Data-driven approach for noise reduction in pressure-sensitive paint data based on modal expansion and time-series data at optimally placed points

Tomoki Inoue, Yu Matsuda, Tsubasa Ikami, Taku Nonomura, Yasuhiro Egami, Hiroki Nagai

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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 Kármán vortex street behind a square cylinder. The reconstructed pressure data agreed very well with the pressures independently measured by pressure transducers. This modal-based approach will be applicable not only to PSP data but other types of experimental data.

Original languageEnglish
Article number077105
JournalPhysics of Fluids
Volume33
Issue number7
DOIs
Publication statusPublished - 2021 Jul 1

ASJC Scopus subject areas

  • Computational Mechanics
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes

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