DMD-based Superresolution Measurement of a Supersonic Jet using Dual Planar PIV and Acoustic Data

Yuta Ozawa, Hiroki Nishikori, Takayuki Nagata, Taku Nonomura, Keisuke Asai, Tim Colonius

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The present study proposes a framework of the superresolution measurement based on the dynamic mode decomposition (DMD) with the Kalman filter and Rauch–Tung–Striebel smoother. The dual-planar particle image velocimetry (PIV) systems were constructed to acquire the paired velocity fields of a Mach 1.1 supersonic jet. The acoustic measurement was simultaneously performed, and the velocity and acoustic data are used for the superresolution. Although the dual PIV system measures the basic characteristics of the velocity fields, all the DMD modes calculated by the exact DMD are decay modes due to the measurement noise. The superresolved velocity field shows smooth convection of the large-scale structures at the downstream side. Therefore, the proposed method is effective to reconstruct the entire flow fluctuation because the DMD modes express the linear dynamical system of the velocity fields.

Original languageEnglish
Title of host publication28th AIAA/CEAS Aeroacoustics Conference, 2022
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624106644
DOIs
Publication statusPublished - 2022
Event28th AIAA/CEAS Aeroacoustics Conference, 2022 - Southampton, United Kingdom
Duration: 2022 Jun 142022 Jun 17

Publication series

Name28th AIAA/CEAS Aeroacoustics Conference, 2022

Conference

Conference28th AIAA/CEAS Aeroacoustics Conference, 2022
Country/TerritoryUnited Kingdom
CitySouthampton
Period22/6/1422/6/17

ASJC Scopus subject areas

  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Mechanical Engineering

Fingerprint

Dive into the research topics of 'DMD-based Superresolution Measurement of a Supersonic Jet using Dual Planar PIV and Acoustic Data'. Together they form a unique fingerprint.

Cite this