Dynamic Mode Decomposition Modes Research Articles

Dynamic Mode Decomposition for Transient Cavitation Bubbles Imaging in Pulsed High-Intensity Focused Ultrasound Therapy.

Pulsed high-intensity focused ultrasound (pHIFU) can induce sparse de novo inertial cavitation without the introduction of exogenous contrast agents, promoting mild mechanical disruption in targeted tissue. Because the bubbles are small and rapidly dissolve after each HIFU pulse, mapping transient bubbles and obtaining real-time quantitative metrics correlated with tissue damage are challenging. Prior work introduced Bubble Doppler, an ultrafast power Doppler imaging method as a sensitive means to map cavitation bubbles. The main limitation of that method was its reliance on conventional wall filters used in Doppler imaging and its optimization for imaging blood flow rather than transient scatterers. This study explores Bubble Doppler enhancement using dynamic mode decomposition (DMD) of a matrix created from a Doppler ensemble for mapping and extracting the characteristics of transient cavitation bubbles. DMD was first tested in silico with a numerical dataset mimicking the spatiotemporal characteristics of backscattered signal from tissue and bubbles. The performance of DMD filter was compared to other widely used Doppler wall filter-singular value decomposition (SVD) and infinite impulse response (IIR) high-pass filter. DMD was then applied to an ex vivo tissue dataset where each HIFU pulse was immediately followed by a plane wave Doppler ensemble. In silico DMD outperformed SVD and IIR high-pass filter and ex vivo provided physically interpretable images of the modes associated with bubbles and their corresponding temporal decay rates. These DMD modes can be trackable over the duration of pHIFU treatment using k-means clustering method, resulting in quantitative indicators of treatment progression.

Identifying Buffet Oscillations Using Sliding-Window Dynamic Mode Decomposition

Buffet is a self-sustaining oscillation that can endanger an aircraft in transonic flight. Early automatic detection of the onset of buffet from simulation data is a necessary step in optimization algorithms used to design new aircraft geometries. We use the Dynamic Mode Decomposition (DMD) eigenvalues to detect the onset of buffet from transient simulations of the OAT15A airfoil across a range of angles of attack. During transients, different eigenvalues may be expressed at different times, and we process shorter segments of the data (sliding windows) in order to obtain robust, time-varying estimates of frequency of oscillation and decay/growth rates. We demonstrate that the onset of oscillation can be identified by analyzing DMD eigenvalues, and that physically different mechanisms of oscillation can additionally be identified from spatial profiles associated with DMD modes. The dominant buffeting modes can be detected through DMD before they become apparent in the physically relevant quantities, such as the coefficient of lift. We expect that such early detection of the onset of buffet could reduce the computational burden of aircraft design and expand the feasible design space.

Optimizing Dynamic Mode Decomposition for Video Denoising via Plug-and-Play Alternating Direction Method of Multipliers

Dynamic mode decomposition (DMD) is a powerful tool for separating the background and foreground in videos. This algorithm decomposes a video into dynamic modes, called DMD modes, to facilitate the extraction of the near-zero mode, which represents the stationary background. Simultaneously, it captures the evolving motion in the remaining modes, which correspond to the moving foreground components. However, when applied to noisy video, this separation leads to degradation of the background and foreground components, primarily due to the noise-induced degradation of the DMD mode. This paper introduces a novel noise removal method for the DMD mode in noisy videos. Specifically, we formulate a minimization problem that reduces the noise in the DMD mode and the reconstructed video. The proposed problem is solved using an algorithm based on the plug-and-play alternating direction method of multipliers (PnP-ADMM). We applied the proposed method to several video datasets with different levels of artificially added Gaussian noise in the experiment. Our method consistently yielded superior results in quantitative evaluations using peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared to naive noise removal methods. In addition, qualitative comparisons confirmed that our method can restore higher-quality videos than the naive methods.

Data-driven identification and pressure fields prediction for parallel twin cylinders based on POD and DMD method

The mode analysis of parallel twin cylinders is conducted in this paper using two data-driven methods: proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD). First, a high-fidelity computational fluid dynamics (CFD) model of parallel twin cylinders is established, and numerical simulations of the model are carried out. Subsequently, the fundamental principles of the POD and DMD algorithms are systematically introduced. Utilizing snapshots obtained from the high-fidelity CFD model, the POD and DMD methods are employed to extract the dominant flow structures. Furthermore, a comparison between the two data-driven methods is conducted by analyzing modal frequencies, pressure distribution, and the reconstruction errors of pressure fields. Finally, the pressure fields of non-sample points are predicted based on the POD–backpropagation neural network (BPNN) surrogate model and the DMD method, and the predicted results are compared with the CFD simulation results. It found that (i) the DMD method is capable of extracting the main coherent structures of the pressure fields, directly obtaining flow modes and their corresponding frequencies, and assessing the stability of flow modes; (ii) the DMD method can capture the main flow features of the pressure fields in both spatial and temporal dimensions, while the POD method is primarily efficient at capturing the spatial features of the pressure fields; (iii) in contrast to the frequency-ranked DMD method, the energy-ranked POD method can reconstruct the pressure fields using a smaller number of modes, indicating that the POD method has an advantage in terms of mode reduction; (iv) in contrast to the energy-ranked POD method, the frequency-ranked DMD method has a wider applicability to the range of flow types and has more advantages in stability analysis of complex dynamic systems; (v) the predicted pressure fields around the cylinder using the first five-order POD modes or DMD modes closely align with CFD calculation results. Additionally, the evolution of pressure fields predicted by the POD–BPNN surrogate model with the first five-order POD modes or the DMD method with the first 200-order DMD modes significantly agrees with CFD simulation results; (vi) the combined use of the POD–BPNN surrogate model and DMD methods allows efficient interpolation and extrapolation of samples, delivering exceptional predictive performance. This study offers insight into the coherent structures in parallel twin cylinders.

Modal analysis of vortex rope using dynamic mode decomposition

The decelerating swirling flow in the draft tube of hydraulic turbines at part load conditions often results in a self-induced instability known as vortex rope. This phenomenon is associated with detrimental pressure pulsations in the hydropower system that need to be mitigated. A deep understanding of such instability is essential for developing effective mitigation and control strategies. The current article exploits the dynamic mode decomposition (DMD) algorithm to perform an in-depth modal analysis of the physical aspects of the vortex rope. DMD can efficiently identify distinct coherent structures with isolated frequencies. The sparsity-promoting variant of DMD is exploited to extract the most influential modes. The computational fluid dynamics (CFD) data is generated via a resolved improved delayed detached eddy simulation using OpenFOAM. Frequency analysis of the CFD data uncovered peaks at the normalized frequencies of f/fn=0.56 and 0.63, whose origins seemed initially unclear. Nevertheless, the DMD modal analysis elucidates that these excitations are associated with the rotation of the reunited vortex and fluctuations of the separated boundary layer, respectively. The non-linear dynamics of the flow field are unveiled through a modal decomposition revealing distinctive coherent structures with isolated frequencies. These include rotational and plunging modes of the vortex rope, traveling wakes of the blades, boundary layer separation due to strong adverse pressure gradient, and a reunited vortex core. The flow field reconstruction through time dynamics of DMD modes highlights while it is possible to achieve a perfect flow field reconstruction considering all recovered modes, the model typically fails to predict future behavior with an acceptable level of accuracy. The chaotic nature of the resolved turbulent flow field presents a substantial challenge for predicting the future through a model built based on prior events. The current modal analysis not only provides a more comprehensive understanding of the physics underlying the vortex rope phenomenon but also lays the groundwork for potential future applications in controlling mechanisms.

Investigations on bifurcation behavior of wind turbine airfoil response at a high angle of attack

Design load and vibration for parked conditions are gaining in importance for large-scale modern wind turbines with increasing flexibility, especially edgewise vibration when the blade is at a high angle of attack. In this work, flow-induced vibration of the wind turbine airfoil at 90 degrees of attack angle is studied with the fluid-structure interaction (FSI) simulation. The unsteady aerodynamic force due to flow separation and vortex shedding at the high angle of attack causes the chordwise vibration of the airfoil. When the vortex shedding frequency fv gets close to the chordwise natural frequency fn of the airfoil, vortex-induced vibration (VIV) of high amplitude occurs accompanied with the frequency lock-in phenomenon. In the post lock-in regime, it is found that period-3 and torus bifurcation occur successively and the vibration response becomes aperiodic. Dynamic mode decomposition(DMD) technique is used to investigate the mechanism of bifurcation from the perspective of energy balance, through analyzing the vorticity field in the wake and pressure distribution on the airfoil surface. For the certain incoming velocity in the post lock-in regime, since the frequency of the DMD mode f=2fv/3 is close to the natural frequencyfn, both the vibration of frequency 2fv/3 and fv get excited, leading to the onset of bifurcation. The Lissajou curves are obtained through reconstructing the transient pressure of each DMD mode, which indicates that energy transfer mainly exists in modes f=fv. In addition, the reconstructed Lissajou curves based on the leading DMD modes agree well with the original time-domain Lissajou curves.

Reduced-order model and attractor identification for large eddy simulation of squirrel cage fan

A large eddy simulation (LES) of a squirrel cage fan (SCF) provides a precise representation of turbulent flows with different degrees of complexity. This study comprehensively analyzes the coherent structures of turbulent flows in an SCF using an LES, proper orthogonal decomposition (POD), dynamic mode decomposition (DMD), and multi-resolution dynamic mode decomposition (mrDMD). An intelligent reduced-order model is established by integrating hierarchical deep learning and the sparse identification of nonlinear dynamics. The result shows that the evolution of the global DMD modes is attenuated due to the spatial distribution variations of localized high-frequency mrDMD modes, along with the fragmented and non-steady development of modal patterns. Unlike POD, DMD quantifies the quality of the impeller inlet environment and captures the antisymmetric low-dimensional flows associated with the shedding of rotating vortex structures. The interaction strength between stationary and dynamic rotating areas is accurately represented by attractors characterized by petal-like structures. The trajectory of the attractors faithfully maps the antisymmetric structural attributes, quasi-periodic behavior, and gradual attenuation characteristics exhibited by DMD modes. The number of petal-like systems and their temporal oscillations are in good agreement with the number of fan blades and their rotational cycles. This study provides new insight into fan engineering to advance flow control strategies and improve the understanding of the underlying flow mechanisms.

Effects of temperature ratio on combustion instability in a non-premixed hydrogen-rich ram combustor

The effects of the burnt-to-unburnt temperature ratio (Tb/Tu) (reciprocal density ratio) on thermoacoustic combustion instability were investigated in a hydrogen-rich ram combustor by controlling the inlet air temperature under cruising conditions. Steady and unsteady combustion dynamics were investigated with the simultaneous measurement of combustion pressure and near-infrared (NIR) emissions. Time-resolved NIR images were analyzed using dynamic mode decomposition (DMD) to extract modes of the combustion instability, which show two oscillation modes: longitudinal (500 Hz) and flame-vortex interaction modes (2000 Hz) with a Strouhal number of approximately 0.5. The thermoacoustic combustion instability was not excited at Tb/Tu = 3.3, but was excited at Tb/Tu= 2.6 and 2.9. The DMD modes show that the structure of the vortex mode is similar to that of the fourth longitudinal mode at Tb/Tu = 2.6 and 2.9, indicating that the coupling of the vortex and the fourth longitudinal modes excited the thermoacoustic combustion instability.

Force decomposition on flapping flexible plate via impulse theory and dynamic mode decomposition

Dynamic mode decomposition (DMD) is a widely used method to extract dynamic information from sequential flow data, aiding our comprehension of fluid dynamics and transport processes. While DMD can unveil internal system laws and predict unsteady flow phenomena, the connection between DMD modes and the nonlinear hydrodynamic behavior of solid bodies remains unexplored. This study investigated the internal relationship between DMD modes and their impact on hydrodynamic forces. We employed a penalty-immersed boundary method to simulate the behavior of a flapping flexible plate in a uniform incoming flow, generating extensive datasets of vorticity fields. By applying DMD to these datasets, we identified key modes governing the flow dynamics, including the shear layer, symmetric vortex street, and antisymmetric vortex street. Furthermore, we utilized the impulse theory to analyze the force characteristics of the plate based on the corresponding DMD modes. The net force is determined by the combined contributions of the impulse force and the vortex force. Our findings reveal that the net horizontal force is primarily influenced by the first two modes. Specifically, mode 1, characterized by a dimensionless frequency of f*=0, contributes to thrust, whereas mode 2, with f*=1, contributes to drag. This physical investigation holds relevance for fluid–structure systems involving the interaction dynamics of flexible structures with unsteady wake vortex systems.

Pilot-wave dynamics: Using dynamic mode decomposition to characterize bifurcations, routes to chaos, and emergent statistics.

We develop a data-driven characterization of the pilot-wave hydrodynamic system in which a bouncing droplet self-propels along the surface of a vibrating bath. We consider drop motion in a confined one-dimensional geometry and apply the dynamic mode decomposition (DMD) in order to characterize the evolution of the wave field as the bath's vibrational acceleration is increased progressively. Dynamic mode decomposition provides a regression framework for adaptively learning a best-fit linear dynamics model over snapshots of spatiotemporal data. Thus, DMD reduces the complex nonlinear interactions between pilot waves and droplet to a low-dimensional linear superposition of DMD modes characterizing the wave field. In particular, it provides a low-dimensional characterization of the bifurcation structure of the pilot-wave physics, wherein the excitation and recruitment of additional modes in the linear superposition models the bifurcation sequence. This DMD characterization yields a fresh perspective on the bouncing-droplet problem that forges valuable new links with the mathematical machinery of quantum mechanics. Specifically, the analysis shows that as the vibrational acceleration is increased, the pilot-wave field undergoes a series of Hopf bifurcations that ultimately lead to a chaotic wave field. The established relation between the mean pilot-wave field and the droplet statistics allows us to characterize the evolution of the emergent statistics with increased vibrational forcing from the evolution of the pilot-wave field. We thus develop a numerical framework with the same basic structure as quantum mechanics, specifically a wave theory that predicts particle statistics.

Nonlinear mode decomposition via physics-assimilated convolutional autoencoder for unsteady flows over an airfoil

I have developed a physics-assimilated convolutional autoencoder (CAE) neural network, namely, PhyAENet, to carry out nonlinear mode decomposition of the unsteady flow field around a National Advisory Committee for Aeronautics 4412 airfoil near stall conditions. The flow field snapshots are mapped into latent modes by the encoder part of the well-trained CAE, which are used for dynamic mode decomposition (DMD) analysis. The computed DMD modes are split into modes covering different frequency ranges. These high and low-frequency DMD modes are used to form reconstructed encoded sequences, which are then mapped back to generate the nonlinear decomposed spatiotemporal modes by the decoder of the CAE. As such, physics is assimilated into the neural network by incorporating the frequencies of the DMD modes into the latent modes in the latent space. The proposed PhyAENet is capable of extracting the dominant features of the flow fields, accounting for the nonlinearity of the underlying dynamics. Furthermore, the extracted nonlinear modes are evolving with time and physically interpretable. It is revealed that the nonlinear modes can be well represented when using more DMD modes for reconstruction of the encoded sequences. The energy spectrum of the nonlinear modes are obtained by ranking the Frobenius norm of the mode vector.

Time series prediction of ship maneuvering motion based on dynamic mode decomposition

In order to reveal the dynamic characteristics and achieve rapid time series prediction of ship maneuvering motion, a reduced-order dynamic mode decomposition (DMD) algorithm is applied to reconstruct and predict the zig-zag and turning circle maneuvering motions. A case study is conducted for a KVLCC2 tanker using its free-running model test data. First, all DMD modes are extracted from the test data, and the dominant DMD modes are selected according to their contributions to the dynamical systems of ship maneuvering motions. Then, the dynamical systems of ship maneuvering motions are reconstructed and predicted by the reduced-order and full-order DMD algorithms. A comparison between reduced-order, full-order DMD algorithms and Gaussian process regression (GPR) is conducted. The dynamic characteristics of the dynamical systems are revealed according to the growth rates and frequencies of the dominant DMD modes. The effects of the truncation rank and input samples are analyzed by a parametric study, which indicates that the truncation rank and input samples are crucial to the prediction accuracy. Besides, the computational time of the different algorithms is compared and analyzed.

Spiral flow instability between a rotor and a stator in high-speed turbomachinery and its relation to fan noise

Hydrodynamic instabilities in the interstage of turbomachinery and their relation to fan broadband noise are investigated over wide operating conditions. By applying compressible linear stability analysis to the velocity profiles of NASA's high-speed fan rig, source diagnostic test (SDT), three distinct unstable regimes are found: the first mode amplifies disturbances over a wide frequency range including the rotor speed. The second mode rotates much slower and develops inside the outer-wall boundary layer. The third mode spins in the opposite direction. An approach equivalent to dynamic mode decomposition (DMD) successfully extracts these modes from a database of improved delayed detached-eddy simulation (IDDES) solving the SDT geometry at approach and cut-back conditions: the extracted DMD modes generally capture the characteristics of eigenfunctions predicted by the linear stability analysis. At the approach condition, the first two unstable modes seem to interact inside the outer-wall boundary layer, while at the cut-back condition, disturbances associated with the first regime migrate away from the wall. Moreover, outer-wall pressure fluctuations of the IDDES database are Fourier decomposed in the interstage: coherent structures extracted using proper orthogonal decomposition are found to be dominated by duct acoustic modes, and they can be mapped not only to interaction tones but also to broadband noise likely associated with the interaction between disturbances in the rotor wakes and quasi-stationary structures near the outer wall. This interaction noise appears to be radiated to both inlet and exhaust with similar azimuthal-mode distributions; however, the importance relative to other noise sources decreases with the engine speed.

Strategies for Producing Reliable Trends Forecasting of COVID-19 Pandemic in Malaysia using Dynamic Mode Decomposition

Dynamic Mode Decomposition (DMD) with time delay embedding is used to predict dynamic patterns in univariate time series. An important pattern that can be extracted using DMD is the trend or global change in a time series which is useful for producing reliable forecast. DMD utilizes the computationally effi cient singular value decomposition (SVD) to produce a low rank approximation of the linear operator that brings about the dynamic patterns in the time series. Trend in the time series is translated as dynamic modes of the operator with low frequencies. The time evolution of this low frequency pattern produces forecast of the time series. In this paper, we outline the strategies for extracting trend component from COVID-19 time series of Malaysia. It is discovered that, other than identifying modes with slow varying frequencies, we need to also resolve the time stamp delay, so that mean-square error of the reconstructed time series is minimal. Information of the magnitude and phase of DMD modes are useful to identify persistent patterns and remove nonstationary ones. We compare the performance of DMD with another SVD-based method which is the singular spectrum analysis (SSA) and our results highlight certain fundamental difference between these two methods. The forecasts from SSA tend to lean towards the direction of maximum variance, producing low reconstruction error but slow to detect sudden changes in the time series. On the other hand, forecasts from DMD captures the phases of dominant modes that dictates the overall global pattern, hence providing a better prediction of future dynamics of the time series.

Characterization of elastic topological states using dynamic mode decomposition

Elastic topological states have been receiving increased attention in numerous scientific and engineering fields due to their defect-immune nature, resulting in applications of vibration control and information processing. Here, we present the data-driven discovery of elastic topological states using dynamic mode decomposition (DMD). The DMD spectrum and DMD modes are retrieved from the propagation of the relevant states along the topological boundary, where their nature is learned by DMD. Applications such as classification and synthesis of wave propagation can be achieved by the underlying characteristics from DMD. We demonstrate the classification between topological and traditional metamaterials using DMD modes. Moreover, the model enabled by the DMD modes realizes the synthesis of topological state propagation along the given interface. Our approach to characterizing topological states using DMD can pave the way towards data-driven discovery of topological phenomena in material physics and more broadly lattice systems.

Application study of Dynamic Mode Decomposition coupled with a high-speed imaging system in jet zone oscillation behavior diagnosis of impinging flames

Dynamic Mode Decomposition (DMD) is a data-driven analysis method for plenty of snapshots, which has good application prospects in the combustion diagnostics base on optical methods. In this study, the self-pulsation combustion instability of four-burner impinging diesel flame was explored based on a bench-scale opposed multi-burner entrained-flow gasifier. The spatiotemporal evolution of the flame structure in the jet zone was studied by DMD analysis. The vortex evolution of the central reaction stream was divided into four stages, and the period of each stage was determined. The boundary streams also have periodic oscillations in the reaction intensity. The modes with high growth factor can accurately identify the oscillation components in different structures of the flame. The corresponding feature DMD modes were in good agreement with the flame structure obtained from the grayscale image, but the local components in the DMD mode may belong to different time scales.

Data-driven discovery of spatiotemporal coherent patterns in pulsating soft coral tentacle motion with dynamic mode decomposition

Tentacles on soft corals exhibit intriguing spatiotemporal dynamics of motions, which may benefit their survival and fitness. Despite their significance, studies of their quantitative properties still remain challenging and unexplored. Such motions are characterized by coherent patterns across both space and time, yet computational methods that address spatiotemporal dynamics are rare. Here, we introduce a data-driven method called dynamic mode decomposition (DMD) to explore the spatiotemporal behavior of tentacles of Anthelia glauca, where the motions of eight tentacles are captured by stereovision and object tracking techniques. The DMD reveals the stochastic motions of the tentacles, which can be well modeled as $1/f$-type motion. Additionally, the pulsation behaviors of our soft corals are also captured by analyzing the DMD spectrum and the sliding-window DMD, where these behaviors emerge as spatial DMD modes with increased power. Finally, the impact of light conditions on the $1/f$-type motion and pulsation behaviors is explored, where certain light conditions can manipulate the $1/f$-type motion and emergence of pulsation behaviors. Our work, combining experimental observation and a data-driven method to characterize spatiotemporal motions of coral tentacles, paves the way to exploring the complex behaviors of individual organisms and colonies, and the effect from changing environmental variables.

On the origin of quadrupole sound from a two-dimensional aerofoil trailing edge

Tonal noise emitted from the trailing edge of an airfoil is considered using modal analysis techniques to investigate secondary quadrupole tones. We examine the origin of quadrupole sound generated from two-dimensional unsteady laminar flow over a NACA0012 airfoil. In this paper, we consider two flow configurations at Mach numbers of $M_\infty = 0.1$ and 0.05 that lead to different acoustic characteristics: the former has a significant high-frequency quadrupole noise source, whereas the latter does not. We use vortex sound theory, dynamic mode decomposition (DMD), and resolvent analysis to analyze the sound source. First, we employ DMD modes to reveal that the quadrupole sound is only observed in the higher Mach number case. Next, the vortex dynamics in the vicinity of the trailing edge are studied to identify the origin of quadrupole sound. It is found that the quadrupole sound is caused by vortex shedding in the vicinity of the trailing edge. The complex vortex interaction between both sides of the airfoil strengthens the quadrupole source in the higher Mach number case, while it is negligible in the lower one. Furthermore, we perform resolvent analysis to examine the vortex generation over the airfoil. The resolvent mode indicates that the interaction between the vortices on both sides of the airfoil causes a multi-scale vortex structure on the suction-side wall.

Numerical investigation of azimuthal thermoacoustic instability in a gas turbine model combustor

Self-excited spinning mode azimuthal instability in an annular combustor with non-swirling flow is investigated using large eddy simulation (LES). Compressible Navier–Stokes equations are solved with a flamelet combustion model to describe the subgrid chemistry-turbulence interactions. Two flamelet models, with and without heat loss effects, are compared to elucidate the non-adiabatic wall effects on the thermoacoustic instability. The azimuthal modes are captured well by both models with only marginal differences in the computed frequencies and amplitudes. By comparing with the experimental measurements, the frequencies given by the LES are approximately 10% higher and the amplitudes are well predicted. Further analysis of the experimental and LES data shows a similar dominant anti-clockwise spinning mode, under which a good agreement is observed for the phase-averaged heat release rate fluctuations. Dynamic mode decomposition (DMD) is applied to shed more light on this spinning mode. The LES and experimental DMD modes reconstructed for their azimuthal mode frequencies agree very well for the heat release fluctuations. The DMD mode structure for the acoustic pressure from the LES shows a considerable non-zero profile at the combustor outlet, which could be essential for azimuthal modes to establish in this annular combustor. Finally, a low-order modelling study was conducted using an acoustic network combined with the flame transfer function extracted from LES. The results show that the dominant mode is associated with the plenum showing a first longitudinal and azimuthal mixed mode structure. By tuning the plenum length to match the effective volume, the predicted frequency becomes very close to the measured value.

Dynamic mode decomposition and reconstruction of the transient propeller wake under a light loading condition

This research aims to extend our understanding of propeller wake dynamics under a light loading condition, thereby laying a foundation for design optimization and flow control of the propeller. Dynamic mode decomposition (DMD) and reconstruction are used to analyze the transient vortical wake structures obtained by large eddy simulation. The propeller wake includes stable tip and hub vortices without interacting evolution at the light loading condition, and elliptical instabilities are observed downstream of the tip vortices. DMD describes the most energetic modes and the corresponding dominant frequencies are the blade passing frequency and its multiples. The coherent structures identified via DMD are primarily associated with the ordered convection of the tip vortices and have little correlation with the hub vortices. Additionally, the propeller wake flow is reconstructed using the first four DMD modes, and the primary wake features are well restored with a maximum reconstructed error of 7.98%. This demonstrates that the flow-field reconstruction based on the DMD reduced-order model is promising for predicting the propeller wake and controlling the propeller operation.