Identification of structures and mechanisms in a flow field by POD analysis for input data obtained from visualization and PIV

Abstract

This paper investigates the application of proper orthogonal decomposition (POD) for data obtained from visualizations. Using the POD method, the flow field behind one and two cylinders in a staggered configuration was analyzed. The data processed by this method were obtained from experimental measurements of flow fields using the particle image velocimetry (PIV) method and visualization. The dominant frequencies of the flow pattern from these data were compared using constant temperature anemometry (CTA) measurements. Attention was mainly focused on the flow at three Reynolds numbers: 500, 1200, and 2500. Velocity and vortex fields were created from PIV measurements in the wind tunnel for Re = 500, and video images of flow fields were obtained from dye visualizations in the hydrodynamic tunnel. The components of velocity, vorticity (both of PIV), and change in grayscale (from visualization) were used as input data for POD analysis. A methodology for data processing from visualizations was developed for subsequent analysis using the POD method. A new technique has been found to identify structures in the wake of the cylinders in a staggered configuration by analyzing POD based on various types of input data. The measured fields of dominant frequencies from the CTA and a thorough analysis of the POD modes and their relative energy values for each type of data made it possible to identify the structures and mechanisms that occur in the wake of cylinders. This analysis facilitated a better understanding of the importance of these structures and mechanisms, which can then be used to control the flow behind the cylinders. Graphic abstract

Dynamic mode structure of active turbulence

“…This result is comparable to the flow structure velocity (≈ 235 µm.s −1 ) measured by manual tracking (Materials and Methods) [37]. A principle application of POD is the identification of coherent structures in flow [47][48][49][50], which is effectively demonstrated here in the case of active turbulence.…”

Fast Prediction of Flow Field around Airfoils Based on Deep Convolutional Neural Network

“…This observation is confirmed by the comparison of accuracies of different level POD reconstructions summarized in Table 8: The reconstructed field could not be as accurate as CNN-DCNN until more than about 50 POD modes are retained, beyond this value, reconstruction is more accurate but the improvement is quite limited as the accuracy is already approaching 100%. Besides, it is worth pointing out that POD by itself is typically working with a dataset that is already available (for modal decomposition and identification [52,53]) rather than a prediction tool such as the proposed CNN-DCNN model, which after trained, is expected to directly provide flow field data that is not necessarily pre-existed. Consequently, as predicted flow fields correlate closely to the leading POD modes that contain almost the entire energy (eigenvalues) and dynamic information (eigen functions), it is reasonable to be optimistic in terms of the capability of the CNN-DCNN model to retain at least part of the intrinsic information of the flow dynamics.…”

A New Method for Analyzing the Aero-Optical Effects of Hypersonic Vehicles Based on a Microscopic Mechanism

“…Although research on aero-optical effects has made some progress through experiments, research on their microscopic mechanism has stagnated [21][22][23]. At present, research on the mechanism of aero-optical effects mainly focuses on the change in the turbulent density field on the optical path.…”