Spanwise-coherent hydrodynamic waves around flat plates and airfoils

Abreu, Leandra I.; Tanarro, Álvaro; Cavalieri, André V. G.; Schlatter, Philipp; Vinuesa, Ricardo; Hanifi, Ardeshir; Henningson, Dan S.

doi:10.1017/jfm.2021.718

Cited by 21 publications

(17 citation statements)

References 52 publications

(93 reference statements)

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“…Note that these authors also took into account the data by Kornilov and Boiko [106] to formulate a more realistic estimate of the power consumption by blowing, and they reported a net-energy saving of around 5%. It is interesting to note that other data-driven methods may help to model the near-wall region and, consequently, may provide novel venues for improved flow control [107][108][109][110][111].…”

Section: Data-driven Methods For Control and Deep Reinforcement Learningmentioning

confidence: 99%

Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

Vinuesa

Lehmkuhl

Lozano-Durán

et al. 2022

Fluids

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In this review, we summarize existing trends of flow control used to improve the aerodynamic efficiency of wings. We first discuss active methods to control turbulence, starting with flat-plate geometries and building towards the more complicated flow around wings. Then, we discuss active approaches to control separation, a crucial aspect towards achieving a high aerodynamic efficiency. Furthermore, we highlight methods relying on turbulence simulation, and discuss various levels of modeling. Finally, we thoroughly revise data-driven methods and their application to flow control, and focus on deep reinforcement learning (DRL). We conclude that this methodology has the potential to discover novel control strategies in complex turbulent flows of aerodynamic relevance.

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Section: Data-driven Methods For Control and Deep Reinforcement Learningmentioning

confidence: 99%

Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

Vinuesa

Lehmkuhl

Lozano-Durán

et al. 2022

Fluids

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“…(2021) and Abreu et al. (2021), are compared with the available SPOD modes. The governing Navier–Stokes equations are linearised around a Blasius profile base flow and cast in an input–output form, as described in § 3.…”

Section: Convergence Analysis Of Spodmentioning

confidence: 99%

“…With the purpose of providing a deeper insight of the intrinsic dynamics of the boundary layer system, global resolvent modes, computed according to the method presented in Kaplan et al (2021) and Abreu et al (2021), are compared with the available SPOD modes.…”

Section: Comparison With Resolvent Analysismentioning

confidence: 99%

“…2019; Abreu et al. 2021) and the identification of coherent structures in channels (Abreu et al. 2020 a ), wakes (Nidhan et al.…”

Section: Introductionmentioning

confidence: 99%

“…Later works employing SPOD include: the development of data-driven ROMs for boundary layer transition control (Sasaki et al 2020), characterisation of noise generated over airfoils (Sano et al 2019;Abreu et al 2021) and the identification of coherent structures in channels (Abreu et al 2020a), wakes (Nidhan et al 2020;Li et al 2021), separated high-speed flows (Lugrin et al 2021) and complex geometries (Zhang, Ooka & Kikumoto 2021;He et al 2021).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved convergence of the spectral proper orthogonal decomposition through time shifting

Blanco

Martini²,

Sasaki³

et al. 2022

J. Fluid Mech.

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Spectral proper orthogonal decomposition (SPOD) is an increasingly popular modal analysis method in the field of fluid dynamics due to its specific properties: a linear system forced with white noise should have SPOD modes identical to response modes from resolvent analysis. The SPOD, coupled with the Welch method for spectral estimation, may require long time-resolved datasets. In this work, a linearised Ginzburg–Landau model is considered in order to study the method's convergence. Spectral proper orthogonal decomposition modes of the white-noise forced equation are computed and compared with corresponding response resolvent modes. The quantified error is shown to be related to the time length of Welch blocks (spectral window size) normalised by a convective time. Subsequently, an algorithm based on a temporal data shift is devised to further improve SPOD convergence and is applied to the Ginzburg–Landau system. Next, its efficacy is demonstrated in a numerical database of a boundary layer subject to bypass transition. The proposed approach achieves substantial improvement in mode convergence with smaller spectral window sizes with respect to the standard method. Furthermore, SPOD modes display growing wall-normal and spanwise velocity components along the streamwise direction, a feature which had not yet been observed and is also predicted by a global resolvent calculation. The shifting algorithm for the SPOD opens the possibility for using the method on datasets with time series of moderate duration, often produced by large simulations.

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Resolvent model for aeroacoustics of trailing edge noise

Demange,

Yuan,

Jekosch

et al. 2024

Theor. Comput. Fluid Dyn.

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This study presents a physics-based, low-order model for the trailing edge (TE) noise generated by an airfoil at low angle of attack. The approach employs incompressible resolvent analysis of the mean flow to extract relevant spanwise-coherent structures in the transitional boundary layer and near wake. These structures are integrated into Curle’s solution to Lighthill’s acoustic analogy to obtain the scattered acoustic field. The model has the advantage of predicting surface pressure fluctuations from first principles, avoiding reliance on empirical models, but with a free amplitude set by simulation data. The model is evaluated for the transitional flow ($$\text {Re} = 5e4$$ Re = 5 e 4 ) around a NACA0012 airfoil at 3 deg angle of attack, which features TE noise with multiple tones. The mean flow is obtained from a compressible large eddy simulation, and spectral proper orthogonal decomposition (SPOD) is employed to extract the main hydrodynamic and acoustic features of the flow. Comparisons between resolvent and SPOD demonstrate that the physics-based model accurately captures the leading coherent structures at the main tones’ frequencies, resulting in a good agreement of the reconstructed acoustic power with that of the SPOD (within 4 dB). Discrepancies are observed at high frequencies, likely linked to nonlinearities that are not considered in the resolvent analysis. The model’s directivity aligns well with the data at low Helmholtz numbers, but it fails at high frequencies where the back-scattered pressure plays a significant role in directivity. This modeling approach opens the way for efficient optimization of airfoil shapes in combination with low-fidelity mean flow solvers to reduce TE noise. Graphical abstract

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Spanwise-coherent hydrodynamic waves around flat plates and airfoils

Cited by 21 publications

References 52 publications

Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

Improved convergence of the spectral proper orthogonal decomposition through time shifting

Resolvent model for aeroacoustics of trailing edge noise

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