Scale evolution, intermittency and fluctuation relations in the near-wake of a wind turbine array. (February 2019)

• Record Type: Journal Article
• Authors: Ali, Naseem; Cal, Raúl Bayoán
• Is Part Of: Chaos, solitons and fractals. Volume 119(2019)
• Page Start: 215
• Page End: 229
• Publication Date: 2019-02
• Subjects: Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2018.12.018
• Languages: English
• ISSNs: 0960-0779

Abstract

Highlights: Near-wake wind turbine array is statistically characterized using multifractal framework. The scale evolution that breaks the self-similarity is quantified. Kolmogorov's assumption is tested via a conditional average technique. The statistical properties of velocity-intermittency quadrant demonstrate a dependence between the velocity and velocity increments. A model for the velocity-intermittency contribution is presented. Abstract: Scale evolution statistics are used to analyze the flow structure of the wake flow in the boundary layer of a wind turbine array. Flow behind a 3 × 3 wind turbine array is examined experimentally via X-type hot-wire anemometry, with data obtained in a 9 × 21 array of measurement locations positioned 0.5 rotor diameter downwind of the third row of turbines. It is observed that the structure evolution tends towards non universality as the spanwise distance from the rotor is reduced. The extended self-similarity result highlights the deviation from the self-similarity is marginal in the second order, and becomes stark with increasing the order. Also, the region below the hub height near the rotor carries the most intermittent flow in comparison with the region outside the swept area. Flow features in the wakes are characterized via correlation between the velocity and multifractal Hölder exponent related to the velocity increment via the Frisch–Parisi conjecture. Flow events are also classified using the sign of the fluctuating streamwise velocity and the Hölder exponent into quadrants. The second and fourth quadrants of the velocity and the intermittency are dominant at the hub height and bottom tip. Hole-size filtering shows variations in the number of records per quadrant and dominance of the fourth quadrant. Further, the statistical properties of velocity-intermittency quadrant demonstrate a dependence between the velocity and velocity increments.