Impact of Turbulent Time Scales on Wake Recovery and Operation

Hodgson, Emily L.; Madsen, M H Aa; Troldborg, Niels; Andersen, Søren Juhl

doi:10.1088/1742-6596/2265/2/022022

Cited by 9 publications

(8 citation statements)

References 21 publications

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“…Whereas tip vortices hinder the transport of momentum to the inner core of the wake (Lignarolo et al 2015), large wake structures, like coherent meandering, can enhance the transport. Frederik et al (2020), Korb et al (2023) and Hodgson et al (2023) have shown that periodic excitation of the wake can lead to faster recovery. The different studies found an optimum excitation frequency around St ≈ 0.3, similar to our results.…”

Section: Discussion About Enhanced Recovery and The Nonlinear Dynamicsmentioning

confidence: 99%

“…(2023) and Hodgson et al. (2023) have shown that periodic excitation of the wake can lead to faster recovery. The different studies found an optimum excitation frequency around , similar to our results.…”

Section: Transition-region Dynamicsmentioning

confidence: 99%

“…After Gupta & Wan (2019), wake meandering occurs in the far wake due to the ‘amplification of upstream disturbances by shear flow instabilities’, a phenomenon also observed by Gambuzza & Ganapathisubramani (2023). Periodic excitation in the range of , where wake meandering naturally occurs, can lead to an early onset of meandering (Mao & Sørensen 2018; Gupta & Wan 2019; Hodgson, Madsen & Andersen 2023). Following this idea, some control strategies, such as the helix approach (a blade pitching control method), trigger instabilities that lead to the formation of large coherent structures in the wake (Frederik et al.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced recovery caused by nonlinear dynamics in the wake of a floating offshore wind turbine

Messmer,

Hölling,

Peinke

2024

J. Fluid Mech.

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An experimental study in a wind tunnel is presented to explore the wake of a floating wind turbine subjected to harmonic side-to-side and fore–aft motions under laminar inflow conditions. The wake recovery is analysed as a function of the frequency of motion $f_p$ , expressed by the rotor-based Strouhal number, $St = f_p D / U_{\infty }$ ( $D$ is the rotor diameter, $U_{\infty }$ the inflow wind speed). Our findings indicate that both directions of motion accelerate the transition to the far-wake compared with the fixed turbine. The experimental outcomes confirm the computational fluid dynamics results of Li et al. (J. Fluid Mech., vol. 934, 2022, p. A29) showing that sideways motions lead to faster wake recovery, especially for $St \in [0.2, 0.6]$ . Additionally, we find that fore–aft motions also lead to better recovery for $St \in [0.3, 0.9]$ . The recovery is closely linked to nonlinear spatiotemporal dynamics found in the shear layer region of the wake. For both directions of motion and $St \in [0.2, 0.55]$ , the noisy wake dynamics lock in to the frequency of the motion. In this synchronised-like state, sideways motions result in large coherent structures of meandering, and fore–aft movements induce coherent pulsing of the wake. For fore–aft motion and $St \in [0.55, 0.9]$ , the wake shows a more complex quasiperiodic dynamic, namely, a self-generated meandering mode emerges, which interacts nonlinearly with the excitation frequency $St$ , as evidenced by the occurrence of mixing components. The coherent structures grow nonlinearly, enhance wake mixing and accelerate the transition to the far-wake, which, once reached, exhibits universal behaviour.

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Section: Discussion About Enhanced Recovery and The Nonlinear Dynamicsmentioning

confidence: 99%

Section: Transition-region Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhanced recovery caused by nonlinear dynamics in the wake of a floating offshore wind turbine

Messmer,

Hölling,

Peinke

2024

J. Fluid Mech.

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“…3 using the same finitevolume solver as for the URANS simulations, although in a three-dimensional version instead, EllipSys3D [12]. The LES setup is the same as was used in a recent cross-code study by Hodgson et al (2023) [13] and details about the numerical setup can be found there. One difference in the setup is however that a slightly different grid is used: The domain size is L x × L y × L z = 2.56 km×2.56 km×1.0 km with horizontal grid spacing ∆x = ∆y = 10 m, while the vertical grid spacing is the same as in the URANS.…”

Section: Numerical Solver and Setupmentioning

confidence: 99%

Simulation of a conventionally neutral boundary layer with two-equation URANS

Baungaard,

Van Der Laan,

Kelly

et al. 2024

J. Phys.: Conf. Ser.

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Simulating conventionally neutral boundary layers (CNBLs) with the unsteady Reynolds-Averaged Navier-Stokes (URANS) technique is investigated in this paper using a modified two-equation linear eddy viscosity turbulence model. For CNBLs over a flat and uniform surface, as typically used as the inflow to wind farm simulations, the governing equations of URANS can be solved with a one-dimensional solver, which makes the simulation of a typical CNBL five to six orders of magnitude faster than with large-eddy simulation (LES) approaches. However, URANS on the other hand requires more modelling than LES, and its accuracy is heavily dependent on the turbulence model employed. Through a cross-code study of a CNBL case with data from five different LES codes, it is found that the length-scale limiter of the employed turbulence model should be removed to correctly predict the atmospheric boundary layer (ABL) height evolution and the qualitative shape of various atmospheric profiles. A parametric study of simulations with varying initial ABL height further demonstrates the prediction capabilities of URANS, although a comparison with LES data shows that modelling of turbulence anisotropy and near-surface turbulence could be improved.

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“…Large-Eddy Simulations (LES) capture unsteadiness, a wide range of turbulence scales and energy fluxes, thereby providing a more detailed description of the instantaneous flow field than RANS and catching the interaction between atmosphere and wind farm, especially in buoyancy-driven flows [25,26]. Previous studies have shown changes to the dynamical inflow due to induction [30,31], and changes in amplitudes and turbulent length scales can potentially affect the wake development [32]. Yet, with blockage being driven mainly by the mean flow characteristics, it is expected to be accurately quantified with well-conducted RANS simulations.…”

Section: Introductionmentioning

confidence: 99%