Separation of Unsteady Scales in a Mixing Layer Using Empirical Mode Decomposition

Ansell, Phillip J.; Balajewicz, Maciej

doi:10.2514/1.j055120

Cited by 14 publications

(3 citation statements)

References 54 publications

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“…This adaptability to signal variability makes it a suitable algorithm for processing complex and diverse nonlinear signals. Recently, this method has been applied in the fluid mechanics research community, and its performance quality has been proven [21,[36][37][38][39][40].…”

Section: Uav Image Processingmentioning

confidence: 99%

Decomposition of Submesoscale Ocean Wave and Current Derived from UAV-Based Observation

Kim,

Lee,

Jeong

et al. 2024

Remote Sensing

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The consecutive submesoscale sea surface processes observed by an unmanned aerial vehicle (UAV) were used to decompose into spatial waves and current features. For the image decomposition, the Fast and Adaptive Multidimensional Empirical Mode Decomposition (FA-MEMD) method was employed to disintegrate multicomponent signals identified in sea surface optical images into modulated signals characterized by their amplitudes and frequencies. These signals, referred to as Bidimensional Intrinsic Mode Functions (BIMFs), represent the inherent two-dimensional oscillatory patterns within sea surface optical data. The BIMFs, separated into seven modes and a residual component, were subsequently reconstructed based on the physical frequencies. A two-dimensional Fast Fourier Transform (2D FFT) for each high-frequency mode was used for surface wave analysis to illustrate the wave characteristics. Wavenumbers (Kx, Ky) ranging between 0.01–0.1 radm−1 and wave directions predominantly in the northeastward direction were identified from the spectral peak ranges. The Optical Flow (OF) algorithm was applied to the remaining consecutive low-frequency modes as the current signal under 0.1 Hz for surface current analysis and to estimate a current field with a 1 m spatial resolution. The accuracy of currents in the overall region was validated with in situ drifter measurements, showing an R-squared (R2) value of 0.80 and an average root-mean-square error (RMSE) of 0.03 ms−1. This study proposes a novel framework for analyzing individual sea surface dynamical processes acquired from high-resolution UAV imagery using a multidimensional signal decomposition method specialized in nonlinear and nonstationary data analysis.

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Section: Uav Image Processingmentioning

confidence: 99%

Decomposition of Submesoscale Ocean Wave and Current Derived from UAV-Based Observation

Kim,

Lee,

Jeong

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…non-orthogonal modes and the mode-mixing problem, it has been successfully applied in turbulence-scale separation. With EMD, Huang et al (2008) studied the scaling properties and intermittency of homogeneous turbulence, and Ansell & Balajewicz (2017) analysed the features of large-scale vortical structures in a turbulent mixing layer. Agostini & Leschziner (2014 used bidimensional empirical mode decomposition (BEMD) to analyse the modulation of large-scale motions on the small-scale eddies in the near-wall region, and later they discussed the scale-specific contributions of large-and small-scale structures to the friction-drag generation by means of the FIK and RD identities (Agostini & Leschziner 2019) in channel flows.…”

Section: Introductionmentioning

confidence: 99%

Decomposition of the mean friction drag on an NACA4412 airfoil under uniform blowing/suction

et al. 2021

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The application of drag-control strategies on canonical wall-bounded turbulence, such as periodic channel and zero- or adverse-pressure-gradient boundary layers, raises the question on how to distinguish consistently the origin of control effects under different reference conditions. We employ the RD identity (Renard & Deck, J. Fluid Mech., vol. 790, 2016, pp. 339–367) to decompose the mean friction drag and investigate the control effects of uniform blowing and suction applied to an NACA4412 airfoil at chord Reynolds numbers $Re_c=200\,000$ and $400\,000$ . The connection of the drag reduction/increase by using blowing/suction with the turbulence statistics (including viscous dissipation, turbulence kinetic energy production and spatial growth of the flow) across the boundary layer, subjected to adverse or favourable pressure gradients, is examined. We found that the inner and outer peaks of the contributions associated with the friction-drag generation show good scaling with either inner or outer units, respectively. They are also independent of the Reynolds number, control scheme and intensity of the blowing/suction. The small- and large-scale structures are separated with an adaptive scale-decomposition method, namely the empirical mode decomposition (EMD), which aims to analyse the scale-specific contribution of turbulent motions to friction-drag generation. Results unveil that blowing on the suction side of the airfoil is able to enhance the contribution of large-scale motions and to suppress that of small scales; however, suction behaves contrarily. The contributions related to cross-scale interactions remain almost unchanged with different control strategies.

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“…It is in principle free from pre-established basis functions and represents the original signal as a superposition of several mono-components and a residual, with the characteristic wavelengths of the signals automatically determined. With EMD, Huang et al (2008) studied the scaling properties and intermittency of homogeneous turbulence, and Ansell and Balajewicz (2017) analyzed the features of large-scale vortical structures in a turbulent mixing layer. Leschziner (2014, 2016) used bidimensional empirical mode decomposition (BEMD) to analyze the modulation of large-scale motions on the small-scale eddies in the nearwall region, and later they discussed the scale-specific contributions of large-and small-scale structures to the friction-drag generation by means of FIK and RD identity (Agostini and Leschziner, 2019) in channel flows.…”

Section: Introductionmentioning

confidence: 99%

Decomposition of the mean friction drag on a NACA4412 airfoil under uniform blowing/suction

Fan,

Atzori,

Vinuesa

et al. 2021

Preprint

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The application of drag-control strategies on canonical wall-bounded turbulence, such as periodic channel and zero-or adverse-pressure-gradient boundary layers, raises the question of how to describe control effects consistently for different reference cases. We employ the RD identity (Renard & Deck, J. Fluid Mech., 790, 2016, pp. 339-367) to decompose the mean friction drag and investigate the control effects of uniform blowing and suction applied to a NACA4412 airfoil at chord Reynolds numbers Re c =200, 000 and 400, 000. The connection of the drag reduction/increase by using blowing/suction with the turbulence statistics (including viscous dissipation, turbulence-kinetic-energy production, and spatial growth of the flow) across the boundary layer, subjected to adverse or favorable pressure gradients, are examined. We found that the peaks of the statistics associated with the friction-drag generation exhibit good scaling in either inner or outer units throughout the boundary layer. They are also independent of the Reynolds number, control scheme, and intensity of the blowing/suction. The small-and large-scale structures are separated with an adaptive scale-decomposition method, i.e. empirical mode decomposition (EMD), aiming to analyze the scale-specific contribution of turbulent motions to friction-drag generation. Results unveil that blowing on the suction side of the airfoil is able to enhance the contribution of large-scale motions and to suppress that of small-scales; on the other hand, suction behaves contrarily. The contributions related to cross-scale interactions remain almost unchanged with different control strategies.

show abstract

Separation of Unsteady Scales in a Mixing Layer Using Empirical Mode Decomposition

Cited by 14 publications

References 54 publications

Decomposition of Submesoscale Ocean Wave and Current Derived from UAV-Based Observation

Decomposition of Submesoscale Ocean Wave and Current Derived from UAV-Based Observation

Decomposition of the mean friction drag on an NACA4412 airfoil under uniform blowing/suction

Decomposition of the mean friction drag on a NACA4412 airfoil under uniform blowing/suction

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