Committee Chair
Hilbert, C. Bruce
Committee Member
Karman, Steve L., Jr.; Sreenivas, Kidambi; Webster, Robert
College
College of Engineering and Computer Science
Publisher
University of Tennessee at Chattanooga
Place of Publication
Chattanooga (Tenn.)
Abstract
Two algorithms are presented which together generate well-spaced point distributions applied to curves, surfaces, and the volume of a computational domain. The first is a force equilibrium simulation which applies a simplified direct solution of the equations of motion at each node. Inter-nodal pair forces are computed based on the desired spacing between nodes and summed to provide a net force on each node. The nodes are allowed to travel a restricted distance with each locally distinct time step. The motion of the point distribution is stabilized by applying friction to each node from its neighboring nodes as well as globally restricting the time step size over the series of iterations. Second, an algorithm for node population adaptation is presented which deletes nodes or inserts new nodes depending on how well the local concentration of nodes matches a desired local spacing prescription, or spacing field. Experimental results are provided which demonstrate the ability of these algorithms to generate smooth distributions of points matching various spacing field function definitions.
Degree
Ph. D.; A dissertation submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Doctor of Philosophy.
Date
12-2017
Subject
Numerical analysis; Differential equations -- Numerical solutions
Document Type
Doctoral dissertations
DCMI Type
Text
Extent
xviii, 124 leaves
Language
English
Rights
https://rightsstatements.org/page/InC/1.0/?language=en
License
http://creativecommons.org/licenses/by-nc-nd/3.0/
Recommended Citation
Fackler, Philip W., "A physics-based adaptive point distribution method for computational domain discretization" (2017). Masters Theses and Doctoral Dissertations.
https://scholar.utc.edu/theses/529
Department
Dept. of Computational Engineering