Dimensionality reduction of embedded boundary models for nonlinear fluid-structure interaction
Abstract/Contents
- Abstract
- Embedded (sometimes immersed) boundary methods (EBMs) for CFD and fluid-structure interaction (FSI) -- also known as immersed boundary methods, Cartesian methods, or fictitious domain methods -- are the most robust solution methods for flow problems past obstacles that undergo large motions, deformations, shape changes, and/or surface topology changes. They can also introduce a high degree of automation in the task of mesh generation and significant flexibility in the meshing of complex geometries. However, despite massive advancements in compute power, the application of CFD- or FSI-based EBMs to the simulation of many realistic engineering systems can still take days or weeks on many cores of a supercomputer, rendering the use of such models in many-query applications challenging, impractical or simply infeasible. The application of projection-based model order reduction (PMOR) to nonlinear CFD and FSI models has been shown in recent years to produce low-dimensional projection-based reduced order models (PROMs) which can be queried in real-time, without sacrificing the accuracy of the underlying high-fidelity, high-dimensional computational model. However, the application of PMOR to EBMs for CFD and FSI remains a challenge, primarily because EBMs dynamically partition the computational fluid domain into real and fictitious subdomains. Consequently, despite their myriad benefits, EBMs hamper the acceleration of the solution of computationally intensive problems by these types of physics-based surrogate models. This dissertation addresses several issues stemming from the real/fictitious partitioning of the computational fluid domain to pave the way for the PMOR of EBMs. Specifically, it motivates, presents, and illustrates a computational framework for constructing EBM-PROMs that incorporates state-of-the-art methodologies for mitigating computational bottlenecks associated with strong nonlinearities in the underlying high-dimensional model. This framework includes a weighted low-rank matrix factorization approach for computing suitable reduced-order bases; and adaptations of the energy-conserving sampling and weighting hyperreduction procedure as well as the piecewise-affine approximation method that account for the real/fictitious dynamic partitioning of the nodes of the embedding CFD mesh. It also employs an adaptive sampling approach for choosing an optimal set of parameter points on which to train a parametric EBM-PROM, based on a novel error indicator. Finally, the performance of the proposed computational framework for the PMOR of embedded boundary models is investigated for two representative, unsteady, nonlinear, turbulent flow problems that necessitate the use of an EBM; as well as two prototypical steady-state, shape-parametric studies of relevance to MDAO.
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 | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Youkilis, Noah Ben |
|---|---|
| Degree supervisor | Farhat, Charbel |
| Thesis advisor | Farhat, Charbel |
| Thesis advisor | Alonso, Juan |
| Thesis advisor | Kochenderfer, Mykel |
| Degree committee member | Alonso, Juan |
| Degree committee member | Kochenderfer, Mykel |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Aeronautics and Astronautics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Noah Youkilis. |
|---|---|
| Note | Submitted to the Department of Aeronautics and Astronautics. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/zm574wr0384 |
Access conditions
- Copyright
- © 2023 by Noah Ben Youkilis
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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