Geometric sensitivity, wake dynamics, and machine learning turbulence modeling on a skewed bump
Abstract/Contents
- Abstract
- A flow is considered geometrically sensitive if a slight perturbation to the geometry results in a major change to the flow structure. This work examines the flow over a wall-mounted, skewed bump, which exhibits geometric sensitivity. The bump is three-dimensional and has elliptical cross-sections with axis ratio of 4/3, and the geometry is modified by placing the bump at different angles with respect to the freestream. A combined experimental and computational approach is used to study the bump flow. The distinguishing features are a large separation bubble and longitudinal vortex structures in the wake. The wake has a quasi-periodic shedding cycle that is examined with Spectral Proper Orthogonal Decomposition (SPOD) and conditional averaging. Experiments are done to examine how the flow changes when the bump surface is rough. Reynolds Averaged Navier-Stokes (RANS) simulations have poor accuracy in this flow, and a method is developed to improve RANS accuracy using machine learning.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Ching, David Sunghwa |
|---|---|
| Degree supervisor | Eaton, John K |
| Thesis advisor | Eaton, John K |
| Thesis advisor | Dabiri, John O. (John Oluseun) |
| Thesis advisor | Elkins, Christopher J |
| Degree committee member | Dabiri, John O. (John Oluseun) |
| Degree committee member | Elkins, Christopher J |
| Associated with | Stanford University, Department of Mechanical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | David S. Ching. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by David Sunghwa Ching
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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