Short-time Proper Orthogonal Decomposition of Time-Resolved Schlieren Images for Transient Jet Screech Characterization

H. D. Lim,X. F. Wei,B. Zang,U. S. Vevek,R. Mariani,T. H. New,Y. D. Cui

Aerospace Science and Technology（2020）SCI 1区

Nanyang Technol Univ | Natl Univ Singapore

Cited 19|Views16

Abstract

Short-time Proper Orthogonal Decomposition (POD) is proposed as an image-based technique to study the transient jet screech characteristics of moderately under-expanded supersonic jets emanating from a circular baseline and two bevelled nozzles. Time-resolved schlieren imaging of turbulent flow structures were performed with an ultrahigh-speed schlieren setup. Short-time POD was performed by systematically sampling image-series with a short time delay, performing PODs and applying spectral analyses on the first POD mode coefficients, and plotting the peak frequencies from the resulting PSDs into a peak frequency-occurrence count histogram. The results are in good agreement with the near-field noise spectra and wavelet transform analysis of the microphone measurements, which revealed intermittent jet screech occurrences at St=0.25 for both baseline and 30° bevelled jets, while none was detected for the 60° bevelled jet. In particular, the occurrence counts of the frequency bins is proposed as a suitable parameter to characterize the intermittent nature of jet screech, with the frequency bin revealing the jet screech frequency if present. The present study demonstrates the advantage of short-time POD analysis on time-resolved schlieren images over traditional image-based POD methods, which includes computational gains from parallelization, the ability to handle much larger datasets and revealing insights into a transient flow and noise phenomenon.

Translated text

Key words

Flow diagnostics,Schlieren,Aeroacoustics,Jet screech,Supersonic jets,Proper orthogonal decomposition

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper