Projection-based model order reduction and hyperreduction of turbulent flow models
Abstract/Contents
- Abstract
- Despite significant advances in simulation-based engineering science in recent decades, time-critical applications struggle to take advantage of high-fidelity, partial differential equation-based computer simulation. This is due to the large processing time and storage requirements associated with large-scale computational models. Projection-based model order reduction (PMOR) methods offer the ability to dramatically reduce this computational cost by generating compact, low-dimensional models for which solutions can be obtained in near real-time while still retaining the accuracy of an associated high-fidelity, high-dimensional model for the time and parameter domain of interest. PMOR is thus an essential technology for the application of model-based control, probabilistic analysis, or design optimization to problems involving increasingly complex engineered systems and physical phenomena. Unfortunately, nonlinear problems, in particular turbulent computational fluid dynamics (CFD) applications, continue to present a number of challenges for constructing stable, accurate, and computationally efficient projection-based reduced order models (PROMs). This thesis addresses some of these challenges, demonstrating PROMs for a number of large-scale, unsteady, turbulent flow applications which showcase the potential of PMOR for nonlinear CFD models with multiscale physics. These results leverage advancements in hyperreduction, a technique for the treatment of nonlinearities in the underlying computational model, to obtain CPU time and wall-clock time speedup factors of several orders of magnitude. This work also presents an investigation of the role of projection in the numerical stability of PMOR for convection-dominated flow problems, ultimately disproving an often-stated claim in the literature with the support of several numerical examples
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 | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Grimberg, Sebastian Johannes |
|---|---|
| Degree supervisor | Farhat, Charbel |
| Thesis advisor | Farhat, Charbel |
| Thesis advisor | Alonso, Juan José, 1968- |
| Thesis advisor | Lele, Sanjiva K. (Sanjiva Keshava), 1958- |
| Degree committee member | Alonso, Juan José, 1968- |
| Degree committee member | Lele, Sanjiva K. (Sanjiva Keshava), 1958- |
| Associated with | Stanford University, Department of Aeronautics & Astronautics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Sebastian J. Grimberg |
|---|---|
| Note | Submitted to the Department of Aeronautics & Astronautics |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Sebastian Johannes Grimberg
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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