MIE504H: Applied Computational Fluid Dynamics
The course is designed for Students with no or little Computational Fluid Dynamics (CFD) knowledge who want to learn CFD application to solve engineering problems. The course will provide a general perspective to the CFD and its application to fluid flow and heat transfer and it will teach the use of some of the popular CFD packages and provides them with the necessary tool to use CFD in specific applications. Students will also learn basics of CFD and will use that basic knowledge to learn Fluent Ansys CFD software. Most CFD packages have a variety of modules to deal with a specific type of flow. Students will be introduced to different modules and their specific applications. They will then be able to utilize the CFD package to simulate any particular problem. Ansys software will be the commercial package that will be used in this course. Ansys Fluent is the most common commercial CFD code available and most of the engineering companies use this code for their research & development and product analysis.
This course will cover the design, modeling, fabrication, and control of miniature robot and micro/nano-manipulation systems for graduate and upper level undergraduate students. Micro and Nano robotics is an interdisciplinary field which draws on aspects of microfabrication, robotics, medicine and materials science. In addition to basic background material, the course includes case studies of current micro/nano-systems, challenges and future trends, and potential applications. The course will focus on a team design project involving novel theoretical and/or experimental concepts for micro/nano-robotic systems with a team of students. Throughout the course, discussions and lab tours will be organized on selected topics.
MIE506H: MEMS Design and Microfabrication
This course will present the fundamental basis of microelectromechanical systems (MEMS). Topics will include: micromachining/microfabrication techniques, micro sensing and actuation principles and design, MEMS modeling and simulation, and device characterization and packaging. Students will be required to complete a MEMS design term project, including design modeling, simulation, microfabrication process design, and photolithographic mask layout.
Introduction to the fundamentals of HVAC system operation and the relationship between these systems, building occupants and the building envelope. Fundamentals of psychrometrics, heat transfer and refrigeration; determination of heating and cooling loads driven by occupant requirements and the building envelope; heating and cooling equipment types and HVAC system configurations; controls and maintenance issues that influence performance; evaluation of various HVAC systems with respect to energy and indoor environmental quality performance.
The issue of design and development of AI systems that have beneficial social impact will be discussed and analyzed. The focus will not be on the mechanics of AI algorithms, but rather on the implementation of AI methods to address societal problems. Topics to be covered will include: Safeguarding of human interests (e.g., fairness, privacy) when AI methods are used; partnering of humans and AI systems to implement AI effectively; evaluation of AI assisted interventions; practical considerations in the selection of AI methods to be used in addressing societal problems. The issues that arise in implementing AI for beneficial social impact will be illustrated in a set of case studies aimed at creating beneficial social impact. Class activities will include lectures, seminars, labs, and take-home assignments.
MIE515H: Alternative Energy Systems
This course covers the basic principles, current technologies and applications of selected alternative energy systems. Specific topics include solar thermal systems, solar photovoltaic systems, wind, wave, and tidal energy, energy storage, and grid connections issues. See also: MIE515H-Syllabus
Introduction to combustion theory. Chemical equilibrium and the products of combustion. Combustion kinetics and types of combustion. Pollutant formation. Design of combustion systems for gaseous, liquid and solid fuels. The use of alternative fuels (hydrogen, biofuels, etc.) and their effect on combustion systems. See also: MIE516H-Syllabus
Thermodynamics and electrochemistry of fuel cell operation and testing; understanding of polarization curves and impedance spectroscopy; common fuel cell types, materials, components, and auxiliary systems; high and low temperature fuel cells and their applications in transportation and stationary power generation, including co-generation and combined heat and power systems; engineering system requirements resulting from basic fuel cell properties and characteristics.
MIE519H: Advanced Manufacturing Technologies
This course is designed to provide an integrated multidisciplinary approach to Advanced Manufacturing Engineering, and provide a strong foundation including fundamentals and applications of advanced manufacturing (AM). Topics include: additive manufacturing, 3D printing, micro- and nano-manufacturing, continuous & precision manufacturing, green and biological manufacturing. New applications of AM in sectors such as automotive, aerospace, biomedical, and electronics.
MIE520H: Biotransport Phenomena
Application of conservation relations and momentum balances, dimensional analysis and scaling, mass transfer, heat transfer, and fluid flow to biological systems, including: transport in the circulation, transport in porous media and tissues, transvascular transport, transport of gases between blood and tissues, and transport in organs and organisms.
MIE523H: Engineering Psychology and Human Performance
An examination of the relation between behavioural science and the design of human-machine systems, with special attention to advanced control room design. Human limitations on perception, attention, memory and decision making, and the design of displays and intelligent machines to supplement them. The human operator in process control and the supervisory control of automated and robotic systems. Laboratory exercises to introduce techniques of evaluating human performance.
Introduction to data mining and machine learning algorithms for very large datasets; Emphasis on creating scalable algorithms using MapReduce and Spark, as well as modern machine learning frameworks. Algorithms for high-dimensional data. Data mining and machine learning with large-scale graph data. Handling infinite data streams. Modern applications of scalable data mining and machine learning algorithms.
MIE533H: Non-Destructive Evaluation
The course is designed for students who are interested in more advanced studies of applying wave principles to engineering applications in the field of non-destructive testing (NDT) and imaging (NDI). Topics will cover: Review of principles and characteristics of sound and ultrasonic waves; thermal waves; optical (light) waves; photons: light waves behaving as particles; black body radiation, continuous wave and pulsed lasers. The course will focus on NDT and NDI applications in component inspection and medical diagnostics using ultrasonics, laser photothermal radiometry, thermography and dynamic infrared imaging.
MIE535H: Electrification Through Electricity Markets
Challenges of meeting net-zero, fundamentals of markets, structures and participants, spot markets, economic dispatch, day-ahead markets, optimal unit commitment, forward markets, settlement process, storage and demand management, renewable and distributed energy resources, trading over transmission networks, nodal pricing, reliability resources, generation and transmission capacity investment models, capacity markets.
This course takes a 360° perspective on product design: beginning at the market need, evolving this need into a concept, and optimizing the concept. Students will gain an understanding of the steps involved and the tools utilized in developing new products. The course will integrate both business and engineering concepts seamlessly through examples, case studies and a final project. Some of the business concepts covered include: identifying customer needs, project management and the economics of product design. The engineering design tools include: developing product specifications, concept generation, concept selection, Product Functional Decomposition diagrams, orthogonal arrays, full and fractional factorials, noises, interactions, tolerance analysis and latitude studies. Specific emphasis will be placed on robust and tunable technology for product optimization. See also: MIE540H-Syllabus
MIE542H: Human Factors Integration
The integration of human factors into engineering projects. Human factors integration (HFI) process and systems constraints, HFI tools, and HFI best practices. Modelling, economics, and communication of HFI problems. Examples of HFI drawn from energy, healthcare, military, and software systems. Application of HFI theory and methods to a capstone design project, including HFI problem specification, concept generation, and selection through an iterative and open-ended design process.
MIE550H: Advanced Momentum, Heat and Mass Transfer
This course observes: conservation of mass, momentum, energy and species; diffusive momentum, heat and mass transfer; dimensionless equations and numbers; laminar boundary layers; drag, heat transfer and mass transfer coefficients; transport analogies; simultaneous heat and mass transfer; as well as evaporative cooling, droplet evaporation and diffusion flames.
MIE 561 is a “capstone” course. Its purpose is to give students an opportunity to integrate the Industrial Engineering tools learned in previous courses by applying them to real world problems. While the specific focus of the case studies used to illustrate the application of Industrial Engineering will be the Canadian health care system, the approach to problem solving adopted in this course will be applicable to any setting. This course will provide a framework for identifying and resolving problems in a complex, unstructured decision-making environment. It will give students the opportunity to apply a problem identification framework through real world case studies. The case studies will involve people from the health care industry bringing current practical problems to the class. Students work in small groups preparing a feasibility study discussing potential approaches. Although the course is directed at Industrial Engineering fourth year and graduate students, it does not assume specific previous knowledge, and the course is open to students in other disciplines.
This course takes a practical approach to scheduling problems and solution techniques, motivating the different mathematical definitions of scheduling with real world scheduling systems and problems. Topics covered include: job shop scheduling, timetabling, project scheduling, and the variety of solution approaches including constraint programming, local search, heuristics, and dispatch rules. Also covered will be information engineering aspects of building scheduling systems for real world problems. See also: MIE562H-Syllabus
MIE563H: Analytical & Numerical Solution of Engineering PDEs
This course explores analytic and numerical solution techniques for heat/mass diffusion and vibration/wave equations. Emphasis is placed on intuitive derivation of these equations, and analytic solution techniques like separation of variations, eigenfunction expansions, Fourier analysis, integral transforms, coordinate transforms, and special functions. Numerical solutions are introduced via finite difference methods. A key learning outcome of this course is understanding the central role that analytic solutions play in developing intuition about engineering physics, and how this is a fundamental step in learning to verify, validate, and properly use advanced computational modelling tools. See also: MIE563H-Syllabus
MIE566H: Decision Making Under Uncertainty
Methods of analysis for decision making in the face of uncertainty and opponents. Topics include subjective discrete and continuous probability, utility functions, decision trees, influence diagrams, bayesian networks, multi-attribute utility functions, static and dynamic games with complete and incomplete information, bayesian games. Supporting software.
MIE567H: Dynamic and Distributed Decision Making
Fundamental concepts and mathematical frameworks for scientific sequential decision making in the presence of uncertainty. Utility theory, uncertainty modeling, theory of games, dynamic programming, and multi-agent system. Discussion of how the decision theories can be applied to design algorithms and processes for real-world cases.
Variational principles and Lagrange´s Equations, Hamilton´s principle. Kinematics of rigid body motion, Euler angles, rigid body equations of motion. Hamilton´s equations, cyclic coordinates, Legendre transformations. Canonical transformations, Hamilton-Jacobi theory.
MIE1005H: Theory of Vibrations
Multi-degree of freedom systems, using both analytical and approximate methods. Vibrations of continuous systems, including strings, bars and membranes. Natural modes of plate vibration – approximate methods such as Rayleigh’s Energy Methods, Rayleigh-Ritz Method, Galerkin’s Method, and assumed mode method. Introduction to finite element analysis.
MIE1010H: Acoustics and Noise Control
The purpose of the course is to introduce the theory and practical application of acoustics noise and vibration control. While the emphasis of the study will be on the built environment, both indoor and outdoor, the methods taught can also apply to other industries, e.g. the automotive industry. Both the physics and perception of sound will be discussed covering such wide ranging topics as concert hall design, speech intelligibility, HVAC noise control design and building isolation from rail noise, to name a few. The course combines theoretical introductions to the subjects of acoustics, noise and vibration and follows them up with case studies from industry.
MIE1050H: Design of Intelligent Sensor Networks (Formerly MIE1453H: Introduction to Sensors and Sensor Networks)
This course will provide students with practical knowledge on sensor network design including sensor selection, calibration, digitization, and digital signal processing. Students will be introduced to theory and operation of various sensor technologies and their applications. Commonly used transducers such as chemical, mechanical, and magnetic as well as the more advanced organic and nuclear transducers are discussed. This course will also cover linear and non-linear multi-parameter calibration. Digitization, and a survey of digital signal processing techniques will be discussed with practical application of commonly used digital filters. Special focus will be placed on optimal design of sensor networks and multi-sensor data fusion. There will be a design project to enforce the lessons learned in class on sensor calibration and digital signal processing.
MIE1052H: Signal Processing (Formerly MIE1452H: Signal Processing)
Linear systems and signal sampling, Fourier transforms & frequency analysis, Laplace transforms, FFT and inverse FFT algorithms, convolution/de-convolution, impulse response, random signals, noise characterization, auto- and cross-correlation, power spectra, adaptive filters, detection and clustering. These topics will be covered with extensive coverage on their applications to various topics in mechanical or biomedical engineering. In mechanical engineering such topics include vibrations, signal timing, spectral/phase analysis, signature analysis, thermal waves, acoustic emission, engine performance analysis, resonant acoustic spectroscopy (RAS), crack detection and location with ultrasound, flow measurements, condition-based monitoring & maintenance, fracture mechanics, etc. In biomedical engineering these topics include modeling of biomedical control systems, analysis of evoked potentials, analysis of electroencephalograms and electrocardiograms.
MIE1064H: Control Analysis Methods with Applications to Robotics
The main purpose of this course is to introduce a series of distinct topics in control to students who have not seen control system design beyond a first course in control, which includes classical methods such as root locus, and Bode design, for example. The topics discussed in MIE 1064 F are selected to give students a broad overview of a variety of control design methods and concepts in stability.
MIE1070H: Intelligent Robots for Society
This course introduces the design of intelligent robots- focusing on the principles and algorithms needed for robots to function in real world environments with people. Topics that will be covered include autonomy, social and rational intelligence, multi-modal sensing, biologically inspired and anthropomorphic robots, and human-robot interaction. Class discussions will centre on the interactive, personal assistive and service robotics fields.
MIE1075H: AI Applications in Robotics
AI-embedded Robotics: applications in Service and Personal Robots. Development of a prototype home-assistance robot. Applications of the robot in the home environment.
MIE1076H: AI Applications in Robotics II
This course builds on the concepts of AI Applications in Robotics I.
MIE1077H: AI Applications in Robotics III
This course will cover the development of AI-Embedded Robotics with Applications to Home and Institutional Care. Image Processing for Object Detection and Identification; Convoluted; Neural Networks (CNN); Computer Vision (CV); Artificial Neural Networks (ANN); Autonomous Navigation; Mapping, Localization, Motion Planning; Collision Avoidance; Grasping and Manipulation; Imitation Teaching/Learning; Reinforcement Learning (RL); Policy Improvement with Path Integrals (PI2); Control; Model-Free Control; Advanced Control via DMP and Potential Functions; Closed Loop Control; Applications: Projects: Dressing, Bathing, Ironing, Laundry folding, Sewing, Other.
This course provides students with knowledge on healthcare robotics including surgical, assistive, and rehabilitation robots. Specific topics include medical imaging-guided surgery; minimally-invasive surgery through miniaturization, novel actuation and sensing; robotic surgery at tissue and cell levels; autonomous robotic systems to assist with daily living activities; multi-modal robot interfaces; robotics-based rehabilitation technologies; upper limb rehabilitation robots; wearable exoskeletons and sensors; implanted neural interfaces. Students are provided with state-of-the-art advances in healthcare robotics.
MIE1101H: Advanced Classical Thermodynamics
A course in which the postulatory approach is used to develop the theory of thermodynamics. The postulates are stated in terms of a variational principle that allows them to be applied to systems subjected to fields, to phase transitions, and to systems in which surface effects are dominant. The thermodynamic stability of systems is examined and examples of stable, metastable and unstable systems are discussed.
MIE1115H: Heat Transfer with Phase Change
In this course you will learn about the phenomena that control phase change of pure substances. Most of the course will be devoted to studying liquid-vapour phase change, with an emphasis on boiling. We will study the thermodynamics of phase change, vapour bubble nucleation and growth, heat transfer during boiling, and fluid mechanics during the flow of a liquid-vapour mixture. All students are expected to have done undergraduate courses in thermodynamics, fluid mechanics and heat transfer.
MIE1120H: Current Energy Infrastructure and Resources
This course covers the basic principles of how global energy is currently supplied, by primary source. The aim is to provide an energy literacy that can inform research, technology development and effective policy in this area. The course content will be roughly divided according to the current global energy mix (i.e. 31% oil, 27% coal, 25% gas, 6.9% hydro, 4.3% nuclear, 2.5% wind, 1.4% solar, and 1.8% geothermal/biomass/biofuels). In each case background reading and critical analyses will be applied to: (a) the characteristics of the resource; (b) the infrastructure for extraction/development of the resource; (c) the usage of the resulting energy; and (d) the implications of usage. Assignments and exams will assess both background knowledge and the ability to apply fluid flow, thermodynamic and heat transfer analyses to energy supply systems. See also: MIE1120H-Syllabus
MIE1123H: Fundamentals of Combustion
This course will deal with the basic theory of combustion in the steady state, with consideration of theories of flame propagation, flame stabilization, limits of inflammability, ignition, quenching, etc., and discussion will include both laminar and premixed flames, diffusion flames, flames and detonation.
MIE1128H: Materials for Clean Energy Technologies
The primary emphasis of the course is materials properties relevant for some clean energy conversion technologies. More specifically, some materials such as inorganic solids and semi-conductors that play key roles in clean electricity production technologies such as fuel cells, gas turbines, and solar cells will be the primary focus, with their ionic and electronic conduction mechanisms and their relevance being the major part of the technical content of the course. That information will be combined with some overview-level information of a few different technologies on a broad level. See also: MIE1128H-Syllabus
MIE1129H: Nuclear Engineering I
A first course in nuclear reactor theory, which introduces students to the scientific principles of nuclear fission chain reactions and lays a foundation for the application of these principles to the nuclear design and analysis of reactor cores. Topics covered include basic nuclear concepts, atomic fission, neutron propagation and interaction with matter, neutron thermalization, diffusion model of a nuclear reactor, criticality, nuclear reactor kinetics, and reactivity effects. See also: MIE1129H-Syllabus
MIE1130H: Nuclear Engineering II
This course covers the basic principles of the thermo-mechanical design and analysis of nuclear power reactors. Topics include reactor heat generation and removal, nuclear materials, diffusion of heat in fuel elements, thermal and mechanical stresses in fuel and reactor components, singlephase and two-phase fluid mechanics and heat transport in nuclear reactors, and core thermomechanical design. See also: MIE1130H-Syllabus
MIE1132H: Heat Exchanger Design
This course provides the fundamentals and applications for thermal and hydraulic design of heat exchangers. it covers a wide range of relevant topics including the main considerations for equipment selection and design, and different methods of analysis for sizing and rating. More specialized design considerations are also introduced. The objective is for students to become familiar with the design and specifications of industrial heat exchangers by solving practical problems using synthesis of other engineering subjects such as thermodynamics, heat transfer, and fluid mechanics.
MIE1135H: Thermal Phenomena, Performance and Management of Electric Vehicles
This course describes the thermal phenomena in Electric Vehicles (EVs), including the main cooling/heating circuits associated with the power train, cabin, and battery. The major focus is on thermal performance and thermal management of batteries, power electronics and electric motors, and it also includes thermal issues related to cabin electronic systems. Emphasis is on Lithium-ion batteries (LIB), which are expected to continue to be the most widely used battery for EVs in the next decade. This course will cover LIB cells and their fundamentals; principles of operation; electrochemical and heat transfer formulation, modelling and simulation; thermal-related effects on LIB performance and longevity, including aging, degradation, safety, and thermal runaway; thermal modelling of EV system- and component-level, LIB, electric drivetrain, cabin, and fast charger. Students in this course are expected to have a basic understanding of electrochemistry terminologies and undergraduate-level fundamental knowledge of fluid mechanics, thermodynamics, heat transfer and numerical methods. See also: MIE1135H-Syllabus
MIE1199H: Special Topics in Thermal Sciences
This course is a means of offering specialty courses in the field of Thermal Sciences, exploring topics that would otherwise not be made available in the core graduate curriculum. The topic of this course will change each time that the course is delivered. The primary means of delivery will be lectures, although the instructor will have the freedom to supplement course delivery as appropriate.
MIE1201H: Advanced Fluid Mechanics I
This fundamental course develops the conservation laws governing the motion of a continuum and applies the results to the case of Newtonian fluids, which leads to the Navier-Stokes equations. From these general equations, some theorems are derived from specific circumstances such as incompressible fluids or inviscid fluids. Basic solutions to, and properties of, the governing equations are explored for the case of viscous, but incompressible, fluids. Topics included involve exact solutions, low-Reynolds-number flows, laminar boundary layers, flow kinematics, and 2D potential flows.
MIE1207H: Structure of Turbulent Flows
This is a first level course in turbulent flows following an exposure to basic undergraduate fluid mechanics. It deals with the governing equations of motion, statistical representation of the turbulent field and describes fundamental shear flows such as jets, wakes and boundary layers. Emphasis is placed on the physical aspects of the motion.
MIE1208H: Microfluidic Biosensors
This course will present the fundamentals and applications of biosensors realized on microfluidic platforms. Topics to be covered include: Microfabrication techniques for constructing silicon, glass, and polymer devices; Microfluidic principles; Biosensing mechanisms; Design and analysis of microfluidic biosensors; Microfluidic immunosensors; Microfluidic nucleic acid sensors; Microfluidic chemical sensors; Other applications of microfluidic biosensors
MIE1210H: Computational Fluid Mechanics and Heat Transfer
MIE1210 is an introductory course that will teach a Finite Volume (FV) and Finite Difference (FD) approaches to Computational Fluid Dynamics (CFD) and Heat Transfer. Since the advent of commercially available computers, CFD has been an important engineering research domain as it gave researchers the ability to solve analytically intractable problems of industrial relevance. In the last two decades, the immense demand for CFD research and expertise has spawned the commercialization of software packages such as Fluent/CFX and FEMlab. Despite these readily available software packages, there is a recognized importance to user expertise, fundamental knowledge, and critical understanding of their inner workings. In addition, home spun research codes are still prominent in academia and industry. This is due in large part to the fact that commercial software packages are geared toward a broad range of research topics, and may not function as efficiently as a code designed with a specific problem in mind, and to the fact that developments in CFD are typically achieved in research before they are adopted by software companies. This course is appropriate both for students who wish to become knowledgeable users of commercial CFD programs, and students who plan to create, develop, or enhance research codes. Therefore, the overreaching goals of this course are threefold: 1. To give you an introduction to fundamental discretization and solution techniques for heat transfer and fluid dynamics problems; 2. To give you an understanding of solution methodologies, advantages, downfalls, considerations (stability, accuracy, efficiency), and the inner workings of CFD software; and 3. To have you gain experience writing programs and solving 1D and 2D problems, and in using these programs to demonstrate and reinforce 1 and 2.
MIE1212H: Convective Heat Transfer
The basic partial differential equations of material transport by fluid flow is derived along with the most significant analytical solutions of these equations, e.g., fully developed laminar flow and heat transfer in pipes and channels. Prediction of heat and mass transfer rates based on analytical and numerical solutions of the governing partial differential equations. Heat transfer in fully developed pipe and channel flow, laminar boundary layers, and turbulent boundary layers. Approximate models for turbulent flows. General introduction to heat transfer in complex flows. Discussion will be centered on boundary conditions for heat transfer, similarity and dimensionless parameters, and boundary layer approximations.
The purpose of this course is tor provide a basic understanding of multiphase flows. In particular, the dynamics of drops and bubbles in various flow conditions will be presented. The course will introduce the important parameters involved in analyzing multiphase flows. The equation of mass, momentum, and energy for such systems will be presented. These equations will be solved for specific conditions. Also, the methodology for solving more complex multiphase flow problems will be described.
MIE1232H: Microfluidics and Laboratory-on-a-Chip Systems
Tremendous opportunities are associated with shrinking large-scale (laboratory) processes to characteristic volumes of 10nL-100µL and translating them to continuous-flow formats. Applications of microfluidic and lab-on-a-chip technologies include assays for biomolecular detection, platforms for the perfusion culture of cells, organs and organisms, microfluidic bioprinting, and miniature chemical factories and energy conversion. The interdisciplinary course considers the different backgrounds of students and consists of a combination of lectures and project work. Projects will consist of individual and group contributions and involve the design, manufacture, testing and live demonstration a microfluidic device. Course participants will receive hands-on experience in several current technologies for the processes for the manufacture of microfluidic devices (soft lithography, hot embossing, 3D printing).
This course is designed to provide students with a comprehensive view of the fundamental concepts of wind power projects, from inception and economic viability to implementation and operation. Students will learn an appreciation for the main components of wind power systems. In addition, this course will cover the identification and quantification of the wind resource, numerical modelling and CFD techniques applied to wind power systems, wind turbine aerodynamics, design and performance, wind turbine noise, wind farm design and economic and environmental evaluation of wind projects. A final project will be undertaken involving specific technology developments in the wind industry and its potential impact on existing facilities. See also: MIE1240H-Syllabus
The main goal of this course is to introduce the concepts and techniques for energy management and utilization. Among the subjects to be discussed will be: energy supply and distribution, energy audits, energy efficiency in the industrial environment, mechanical and electrical applications, energy conservation, and an introduction to energy storage strategies. Practical applications include mining, manufacturing, construction (LEED, HVAC, lightning, etc.), power and process plants, oil and gas and food processing. The fundamental principles of thermodynamics, fluid mechanics and heat transfer will be used for analyzing these energy systems. See also: MIE1241H-Syllabus
MIE1242H: Applied Thermal Management
In this course, we discuss thermal management of industrial systems. The course will start with an introduction to what is involved in thermal management, why it is important, and discuss different aspects of thermal management in selected industrial applications, namely: i. Electric Vehicles ; ii. Autonomous Self Driving Systems ; iii. Consumer Electronics ; iv. Datacenters and Supercomputers. After the introduction, the course will discuss the steps of thermal management in industry and its different aspects from a practical perspective. See also: MIE1242H-Syllabus
MIE1299H: Special Topics in Fluid Mechanics
This course will teach students how to apply fundamental fluid mechanics to the study of biological systems. The course is divided into three modules, with the focus of the first two modules on the human circulatory and respiratory systems, respectively. Topics covered will include blood rheology, blood flow in the heart, arteries, veins and microcirculation, the mechanical properties of the heart as a pump; air flow in the lungs and airways, mass transfer across the walls of these systems, the fluid mechanics of the liquid-air interface of the alveoli, and artificial mechanical systems and devices for clinical aid. The third and final module will cover a range of other fluid problems in modern biology.
Review of tensor notation; analysis of stress in a continuum including principal stress, invariants, spherical and deviator tensors; analysis of deformation and strain in a continuum including Lagrangian and Eulerian descriptions, spherical and deviator tensors, strain rate tensors and compatibility equations; equilibrium equations; constitutive relations for general linear solid, application to elastic, plastic and viscoelastic solids; anisotropic elasticity, orthotropic materials.
This course offers graduate students an in-depth study of fracture mechanics as applied to real engineering problems. The course is divided into three main components: failure analysis using fracture mechanics concepts, diagnostics using replicas of engineering failures, and failure prevention techniques. Mofdes of failure, brittle fracture, linear elastic fracture mechanics (LEFM), elastoplastic fracture mechanics (EPFM) and fatigue crack initiation and growth will constitute the failure analysis component. In-laboratory examinations of typical fractures will constitute the diagnostics component. Design considerations, Surface treatment and different processing techniques for crack arrest will conclude the final component. The course is supported by numerous aerospace case studies.
MIE1359H: Engineering Cell Biology and Micro/Nanoengineered Platforms
Motivation/Objectives: A cell is the basic unit of life in all organisms. Understanding cellular structures and how cells function is fundamental to all aspects of biosciences and is the basis for disease diagnostics/therapeutics and drug discovery. For single cell studies, the development of enabling micro and nanoengineered techniques/systems is a highly active field. The objectives of this course are two folds: (1) The course targets engineering graduate students to introduce essential topics in cell biology. (2) The course will also discuss micro/nano fabricated/engineered techniques/systems for manipulating cells, stimulating cells, and quantitatively measuring cellular activities.
MIE1401H: Human Factors Engineering
This course introduces the principles, methods, and tools essential for the analysis, design, and evaluation of human-centered systems. It explores the impact of human perceptual and cognitive factors on the design and use of engineered systems. Key topics include human information processing, decision making, workload, and human error. Students will learn and apply the human-centered systems design process, encompassing task analysis, user requirements generation, prototyping, and evaluation. Additionally, it addresses the design of procedures, displays and controls, and training systems; designing for error prevention; and human-computer interaction. See also: MIE1401H-Syllabus
MIE1402H: Experimental Methods in Human Factors Research
The course deals with practical problems associated with the design of experiments in Human Factors research, with an emphasis on the use of statistical packages and data analysis tools. Topics covered will include analysis of variance, non- parametric statistics, balanced and unbalanced block designs (including Latin squares), confidence intervals, etc. Stress is given to practical problems and the intuitive understanding of applied statistics. See also: MIE1402H-Syllabus
MIE1403H: Analytical Methods in Human Factors Research
The course covers a variety of topics in Human Factors / Ergonomics research related to the acquiring, analysing, and modelling of human behavioural data. Topics to be covered include the following (in approximate order of presentation): Selecting Measures for Human Factors Research; Psychophysical methods of measurement: – Classical psychophysical methods – Signal Detection Theory – Indirect and direct subjective scaling; Protocol Analysis – Interviewing and Questionnaires – Knowledge Elicitation; Estimating mental workload & situational awareness; Manual Control – Tracking paradigms – Modelling of human manual control performance
MIE1411H: Design of Work Places
Introduction to ergonomics in industrial settings. Biomechanics related to manual materials handling, repetitive strain injuries, visual and auditory limitations, human information processing and short term memory limitations, psychomotor skill, anthropometry and workspace layout, population stereotypes, design of controls and displays, circadian rhythms and design of shift work schedules. (MIE343 anti-requisite.) See also: MIE1411H-Syllabus
MIE1412H: Human-Automation Interaction
A survey of theoretical and applied issues in human interaction with automation. Topics included are: philosophy of human-machine systems, types and levels of automation, models of human-automation interaction, function allocation, mode error, bias, trust, workload and situation awareness, automation interfaces, decision-aiding, adaptable and adaptive (intelligent) automation, supervisory control, and management of human-automation systems. See also: MIE1412H-Syllabus
MIE1413H: Statistical Models in Empirical Research
This course covers various statistical models used in empirical research, in particular human factors research, including linear regression, mixed linear models, non-parametric models, generalized linear models, time series modeling, and cluster analysis. For various observational and experimental data, students will be proficient in generating relevant hypotheses to answer research questions, selecting and building appropriate statistical models, and effectively communicating these results through interpretation and presentation of results. Basic knowledge in probability, statistics, and experimental design is required. The course will not focus on the design of experiments. In addition to homework assignments and exams, the students will review and critique journal articles and conference papers for the validity of the use of various statistical models. The students will work on a term long project of their choice and will be encouraged to relate this assignment to their current research projects. The examples used in class and the assignments will be drawn from human factors research. However, the students will not be required to use human factors data for their project. See also: MIE1413H-Syllabus
MIE1414H: Human Factors in Transportation
The course will cover a wide range of human factors topics related to road transportation, in particular motor vehicle safety. The course provides an understanding of road user characteristics and limitations and how these affect design of traffic control devices and the roadway. The course topics include: history and scope of human factors in transportation; vision and information processing in the context of driving; driver adaptation; driver education, driver licensing and regulation; traffic control devices; crash types, causes, and countermeasures; alcohol, drug, and fatigue effects; forensic human factors. See also: MIE1414H-Syllabus
MIE1415H: Analysis and Design of Cognitive Work
Frameworks, tools and methods to analyze and design support for cognitive work. The course will emphasize computer-based work in complex production- and/or safety-critical systems. Primary frameworks include Cognitive Work Analysis and Ecological Interface Design, with consideration of complementary perspectives in Cognitive Systems Engineering. The design element will emphasize the human-machine interface.
MIE1416H: Human Factors in Healthcare
This course provides an introduction to the application of human factors (HF) in the analysis of healthcare systems using case studies and current events. Various healthcare models are explored with a focus on aims of healthcare systems in Canada and the US. Applicable HF theory, models, principles and methods are covered. Emphasis is placed on the use of HF in prospective and retrospective system safety evaluation and integration of technology (including ML/AI) in clinical environments. Equity as a cross-cutting dimension of quality care, engagement of patients in system redesign processes, and research ethics and misconduct are also covered.
MIE1444H: Engineering for Psychologists
The objective of the course is to convey engineering thinking to non engineers, and specifically psychology graduate students, to support the Collaborative Specialization in Psychology and Engineering (PsychEng). The aim is for psychology students to be able to understand engineering language and common methods to be able to participate in design activities.
The course will introduce the problem-solving focus of engineering work, including the use of: engineering assumptions, models (formation, interpretation, limits), codes / standards and heuristics, problem statements, design objectives and functions, and processes for selecting design alternatives.
Considerable attention in the course is devoted to existing applications of psychology in engineering, e.g., in design theory and methodology and human factors, etc. The problem-solving perspective of engineering enables clarification of not just where psychological theories are applicable, but may also inform where such theories may require further development. For example, applying social psychological theories and models, e.g., Higgins’ Regulatory Focus Theory, to solve engineering problems can be quite challenging, and may add at least a physical dimension to such models.
Projects in human factors, design methodology, and other areas of engineering that can benefit from application of psychology are offered to be completed as course projects. Finally, Psychologists are also guided on how to present their work to engineering audiences.
MIE1499H: Special Topics in Human Factors and Ergonomics
Field research is a process where data is collected through qualitative methods. The objective of field study is to observe and interpret a subject of study in its natural environment. Field research employs qualitative methods, including interviews, direct observation, focus groups and artifact analyses. In human factors, field work is used in a variety of ways to identify errors in systems, understand human needs, and to evaluate designs. In this course students will learn core qualitative methodologies, philosophies and their application to human factors contexts and problems. Learners will critique qualitative methodologies and have an opportunity to practice techniques through the design of a field research study. See also: MIE1499H-Syllabus
MIE1501H: Knowledge Modelling and Management
Information Engineering focuses on the representation and use of information in the context of the web. The first part of the course covers the Semantic Web, including XML, RDF, Linked Data, Provenance, Trust and Data Mashup. The second part covers web-based Knowledge Representations, including: Description Logic, OWL, SWRL, and Ontologies.
MIE1505H: Enterprise Modelling
To remain competitive, enterprises must become increasingly agile and integrated across their functions. Enterprise models play a critical role in this integration, enabling improved designs for enterprises, analysis of their performance, and management of their operations. This course motivates the need for enterprise models and introduces the concepts of generic and deductive enterprise models. It reviews research to date on enterprise modelling, including emerging standards and implementation technologies.
MIE1510H: Formal Techniques in Ontology Engineering
This course will explore theoretical techniques for the design and analysis of formal ontologies. Topics will include the design of verified ontologies, methodologies for proving properties about ontologies, and applications of classification theorems from mathematics. These techniques will be applied to ontologies that are currently being used in government and industry. See also: MIE1510H-Syllabus
This course is a research seminar that focuses on recent developments in the area of Data Management for Analytics. Science, businesses, society, and government are been revolutionized by data-driven methods that benefit heavily from scalable data management techniques. The course provides an overview of data management concepts applied to analytics, covering methods and techniques, including distributed computations on massive datasets and frameworks for enabling large-scale parallel data processing on clusters of commodity servers. Emphasis is given to data management techniques for analyzing Web Data and Open Datasets. The course evaluation is based on student presentations, a focused bibliography survey, a hands on invigilated lab, and a course project (the last two using computational notebooks on scalable platforms). The project goal is to reproduce high quality published research in the area of data analytics, emphasizing data management aspects.
MIE1513H: Decision Support Systems
This course provides students with an understanding of the role of a decision support system in an organization, its components, and the theories and techniques used to construct them. The course will focus on information analysis to support organizational decision-making needs and will cover topics including information retrieval, descriptive and predictive modeling using machine learning and data mining, recommendation systems, and effective visualization and communication of analytical results.
MIE1514H: Systems Design and Engineering: A Product Perspective
The course objective is to familiarize students with the principles and methods of systems engineering in the design of products. It includes specific practical examples and projects to aid in understanding and appreciating fundamental principles. Students will apply the various systems engineering methods and techniques as appropriate across all phases of a product’s life cycle. The course will prepare students who are or will be involved in high technology complex systems, and the preliminary and detailed design of products.
MIE1516H: Structured Learning and Inference
This Research Course will provide students with the conceptual, theoretical, and implementational foundations of fundamental tools for structured learning and inference: probabilistic graphical models, probabilistic programming, and deep neural networks. The course will focus on the design and training of structured models for specific application use cases such as answering probabilistic queries over data, sequence tagging and classification, and image recognition through programming intensive projects including a final independently proposed research project with report component.
MIE1517H: Introduction to Deep Learning
This course will provide an overview of deep learning techniques with engineering applications. Topics covered include: neural network architectures (CNNs, RNNs, and more.); model training and regularization; data augmentation; transfer learning; generative models; Ethics and fairness will play a prominent role in the course discussions. The course will follow an applied approach through several skill building assignments and a team-based project. See also: MIE1517H-Syllabus
MIE1520H: Learning with Graphs and Sequences
Complex data in a variety of applications can often lend themselves to a sequence or graph representation. In recent years, many tools and techniques were developed to efficiently learn from sequence and graph data. In particular, specialized deep neural network architectures, such as graph neural networks and transformers, have obtained state-of-the-art performance in tasks such as natural language processing and recommender systems. This course will provide students with advanced conceptual, theoretical, and implementational skills for developing machine learning approaches for processing sequences and graphs. The course will cover the design and training of both fundamental models and recent state-of-the-art models, and will prepare students to conduct research that involves the development or application of machine learning techniques for sequences or graph data. Knowledge of machine learning, algorithms, and programming is required, while knowledge of deep learning is recommended. The course is a research-oriented course. Students are expected to read and present research papers and the main evaluation will be an individual research project.
MIE1603H: Integer Programming (for Research students)
Formulation of integer programming problems and the characterization of optimization problems representable as integer and mixed-integer programs. The degree of difficulty of classes of integer programs and its relation to the structure of their feasible sets. Optimality conditions. Branchand-bound, cutting plane, and decomposition methods for obtaining solutions or approximating solutions.
MIE1605H: Stochastic Processes
This course is an introduction to stochastic processes with an emphasis on applications to queueing theory and service Engineering
MIE1607H: Stochastic Modeling and Optimization
A course in renewal theory, Markov renewal theory, regenerative and semi-regenerative processes, Markov and semi-Markov processes and decision processes with emphasis on applications in production/inventory control, maintenance, communication systems, flexible manufacturing systems.
MIE1612H: Stochastic Programming and Robust Optimization
Official course description: Stochastic programming and robust optimization are optimization tools dealing with a class of models and algorithms in which data is affected by uncertainty, i.e., some of the input data are not perfectly known at the time the decisions are made. Topics include modeling uncertainty in optimization problems, two-stage and multistage stochastic programs with recourse, chance constrained programs, computational solution methods, approximation and sampling methods, and applications. Knowledge of linear programming, probability and statistics are required, while programming ability and knowledge of integer programming are helpful.
MIE1613H: Stochastic Simulation
This course is an introduction to modelling and analysis of stochastic dynamical systems using computer simulation. The course will provide a rigorous yet accessible treatment of the probability foundations of simulation, and discuss programming simulation models in a lower-level language (e.g., Python). Design and analysis of simulation experiments will also be covered. Applications in service and financial engineering will be emphasized.
MIE1615H: Markov Decision Processes
This is a course to introduce the students to theories of Markov decision processes. Emphasis will be on the rigorous mathematical treatment of the theory of Markov decision processes. Topics will include MDP finite horizon, MDP with infinite horizon, and some of the recent development of solution method.
MIE1616H: Research Topics in Healthcare Engineering
This is a seminar-based course in which we will review a variety of papers in the field of healthcare OR. We will survey and evaluate several papers within topic areas and try to identify areas for potential future research. Some papers will be distinctly OR, while others will come from researchers in the field of health policy and health economics. One thing that you will notice as we go through the literature is that the area of healthcare engineering is interdisciplinary in nature and encourages solutions that are derived from various areas of expertise. This interdisciplinary approach is also encouraged through the many funding bodies that currently support healthcare engineering research in North America. The Canadian Institute of Health Research, CIHR, (http://www.cihr-irsc.gc.ca) funds the majority of healthcare research in Canada. It is composed of 14 virtual ‘institutes’ that represent all facets of health research. The Institute of Health Services and Policy Research, IHSPR, is most related to the type of collaborative research discussed above. It supports innovative research, capacity-building and knowledge translation in order to improve health care service delivery. In 2001 and 2004, IHSPR was involved with national consultations on health services priorities entitled “Listening for Direction”. The result of these consultations was a set of priorities for Canadian researchers in the area of health care policy and management. Of course, not all of the topics are relevant to Healthcare Engineering, but many of the readings and articles discussed in this class will align with the most recent set of priorities:
MIE1619H: Constraint Programming and Hybrid Optimization
The topic of MIE1619 is the “non-traditional” optimization technique Constraint Programming (CP) and hybrids of CP with approaches in OR. Heavy emphasis will be placed on similarities and differences between CP and mathematical programming including the unified framework of search, relaxation, and inference. The primary hybrid approaches will be based on constraint generation approaches including Logic-based Benders Decomposition and SAT Modulo Theory. This is an advanced graduate level course intended for research-stream students. MEng students are not admitted without special permission from the instructor. The course will be challenging. Students are expected to read material in preparation for each lecture and, in a few cases, view online lectures. An objective of this course is to impart skills necessary for an academic career such as paper writing, presentation skills, and writing peer reviews. The main evaluation will be a project where the student is expected to apply techniques discussed in the course to their own research interests: you should do something you weren’t already planning to do as part of your research. A goal of this course is that these projects will be publishable in a peer-reviewed forum.
MIE1620H: Linear Programming and Network Flows
Rigorous introduction to the theory of linear programming. Simplex method, revised simplex method, duality, dual simplex method. Post-optimality analysis. Interior point methods. Decomposition methods. Network flow algorithms. Maximum flow, shortest path, assignment, min cost flow problems.
MIE1621H: Non-Linear Optimization
Theory and computational methods of non-linear optimization. Convex sets, convex and concave functions. Unconstrained and Constrained Optimization. Quadratic Programming. Optimality conditions and convergence results. Karush-Kuhn-Tucker conditions. Introduction to penalty and barrier methods. Duality in nonlinear programming.
MIE1622H: Computational Finance and Risk Management
The objective of the course is to examine the construction of computational algorithms in solving financial problems, such as risk-aware decision-making, asset pricing, portfolio optimization and hedging. Considerable attention is devoted to the application of computational and programming techniques to financial, investment and risk management problems. Materials in this course are quantitative and computational in nature as well as analytical. Topics include mean-variance portfolio optimization, simulation (Monte Carlo) methods, scenario-based risk optimization, hedging, uncertainty modeling, asset pricing, simulating stochastic processes, and numerical solutions of differential equations. Python is the primary computational and modeling software used in this course, we also briefly describe other programming environments such as R, Matlab and C/C++ used in financial engineering. Practical aspects of financial and risk modeling, which are used by industry practitioners, are emphasized. See also: MIE1622H-Syllabus
MIE1623H: Introduction to Healthcare Engineering
This course illustrates the use of industrial engineering techniques in the field of healthcare. Common strategic, tactical, and operational decision-making problems arising in healthcare will be approached from an operations research perspective. Unique aspects of healthcare compared to other industries will be discussed. Real-world datasets will be provided to illustrate the complexity of applying standard operations research methods to healthcare. A background in operations research is required.
MIE1624H: Introduction to Data Science and Analytics
The objective of the course is to learn analytical models and overview quantitative algorithms for solving engineering and business problems. Data science or analytics is the process of deriving insights from data in order to make optimal decisions. It allows hundreds of companies and governments to save lives, increase profits and minimize resource usage. Considerable attention in the course is devoted to applications of computational and modeling algorithms to finance, risk management, marketing, health care, smart city projects, crime prevention, predictive maintenance, web and social media analytics, personal analytics, etc. We will show how various data science and analytics techniques such as basic statistics, regressions, uncertainty modeling, simulation and optimization modeling, data mining and machine learning, text analytics, artificial intelligence and visualizations can be implemented and applied using Python. Python and IBM Watson Analytics are modeling and visualization software used in this course. Practical aspects of computational models and case studies in Interactive Python are emphasized. See also: MIE1624H-Syllabus
MIE1626H: Data Science Methods and Statistical Learning
This course will equip the students with the fundamental skills and knowledge for: understanding the statistical foundation of data science and machine learning methods; approaching active and passive data as artifacts for scientific evaluation; combining, pre-processing, and cleaning data in practical data science projects; performing exploratory data analysis and uncovering patterns in data; analyzing data and making inference using methods from statistical learning; resampling data and evaluate the error of any computational estimate; using confidence intervals, analysis of variance, and hypothesis testing to explain data; implementing linear and nonlinear regression models for prediction and inference; designing and understanding tree-based models and support vector machines; detecting and avoiding misleading statistical figures, information visualization, and other forms of data presentation which lack a logical coherence. This is an intensive and high-demand course which requires active engagement and participation. See also: MIE1626H-Syllabus
MIE1628H: Cloud-Based Data Analytics (formerly Big Data Science)
This course covers Big Data fundamentals including an overview of Hadoop MapReduce and Spark. Covers Cloud fundamentals and Big Data Analytics on Cloud-based platforms including an introduction to a specific Cloud platform such as Microsoft Azure, Amazon Web Services, or Google Cloud Platform along with common practices for this platform. Covers Cloud technologies to store and process structured, unstructured and semi-structured data. Covers Cloud-based implementation of Real-time Analytics and Machine Learning.
MIE1630H: Reinforcement Learning for Research
This course is to provide fundamental concepts and mathematical frameworks for reinforcement learning. Specific topics include Markov decision processes, tabular reinforcement learning, policy gradient methods, and function approximation such as deep reinforcement learning. Optional topics are distributional reinforcement learning, model-based methods, off-line learning, inverse reinforcement learning and multi-agent reinforcement learning. The course is designed to allow research students to experience research on reinforcement learning from the perspective of methodological development or application of reinforcement learning to applications. See also: MIE1630H-Syllabus
MIE1653H: Integer Programming Applications (for M.Eng. students)
Formulation of integer programming problems and the characterization of optimization problems representable as integer and mixed-integer programs. The degree of difficulty of classes of integer programs and its relation to the structure of their feasible sets. Optimality conditions. Branchand-bound, cutting plane, and decomposition methods for obtaining solutions or approximating solutions.
MIE1666H: Machine Learning for Mathematical Optimization
Mathematical optimization algorithms are used to solve a wide variety of decision-making tasks. The design of optimization algorithms often requires substantial theoretical insights or algorithmic engineering, both of which are manual, tedious tasks. This course introduces automated machine learning approaches for improving optimization algorithms in the presence of a historical dataset or a generator of problem instances from a domain of interest. Topics include automated algorithm configuration, modeling iterative heuristics in the reinforcement learning framework, deep neural networks for modeling combinatorial optimization problems, guiding exact solvers with learned search strategies, learning-theoretic guarantees, and benchmarking/computational considerations. The focus will be on discrete optimization in the integer programming framework, both exact and heuristic. Knowledge of integer programming, algorithm design, machine learning, and programming are required, while knowledge of deep learning is helpful.
MIE1699H: Special Topics in Operations Research
This course will provide an introduction to System Dynamics (SD) modelling.
MIE1705H: Thermoplastics Polymer Processing
This course is designed to provide the background for an understanding of the wide field of polymer processing, and provide a strong foundation including fundamentals and applications of polymer processing. Topics include: fundamentals of polymers, extrusion, injection molding, die forming, mixing, and other common plastics processes such as fiber spinning, blow molding, rotational molding, coating, etc. See also: MIE1705H-Syllabus
MIE1706H: Manufacturing of Cellular and Microcellular Polymers
Manufacturing and design issues in foamed materials processing. Solution and diffusion of gas in polymers. Sorption experiments for determining the solubility and diffusivity. Plasticizing effect of gas in a polymer. Bubble nucleation theories. Processing strategies for the production of high nucleation density foams. Mathematical model of bubble growth. Processing strategies for the bubble growth control. Effect of melt strength on bubble coalescence. Continuous processing of microcellular foamed polymers.
MIE1707H: Structure-Property Relationships of Thermoplastic and Composite Foams
This course provides the structure to property relationships of thermoplastic and composite foams. The crystal morphology (crystallinity, crystal size, crystal kind, crystal number, etc.), the cellular morphology (cell density, cell size, void fraction, uniformity, open cell content, etc.), and the composite structures (the fiber/platelet kind, the fiber/platelet aspect ratio, fiber/platelet orientation, the interface of fiber/platelet and matrix, etc.) affect various properties of the final products such as the thermal conductivity, the electrical conductivity, the mechanical properties, of various thermoplastic and composite foams. The mechanical properties (tensile properties, flexural properties, impact strength, etc.), the thermal conductivity (polymer conduction, gas conduction and radiation), and the electric conductivity are described as a function of the aforementioned structural parameters. The effects of the nano particles (carbon nanotube (CNT), graphene nano platelets (GNP), nanofibrils, etc.) on the properties are also discussed. Nanofibril compositess and their processing, structure characterization and property testing are also intensely discussed.
MIE1708H: Collision Reconstruction
This course provides the participant with a comprehensive understanding of widely-accepted techniques of vehicular collision reconstruction based on physical and engineering principles. The course covers Energy, Impulse and Momentum fundamentals and how they are engaged to obtain valuable information from collisions, in order to answer important questions about culpability in various litigation arenas. Content is reinforced with real-world examples. A wide variety of collision types (passenger vehicle, motorcycle, cyclist, pedestrian, heavy truck) and modes (high speed, low speed, rollover, tire failure) are addressed in the context of various contributors to collisions, whether they be from the operator, vehicle, or the roadway environment. Specialized techniques for evaluation of the use, performance, and effectiveness of restraint systems, and the avoidability of collisions are also covered. The latest technologies for harvesting data from ‘black boxes’ are covered, and state of the art computer simulation techniques are incorporated into the teachings.
Continuum Mechanics is the study of the response of the matter on a macroscopic scale to different loading conditions, neglecting the structure of the matter on the smaller scale (i.e., molecular scale). It brings out the general principles common to all media and discusses the assumptions for developing constitutive equations of idealized materials (e.g., solid and fluid). The developed fundamentals can be applied to engineering problems such as elasticity, viscoelasticity, plasticity, linearly viscous fluid, etc.
MIE1710H: Sustainable Development (manufacturing) of Circular Materials for Healthy Climate
This course will explore advanced research and developments in manufacturing, engineering principles and design fundamentals for creating sustainable materials with circularity approach from renewable, after-market and waste resources minimizing resource depletions. Process transformation may include eco-thermal, photo-chemical, biochemical, and biological pathways that can result in materials circularity for product and process developments. This advancement of knowledge transformation will be exemplified by evaluating applications such as thinner, lighter and multi-functional, durable and/or disposable products for construction, building, electronics transportation and biomedical sectors with attributes such as facile deconstruction and re-integration in the industrial ecosystem. Graduate students will have the opportunity to learn advanced research methodology to tackle climate change by developing new concept, theory, and analytical validation that directly implies to manufacturing of biochemical, advanced polymers, energy, and energy materials. Biogenesis of renewables in climate mitigation, a circular approach by advancing biomolecular engineering and photo-chemical reactor design.
Engineering is the science of predictive modelling based on application of Physical Laws, and prototyping to verify designs. This applies to all fields. Good Engineering prevents Failure. The course centers on the Theory of Failure Analysis and how it directs engineering activity: design, research, quality systems, continuous improvement, innovation, new knowledge creation, systemic failure, and business management. See also: MIE1714H-Syllabus
MIE1715H: Life Cycle Engineering
This course introduces the fundamentals of both product and process engineering with an emphasis on life cycle models. A mixture of practical and theoretical topics, methodologies, principles, and techniques are covered such as Life Cycle Analysis, Design For Assembly (DFA), Design For Manufacturing (DFM), Design For Environment (DFE), etc. Students develop an understanding of the performance, cost, quality and environment implications of both product design and manufacture and become capable of translating these into engineering “cradle-tograve” responsibility requirements, goals, and specifications in order to maximize the values of products and the effectiveness of supply chain management while containing the costs to manufacturer, the user, and the society.
MIE1718H: Computer Integrated Manufacturing
The course will focus on the integration of facilities (machine tools, robotics) and the automation protocols required in the implementation of computer integrated manufacturing. Specific concepts addressed include flexible manufacturing systems (FMS); interfaces between computer aided design and computer aided manufacturing systems.
MIE1720H: Creativity in Conceptual Design
This course will present established methods that aim to enhance creativity during conceptual design, along with more recent research relevant to creativity and conceptual design. Students will select current creativity research from multiple disciplines, identify limitations of reported results, determine and perform further research that can be conducted within a course, and report results.
The goal of the course is to introduce students to principles of reliability from a practical point of view. The course covers principles of quality, principles of reliability, reliability of systems, failure rate data and models, quality and reliability in design and manufacturing, and reliability and availability in maintenance including cost models. Some other topics could be covered, depending on timing. A moderate knowledge of probability and statistics is a requirement.
MIE1723H: Engineering Asset Management
This course is concerned with the determination of optimal maintenance and replacement practices for components and capital equipment. Short-term deterministic replacement; short-term probabilistic replacement. Inspection optimization of assets with hidden failures or soft failures and equipment under continuous operation. Sustainable asset management along with the application of sustainable asset management for utilization, purchase, and disposal of a fleet of assets. Identification of an items failure distribution and reliability function using failure data. **Anti-requisite MIE469
MIE1724H: Additive Manufacturing in Engineering Applications
The aim of this course is to help students understand the concepts of AM and their role in design and fabrication of complex structures. Also, the course will introduce state-of-the-art approaches to “3D printing”, which is the more common term to the more professionally utilized “Additive Manufacturing” (AM) term. Students will be able to follow a design paradigm through careful analysis of complex structures and complete an AM process flow through CAD conceptualization, conversion to STL files, transfer to AM machine, machine conditioning, removal/clean up and post-processing. Also, design for AM (DfAM) is introduced to optimize product fabrication, controlled by part orientation, support design, hollowing out components, constraining features/undercuts, interlock structures and multi-material compatibilities. Case studies will be introduced with AM for investment casting and part fabrication without a conventional CAD file, with focus on medical modeling and reverse engineering data. In recent years, new approaches to AM solutions have produced a large range of controllability and size ranges. Examples of emerging technologies are Multi-Jet Printing (MJP), AM+CNC, two-photon lithography (for nanoscale AM) and Volumetric 3D Printing. Ultimately, students will be able to apply and scale models from the most focused technical perspective to eventual AM fabrication of complex lightweight designs… and never rely on randomized approaches to AM.
MIE1725H: Soft Materials and Machines
MIE1727H: Statistical Methods of Quality Assurance
Awareness of the importance of quality has increased dramatically. Understanding and improving quality is a key factor leading to company’s success and its enhanced competitive position. The course focuses on the following topics in Quality Assurance: Introduction to quality engineering, TQM, costs of quality, quality and productivity, statistical process control, process capability analysis and supplier-producer relations, quality standards and certification, six sigma philosophy and methodology, quality/process improvement using designed experiments, and an overview of acceptance sampling. See also: MIE1727H-Syllabus
MIE1740H: Smart Materials and Structures
Smart materials are a novel class of materials characterized by new and unique properties that can be altered in response to environmental stimuli. They can be used in a wide range of applications since they can exceed the current abilities of traditional materials especially in environments where conditions are constantly changing. This course is designed to provide an integrated and complete knowledge to smart materials and structures, which makes a strong foundation for further studies and research on these materials. Topics include: design, manufacturing, properties of smart materials; Electrical, thermal, magnetic and optical active smart materials systems; Examples are piezoelectrics, ferroelectrics, electrostrictive materials, shape memory materials, magnetostrictive materials; self healing and optical activated materials; Design, and optimization of smart materials based devices and their applications.
MIE1744H: Nanomechanics of Materials
Materials can exhibit dramatically altered mechanical properties and physical mechanisms when they have characteristic dimensions that are confined to small length-scales of typically below ~ 100 nm. These size-scale effects in mechanics result from the enhanced role of surfaces and interfaces, defects and material variations, and quantum effects. Nanostructured materials which exhibit these size-scale effects often have extraordinary mechanical properties as compared to their macroscopic counterparts. This course is designed to provide an introduction to nanomechanics and size-scale mechanical phenomena exhibited by nanostructured materials, and provide a platform for future advanced studies in the areas of computational/experimental nanomechanics and nanostructured materials design and application. Topics include: an introduction to nanomechanics; atomic/molecular structure of materials & nanomaterials synthesis; limitations of continuum mechanics, nanomechanical testing techniques (AFM, nanoindentation, in situ SEM/TEM); atomistic modeling techniques (DFT, MD, Course-grained MD); size-scale strength, plasticity, and fracture ; Hall-Petch strengthening, superplasticity; nanotribology, atomistic origins of friction, nanoscale wear; nano-bio-mechanics; mechanics of nanocomposites. See also: MIE1744H-Syllabus
One materials-related topic that is important for mechanical, civil engineers is the interactions between solids and liquids. Why do some materials absorb water when others do not? How does broccoli remain dry after washing it? How to non-stick pans work? Why is the build plate adhesion of 3D printers so important? What properties of the molten plastics are important for additive manufacturing? This course will discuss how liquids interact with solids, and how these interactions are affected by the chemical, physical, and mechanical properties of the solid, in addition to the viscosity, surface tension, and chemical structure of the liquid. The objective is for students to gain a deep understanding about how liquids and solids interact at interfaces. Examples will be drawn from all fields of engineering and the course is not tilted towards any one discipline in particular. See also: MIE1745H-Syllabus
MIE1804H: Finite Element Analysis in Engineering Design
Starting with the analysis of simple discrete systems, the essential ideas of building up the governing equations of the system from those of its constituent parts is illustrated. The techniques of deriving a discrete set of equations for continuous systems are then outlined; specifically the variational and weighed residual procedures are examined and illustrated through some simple examples. The course then concentrates on applications to structural mechanics of solids. Programming for finite elements is also covered and students are encouraged to design and develop FEM software. See also: MIE1804H-Syllabus
MIE1809H: Advanced Mechatronics
This course provides students with tools to design, model, analyze and control precision mechatronic systems. Specifically, the class provides techniques for the modeling of various system components into a unified approach and tools for the simulation of the performance of these systems. The class also lists techniques and issues that arise when interfacing various components in order to form complex mechatronic systems. The class presents the properties and characteristics of smart material based sensors and actuators with a focus on piezoceramics, its processing and its implementation into various sensors and actuator configurations.
MIE2002H: Readings in Industrial Engineering I
Students may take only one reading course for credit in a degree program, unless special authorization has been granted by the Graduate Studies Committee.
MIE2003H: Readings in Industrial Engineering II
Students may take only one reading course for credit in a degree program, unless special authorization has been granted by the Graduate Studies Committee.
MIE2004H: Readings in Mechanical Engineering I
Students may take only one reading course for credit in a degree program, unless special authorization has been granted by the Graduate Studies Committee.
MIE2005H: Readings in Mechanical Engineering II
Students may take only one reading course for credit in a degree program, unless special authorization has been granted by the Graduate Studies Committee.
