Degree Type

Thesis

Date of Award

2021

Degree Name

Master of Science

Department

Mechanical Engineering

Major

Mechanical Engineering

First Advisor

Adarsh Krishnamurthy

Abstract

Recently, immersogeometric analysis (IMGA) was successfully applied to simulate compressibleand incompressible fluid flows over CAD models represented using triangles, non-uniform rational B-splines (NURBS), and analytic surfaces. However, performing flow analysis over real-life objects requires CAD model reconstruction, which can be as tedious as the mesh generation process itself. In a point cloud geometry, the object is represented as an unstructured collection of points. Point cloud representation has proliferated as a form of acquiring geometric information in digital format using LIDAR scanners, optical scanners, or other passive methods like multi-view stereo images. In this work, we perform IMGA directly on point cloud representation of geometry, thus enabling flow analysis over as-manufactured components. Due to the absence of topological information in a point cloud, there are no guarantees that the geometric representation is watertight, which makes performing inside-outside tests on the background mesh challenging. To address this, we first develop methods for generating topological properties on a point cloud and compute inside- outside information directly from the resulting topology. Then, validations are performed for these geometric estimation methods, as well as for point cloud IMGA (PC-IMGA) incompressible flow results. We finally demonstrate additional features and scalability of our approach by performing PC-IMGA on large construction machinery represented by a dense cloud of more than 12 million points, along with our other PC-IMGA developments, including weak thermal boundary conditions and transient boundaries.

DOI

https://doi.org/10.31274/etd-20210609-86

Copyright Owner

Joel Khristy

Language

en

File Format

application/pdf

File Size

94 pages

Available for download on Saturday, June 04, 2022

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