Multi-scale/fractal processes in the wake of a wind turbine array boundary layer

Ali, Naseem; Fuchs, André; Neunaber, Ingrid; Peinke, Joachim; Cal, Raúl Bayoán

doi:10.1080/14685248.2019.1590584

Cited by 16 publications

(12 citation statements)

References 76 publications

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“…This is even more the case in field studies where stability or flow discharge are unlikely to be stationary for such durations. Hence, a velocity-intermittency analysis framework better suited to the study of shorter duration time series has been proposed for [29] and applied in various experimental and numerical contexts [20,38,53].…”

Section: Turbulence and Intermittencymentioning

confidence: 99%

“…It was observed in earlier work that the pointwise Hölder regularity of the velocity time series (a measure of the intermittency of the time series signal, defined more formally, below) was highly correlated between velocity components [34], and that the dimensionality of "active periods" within the flow could be determined using this measure [35]. From these observations a velocity-intermittency technique has been developed to illustrate the difference in turbulent structure between jets, wakes, and boundary-layers [29], and to show how the flow over bed-forms and in the wake of wind turbines has a unique structure compared to these canonical flows [36][37][38]. In this study, the technique is further developed and applied to both atmospheric and aquatic vegetation flows to unfold their common characteristics, thereby opening up the possibility to formulate new Lagrangian models for turbulent canopy flows.…”

Section: Introductionmentioning

confidence: 99%

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A joint velocity-intermittency analysis reveals similarity in the vertical structure of atmospheric and hydrospheric canopy turbulence

et al. 2019

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Turbulent flow through and over vegetation continues to draw significant research attention given its relevance to a plethora of applications in earth and environmental science. Canopy flows are characterized by three-dimensional coherent vortical motions not directly accessible from single-point measurements, which pose a challenge to formalizing links between vegetation structure and turbulent motion. A joint velocity-intermittency technique is applied to velocity data collected within and above aquatic vegetation in a hydraulic flume and above a forested canopy. The approach reveals behavior that provides greater insight into canopy flow dynamics than may be inferred from the vertical profiles of mean velocity, turbulence intensity and Reynolds stresses, which are the quantities usually studied. There is a remarkable similarity in the structure of such flows between the forest canopy and the flume study despite large differences in morphology and stem rigidity. In particular, these results determine an outer flow type arising above 1.5 canopy heights, while turbulent in-rushing events are most significant at the zero-plane displacement. The approach also implies ways in which improved models for canopy turbulence may be developed.

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Section: Turbulence and Intermittencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A joint velocity-intermittency analysis reveals similarity in the vertical structure of atmospheric and hydrospheric canopy turbulence

et al. 2019

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“…These methods can be applied easily to other wind quantities like the temporal behaviour of shears, or wind veers, eventually combined in higherdimensional stochastic processes (Siefert and Peinke, 2006). The results reported in Ali et al (2019) show that such a stochastic modelling can also be used for wake flows.…”

Section: Discussionmentioning

confidence: 97%

“…The downside of this increasing share of fluctuating renewable energy sources is their integration into the power grid. By analysing measurements of fed-in wind and solar power, it could be shown that their fluctuations strongly deviate from Gaussian behaviour on timescales ranging from hours to seconds (Anvari et al, 2016) and for wind power even for scales below 1 s (Haehne et al, 2018). This survival of the atmospheric intermittency in the power grid poses the grid operators with the great challenge to ensure stable power supply, even under highly volatile conditions.…”

Section: Introductionmentioning

confidence: 99%

Multipoint reconstruction of wind speeds

2020

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Abstract. The most intermittent behaviour of atmospheric turbulence is found for very short timescales. Based on a concatenation of conditional probability density functions (cpdf's) of nested wind speed increments, inspired by a Markov process in scale, we derive a short-time predictor for wind speed fluctuations around a non-stationary mean value and with a corresponding non-stationary variance. As a new quality this short-time predictor enables a multipoint reconstruction of wind data. The used cpdf's are (1) directly estimated from historical data from the offshore research platform FINO1 and (2) obtained from numerical solutions of a family of Fokker–Planck equations in the scale domain. The explicit forms of the Fokker–Planck equations are estimated from the given wind data. A good agreement between the statistics of the generated and measured synthetic wind speed fluctuations is found even on timescales below 1 s. This shows that our approach captures the short-time dynamics of real wind speed fluctuations very well. Our method is extended by taking the non-stationarity of the mean wind speed and its non-stationary variance into account.

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“…This methods can be applied easily to other wind quantities like the temporal behaviour of shears, or wind veers, eventually combined in higher dimensional stochastic processes (SIEFERT and PEINKE, 2006). The results reported in (Ali et al, 2019) show that such a stoachastic modelling can also be used for wake flows.…”

mentioning

confidence: 96%

Multipoint Reconstruction of Wind Speeds

Behnken¹,

Wächter²,

Peinke³

2020

Preprint

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Abstract. The most intermittent behavior of atmospheric turbulence is found for very short time scales. Based on a concatenation of conditional probability density functions (cpdfs) of nested wind speeds increments, inspired by a Markov process in scale, we derive a short-time predictor for wind speed fluctuations around a non-stationary mean value and with a corresponding non-stationary variance. As a new quality this short time predictor enables a multipoint reconstruction of wind data. The used cpdfs are (1) directly estimated from historical data from the offshore research platform FINO1 and (2) obtained from numerical solutions of a family of Fokker-Planck equations in the scale domain. The explicit forms of the Fokker-Planck equations are estimated from the given wind data. A good agreement between the statistics of the generated synthetic wind speed fluctuations and the measured is found even on time scales below 1 s. This shows that our approach captures the short-time dynamics of real wind speed fluctuations very well. Our method is extended by taking the non-stationarity of the mean wind speed and its non-stationary variance into account.

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Multi-scale/fractal processes in the wake of a wind turbine array boundary layer

Cited by 16 publications

References 76 publications

A joint velocity-intermittency analysis reveals similarity in the vertical structure of atmospheric and hydrospheric canopy turbulence

A joint velocity-intermittency analysis reveals similarity in the vertical structure of atmospheric and hydrospheric canopy turbulence

Multipoint reconstruction of wind speeds

Multipoint Reconstruction of Wind Speeds

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