Decomposition of unsteady sheet/cloud cavitation dynamics in fluid-structure interaction via POD and DMD methods

Liu, Yunqing; Wu, Qing; Huang, Biao; Zhang, Hanzhe; Liang, Wendong; Wang, Guoyu

doi:10.1016/j.ijmultiphaseflow.2021.103690

Cited by 55 publications

(7 citation statements)

References 43 publications

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“…Unsteady cavitating flows are challenging to analyze due to high-dimensional data. Reduced Order Models (ROMs), like POD [5], have been used to reduce costs. Advanced machine learning techniques like LSTM have been introduced in fluid mechanics.…”

Section: Introductionmentioning

confidence: 99%

Reduced-order modelling of unsteady cavitating flow around a Clark-Y hydrofoil

Zhang,

Liu,

et al. 2024

J. Phys.: Conf. Ser.

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This paper proposes a novel approach that combines Proper Orthogonal Decomposition (POD) reduced-order system with Long Short-Term Memory (LSTM) neural network to predict flow velocity. Large Eddy Simulation (LES) is used to simulate the cavitating flow around a NACA66 hydrofoil. POD is adopted to reduce the dimensionality of the high-dimensional data. It was found that 66.81% of the flow field energy and dominant coherent structures can be captured with first eight POD modes. The LSTM network model was further used to predict the temporal data of the POD mode coefficients, and the error of the predicted coefficients was within an acceptable range. The reconstructed flow field agrees well with the real flow field and the cavitation development has also been well illustrated. This method provides a promising and efficient alternative for flow prediction and has potential for applications in fluid dynamics, aerospace engineering, and hydrodynamics.

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Section: Introductionmentioning

confidence: 99%

Reduced-order modelling of unsteady cavitating flow around a Clark-Y hydrofoil

Zhang,

Liu,

et al. 2024

J. Phys.: Conf. Ser.

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“…The conventional analysis methods, due to the turbulence characteristics, have limitations in obtaining flow information. To overcome this, the modal analysis method, such as proper orthogonal decomposition, has been employed to process experimental data, enabling the acquisition of more detailed information and facilitating the understanding of these physical mechanisms 11–13 . Moreau et al 14 conducted experimental research on the flow characteristics stirred by the Rushton turbine using particle image velocimetry (PIV) technology and analyzed the velocity field through the POD method.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this, the modal analysis method, such as proper orthogonal decomposition, has been employed to process experimental data, enabling the acquisition of more detailed information and facilitating the understanding of these physical mechanisms. [11][12][13] Moreau et al 14 conducted experimental research on the flow characteristics stirred by the Rushton turbine using particle image velocimetry (PIV) technology and analyzed the velocity field through the POD method. The results indicate the effective extraction of periodic fluctuation components of turbulent flow through this approach.…”

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confidence: 99%

Flow stirred by open turbine: A modal analysis of multiscale flow characteristics

Jin,

Bu,

Fan

et al. 2024

Asia-Pacific J Chem Eng

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The turbulence observed in the stirred tank is a result of the superposition of flows at different scales, each exerting various influences on the operational processes. To understand the flow characteristics associated with stirring, this paper undertakes particle image velocimetry (PIV) experimental research on an unbaffled tank stirred by an open turbine featuring four distinct blade deflection angles. The flow field in the unbaffled tank manifests as a double‐circulation flow pattern; however, it is worth noting that the blade deflection angle exerts a significant impact on the stirring process. Specifically, when the blade deflection angle reached 90°, the flow discharge aligned closely with the radial direction, accompanied by pronounced fluctuations in the high‐velocity region. The Lagrangian coherent structures (LCS) formed were subsequently extracted and subjected to finite‐time Lyapunov exponent (FTLE) analysis. Notably, a more regular wake pattern emerged in proximity to the blade tip, while a larger scale flow structure persisted along the flow direction. To systematically analyze the flow field stirred by the open turbine, modal analysis was employed. By reconstructing the flow field based on the principle of 50% energy content, the decomposition of flows at different scales was achieved. The turbulent kinetic energy (TKE) generated by the various scale flows was subsequently calculated, revealing that the high TKE values primarily originate from the large‐scale flow structure. Conversely, the TKE generated by the small‐scale flow exhibits a uniform distribution across different regions. Notably, in regions characterized by higher velocities, the peak TKE associated with the small‐scale flow also manifests; however, it is significantly smaller in magnitude compared to that generated by the original flow or the large‐scale flow.

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“…POD interpolation relies on the number of variables, while POD projection relies on the governing equation for solving [9]. For example, the dominant coherent structure of cavitation flow is studied using the POD and DMD methods by Liu et al [10]. Lu et al [11] applied the POD method to double rotor bearing downscaling and investigated the frequency characteristics of the deviation response of a double rotor bearing coupling.…”

Section: Introductionmentioning

confidence: 99%

Rapid Prediction of the In Situ Pyrolysis Performance of Tar-Rich Coal Using the POD Method

Wang,

Ye,

et al. 2023

Processes

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In this paper, a POD reduced-order interpolation model for solving the in situ pyrolysis process of tar-rich coal is employed to predict the flow and heat transfer performance in the porous media region so as to save computational resources and realize fast calculations. Numerical simulation using the finite volume method (FVM) is firstly used to obtain sample data, based on the samples through the primary function and spectral coefficients of the solutions. The physical field information and parameter distribution under different conditions of inlet temperature, inlet velocity and permeability are predicted. The results are compared with those of FVM to verify the accuracy of the calculated results. The relative mean deviation (RME) of the results of the POD prediction of each parameter for each working condition was synthesized to be no more than 5%. The performance of in situ pyrolysis of tar-rich coal is then investigated, and the oil and gas production are predicted. As the inlet velocity increases from 0.3 m/s to 0.9 m/s, the fraction of high-quality oil and gas production reaches 0.47 and then decreases to 0.38. Increasing the inlet temperature and permeability has a negative effect on the fraction of high-quality hydrocarbon production, after which the quality fraction of high-quality oil and gas dropped sharply to about 0.22. Porosity has a positive impact on the oil and gas production. When the porosity reaches 0.3, the quality fraction of high-quality oil and gas can reach 0.27.

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Decomposition of unsteady sheet/cloud cavitation dynamics in fluid-structure interaction via POD and DMD methods

Cited by 55 publications

References 43 publications

Reduced-order modelling of unsteady cavitating flow around a Clark-Y hydrofoil

Reduced-order modelling of unsteady cavitating flow around a Clark-Y hydrofoil

Flow stirred by open turbine: A modal analysis of multiscale flow characteristics

Rapid Prediction of the In Situ Pyrolysis Performance of Tar-Rich Coal Using the POD Method

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