Master’s Thesis Presentation • Scientific Computation • A Scalable Method for Many Object Fluid-Structure Interaction Simulations

Thursday, December 16, 2021 1:00 pm - 1:00 pm EST (GMT -05:00)

Please note: This master’s thesis presentation will be given online.

Connor Tannahill, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Justin Wan

Fluid-Structure Interaction (FSI) Simulations are an important technology in many areas of research, including, but not limited to, Computer Graphics, Computational Physics and Engineering. This area is concerned with the realistic simulation of fluids, solids and their interaction. To accurately realize a simulation of an FSI scenario typically requires a large amount of computation and specialized numerical methods to ensure stability and accuracy.

In this thesis, we consider the development of a numerical method for dealing with two and three-dimensional FSI scenarios where a large number of deformable objects are immersed in a incompressible fluid. The fluid component of the model is solved using standard CFD approaches, while the solid models are computed using an efficient heuristic. Their interaction is coordinated through the use of a specialized algorithm based on the level set method to coordinate the fluid solver and the solid models, transfer the necessary information between these model components, as well as object-with-object collisions, using a novel collision handling algorithm for arbitrarily shaped deformable objects.

The method is described along with the motivations behind each model component in how they help us reach the goal of a scalable FSI object for this general scenario. Several test cases are presented to demonstrate the capability of the method in producing realistic FSI simulations. These experiments are then analysed to establish the scaling performance of our solver in terms of relevant performance metrics.


To join this master’s thesis presentation on Zoom, please go to https://uwaterloo.zoom.us/j/98868316309?pwd=djc1MHl6Z2plaXJHOEVpeTlRVTNLZz09.