Bulletin of the American Physical Society
2023 APS March Meeting
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session UU03: V: Fluids III
5:00 AM–7:00 AM,
Wednesday, March 22, 2023
Room: Virtual Room 3
Sponsoring
Unit:
DFD
Chair: Brad Rubin, American Physical Society
Abstract: UU03.00004 : Uncertainty Quantification Integrated with the Reduced-Dimensional Modeling of Supersonic Shear Flows
5:36 AM–5:48 AM
Presenter:
Kevin Hartman
(University of Tennessee Space Institute)
Authors:
Kevin Hartman
(University of Tennessee Space Institute)
Ragini Acharya
(University of Tennessee Space Institute)
The authors present a novel approach where reduced-dimensional modeling leveraging K-means clustering [3] and uncertainty quantification has been integrated with reduced-order modeling. The approach is demonstrated by modeling the supersonic flow over a double-fin-on-cylinder test article [4] where shock-boundary layer interactions are dominant. Heterogeneous experimental data for 20-degree and 5-degree fins was used as the so-called training data to develop a reduced-order model of the surface pressure distribution for a fin with a 10-degree incident angle. Further, Reynolds averaged Navier Stokes (RANS) computational fluid dynamics (CFD) calculations are conducted for the same 10-degree configuration, and the results are leveraged as additional training data. The experimental data obtained from various facilities can have different dimensionality and CFD data can have different dimensionality depending upon the grid density and numerical methods employed. If the dimensionality of each dataset is reduced to the same dimensionality, all data can be fused and the resulting large dataset can be used to build the reduced-order model. The research challenges in performing this dimensionality reduction are (1) maintaining an acceptably accurate representation of the relevant physical interactions and (2) quantifying both the contribution of the error and imprecision in dimensionality reduction and uncertainty inherent in the training data to the uncertainty of a reduced-order model’s predictions. This research will show the results from dimensionality reduction and reduced-order modeling, and implement existing uncertainty quantification (UQ) methods that address the above research challenges.
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