Sustainable Energy Engineering
The Master of Applied Science (MASc) in Sustainable Energy Engineering (SEE), offered through the Faculty of Applied Sciences, is a research-intensive program that has a primary emphasis on the MASc thesis. The program aims to offer a unique ecosystem for advanced research in sustainable energy engineering. Through training in formal coursework and hands-on research, SEE graduates will be capable of working with integrity to invent, improve, design and deploy sustainable clean energy technologies addressing the clean energy needs for now and the future. Candidates will develop a strong aptitude for research and exceptional quantitative, analytical, and design skills in areas such as sustainable harvesting, conversion, storage, distribution, utilization, transition, and management of energy and environmental resources.
Admission is competitive. Applicants must satisfy the University admission requirements as stated in Graduate General Regulation 1.3 in the SFU Calendar, and have the following:
- An undergraduate (bachelor's) degree in a related field;
- Submitted evidence of capability to undertake substantial original research;
- Identified a faculty member as a supervisor.
This program consists of course work (12 units) and a thesis (18 units) for a minimum of 30 units. Students who lack the necessary background knowledge may, at the discretion of the supervisor or the supervisory committee, be asked to complete additional courses beyond the program requirements in order to broaden the students' preparation for undertaking thesis work.
Students must complete
Presentation and discussion of research topics and progress in seminar and publication formats. MASc students must enroll in SEE 896 during every term during which they are registered, until all program requirements have been met.
Fr 1:30 PM – 4:30 PM
REMOTE LEARNING, Burnaby
and three of (with a minimum of two SEE courses)
State-space analysis of finite dimensional continuous and discrete time linear systems. Linear vector spaces, linear operators, normed linear spaces, and inner product spaces. Fundamentals of matrix algebra; generalized inverses, solution of Ax=y and AXB=Y, least square and recursive least square estimation, induced norm and matrix measures, functions of a square matrix, Cayley-Hamilton and Sylvester's theorems, Singular Value Decomposition (SVD) with applications. Analytical representation of linear systems, state-space formulation, solution of the state equation and determination of the system's response. Controllability, observability, duality, canonical forms, and minimal realization concepts. Stability analysis and the Lyapunov's method. Prerequisite: Graduate standing.
The application of theories in probability, random variables and stochastic processes in the analysis and modelling of engineering systems. Topics include: a review of probability and random variables; random deviate generation; convergence of random sequences; random processes; auto correlation and power spectral-density; linear systems with stochastic inputs; mean-square calculus; AR and ARMA models; Markov chains; elementary queuing theory; an introduction to estimation theory. Areas of application include digital communications, speech and image processing, control, radar and Monte Carlo simulations. Prerequisite: Graduate standing.
Mo, We 5:00 PM – 6:20 PM
REMOTE LEARNING, Burnaby
Processing techniques for continuous and discrete signals with initially unknown or time-varying characteristics. Parameter estimation; Bayes, MAP, maximum likelihood, least squares the Cramer-Rao bound. Linear estimation, prediction, power spectrum estimation, lattice filters. Adaptive filtering by LMS and recursive least squares. Kalman filtering. Eigenmethods for spectral estimation. Implementation issues and numerical methods of computation are considered throughout. Prerequisite: ENSC 802 and 429 or their equivalents.
This course covers the techniques needed to understand and analyse modern communications networks. The main topics are as follow: practical techniques for the design and performance analysis of data communication networks; performance analysis of error control, flow and congestion control, and routing; networks of queues using stochastic and mean value analysis; polling and random access LANs and MANs; wireless networks; broadband integrated services digital networks and asynchronous transfer mode; optical networks. Prerequisite: ENSC 802 or permission of instructor.
Techniques needed to understand and analyze modern data communications networks. Basic architecture of packet networks and their network elements (switches, routers, bridges), and the protocols used to enable transmission of packets through the network. Techniques for collection, characterization, and modeling of traffic in packet networks. Aspects of traffic management, such as call admission control and congestion control algorithms in packet networks and the influence of traffic on network performance. Prerequisite: ENSC 427 or permission of the instructor.
Microelectronic transducer principles, classification, fabrication and application areas. Silicon micromachining and its application to integrated microelectronic sensors and actuators. CMOS compatible micromachining, static, dynamic and kinematic microactuator fabrication. Integrated transducer system design and applications. Students will be required to complete a micromachining project in the microfabrication lab at ENSC. Prerequisite: ENSC 475 and ENSC 495 or permission of instructor.
Scientific and engineering principles of fuel cell systems, including fundamental electrochemistry, applied thermodynamics, and transport phenomena. Types of fuel cells: low temperature and high temperature fuel cell systems and applications. Students are required to complete a project.
Overview of manufacturing systems: industrial robotics, numerical control and metal cutting, manufacturing system components and definitions, material handling systems, production lines, assembly systems, robotic cell design, cellular manufacturing, flexible manufacturing systems, quality control, and manufacturing support systems. Students are required to complete a project.
Advanced course on conduction heat and mass transfer. Fundamental elements of heat conduction. Laplace's equation and its applications. Analysis and modelling of engineering systems involving conduction heat transfer. Experimental methods related to conductive heat transfer. Introduction to cooling systems commonly used in microelectronics industry. Recommended: MSE 223 and MSE 321 or their equivalents.
Advanced course on convection heat and mass transfer. Fundamental elements of fluid flow and heat transfer using conservation principles. Analysis and modelling of engineering systems involving convective heat transfer. Experimental methods related to convective heat transfer. Heat/mass transfer and cooling/heating systems commonly used in energy management systems such as microelectronics industry, HVAC systems, fuel cell technologies, and automotive industry. Recommended: MSE 223 and MSE 321 or their equivalents.
Modern engineering materials design for energy system applications. Predictive modelling and design implications applied to energy systems. Advanced theoretical and experimental investigations will be discussed to understand the methodologies for design of materials and machinery to be applied to the energy conversion. Corequisite: SEE 896 or SEE 897. Recommended: SEE 222.
Water usage and global water shortages; principles of membrane separation including microfiltration, ultrafiltration, nanofiltration and reverse osmosis; physico-chemical criteria for separations and membrane materials; basic mass transport in mixed solute systems; polarization and fouling; prediction of membrane performance; operational issues, limitations, energy requirements and system configurations. Corequisite: SEE 896 or SEE 897. Recommended: SEE 224 and SEE 225.
and one three unit graduate elective course in consultation with the supervisor
and a thesis
* Students must enroll in this course every term.
Students are expected to complete the program requirements within six terms.
Academic Requirements within the Graduate General Regulations
All graduate students must satisfy the academic requirements that are specified in the Graduate General Regulations, as well as the specific requirements for the program in which they are enrolled.