Enhancement of signal-to-noise ratio of schlieren visualization measurements in low-density wind tunnel tests using modal decomposition

Tsuyoshi Shigeta, Takayuki Nagata, Taku Nonomura, Keisuke Asai

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Abstract: Signal processing methods that remove noise due to atmospheric fluctuation and image sensors and extract fluid phenomena from schlieren images obtained in the low-density wind tunnel test were developed together with the highly sensitive schlieren measurement setup. Time-series schlieren images of the flow around a triangular airfoil were analyzed, and the effectiveness of noise reduction methods using the randomized singular value decomposition and band-pass filtering using the fast Fourier transform (FFT) and the inverse FFT were investigated. The proposed method succeeded in removing noise by taking advantage of the frequency difference between the noise and fluid phenomena, and the fluid phenomena around the airfoil were clearly visualized at a Reynolds number of 3000 and a Mach number of 0.15. Graphical abstract: [Figure not available: see fulltext.]

Original languageEnglish
Pages (from-to)697-712
Number of pages16
JournalJournal of Visualization
Volume25
Issue number4
DOIs
Publication statusPublished - 2022 Aug

Keywords

  • Digital image processing
  • Flow visualization
  • Low Reynolds number

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