Postdoctoral fellow: Dr. Xiaodong Wang, Institute for Aerospace Studies, University of Toronto
Lead faculty member: Dr. David Zingg, Institute for Aerospace Studies, University of Toronto
Modern engineering designs require fast and high credible scientific computations which usually run in a parallel way. The proposed research focuses on the development of the parallel preconditioning technology used in large scale scientific computations. A multi-level recursive strategy is developed to improve the parallel computing performance when a large number of processors (up to at least 5000) are used. An existing Newton-Krylov flow solver will be improved by coupling with this multi-level preconditioner.
Advanced Mathematical Modeling and Parallel Simulation Algorithms for Analysis and Design of Electrical Power Systems and Smart Grid Technologies
Postdoctoral fellow: Dr. Natalie Nakhla, Electronics, Carleton University
Lead faculty member: Dr. Q. J. Zhang, Electronics, Carleton University
With today’s rapidly increasing energy demands and the emergence of smart grids and renewable energy resources, the current energy and power technologies need to be advanced to keep up with these changes. Simulation and modeling plays a vital role in understanding, designing and planning of electrical power systems. The proposed research aims at developing a new generation of advanced mathematical models and simulation tools for electrical power systems and smart grids.
Dr. François Anctil, Université Laval
The goal of this project is to evaluate if mesoscale (35 km) meteorological ensemble forecasts coupled to a short-range hydrological forecasting system can lead to improved forecasts, and thus help maximize hydropower production and minimize flood risks. Positive results would pave the way for a full project which would aim to design an efficient short-range hydrological ensemble forecasting system adapted to the climate and hydrology of the Great-Lakes and Saint Lawrence River basin.