| Author(s): |
Marcondes, Rebeca
;
Ricciardi, Tulio
;
Wolf, William
Total Authors: 3
|
| Document type: | Journal article |
| Source: | AIAA SCITECH 2021 FORUM; v. N/A, p. 15-pg., 2021-01-01. |
| Abstract | |
Proper orthogonal decomposition, POD, has been widely used in the community of fluid dynamics. POD finds application in analysis of coherent flow structures, data compression and construction of reduced-order models, ROMs. However, in the latter case, ROMs are typically unstable for problems that present strong non-linearities, such as shock waves and contact surfaces, or a broad range of temporal and spatial scales, in turbulent flows. The Kernel Proper Orthogonal Decomposition KPOD is a promising method for overcoming the previous issues since it maps the non-linear data into a higher dimension, known as feature space, using kernel functions. One expects that non-linear features are incorporated into the KPOD basis such that fewer modes are required for a better approximation of the data. In this work, we employ both the POD and KPOD for the reconstruction of non-linear solutions from a modified Shu-Osher shock tube problem and the Ginzburg-Landau equation. A Radial Basis Function Neural Network is applied for the reconstructions and results show that the KPOD is a promising technique compared to the standard POD. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 13/08293-7 - CCES - Center for Computational Engineering and Sciences |
| Grantee: | Munir Salomao Skaf |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |