Pengdi, Zhang
(2021)
Combining Low-dimensional Models with High-fidelity Data: A Multi-fidelity Approach to Transient Heat Transfer Problems.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
We present a generic multi-fidelity approach for combining constructing multi-fidelity surrogate models for transient heat transfer applications. We apply our developed methodology to build various surrogate models for mixing the temperature of two different fluids. This is a classical heat transfer problem with numerous applications in diverse industries and it is considered in this work as a template problem for our methodology. In the presented framework, data from various levels of fidelity can be combined in a principled manner. More broadly, our target applications are problems where relying on high-fidelity data alone is not sufficient to build satisfactory surrogate models due to the high expense often associated with high-fidelity data acquisition. On the other hand, it may be possible to build reduced-order models that can be sampled at a high rate with insignificant computational cost. However, the ROM may be inaccurate due to physics deficiency of the full-dimensional model as well as the reduction errors. To this end, we utilize reduced-order models of heat transfer mixing, i.e. low fidelity model, and high-fidelity measurements, which are obtained by performing direct numerical simulations. We will combine these two data sources using Gaussian process regression (GPR) and auto-regressive stochastic strategy. GPR is a non-parametric Bayesian regression technique that has a fully probabilistic workflow, in which the prediction uncertainties can be quantified in a principled manner. In the following research, we will successively verify the accuracy and computational effectiveness of multi-fidelity results for different quantities of parameters and the different sizes of training data.
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Details
| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
3 September 2021 |
| Date Type: |
Publication |
| Defense Date: |
5 April 2021 |
| Approval Date: |
3 September 2021 |
| Submission Date: |
4 May 2021 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
58 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Mechanical Engineering and Materials Science |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Multi-fidelity Gaussian process regression; Spectral/hp element method; Proper orthogonal decomposition. |
| Date Deposited: |
03 Sep 2021 18:50 |
| Last Modified: |
03 Sep 2021 18:50 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/40947 |
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