhesion performance, coatings should be able to completely wet the surface. Wettability is measured through analysis of contact angle formed between a droplet of liquid with the substrate at certain times. The lower the contact angle, the better is the wettability so its adhesion. However, when it comes to wood as a biological material with substantial variations among and between different species, many other parameters will come into play. Depending on the grain orientation, surface morphology, moisture contact and treatment, there are significant differences in density, pore size and other surface properties of the wood. This study is focused on finding correlation between coatings’ surface tensions, base (water-based vs solvent-based) and resin types (alkyd, acrylic and PU) with their wetting properties on chemically modified and unmodified wood samples. The student working on this project will have the opportunity to learn how to measure surface tensions of liquid coatings using Tensiometer and contact angles of a wide range of coatings using a high speed camera, and calculating dynamic and static contact angles of different coatings on wood using MATLAB software. Additionally, student will need to use advanced multivariate statistical analysis techniques to model correlation between coating properties with their wetting performances.

    Research area: Thermofluids

  • MEng project with specialty LED lighting manufacturer
    Faculty advisor: Professors J.K. Spelt and F. Azhari

    The project involves working with a manufacturer of LED lighting products to develop a finite element model of one of their circuit board assemblies.
    The objective is to model the stresses that are created in the circuit board by changes in the temperature of the assembly.
    Some experimental strain measurement may be part of the project as a means of verifying the model.

    Research area: Mechanics & Design