Narrow Results

Synthetic Jet Actuator (SJA) works cyclically with the directionally transportation of fluid near the exit, and the paper presents the performance of a loudspeaker driven SJA in static flow field. Time-Resolved Particle Image Velocimetry (TR-PIV) system is used to measure the flow field characteristics near the SJA slot exit with input voltage changing, and the flow field snapshots obtained by TR-PIV are modality analyzed by Dynamic Mode Decomposition (DMD) method. The PIV experiments show that by varying input voltage at fixed oscillating frequency, the loudspeaker diaphragm vibration displacement is the parameter that affects the jet velocity and the performance of the SJA. The traveling vortex vanishes at high voltage due to the interaction between vortex structures and the synthetic main jet. In DMD method, the first three-order modes can characterize the main information of the original flow field with reverting the flow field snapshot sequence. It indicates that the DMD method is applicable in the SJA flow field research and the reduced order model can effectively simplify the analysis of flow field.

In this work, the performance of flapping airfoil propulsion at low Reynolds number of Re = 100–400 is studied numerically with the lattice Boltzmann method (LBM). Combined with immersed boundary method (IBM), the LBM has been widely used to simulate moving boundary problems. The influences of the reduced frequency on the plunging and pitching airfoil are explored. It is found that the leading-edge vertex separation and inverted wake structures are two main coherent structures, which dominate the flapping airfoil propulsion. However, the two structures play different roles in the flow and the combination effects on the propulsion need to be clarified. To do so, we adopt the dynamic mode decomposition (DMD) algorithm to reveal the underlying physics. The DMD has been proven to be very suitable for analyzing the complex transient systems like the vortex structure of flapping flight.

The recently emerged data-driven dynamic mode decomposition (DMD) method was employed to investigate the free surface sloshing dynamics of a partially filled rigid tank excited by horizontal harmonic motions. The volume of fluid algorithm was adopted for liquid–gas free surface tracking, and DMD was utilized for decomposition with physical interpretations of the DMD modes and the eigenvalues. Our results demonstrate that DMD works well for both data reconstruction and future-state prediction in terms of either free surface profiles or the sloshing pressure exerted on the rigid wall. DMD presents an efficient and versatile approach for accelerated reduced-order modeling and future-state forecasting. Our efforts provide the first reference on use of DMD for free surface sloshing problems to the best knowledge of the authors. We publicly share our data and codes for all the implementation.

In this work, we present a method that determines optimal multistep Dynamic Mode Decomposition (DMD) models via Entropic Regression (ER), which is a nonlinear information flow detection algorithm. Motivated by the Higher-Order DMD (HODMD) method of [Le Clainche & Vega, 2017], and the ER technique for network detection and model construction found in [Sun et al., 2015; AlMomani et al., 2020], we develop a method that we call ERDMD, which produces high fidelity time-delay DMD models that allow for nonuniformity in the delays. This optimal choice of delays is discovered by maximizing informativity as measured through ER. These models are shown to be highly efficient and robust. We test our method over several data sets generated by chaotic attractors and show that we are able to build excellent reconstructions using relatively minimal models. We likewise are able to better identify multiscale features via our models which enhances the utility of DMD.

While the detailed dynamics of transitional and turbulent flows are complex and high-dimensional, many of the important characteristics can be captured by models of surprisingly low dimension. We review two approaches for constructing low-dimensional models from data: in particular, we discuss balanced proper orthogonal decomposition, which applies to linear systems and can give insight into transitional flows; and Koopman spectral analysis, which we use to extract coherent structures from snapshots of turbulent flows.