A data-based, reduced-order, dynamic estimator for reconstruction of non-linear flows exhibiting limit-cycle oscillations
File(s)
Accepted version
Author(s)
Guzman Inigo, Juan
Sodar, Markus
Papadakis, Georgios
Type
Journal Article
Abstract
We apply a data-based, linear dynamic estimator to reconstruct the velocity field from measurements at a single sensor point in the wake of an aerofoil. In particular, we consider a NACA0012
aerofoil at Re = 600 and 16◦ angle of attack. Under these conditions, the flow exhibits a vortex
shedding limit cycle. A reduced order model (ROM) of the flow field is extracted using proper orthogonal decomposition (POD). Subsequently, a subspace system identification algorithm (N4SID)
is applied to extract directly the estimator matrices from the reduced output of the system (the
POD coefficients). We explore systematically the effect of the number of states of the estimator,
the sensor location, the type of sensor measurements (one or both velocity components), and the
number of POD modes to be recovered. When the signal of a single velocity component (in the
stream wise or cross stream directions) is measured, the reconstruction of the first two dominant
POD modes strongly depends on the sensor location. We explore this behaviour and provide a
physical explanation based on the non-linear mode interaction and the spatial distribution of the
modes. When however, both components are measured, the performance is very robust, and is
almost independent of the sensor location when the optimal number of estimator states is used.
Reconstruction of the less energetic modes is more difficult, but still possible. Finally, we assess
the robustness of the estimator at off-design conditions, at Re = 550 and 650.`
aerofoil at Re = 600 and 16◦ angle of attack. Under these conditions, the flow exhibits a vortex
shedding limit cycle. A reduced order model (ROM) of the flow field is extracted using proper orthogonal decomposition (POD). Subsequently, a subspace system identification algorithm (N4SID)
is applied to extract directly the estimator matrices from the reduced output of the system (the
POD coefficients). We explore systematically the effect of the number of states of the estimator,
the sensor location, the type of sensor measurements (one or both velocity components), and the
number of POD modes to be recovered. When the signal of a single velocity component (in the
stream wise or cross stream directions) is measured, the reconstruction of the first two dominant
POD modes strongly depends on the sensor location. We explore this behaviour and provide a
physical explanation based on the non-linear mode interaction and the spatial distribution of the
modes. When however, both components are measured, the performance is very robust, and is
almost independent of the sensor location when the optimal number of estimator states is used.
Reconstruction of the less energetic modes is more difficult, but still possible. Finally, we assess
the robustness of the estimator at off-design conditions, at Re = 550 and 650.`
Date Issued
2019-11-21
Date Acceptance
2019-10-29
Citation
Physical Review Fluids, 2019, 4 (11)
ISSN
2469-990X
Publisher
American Physical Society
Journal / Book Title
Physical Review Fluids
Volume
4
Issue
11
Copyright Statement
©2019 American Physical Society.
Subjects
Science & Technology
Physical Sciences
Physics, Fluids & Plasmas
Physics
STOCHASTIC ESTIMATION
SENSOR PLACEMENT
IDENTIFICATION
REDUCTION
physics.flu-dyn
physics.flu-dyn
0102 Applied Mathematics
0203 Classical Physics
0913 Mechanical Engineering
Publication Status
Published
Article Number
ARTN 114703