# A data-based, reduced-order, dynamic estimator for reconstruction of non-linear flows exhibiting limit-cycle oscillations

## TL;DR

This paper develops a data-driven linear estimator to reconstruct non-linear flow fields with limit-cycle oscillations from minimal sensor data, using reduced-order modeling and system identification techniques.

## Contribution

It introduces a systematic approach combining POD and N4SID for flow reconstruction, analyzing sensor placement and measurement types for optimal performance.

## Key findings

- Robust flow reconstruction when measuring both velocity components.
- Sensor location critically affects reconstruction with single-component measurements.
- Performance is nearly independent of sensor location when both velocity components are measured.

## 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 airfoil at $Re=600$ and $16^{\deg}$ 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.