Machine learning for model uncertainties in turbulence models and Monte Carlo integral approximation
Abstract/Contents
- Abstract
- While computational fluid dynamics (CFD) is playing an ever-increasing role in the design process, physical experiments are still required for final verification. There is a demand for certification through simulation, but there is a gap in predictive quality. Reynolds-averaged Navier-Stokes flow simulations have known deficiencies, especially for high Reynolds number flows with turbulent transition and separation, and higher fidelity Large Eddy Simulations (LES) and Direct Numerical Simulations (DNS) are not generally affordable. Quantification and reduction of uncertainty in simulation results is necessary, and yet it is rare for error bounds to be returned by a simulation, and progress towards more accurate turbulent closures in RANS models seems to have stalled. Today, however, the community is better equipped than ever to address this challenge. The rise in data science has driven the creation of tools and techniques to analyze and synthesize massive data sets. Most importantly, the data needed for statistical inference is available; computational budgets allow for RANS calculations on a number of input conditions and design settings, LES advances to increasingly complex geometries, and DNS continues to expand its Reynolds-number range. This dissertation harnesses data-driven approaches to address issues of uncertainty in predictive tools. First, the dissertation explores creating accurate models from data by replicating the behavior of a known model. Computational data is collected from the Spalart-Allmaras turbulence model, a neural network algorithm is trained on this data, and the learned model is re-embedded within a CFD flow solver. The robustness and accuracy of this procedure is explored as influenced by loss function choice, feature selection, and training data. Next, the dissertation considers model uncertainty in low-fidelity models. High-fidelity data from DNS of combustion (using finite-rate chemistry) are used to augment the low-fidelity flamelet progress variable-based RANS approach (FPVA). Supervised learning approaches are used to construct two error models, one for the local inaccuracies in the model and a second addressing the spatial correlation of these errors. These uncertainty models are combined to estimate the uncertainty in the FPVA model. Finally, a methodology is presented for quantifying the effects of input uncertainty on an output variable of interest. This is done by constructing an approximate model of the system using available data samples, and then using this as a control variate to reduced the squared estimation error in the output. Results are presented which demonstrate improved accuracy for a wide range of problem dimensions, function types, and sampling types. Taken together, these approaches indicate the potential of data-driven techniques to identify and reduce uncertainties in complex flow simulations.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2015 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Tracey, Brendan D |
|---|---|
| Associated with | Stanford University, Department of Aeronautics and Astronautics. |
| Primary advisor | Alonso, Juan José, 1968- |
| Thesis advisor | Alonso, Juan José, 1968- |
| Thesis advisor | Duraisamy, Karthikeyan |
| Thesis advisor | Iaccarino, Gianluca |
| Thesis advisor | Kochenderfer, Mykel J, 1980- |
| Advisor | Duraisamy, Karthikeyan |
| Advisor | Iaccarino, Gianluca |
| Advisor | Kochenderfer, Mykel J, 1980- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Brendan D. Tracey. |
|---|---|
| Note | Submitted to the Department of Aeronautics and Astronautics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2015. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2015 by Brendan Tracey
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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