Experimental Investigation of Coherent Structure Dynamics in a Submerged Forced Jet

2013 ◽  
Vol 8 (1) ◽  
pp. 56-64
Author(s):  
Sergey Abdurakipov ◽  
Vladimir Dulin ◽  
Dmitriy Markovich
Keyword(s):  
Coherent Structures ◽  
Vortex Formation ◽  
Velocity Fields ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Analysis Tool ◽  
Modulation Amplitude ◽  
Mode Decomposition ◽  
Image Velocimetry ◽  
Statistical Analysis Tool

The present work investigates the dynamics of coherent structures, including their scales and intensity, in an initial region of a submerged round forced jet by a Particle Image Velocimetry (PIV) technique for measurements of instantaneous velocity fields and statistical analysis tool Dynamic Mode Decomposition (DMD). The PIV measurements were carried out with 1,1 kHz acquisition rate. Application of DMD to the measured set of the velocity fields provided information about dominant frequencies, contained in DMD spectrum, of velocity fluctuations in different flow regions and about scales of the corresponding spatial coherent structures, contained in DMD modes. Additional calculations of time-spectra from turbulent fluctuations showed good agreement between frequencies of the main harmonics and characteristic frequencies of the dominant dynamic modes. Superposition of relevant DMD modes approximately described nonlinear interaction of coherent structures: vortex formation, their quasi-periodic pairing with modulation amplitude of generated harmonics

scholarly journals Tomographic Particle Image Velocimetry and Dynamic Mode Decomposition (DMD) in a Rectangular Impinging Jet: Vortex Dynamics and Acoustic Generation

Fluids ◽  
2021 ◽  
Vol 6 (12) ◽  
pp. 429
Author(s):  
Hassan H. Assoum ◽  
Jana Hamdi ◽  
Marwan Alkheir ◽  
Kamel Abed Meraim ◽  
Anas Sakout ◽  
...  
Keyword(s):  
Particle Image Velocimetry ◽  
Acoustic Field ◽  
Coherent Structures ◽  
Particle Image ◽  
Flow Dynamics ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Tomographic Particle Image Velocimetry ◽  
Mode Decomposition ◽  
Image Velocimetry

Impinging jets are encountered in ventilation systems and many other industrial applications. Their flows are three-dimensional, time-dependent, and turbulent. These jets can generate a high level of noise and often present a source of discomfort in closed areas. In order to reduce and control such mechanisms, one should investigate the flow dynamics that generate the acoustic field. The purpose of this study is to investigate the flow dynamics and, more specifically, the coherent structures involved in the acoustic generation of these jets. Model reduction techniques are commonly used to study the underlying mechanisms by decomposing the flow into coherent structures. The dynamic mode decomposition (DMD) is an equation-free method that relies only on the system’s data taken either through experiments or through numerical simulations. In this paper, the DMD technique is applied, and the spatial modes and their frequencies are presented. The temporal content of the DMD’s modes is then correlated with the acoustic signal. The flow is generated by a rectangular jet impinging on a slotted plate (for a Reynolds number Re = 4458) and its kinematic field is obtained via the tomographic particle image velocimetry technique (TPIV). The findings of this research highlight the coherent structures signature in the DMD’s spectral content and show the cross correlations between the DMD’s modes and the acoustic field.

scholarly journals Dynamic mode decomposition of inertial particle caustics in Taylor–Green flow

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Omstavan Samant ◽  
Jaya Kumar Alageshan ◽  
Sarveshwar Sharma ◽  
Animesh Kuley
Keyword(s):  
Turbulent Flows ◽  
Particle Image ◽  
Experimental System ◽  
Stokes Number ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Complex Structures ◽  
Inertial Particles ◽  
Mode Decomposition ◽  
Image Velocimetry

AbstractInertial particles advected by a background flow can show complex structures. We consider inertial particles in a 2D Taylor–Green (TG) flow and characterize particle dynamics as a function of the particle’s Stokes number using dynamic mode decomposition (DMD) method from particle image velocimetry (PIV) like-data. We observe the formation of caustic structures and analyze them using DMD to (a) determine the Stokes number of the particles, and (b) estimate the particle Stokes number composition. Our analysis in this idealized flow will provide useful insight to analyze inertial particles in more complex or turbulent flows. We propose that the DMD technique can be used to perform similar analysis on an experimental system.

Dynamic-mode decomposition based analysis of shear coaxial jets with and without transverse acoustic driving

2016 ◽  
Vol 790 ◽  
pp. 5-32 ◽  
Author(s):  
Jia-Chen Hua ◽  
Gemunu H. Gunaratne ◽  
Douglas G. Talley ◽  
James R. Gord ◽  
Sukesh Roy
Keyword(s):  
Coherent Structures ◽  
Flux Ratio ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Oscillatory Behaviour ◽  
Natural Approach ◽  
Coaxial Jets ◽  
Mode Decomposition ◽  
Transverse Acoustic ◽  
Injector Flows

Modal decompositions of unperturbed and acoustically driven injector flows from shear coaxial jets are implemented using dynamic-mode decomposition, which is a natural approach in the search for collective oscillatory behaviour in nonlinear systems. Previous studies using proper orthogonal decomposition had revealed the most energetic pairs of coherent structures in injector flows. One of the difficulties in extracting lower-energy coherent structures follows from the need to differentiate robust flow constituents from noise and other irregular facets of a flow. The identification of robust features is critical for applications such as flow control as well, since only they can be used for the tasks. A dynamic-mode decomposition based algorithm for this differentiation is introduced and used to identify different classes of robust dynamic modes. They include (1) background modes located outside the injector flow that decay rapidly, (2) injector modes – including those presented in earlier studies – located in the vicinity of the flow, (3) modes that persist under acoustic driving, (4) modes responding linearly to the driving and, most interestingly, (5) a mode whose density exhibits antiphase oscillatory behaviour in the observation plane and that appears only when $J$, the outer-to-inner-jet momentum flux ratio, is sufficiently large; we infer that this is a projection of a mode rotating about the symmetry axis and born via a spontaneous symmetry breaking. Each of these classes of modes is analysed as $J$ is increased, and their consequences for the flow patterns are discussed.

scholarly journals Dynamic reconstruction and data reconstruction for subsampled or irregularly sampled data

2017 ◽  
Vol 825 ◽  
pp. 133-166
Author(s):  
Jakub Krol ◽  
Andrew Wynn
Keyword(s):  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Decomposition Algorithms ◽  
Cylinder Wake ◽  
Sampled Data ◽  
Mode Decomposition ◽  
Image Velocimetry ◽  
Flow Evolution ◽  
Irregularly Sampled Data ◽  
Low Order

The Nyquist–Shannon criterion indicates the sample rate necessary to identify information with particular frequency content from a dynamical system. However, in experimental applications such as the interrogation of a flow field using particle image velocimetry (PIV), it may be impracticable or expensive to obtain data at the desired temporal resolution. To address this problem, we propose a new approach to identify temporal information from undersampled data, using ideas from modal decomposition algorithms such as dynamic mode decomposition (DMD) and optimal mode decomposition (OMD). The novel method takes a vector-valued signal, such as an ensemble of PIV snapshots, sampled at random time instances (but at sub-Nyquist rate) and projects onto a low-order subspace. Subsequently, dynamical characteristics, such as frequencies and growth rates, are approximated by iteratively approximating the flow evolution by a low-order model and solving a certain convex optimisation problem. The methodology is demonstrated on three dynamical systems, a synthetic sinusoid, the cylinder wake at Reynolds number $Re=60$ and turbulent flow past the axisymmetric bullet-shaped body. In all cases the algorithm correctly identifies the characteristic frequencies and oscillatory structures present in the flow.

scholarly journals Data-Driven Pulsatile Blood Flow Physics with Dynamic Mode Decomposition

Fluids ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 111
Author(s):  
Milad Habibi ◽  
Scott T. M. Dawson ◽  
Amirhossein Arzani
Keyword(s):  
Blood Flow ◽  
Flow Rate ◽  
Coherent Structures ◽  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Patient Specific ◽  
Dynamic Mode ◽  
Flow Data ◽  
Reduced Order ◽  
Mode Decomposition

Dynamic mode decomposition (DMD) is a purely data-driven and equation-free technique for reduced-order modeling of dynamical systems and fluid flow. DMD finds a best fit linear reduced-order model that represents any given spatiotemporal data. In DMD, each mode evolves with a fixed frequency and therefore DMD modes represent physically meaningful structures that are ranked based on their dynamics. The application of DMD to patient-specific cardiovascular flow data is challenging. First, the input flow rate is unsteady and pulsatile. Second, the flow topology can change significantly in different phases of the cardiac cycle. Finally, blood flow in patient-specific diseased arteries is complex and often chaotic. The objective of this study was to overcome these challenges using our proposed multistage dynamic mode decomposition with control (mDMDc) method and use this technique to study patient-specific blood flow physics. The inlet flow rate was considered as the controller input to the systems. Blood flow data were divided into different stages based on the inlet flow waveform and DMD with control was applied to each stage. The system was augmented to consider both velocity and wall shear stress (WSS) vector data, and therefore study the interaction between the coherent structures in velocity and near-wall coherent structures in WSS. First, it was shown that DMD modes can exactly represent the analytical Womersley solution for incompressible pulsatile flow in tubes. Next, our method was applied to image-based coronary artery stenosis and cerebral aneurysm models where complex blood flow patterns are anticipated. The flow patterns were studied using the mDMDc modes and the reconstruction errors were reported. Our augmented mDMDc framework could capture coherent structures in velocity and WSS with a fewer number of modes compared to the traditional DMD approach and demonstrated a close connection between the velocity and WSS modes.

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