Data-Driven Approach for Noise Reduction in Pressure-Sensitive Paint Data Based on Modal Expansion and Time-Series Data at Optimally Placed Points

TL;DR

This paper introduces a novel noise reduction technique for unsteady pressure-sensitive paint data using modal expansion with POD modes and optimal sensor placement, achieving high accuracy without additional pressure tap data.

Contribution

The study presents a self-contained noise reduction method based on POD and sensor optimization, eliminating the need for external pressure measurements.

Findings

Reconstructed pressure data closely matches independent measurements.

Method effectively reduces noise in unsteady PSP data.

Demonstrated on flow behind a square cylinder with successful results.

Abstract

We propose a noise reduction method for unsteady pressure-sensitive paint (PSP) data based on modal expansion, the coefficients of which are determined from time-series data at optimally placed points. In this study, the proper orthogonal decomposition (POD) mode calculated from the time-series PSP data is used as a modal basis. Based on the POD modes, the points that effectively represent the features of the pressure distribution are optimally placed by the sensor optimization technique. Then, the time-dependent coefficient vector of the POD modes is determined by minimizing the difference between the time-series pressure data and the reconstructed pressure at the optimal points. Here, the coefficient vector is assumed to be a sparse vector. The advantage of the proposed method is a self-contained method, while existing methods use other data such as pressure tap data for the reduction of the noise. As a demonstration, we applied the proposed method to the PSP data measuring the K\'arm\'an vortex street behind a square cylinder. The reconstructed pressure data is agreed very well with the pressures independently measured by pressure transducers.