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Optimal multistage relaxation with automatic parameter selection

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thesis
posted on 23.05.2021, 17:42 by Alvin Wong
This research developed a numerical method that solves complicated fluid flow problems without requiring end-user expertise with the solver. This method is capable of obtaining a spatially accurate solution in the same time or better as a skilled user with a conventional solver. An explicit preconditioned multigrid solver was used in this research with a multistage relaxation method. The prosposed method utilizies a database with optimized relaxation method parameters for different local flow and mesh conditions. The parameters are optimized for the relaxation such that the error modes in a complex Fourier series expansion of the residual can be quickly reduced. The convergence time and iteration count of this method was compared against the same solver using default input values, as well as a pre-optimized solver, to simulate a skilled user for various geometries. Improvements in both comparisons were demonstrated.

History

Language

eng

Degree

Master of Applied Science

Program

Mechanical and Industrial Engineering

Granting Institution

Ryerson University

LAC Thesis Type

Thesis

Thesis Advisor

Jason V. Lassaline