Comparison of Dynamic Mode Decomposition and Deep Learning Techniques for Two-Phase Flows Analysis

Ramos, Eliaquim Monteiro; Darze, Gabriella Machado; Nascimento, Francisco Rogério Teixeira do; Faccini, José Luiz Horacio; Giraldi, Gilson A.

doi:10.1007/s10494-020-00151-z

Cited by 10 publications

(2 citation statements)

References 33 publications

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“…Sun Guoyong et al [88] used DMD to reconstruct the two-dimensional flow field around the cylinder, and the error was less than 10 −10 . However, for a more complex flow system, before using DMD to decompose the flow field, the complexity of the flow field should be analyzed to determine the amount of data to be obtained, to reduce errors, and to avoid distortion when reconstructing the flow field [89]. DMD can also be used for flow field predictions.…”

Section: Mode Decomposition and The Order Reduction Modelmentioning

confidence: 99%

Research, Application and Future Prospect of Mode Decomposition in Fluid Mechanics

Long,

Guo,

Xiao

2024

Symmetry

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In fluid mechanics, modal decomposition, deeply intertwined with the concept of symmetry, is an essential data analysis method. It facilitates the segmentation of parameters such as flow, velocity, and pressure fields into distinct modes, each exhibiting symmetrical or asymmetrical characteristics in terms of amplitudes, frequencies, and phases. This technique, emphasizing the role of symmetry, is pivotal in both theoretical research and practical engineering applications. This paper delves into two dominant modal decomposition methods, infused with symmetry considerations: Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD). POD excels in dissecting flow fields with clear periodic structures, often showcasing symmetrical patterns. It utilizes basis functions and time coefficients to delineate spatial modes and their evolution, highlighting symmetrical or asymmetrical transitions. In contrast, DMD effectively analyzes more complex, often asymmetrical structures like turbulent flows. By performing iterative analyses on the flow field, DMD discerns symmetrical or asymmetrical statistical structures, assembling modal functions and coefficients for decomposition. This method is adapted to extracting symmetrical patterns in vibration frequencies, growth rates, and intermodal coupling. The integration of modal decomposition with symmetry concepts in fluid mechanics enables the effective extraction of fluid flow features, such as symmetrically or asymmetrically arranged vortex configurations and trace evolutions. It enhances the post-processing analysis of numerical simulations and machine learning approaches in flow field simulations. In engineering, understanding the symmetrical aspects of complex flow dynamics is crucial. The dynamics assist in flow control, noise suppression, and optimization measures, thus improving the symmetry in system efficiency and energy consumption. Overall, modal decomposition methods, especially POD and DMD, provide significant insights into the symmetrical and asymmetrical analysis of fluid flow. These techniques underpin the study of fluid mechanics, offering crucial tools for fluid flow control, optimization, and the investigation of nonlinear phenomena and propagation modes in fluid dynamics, all through the lens of symmetry.

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Section: Mode Decomposition and The Order Reduction Modelmentioning

confidence: 99%

Research, Application and Future Prospect of Mode Decomposition in Fluid Mechanics

Long,

Guo,

Xiao

2024

Symmetry

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“…2. This flow regime induces large variations in mass flow rate, pressure, and velocity in both radial and axial directions being inherently unsteady (Ramos et al, 2020). This behavior induces severe consequences for industrial installations as the flow-accelerated corrosion, liquid impact-induced erosion, flow-induced vibration (Arabi et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Horizontal Gas-liquid Intermittent Flow Sub-Regimes Transitions: Physical Mechanisms and Flow Maps

Arabi,

salhi,

Zenati

et al. 2023

Preprint

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These recent years, several experimental investigations were carried out on horizontal gas-liquid two-phase sub-regimes including plug, Less Aerated Slug (LAS) and Highly Aerated Slug (HAS) flows. Such classification is based on the shape of the elongated bubble/liquid slug interface as well as the aeration in the liquid slugs. In this paper, an extensive experimental visualization was performed using air-water mixture in a 30 mm ID horizontal pipe covering a total of 189 operating conditions. It was reported, for the first time, that each sub-regime had a specific mechanism of aeration of the liquid slug flow. For instance, the presence of gas bubbles within liquid slugs, in case of plug flow, is due to the interfacial shear induced waves mechanism. The transitions to LAS and HAS flows are accompanied by the appearance of shearing-off and scooping mechanisms of bubbles, in the liquid film, into the liquid slug, respectively. In addition, the reported flow transition lines, as well as those existing in the literature, were analyzed using different existing coordinate systems. This analysis showed that the X-T system is the most suitable for predicting the three flow sub-regimes.

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Reconstruction and prediction of rising bubble by Lagrange DMD in data-driven

Yin

Jia

Yuan

et al. 2023

Chemical Engineering Research and Design

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Comparison of Dynamic Mode Decomposition and Deep Learning Techniques for Two-Phase Flows Analysis

Cited by 10 publications

References 33 publications

Research, Application and Future Prospect of Mode Decomposition in Fluid Mechanics

Research, Application and Future Prospect of Mode Decomposition in Fluid Mechanics

Analysis of Horizontal Gas-liquid Intermittent Flow Sub-Regimes Transitions: Physical Mechanisms and Flow Maps

Reconstruction and prediction of rising bubble by Lagrange DMD in data-driven

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