Abstract
The present work introduces an Adaptive Reduced Order technique, that aims at reconstructing the flow field around lifting bodies by adaptively selecting a Reduced Order Method that provides the highest accuracy among the existing model reduction approaches. The proposed adaptive approach automatically selects the most accurate order reduction methods between the classical snapshot Proper Orthogonal Decomposition and the Isomap Manifold Learning. The choice of the best method is performed by estimating the reconstruction error as different reduced order solutions are used. Problems of relevance to the aerodynamics field are considered to evaluate the performances of the proposed approach such as the parametric study of airfoils and wings aerodynamic performance.
Original language | English |
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Title of host publication | AIAA Scitech 2021 Forum |
Place of Publication | Reston, VA |
Number of pages | 16 |
DOIs | |
Publication status | Published - 21 Jan 2021 |
Event | AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021 - Virtual, Online Duration: 11 Jan 2021 → 15 Jan 2021 |
Publication series
Name | AIAA Scitech 2021 Forum |
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Conference
Conference | AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021 |
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City | Virtual, Online |
Period | 11/01/21 → 15/01/21 |
Funding
The authors wish to acknowledge the support the EPSRC funded ARCHIE-WeSt (www.archie-west.ac.uk) and Cirrus (www.cirrus.ac.uk) High Performance Computer Facilities.
Keywords
- aerodynamic loads
- aerodynamic performance
- lifting body
- CFD simulation
- high aspect ratio
- flow conditions
- Navier Stokes equations
- Reynolds Averaged Navier Stokes (RANS) equations
- supercritical airflows
- singular value decomposition