Dynamic Mode Decomposition of Fast Pressure Sensitive Paint Data

Abstract

Fast-response pressure sensitive paint (PSP) is used in this work to measure and analyze the acoustic pressure field in a rectangular cavity. The high spatial resolution and fast frequency response of PSP effectively captures the spatial and temporal detail of surface pressure resulting in the acoustic pressure field. In this work, a high-speed camera is used to generate a continuous time record of the acoustic pressure fluctuations with PSP. Since the level of the acoustic pressure is near the resolution limit of the sensor system, advanced analysis techniques are used to extract the spatial modes of the pressure field. Both dynamic mode decomposition (DMD) and proper orthogonal decomposition (POD) are compared with phase averaging for data analysis. While all three techniques effectively extract the pressure field and reduce the impact of sensor noise, DMD and POD are more robust techniques that can be applied to aperiodic or multi-frequency signals. Furthermore, DMD is better than POD at suppressing noise in particular regions of the spectrum and at effectively separating spectral energy when multiple acoustic excitation frequencies are present.

POD-Galerkin Based Reduced Order Modelling for Flow-Structure Interactions

“…The attractiveness of the POD method lies in the fact that it is a linear procedure and efficient in the design of robust feedback controllers leading to bettercontrolled airflow over aerofoils. Afterwards, much computational and experimental work has been performed by using this method to validate its effectiveness [6][7][8].…”

Data-driven approach for noise reduction in pressure-sensitive paint data based on modal expansion and time-series data at optimally placed points

“…In this method, noise reduction was conducted by truncating the SVD (POD). The dynamic mode decomposition (DMD), 36 Kalman-filter DMD, 37 and extended-Kalman-filter-based DMD 38 have also been applied. There are many choices for the truncation rank of the POD, which also relates to the DMD calculation, and for the mode selection of the DMD to reconstruct the pressure distribution data.…”

Stable Dynamic Mode Decomposition Algorithm for Noisy Pressure-Sensitive Paint Measurement Data

“…A N increasing number of large-scale time-series datasets are being generated with the development of numerical and experimental techniques. For example, pressure-sensitive paint (PSP) [1][2][3] and particle image velocimetry (PIV) [4][5][6] have been used for aerospace fluid analysis to obtain the spatial and temporal distribution of the flow field. To gain deeper insights from multidimensional time-series data and utilize them for modeling fluid flow, data analysis techniques based on modal decomposition are being actively studied [7][8][9].…”

“…Dynamic mode decomposition (DMD) [10] is one of the most commonly used modal analysis methods along with proper orthogonal decomposition (POD) [11]. DMD has been applied in various studies [2,[12][13][14][15][16] due to its advantages in extracting both spatial modes and their associated temporal behavior.…”