Dynamic mode decomposition of the geomagnetic field over the last two decades

Earth’s magnetic field,which is generated in the liquid outer core through the dynamo action,undergoes changes on timescales of a few years to several million years,yet the underlying mechanisms responsible for the field variations remain to be elucidated.In this study,we apply a novel data analysis technique developed in fluid dynamics,namely the dynamic mode decomposition,to analyze the geomagnetic variations over the last two decades when continuous satellite observations are available.The dominant dynamic modes are extracted by solving an eigen-value problem,so one can identify modes with periods longer than the time span of data.Our analysis show that similar dynamic modes are extracted from the geomagnetic secular variation and secular acceleration,justifying the validity of applying the dynamic mode decomposition method to geomagnetic field.We reveal that the geomagnetic field variations are characterized by a global mode with period of 58 years,a localized mode with period of 16 years and an equatorially trapped mode with period of 8.5 years.These modes are possibly related to magnetohydrodynamic waves in the Earth’s outer core.

Application of Hankel Dynamic Mode Decomposition for Wide Area Monitoring of Subsynchronous Resonance

In recent years,subsynchronous resonance(SSR)has frequently occurred in DFIG-connected series-compensated systems.For the analysis and prevention,it is of great importance to achieve wide area monitoring of the incident.This paper presents a Hankel dynamic mode decomposition(DMD)method to identify SSR parameters using synchrophasor data.The basic idea is to employ the DMD technique to explore the subspace of Hankel matrices constructed by synchrophasors.It is analytically demonstrated that the subspace of these Hankel matrices is a combination of fundamental and SSR modes.Therefore,the SSR parameters can be calculated once the modal parameter is extracted.Compared with the existing method,the presented work has better dynamic performances as it requires much less data.Thus,it is more suitable for practical cases in which the SSR characteristics are timevarying.The effectiveness and superiority of the proposed method have been verified by both simulations and field data.

Parameters Identification for Extended Debye Model of XLPE Cables Based on Sparsity-Promoting Dynamic Mode Decomposition Method

To identify the parameters of the extended Debye model of XLPE cables,and therefore evaluate the insulation performance of the samples,the sparsity-promoting dynamicmode decomposition(SPDMD)methodwas introduced,aswell the basics and processes of its applicationwere explained.The amplitude vector based on polarization current was first calculated.Based on the non-zero elements of the vector,the number of branches and parameters including the coefficients and time constants of each branch of the extended Debye model were derived.Further research on parameter identification of XLPE cables at different aging stages based on the SPDMD method was carried out to verify the practicability of the method.Compared with the traditional differential method,the simulation and experiment indicated that the SPDMD method can effectively avoid problems such as the relaxation peak being unobvious,and possessing more accuracy during the parameter identification.And due to the polarization current being less affected by the measurement noise than the depolarization current,the SPDMD identification results based on the polarization current spectral line proved to be better at reflecting the response characteristics of the dielectric.In addition,the time domain polarization current test results can be converted into the frequency domain,and then used to obtain the dielectric loss factor spectrum of the insulation.The integral of the dielectric loss factor on a frequency domain can effectively evaluate the insulation condition of the XLPE cable.

Dynamic mode decomposition and reconstruction of transient cavitating flows around a Clark-Y hydrofoil

The transient cavitating flow around the Clark-Y hydrofoil is numerically investigated by the dynamic mode decomposition with criterion.Based on the ranking dominant modes,frequencies of the first four modes are in good accordance with those obtained by fast Fourier transform.Furthermore,the cavitating flow field is reconstructed by the first four modes,and the dominant flow features are well captured with the reconstructed error below 12%when compared to the simulated flow field.This paper offers a reference for observing and reconstructing the flow fields,and gives a novel insight into the transient cavitating flow features.

Practical Implementation and Operational Experience of Dynamic Mode Decomposition in Wide-area Monitoring Systems of Italian Power System

This study presents the assumptions and strategies for the practical implementation of the dynamic mode decomposition approach in the wide-area monitoring system of the Italian transmission system operator,Terna.The procedure setup aims to detect poorly damped interarea oscillations of power systems.Dynamic mode decomposition is a data-driven technique that has gained increasing attention in different fields;the proposed implementation can both characterize the oscillatory modes and identify the most influenced areas.This study presents the results of its practical implementation and operational experience in power system monitoring.It focuses on the main characteristics and solutions identified to reliably monitor the interarea electromechanical modes of the interconnected European power system.Moreover,conditions to issue an appropriate alarm in case of critical operating conditions are described.The effectiveness of the proposed approach is validated by its application in three case studies:a critical oscillatory event and a short-circuit event that occurred in the Italian power system in the previous years,and a 15-min time interval of normal grid operation recorded in March 2021.

A dynamic-mode-decomposition-based acceleration method for unsteady adjoint equations at low Reynolds numbers

The computational cost of unsteady adjoint equations remains high in adjoint-based unsteady aerodynamic op-timization.In this letter,the solution of unsteady adjoint equations is accelerated by dynamic mode decomposi-tion(DMD).The pseudo-time marching of every real-time step is approximated as an infinite-dimensional linear dynamical system.Thereafter,DMD is utilized to analyze the adjoint vectors sampled from these pseudo-time marching.First-order zero frequency mode is selected to accelerate the pseudo-time marching of unsteady adjoint equations in every real-time step.Through flow past a stationary circular cylinder and an unsteady aerodynamic shape optimization example,the efficiency of solving unsteady adjoint equations is significantly improved.Re-sults show that one hundred adjoint vectors contains enough information about the pseudo-time dynamics,and the adjoint dominant mode can be precisely predicted only by five snapshots produced from the adjoint vectors,which indicates DMD analysis for pseudo-time marching of unsteady adjoint equations is efficient.

A transition prediction method for flow over airfoils based on high-order dynamic mode decomposition

This article presents a novel approach for predicting transition locations over airfoils,which are used to activate turbulence model in a Reynolds-averaged Navier-Stokes flow solver.This approach combines Dynamic Mode Decomposition(DMD)with e^Ncriterion.The core idea is to use a spatial DMD analysis to extract the modes of unstable perturbations from a steady flowfield and substitute the local Linear Stability Theory(LST)analysis to quantify the spatial growth of Tollmien–Schlichting(TS)waves.Transition is assumed to take place at the stream-wise location where the most amplified mode’s N-factor reaches a prescribed threshold and a turbulence model is activated thereafter.To improve robustness,the high-order version of DMD technique(known as HODMD)is employed.A theoretical derivation is conducted to interpret how a spatial highorder DMD analysis can extract the growth rate of the unsteady perturbations.The new method is validated by transition predictions of flows over a low-speed Natural-Laminar-Flow(NLF)airfoil NLF0416 at various angles of attack and a transonic NLF airfoil NPU-LSC-72613.The transition locations predicted by our HODMD/e^Nmethod agree well with experimental data and compare favorably to those obtained by some existing methods■.It is shown that the proposed method is able to predict transition locations for flows over different types of airfoils and offers the potential for application to 3D wings as well as more complex configurations.

Dynamical mode decomposition of Gurney flap wake flow

The present work uses dynamic mode decomposition(DMD) to analyze wake flow of NACA0015 airfoil with Gurney flap.The physics of DMD is first introduced.Then the PIV-measured wake flow velocity field is decomposed into dynamical modes.The vortex shedding pattern behind the trailing edge and its high-order harmonics have been captured with abundant information such as frequency,wavelength and convection speed.It is observed that high-order dynamic modes convect faster than low-order modes;moreover the wavel...

Investigation of flow unsteadiness in a highly-loaded compressor cascade using a dynamic mode decomposition method

Unsteady flow in the hub endwall region has long been a hot topic in the turbomachinery community.However important it is to the performance of the whole engine,the coherent unsteady flow phenomena are still not well understood.In this paper,the complex flow field in the hub endwall of a cantilevered compressor cascade has been investigated through numerical approach.The predicted results were validated by experimental data.To highlight the dominant flow structures among irregular and chaotic motions of various vortices,a Dynamic Mode Decomposition(DMD)method was utilized.The results show that there exist three dominant periodic flow structures:the oscillation of the leakage vortex,a circumferential migration of a Breakdown Induced Vortex(BIV)and the fluctuation of the passage vortex.These three coherent structures all together form a self-sustained closed loop which accounts for the flow unsteadiness of the studied cascade.During this process,the BIV plays a key role in inducing the flow unsteadiness.Only if the BIV is strong enough to affect the passage vortex,the flow unsteadiness occurs.This study expands current knowledge base of flow unsteadiness in a compressor environment,and shows the efficacy of the DMD method for revealing the origin of flow unsteadiness.

Visualization and selection of Dynamic Mode Decomposition components for unsteady flow

Dynamic Mode Decomposition(DMD)is a data-driven and model-free decomposition technique.It is suitable for revealing spatio-temporal features of both numerically and experimentally acquired data.Conceptually,DMD performs a low-dimensional spectral decomposition of the data into the following components:the modes,called DMD modes,encode the spatial contribution of the decomposition,whereas the DMD amplitudes specify their impact.Each associated eigenvalue,referred to as DMD eigenvalue,characterizes the frequency and growth rate of the DMD mode.In this paper,we demonstrate how the components of DMD can be utilized to obtain temporal and spatial information from time-dependent flow fields.We begin with the theoretical background of DMD and its application to unsteady flow.Next,we examine the conventional process with DMD mathematically and put it in relationship to the discrete Fourier transform.Our analysis shows that the current use of DMD components has several drawbacks.To resolve these problems we adjust the components and provide new and meaningful insights into the decomposition:we show that our improved components capture the spatio-temporal patterns of the flow better.Moreover,we remove redundancies in the decomposition and clarify the interplay between components,allowing users to understand the impact of components.These new representations,which respect the spatio-temporal character of DMD,enable two clustering methods that segment the flow into physically relevant sections and can therefore be used for the selection of DMD components.With a number of typical examples,we demonstrate that the combination of these techniques allows new insights with DMD for unsteady flow.

Numerical analysis of the correlation between fluid dynamic modes and hydrodynamic noise in flows around a three-dimensional circular cylinder

The flow around a circular cylinder for Re=1000 is characterized by flow separation and Karman vortex street.The typical flow features can be captured to study the correlation between fluid fields and sound fields.In this paper,the three-dimensional circular cylinder is taken as the research object,and the probes of surface fluctuating pressure and far field sound pressure are arranged every 10°.The directional diagram and the coherence of fluctuating pressure and sound pressure are analyzed.The relationship between the flow mode and hydrodynamic noise is studied by using dynamic mode decomposition(DMD).The characteristics of the dipole and quadrupole sound source term of a long span cylinder are studied.The results show that at the angles between 30°–120°and 190°–350°,the fluctuating pressure contributes more to the generation of dipole sounds.The quadrupole sound source shows three-dimensional effects,which is more obvious in a cylinder with large spanwise length.

Fast Model-based Design of High Performance Permanent Magnet Machine for Next Generation Electric Propulsion for Urban Aerial Vehicle Application

Model order reduction(MOR)is considered as a good alternative to reduce the computational scale for electro-magnetic problems.The aim of this work is to introduce the use of dynamic mode decomposition(DMD)as a promising tool for MOR to analyze its effectiveness in creating a fast model-based design platform for the permanent magnet motor design for ur-ban aerial vehicles(UAVs).Using a singular value decomposition(SVD)based DMD,the design process is constructed and verified against different scenarios.

Constructing reduced model for complex physical systems via interpolation and neural networks

The work studies model reduction method for nonlinear systems based on proper orthogonal decomposition (POD)and discrete empirical interpolation method (DEIM). Instead of using the classical DEIM to directly approximate thenonlinear term of a system, our approach extracts the main part of the nonlinear term with a linear approximation beforeapproximating the residual with the DEIM. We construct the linear term by Taylor series expansion and dynamic modedecomposition (DMD), respectively, so as to obtain a more accurate reconstruction of the nonlinear term. In addition, anovel error prediction model is devised for the POD-DEIM reduced systems by employing neural networks with the aid oferror data. The error model is cheaply computable and can be adopted as a remedy model to enhance the reduction accuracy.Finally, numerical experiments are performed on two nonlinear problems to show the performance of the proposed method.

Comparative study of reduced-order modeling method for the cavitating flow over a hydrofoil

As a high-dimensional complex nonlinear dynamic system,the analysis of the essence of flow has always been a difficult problem,especially in the flow including phase change.In recent years,it has become a feasible method to reduce the dimension of flow structure by reduced-order modeling(ROM)methods.In this paper,through the cavitation numerical simulation of NACA0015 hydrofoil,two ROM methods are used to reduce and restore three different cavitation respectively-proper orthogonal decomposition(POD)and dynamic mode decomposition(DMD).The applicability of two methods in cavitation is discussed and reasons are analyzed.The results show that for stable cavitation,POD,DMD methods can accurately restore the flow field of a few modes with high energy.For unstable cavitation,only POD method can restore real flow field well.This situation is mainly due to the fact that POD,DMD method are applicable to different energy ratios,and different main mode selection criterion of DMD will lead to different main mode.ROM can greatly simplify the complexity of flow.Selecting a reasonable ROM can improve the accuracy of a small amount of database,and provide a basis for intelligent prediction of flow analysis.

Modal Analysis of Axial Compressor Tip Rotating Instability under Varying Operating Conditions

The full annulus numerical research was performed on a low-speed compressor rotor to investigate the rotating instability in the tip region.The frequency spectra show the existence of rotating instability at narrow stable operating range.With the decrease of flow rate,31 cells of flow disturbance can be found in the instantaneous flow field.The distribution of vortex suggests that the circumferential propagation of the interaction between tip leakage vortex and adjacent blade brings about these cells.The dynamic mode decomposition(DMD)method and spatial discrete Fourier transform(SDFT)were applied to obtain the circumferential mode features,and the results indicate that the rotating instability is associated with the 31 cells of flow disturbance.Then the DMD method was further applied on the pressure data from a circle and an annulus domain,so as to extract different mode components with the corresponding spatial structures,frequencies and amplitudes.The results suggest that DMD modes can display the flow feature and explore the evolution of each instability source in the tip flow field.

A Confined Laminar Slot Impinging Jet at Low Reynolds Numbers:Unsteady Flow and Heat Transfer Characteristics

In this study,the unsteady flow and heat transfer characteristics of a laminar slot jet at low Reynolds numbers impinging on an isothermal plate surface in a two-dimensional confined space are numerically investigated.The investigations are performed at Reynolds numbers of 120,150 and 200 based on the nozzle width and mean inlet velocity of the jet.Results show that the Reynolds numbers of 120,150 and 200 correspond to different flow features,namely,a steady flow,an intermittent flapping motion of jet column and a continuous sinusoidal flapping state,respectively.Based on some time snapshots of the flow field,the dynamic characteristics and driving mechanism of the intermittent flapping motion of the jet column and the continuous sinusoidal flapping state are explained.When the jet flaps at the Reynolds number 150 and 200,there are other Nusselt number peaks outside the stagnation zone,which are related to the interference between the vortices shedding on both sides of the jet and the boundary layers of the plate surface.Furthermore,the dynamic mode decomposition is implemented to accurately extract flow modes with characteristic frequencies.For a Reynolds number of 150,there is a flapping mode,which describes the lateral flapping motion of the jet column.When the Reynolds number is 200,there are multiple modes related to the flapping motion of the jet,as well as a low-frequency mode,which reflects the periodic changes of the boundary contour and position of the recirculation zone.

Soft Sensors for Property‑Controlled Multi‑Stage Press Hardening of 22MnB5

In multi-stage press hardening,the product properties are determined by the thermo-mechanical history during the sequence of heat treatment and forming steps.To measure these properties and finally to control them by feedback,two soft sensors are developed in this work.The press hardening of 22MnB5 sheet material in a progressive die,where the material is first rapidly austenitized,then pre-cooled,stretch-formed,and finally die bent,serves as the framework for the development of these sensors.To provide feedback on the temporal and spatial temperature distribution,a soft sensor based on a model derived from the Dynamic mode decomposition(DMD)is presented.The model is extended to a parametric DMD and combined with a Kalman filter to estimate the temperature(-distribution)as a function of all process-relevant control vari-ables.The soft sensor can estimate the temperature distribution based on local thermocouple measurements with an error of less than 10°C during the process-relevant time steps.For the online prediction of the final microstructure,an artificial neural network(ANN)-based microstructure soft sensor is developed.As part of this,a transferable framework for deriving input parameters for the ANN based on the process route in multi-stage press hardening is presented,along with a method for developing a training database using a 1-element model implemented with LS-Dyna and utilizing the material model Mat248(PHS_BMW).The developed ANN-based microstructure soft sensor can predict the final microstructure for specific regions of the formed and hardened sheet in a time span of far less than 1 s with a maximum deviation of a phase fraction of 1.8%to a reference simulation.

Flow characteristics around airfoils near transonic buffet onset conditions

In transonic flow,buffet is a phenomenon of flow instability caused by shock wave/boundary layer interaction and flow separation.The phenomenon is common in transonic flow,and it has serious impact on the structural strength and fatigue life of aircraft.In this paper,three typical airfoils:the supercritical OAT15A,the high-speed symmetrical NACA64A010,and the thin,transonic/supersonic NACA64A204 are selected as the research objects.The flow fields of these airfoils under pre-buffet and buffet onset conditions are simulated by Unsteady Reynolds Averaged Navier-Stokes (URANS) method,and the mode analysis of numerical results is carried out by Dynamic Mode Decomposition (DMD).Qualitative and quantitative analysis of the shock wave motion,shock wave intensity,shock foot bubble and trailing edge separation,and pressure coefficient fluctuation were performed to attain deep insight of transonic buffet flow features of different airfoils near buffet onset conditions.The results of DMD analysis show that the energy proportion of the steady mode of these airfoils decreases dramatically when approaching the buffet onset angle of attack,while the growth rate of the primary mode increases inversely.It was found that at the onset of buffet,there exist different degrees of merging behavior between shock foot bubble and trailing edge separation during one buffet cycle,and the instability of shock wave and separation induced shear layer are closely related to the merging behavior.

Effects of temperature on drag reduction in a subsonic turbulent boundary layer via micro-blowing array

A comparative study of two micro-blowing temperature cases has been performed to investigate the characteristics of drag reduction in a subsonic flat-plate flow(where the freestream Mach number is 0.7) by means of Direct Numerical Simulation(DNS). With minute amount of blowing gas injected from a 32 × 32 array of micro-holes arranged in a staggered pattern, the porosity of micro-holes is 23% and the blowing coefficient is 0.125%. The simulation results show that a drag reduction is achieved by micro-blowing, and a lower wall-friction drag can be obtained at a higher blowing temperature. The role of micro-blowing is to redistribute the total kinetic energy in the boundary layer, and the proportion of stream-wise kinetic energy decreases, resulting in the thickened boundary layer. Increasing micro-blowing temperature can accelerate this process and obtain an enhanced drag reduction. Moreover, an explanation of drag reduction by microblowing related to the micro-jet vortex clusters is proposed that these micro-jet vortex clusters firmly attached to the wall constitute a stable barrier, which is to prevent the direct contact between the stream-wise vortex and the wall. By Dynamic Mode Decomposition(DMD) from temporal/spatial aspects, it is revealed that small structures in the near-wall region play vital role in the change of turbulent scales. The high-frequency patterns are clearly strengthened, and the lowfrequency patterns just maintain but are lifted up.

Automatic detection and assessment of crack development in ultra-high performance concrete in the spatial and Fourier domains

Automatic detection and assessment of surface cracks are beneficial for understanding the mechanical performance of ultra-high performance concrete(UHPC).This study detects crack evolution using a novel dynamic mode decomposition(DMD)method.In this method,the sparse matrix‘determined’from images is used to reconstruct the foreground that contains cracks,and the global threshold method is adopted to extract the crack patterns.The application of the DMD method to the three-point bending test demonstrates the efficiency in inspecting cracks with high accuracy.Accordingly,the geometric features,including the area and its projection in two major directions,are evaluated over time.The relationship between the geometric properties of cracks and load-displacement curves of UHPC is discussed.Due to the irregular shape of cracks in the spatial domain,the cracks are then transformed into the Fourier domain to assess their development.Results indicate that crack patterns in the Fourier domain exhibit a distinct concentration around a central position.Moreover,the power spectral density of cracks exhibits an increasing trend over time.The investigation into crack evolution in both the spatial and Fourier domains contributes significantly to elucidating the mechanical behavior of UHPC.