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Applied mathematics

Project Leader(s): 

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.

Project Leader(s): 

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.

Project Leader(s): 

Dr. François Anctil, Université Laval

Project team: 
Dr. Anne-Catherine Favre, Université Laval
Dr. Vincent Fortin, Environment Canada
Dr. Christian Genes, Université Laval
Dr. Barbara Lence, University of British Columbia
Dr. Peter Yau, McGill University
Funding period: 
October 1, 2021 – March 31, 2021

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.