Calibration of projection-based reduced-order models for unsteady compressible flows
Victor Zucatti, William R. Wolf, Michel Bergmann

TL;DR
This paper evaluates calibration strategies for projection-based reduced-order models in unsteady compressible flows, demonstrating improved stability and accuracy through novel calibration and hyper-reduction techniques across different flow regimes.
Contribution
It introduces a new calibration strategy for LSPG ROMs and assesses hyper-reduction with MPE, comparing Galerkin and LSPG methods in complex unsteady flow scenarios.
Findings
Calibration enhances stability and long-term accuracy.
Hyper-reduction with MPE is effective for subsonic flows.
Galilean models outperform LSPG in capturing high-frequency flow features.
Abstract
An analysis of calibration for reduced-order models (ROMs) is presented in this work. The Galerkin and least-squares Petrov-Galerkin (LSPG) methods are tested on compressible flows involving a disparity of temporal scales. A novel calibration strategy is proposed for the LSPG method and two test cases are analyzed. The first consists of a subsonic airfoil flow where boundary layer instabilities are responsible for trailing-edge noise generation and the second comprises a supersonic airfoil flow with a transient period where a detached shock wave propagates upstream at the same time that shock-vortex interaction occurs at the trailing edge. Results show that calibration produces stable and long-time accurate for both cases. In order to reduce the computational costs of the LSPG models, an accelerated greedy missing point estimation (MPE) algorithm is employed for hyper-reduction. For the…
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