The research project focuses on the development of the parallel preconditioning technology used in large scale scientific computations.
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. The computational time of simulations with large size grid is expected to be reduced greatly. The coupled flow solver can be widely used for aerodynamic analysis and designs in aviation companies. For instance, the prediction of the lift and drag of an airplane, the performance optimization of an engine, the noise control of high speed trains, and so on.