Dynamic mode decomposition for flow field and scalar field of coaxial jet with swirl

2017 ◽  
Vol 2017.66 (0) ◽  
pp. 423
Author(s):  
Yuta MAEKAWA ◽  
Yasumasa ITO ◽  
Yasuhiko SAKAI ◽  
Koji IWANO ◽  
Koji NAGATA ◽  
...  
Keyword(s):  
Scalar Field ◽  
Flow Field ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Mode Decomposition

scholarly journals Analysis of transonic buffet using dynamic mode decomposition

2021 ◽  
Vol 62 (4) ◽  
Author(s):  
Antje Feldhusen-Hoffmann ◽  
Christian Lagemann ◽  
Simon Loosen ◽  
Pascal Meysonnat ◽  
Michael Klaas ◽  
...  
Keyword(s):  
Shock Wave ◽  
Flow Field ◽  
Acoustic Waves ◽  
Feedback Loop ◽  
Measurement Data ◽  
Dynamic Mode Decomposition ◽  
Recirculation Region ◽  
Dynamic Mode ◽  
Mode Decomposition ◽  
Transonic Buffet

AbstractThe buffet flow field around supercritical airfoils is dominated by self-sustained shock wave oscillations on the suction side of the wing. Theories assume that this unsteadiness is driven by a feedback loop of disturbances in the flow field downstream of the shock wave whose upstream propagating part is generated by acoustic waves. High-speed particle-image velocimetry measurements are performed to investigate this feedback loop in transonic buffet flow over a supercritical DRA 2303 airfoil. The freestream Mach number is $$M_{\infty } = 0.73$$ M ∞ = 0.73 , the angle of attack is $$\alpha = 3.5^{\circ }$$ α = 3 . 5 ∘ , and the chord-based Reynolds number is $${\mathrm{Re}}_{c} = 1.9\times 10^6$$ Re c = 1.9 × 10 6 . The obtained velocity fields are processed by sparsity-promoting dynamic mode decomposition to identify the dominant dynamic features contributing strongest to the buffet flow field. Two pronounced dynamic modes are found which confirm the presence of two main features of the proposed feedback loop. One mode is related to the shock wave oscillation frequency and its shape includes the movement of the shock wave and the coupled pulsation of the recirculation region downstream of the shock wave. The other pronounced mode represents the disturbances which form the downstream propagating part of the proposed feedback loop. The frequency of this mode corresponds to the frequency of the acoustic waves which are generated by these downstream traveling disturbances and which form the upstream propagating part of the proposed feedback loop. In this study, the post-processing, i.e., the DMD, is highlighted to substantiate the existence of this vortex mode. It is this vortex mode that via the Lamb vector excites the shock oscillations. The measurement data based DMD results confirm numerical findings, i.e., the dominant buffet and vortex modes are in good agreement with the feedback loop suggested by Lee. Graphic abstract

Analysis of dynamic stall flow field of high bypass fan rotor based on airworthiness certification

2020 ◽  
Vol 234 (11) ◽  
pp. 1757-1769
Author(s):  
Kai Zhang ◽  
AJ Wang
Keyword(s):  
Flow Field ◽  
Unsteady Flow ◽  
Field Data ◽  
Decomposition Methods ◽  
Modal Identification ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Complex Flow ◽  
Mode Decomposition ◽  
Proper Orthogonal

In order to ensure flight safety, the stall test is one of the most important steps in the airworthiness certification phase of civil aircraft. The twisted-swept fan is one of the most important components of the high bypass ratio engine. The unsteady flow field of the fan rotor stall condition is obtained by numerical simulation. At the same time, the time series flow field data of the stall condition flow field is acquired. The modal analysis of the unsteady flow field at stall condition was performed using the dynamic mode decomposition and proper orthogonal decomposition methods. Through modal identification of a large number of unsteady flow field data, the eigenvalues and corresponding modal information about the unsteady flow field change process are obtained. Finally, the evolution process of the unsteady flow field of the fan rotor under stall condition is visually demonstrated, and the coherent structures of different scales in the complex flow field under stall condition are revealed.

scholarly journals Identification of Vortex Structures in Flow Field Images Based on Convolutional Neural Network and Dynamic Mode Decomposition

2019 ◽  
Vol 36 (6) ◽  
pp. 501-506 ◽  
Author(s):  
Zhixian Ye ◽  
Qian Chen ◽  
Yang Zhang ◽  
Jianfeng Zou ◽  
Yao Zheng
Keyword(s):  
Neural Network ◽  
Flow Field ◽  
Convolutional Neural Network ◽  
Vortex Structures ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Mode Decomposition

Dynamic Mode Decomposition for Extracting Cycle-to-Cycle Variation of SIDI Engine In-Cylinder Flow Under Motoring Condition

2020 ◽  
Author(s):  
Mengqi Liu ◽  
Fengnian Zhao ◽  
Xuesong Li ◽  
Min Xu ◽  
David L. S. Hung
Keyword(s):  
Flow Field ◽  
Direct Injection ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Field Variation ◽  
Dynamic Features ◽  
Time Resolved ◽  
Cycle Variation ◽  
Mode Decomposition ◽  
Cylinder Flow

Abstract Cycle-to-cycle variation (CCV) of in-cylinder flow strongly affects the performance and efficiency of spark ignition direct injection (SIDI) engines. In order to achieve a precise flow control inside the engine, the underlying dynamic features of flow field CCV must be thoroughly investigated. In this work, large-eddy simulations (LES) with 50 consecutive cycles are employed for high fidelity numerical realizations of engine flow under motoring condition. To supplement the numerical analysis, time-resolved particle image velocimetry (PIV) measurements are also conducted in several cutting planes. Although the velocity root mean square (RMS) is calculated to quantify the cyclic variation intensity of simulation and experiment results, some important dynamic characteristics cannot be observed directly from velocity data. Therefore, dynamic mode decompositions (DMD), which is a widely used modal decomposition algorithm on fluid study, is used to decompose flow fields into modes with specific frequencies and provide growth rates of corresponding flow structures. This spectral information of in-cylinder flow field is ponderable for uncovering dynamic features of engine CCV. In this study, DMD algorithm is applied on both LES and PIV datasets. The frequency and growth rate differences are employed to elucidate the CCV feature deviations captured by LES and PIV. This research provides a guideline for extracting engine flow field cyclic variability feature using DMD algorithm. Based on the discussion for spectral features and potential sources of flow field variation, the capability of LES to capture CCV features is evaluated. The DMD spectrum differences between PIV and LES can guide the boundary condition perturbations used for simulation fidelity improvements.

scholarly journals Numerical Analysis of the Flow around Two Square Cylinders in a Tandem Arrangement with Different Spacing Ratios Based on POD and DMD Methods

Processes ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 903
Author(s):  
Feng Wang ◽  
Xiaodong Zheng ◽  
Jianming Hao ◽  
Hua Bai
Keyword(s):  
Flow Field ◽  
Flow Characteristics ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Square Cylinder ◽  
Main Mode ◽  
Square Cylinders ◽  
Mode Decomposition ◽  
The Difference ◽  
Proper Orthogonal

To more clearly understand the changes in flow characteristics around two square cylinders with different spacing ratios, the main mode of the flow field was extracted by using the Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD) methods. The changes in the main mode of the flow field at different spacing ratios and the difference of the time series were analyzed and compared. This processing can separate the mixed information in the flow field and obtain the dominant modes in the flow field. These main modes can clearly reflect the dominant flow characteristics in the flow field. The analysis results show that when L/D = 2, the flow field structure is consistent with the flow field around a single square cylinder. When L/D = 2.5–3.5, the vortex shedding from upstream cylinders combines with the vortex near the downstream cylinders. This mutual coupling causes a significant change in the drag coefficient value of the downstream cylinder. When L/D = 4, the main vortex from the upstream cylinder can be completely shed, which means that the upstream and downstream square cylinder vortices start to become independent. The main focus of this paper is to use the advantages of POD and DMD to obtain several modes with higher energy in the flow field. Furthermore, it can be considered that these main modes can fully reflect the flow characteristics of the flow field.

scholarly journals Study on Flow Characteristics in Volute of Centrifugal Pump Based on Dynamic Mode Decomposition

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Yi-bin Li ◽  
Chang-hong He ◽  
Jian-zhong Li
Keyword(s):  
Flow Field ◽  
Unsteady Flow ◽  
Centrifugal Pump ◽  
Flow Characteristics ◽  
Internal Flow ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Rate Condition ◽  
Blade Passing Frequency ◽  
Mode Decomposition

To investigate the unsteady flow characteristics and their influence mechanism in the volute of centrifugal pump, the Reynolds time-averaged N-S equation, RNG k-ε turbulence model, and structured grid technique are used to numerically analyze the transient flow-field characteristics inside the centrifugal pump volute. Based on the quantified parameters of flow field in the volute of centrifugal pump, the velocity mode contours and oscillation characteristics of the mid-span section of the volute of centrifugal pump are obtained by dynamic mode decomposition (DMD) for the nominal and low flow-rate condition. The research shows that the first-order average flow mode extracted by DMD is the dominant flow structure in the flow field of the volute. The second-order and third-order modes are the most important oscillation modes causing unsteady flow in the volute, and the characteristic frequency of the two modes is consistent with the blade passing frequency and the 2x blade passing frequency obtained by the fast Fourier transform (FFT). By reconstructing the internal flow field of the volute with the blade passing frequency for the nominal flow-rate condition, the periodic variation of the unsteady flow structure in the volute under this frequency is visually reproduced, which provides some ideas for the study of the unsteady structure in the internal flow field of centrifugal pumps.

Investigation of rotating instability characteristics in an axial compressor with different tip clearances

2021 ◽  
pp. 095441002199362
Author(s):  
Yadong Wu ◽  
Tao Li ◽  
Shengzhi Lai ◽  
Jie Tian ◽  
Hua Ouyang
Keyword(s):  
Flow Field ◽  
Strong Stability ◽  
Tip Clearance ◽  
Dynamic Mode Decomposition ◽  
Field Energy ◽  
Dynamic Mode ◽  
Mode Decomposition ◽  
Blade Tip ◽  
Rotating Instability ◽  
Blade Tip Clearance

It is believed that the rotating instability phenomenon originating in the compressor tip region is due to leakage flow, which is closely associated with the blade tip clearance. In this work, we have studied the correlation between the dynamic characteristics of blade tip flow and the size of tip clearance for a single-stage low-speed compressor rotor, so as to unveil the mechanism of rotating instability. The full-passage numerical simulations were carried out to obtain the variations in frequency, circumferential mode, and spatial flow field associated with rotating instability. The results of spatial mode decomposition with open clearance show the number of predominate instability modes identified are 25 and 30, respectively. By diminishing the blade tip clearance, all these unstable modes greatly diminished. The formation and propagation of the tip leakage vortex were described in detail to show the development of rotating instability. Two flow field reduced-order methods, proper orthogonal decomposition and dynamic mode decomposition, were used to analyze the flow field, energy proportion, and stability of related modes under different tip clearances. The results show that the first several modes with strong stability account for a large proportion of energy and make a major contribution to flow unsteadiness. The energy proportion and stability of rotating instability decrease as the tip clearance becomes smaller. The blade-passing frequency and its multiples emerge as the main components of the flow field.

scholarly journals Dynamic Mode Decomposition Analysis of Spatially Agglomerated Flow Databases

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2134
Author(s):  
Binghua Li ◽  
Jesús Garicano-Mena ◽  
Yao Zheng ◽  
Eusebio Valero
Keyword(s):  
Flow Field ◽  
Clustering Algorithms ◽  
Decomposition Analysis ◽  
Turbulent Channel Flow ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Complex Flow ◽  
Computational Performance ◽  
Mode Decomposition ◽  
Multiple Variables

Dynamic Mode Decomposition (DMD) techniques have risen as prominent feature identification methods in the field of fluid dynamics. Any of the multiple variables of the DMD method allows to identify meaningful features from either experimental or numerical flow data on a data-driven manner. Performing a DMD analysis requires handling matrices V ∈ R n p × N , where n p and N are indicative of the spatial and temporal resolutions. The DMD analysis of a complex flow field requires long temporal sequences of well resolved data, and thus the memory footprint may become prohibitively large. In this contribution, the effect that principled spatial agglomeration (i.e., reduction in n p via clustering) has on the results derived from the DMD analysis is investigated. We compare twelve different clustering algorithms on three testcases, encompassing different flow regimes: a synthetic flow field, a R e D = 60 flow around a cylinder cross section, and a R e τ ≈ 200 turbulent channel flow. The performance of the clustering techniques is thoroughly assessed concerning both the accuracy of the results retrieved and the computational performance. From this assessment, we identify DBSCAN/HDBSCAN as the methods to be used if only relatively high agglomeration levels are affordable. On the contrary, Mini-batch K-means arises as the method of choice whenever high agglomeration n p ˜ / n p ≪ 1 is possible.

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