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Trailing edge noise around wind turbine blades due to turbulent coherent structures


Horizontal axis wind turbines are widely used as regenerative power plants. However, the noise from wind turbines is a major concern for the widespread application of onshore wind energy since the swishing character experienced is more annoying than other sound sources of equal level (such as from cars or airplanes). Trailing edge (TE) noise has been identified as the strongestsource of sound emission.

In general, TE noise is either broadband or tonal in character, which is directly linked to the turbulent structures in the flow around the TE. For a sharp trailing edge and low to moderate angle of attack the boundary layer is attached to the wing surface and the flow is convectively unstable. Incoming external noise is amplified in the boundary layer leading to coherent structures with a wide spectral range. The sharp TE scatters the hydrodynamic pressure fluctuations as acoustic waves, leading to strong broadband noise in the far field. For a blunt trailing edge or a detached boundary layer at higher angle of attack, the flow downstream becomes unstable to an intrinsic vortex shedding mode. These flow oscillations lead to narrowband tonal noise.

Turbulent coherent flow structures and TE noise of a 2D airfoil at low and high angle of attack (AoA).

The goal of this research project is a detailed understanding of the TE noise generation mechanisms associated with the coherent structures as well as their mitigation. Proper orthogonal decomposition (POD) and linear stability analysis (LSA) have proven to be valuable methods for identifying and modeling coherent structures. Using the LSA, a low-dimensional model for the coherent structures can be used to reconstruct the surface pressure fluctuations on the airfoil that cause TE noise. Subsequently, this model can be validated against the coherent fluctuations empirically extracted by POD. The POD modes themselves are correlated to the sound pressure level spectrum by linear stochastic estimation to identify the coherent fluctuations that contribute the most to TE noise.

On the one hand, the reduced-order modeling is useful for data assimilation. It enables engineers at an early design state to estimate the intensity of coherent fluctuations and noise based solely on mean flow data and at least one time-resolved reference pressure signal, such as a simple surface pressure sensor. This avoids the necessity of demanding numerical simulations such as LES or DNS or expensive experimental setups.On the other hand, the precise identification and modeling of the responsible noisy coherent modes allows for new insights into the physical mechanisms leading to TE noise. Thereby, existing passive and active flow control designs can be improved or even novel control approaches can be developed to reduce TE noise.

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