Minimal multi-scale dynamics of near-wall turbulence

Doohan, Patrick; Willis, Ashley P.; Hwang, Yongyun

doi:10.1017/jfm.2020.1182

Cited by 30 publications

(147 citation statements)

References 65 publications

(140 reference statements)

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“…It is therefore hoped that the GQL approximation offers a new perspective for these issues, while confirming the previous findings obtained by 'non-intrusive' analysis (e.g. Cho et al 2018;Doohan et al 2021).…”

Section: Introductionsupporting

confidence: 86%

“…λ z ≈ 57η, where η is the Kolmogorov scale). Finally, the turbulent transport spectra are weakly positive in the region very close to the wall (y + < 10) over a wide range of the spanwise wavelength scales (200δ ν λ z 1h) and this is due to an inverse energy transfer from small to large scales (Cho et al 2018;Doohan et al 2021).…”

Section: Spectral Energy Transfermentioning

confidence: 98%

“…This is also a direct extension of the previous studies of the QL model (Thomas et al 2014(Thomas et al , 2015Farrell et al 2017;Pausch et al 2019;Hernández & Hwang 2020) using the GQL approximation, as the QL model offers the dynamics of such energy-containing streamwise waves in a minimal manner. The latter case is more directly related to the scale interaction and energy transfer between the self-sustaining processes at different integral length scales, the issues of which have been studied recently by Cho et al (2018) and Doohan, Willis & Hwang (2021). The spanwise length scale has been understood to represent the size of coherent structures sustained by the self-sustaining process (Hwang 2015).…”

Section: Introductionmentioning

confidence: 99%

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Generalised quasilinear approximations of turbulent channel flow. Part 1. Streamwise nonlinear energy transfer

2022

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A generalised quasilinear (GQL) approximation (Marston et al., Phys. Rev. Lett., vol. 116, 2016, 104502) is applied to turbulent channel flow at $Re_\tau \simeq 1700$ ( $Re_\tau$ is the friction Reynolds number), with emphasis on the energy transfer in the streamwise wavenumber space. The flow is decomposed into low- and high-streamwise-wavenumber groups, the former of which is solved by considering the full nonlinear equations whereas the latter is obtained from the linearised equations around the former. The performance of the GQL approximation is subsequently compared with that of a QL model (Thomas et al., Phys. Fluids, vol. 26, 2014, 105112), in which the low-wavenumber group only contains zero streamwise wavenumber. It is found that the QL model exhibits a considerably reduced multi-scale behaviour at the given moderately high Reynolds number. This is improved significantly by the GQL approximation which incorporates only a few more streamwise Fourier modes into the low-wavenumber group, and it reasonably well recovers the distance-from-the-wall scaling in the turbulence statistics and spectra. Finally, it is proposed that the energy transfer from the low- to the high-wavenumber group in the GQL approximation, referred to as the ‘scattering’ mechanism, depends on the neutrally stable leading Lyapunov spectrum of the linearised equations for the high-wavenumber group. In particular, it is shown that if the threshold wavenumber distinguishing the two groups is sufficiently high, the scattering mechanism can be completely absent due to the linear nature of the equations for the high-wavenumber group.

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Section: Introductionsupporting

confidence: 86%

Section: Spectral Energy Transfermentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Generalised quasilinear approximations of turbulent channel flow. Part 1. Streamwise nonlinear energy transfer

2022

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“…Finally, the observations made for transition in the present study offer some important outlooks for the relevance of the Orr mechanism to fully-developed turbulent flow. The existence of the Orr mechanism has repeatedly been reported by several previous studies [61][62][63][64]. These studies are primarily concerned with the generation of wall-normal velocity structure via the Orr mechanism in relation to the self-sustaining process of the energy-containing eddies (or coherent structures) in wall-bounded turbulence [53,[65][66][67].…”

Section: Discussionmentioning

confidence: 69%

“…These studies are primarily concerned with the generation of wall-normal velocity structure via the Orr mechanism in relation to the self-sustaining process of the energy-containing eddies (or coherent structures) in wall-bounded turbulence [53,[65][66][67]. However, the precise role and origin of the Orr mechanism in fully-developed turbulence are still matters of debate: it was initially proposed that the Orr mechanism is a part of the self-sustaining process at a given length scale for the generation of the wall-normal velocity structure [61][62][63], but a recent study showed that the Orr mechanism can well be initiated by the wall-normal velocity structure originating from the energy cascade of 'larger' energy-containing eddies [64]. In any case, it should be pointed out that all these investigations ignore the amplification of the spanwise velocity structures caused by the lift-up effect following the Orr mechanism.…”

Section: Discussionmentioning

confidence: 99%

Orr mechanism in transition of parallel shear flow

2021

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The Orr mechanism is revisited to understand its precise role in transition of plane Couette flow. By considering homogenous shear flow and plane Couette flow, it is identified that the Orr mechanism induces a lift-up effect which significantly amplifies spanwise velocity. An optimal perturbation analysis for individual velocity component reveals that the amplification of spanwise velocity is most active at the streamwise length comparable to the given spanwise length of the perturbation. The relevance of this mechanism to transition is subsequently examined in plane Couette flow. To this end, a set of initial conditions, which combines the optimal perturbation for spanwise velocity with the one for all the velocity components, is considered by varying their amplitudes. Two representative transition scenarios are found: oblique and streak transitions. In the former, the spanwise velocity perturbation amplified with the Orr mechanism initiates both streak amplification and breakdown, whereas, in the latter, its role is limited only to the streak breakdown at the late stage of transition. As such, the oblique transition offers a route to turbulence energetically more efficient than the streak transition at least for the cases examined in the present study. Finally, the oblique transition is found to share many physical similarities to the transition by the minimal seed.

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Deep Reinforcement Learning for the Management of the Wall Regeneration Cycle in Wall-Bounded Turbulent Flows

Cavallazzi,

Guastoni,

Vinuesa

et al. 2024

Flow Turbulence Combust

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The wall cycle in wall-bounded turbulent flows is a complex turbulence regeneration mechanism that remains not fully understood. This study explores the potential of deep reinforcement learning (DRL) for managing the wall regeneration cycle to achieve desired flow dynamics. To create a robust framework for DRL-based flow control, we have integrated the StableBaselines3 DRL libraries with the open-source direct numerical simulation (DNS) solver CaNS. The DRL agent interacts with the DNS environment, learning policies that modify wall boundary conditions to optimise objectives such as the reduction of the skin-friction coefficient or the enhancement of certain coherent structures’ features. The implementation makes use of the message-passing-interface (MPI) wrappers for efficient communication between the Python-based DRL agent and the DNS solver, ensuring scalability on high-performance computing architectures. Initial experiments demonstrate the capability of DRL to achieve drag reduction rates comparable with those achieved via traditional methods, although limited to short time intervals. We also propose a strategy to enhance the coherence of velocity streaks, assuming that maintaining straight streaks can inhibit instability and further reduce skin-friction. Our results highlight the promise of DRL in flow-control applications and underscore the need for more advanced control laws and objective functions. Future work will focus on optimising actuation intervals and exploring new computational architectures to extend the applicability and the efficiency of DRL in turbulent flow management.

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Minimal multi-scale dynamics of near-wall turbulence

Abstract: Abstract

Cited by 30 publications

References 65 publications

Generalised quasilinear approximations of turbulent channel flow. Part 1. Streamwise nonlinear energy transfer

Generalised quasilinear approximations of turbulent channel flow. Part 1. Streamwise nonlinear energy transfer

Orr mechanism in transition of parallel shear flow

Deep Reinforcement Learning for the Management of the Wall Regeneration Cycle in Wall-Bounded Turbulent Flows

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