Short-time proper orthogonal decomposition of time-resolved schlieren images for transient jet screech characterization
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 stru...
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Main Authors: | , , , , , , |
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Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2022
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/160904 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | 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. |
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