Spatiotemporal Superresolution Measurement based on POD and Sparse Regression applied to a Supersonic Jet measured by PIV and Near-field Microphone

TL;DR

This paper introduces a novel spatiotemporal superresolution measurement framework combining POD and sparse regression, enabling detailed reconstruction of unsteady flow and acoustic fields in a supersonic jet from limited data.

Contribution

The study presents a new method integrating POD and sparse regression for superresolution measurement of combined flow and acoustic data, enhancing analysis of unsteady jet phenomena.

Findings

Successfully reconstructed velocity fluctuations related to screech tone.

Reconstructed unsteady fluctuations with multiple frequency components.

Limited reconstruction of phenomena not reflected in microphone data.

Abstract

The present study proposed the framework of the spatiotemporal superresolution measurement based on the sparse regression with dimensionality reduction using the proper orthogonal decomposition (POD). The non-time-resolved particle image velocimetry (PIV) and the time-resolved near-field acoustic measurements using microphones were simultaneously performed for a Mach 1.35 supersonic jet. POD is applied to PIV and microphone data matrices and the sparse linear regression model of the reduced-order data is calculated using the least absolute shrinkage and selection operator regression. The effects of the hyperparameters of the superresolution measurement were quantitatively evaluated through randomized cross-validation. The superresolved velocity field indicated the smooth convection of the velocity fluctuations associated with the screech tone, while the convection of the large-scale structures at the downstream side was not observed. The proposed framework can reconstruct the unsteady fluctuation with multiple frequency phenomena, although the reconstruction is limited to the phenomena that are associated with the microphone output.