Abstract. This paper describes the noise mechanisms and categories of modern large wind turbine and main noise sources. Then the latest progresses in wind turbine noise researches are described from three aspects: noise prediction model, detection of noise sources by microphone array technique and methods for noise reduction. Although the turbine is restricted to horizontal axis wind turbines, the noise prediction model and reduction methods also can be applied to other turbines when the noise mechanisms are similar. Microphone array technique can be applied to locate any kind of noise sources.
Direct numerical simulations (DNS) of turbulent channel flows up to
${Re}_{\tau} \approx 1000$
are conducted to investigate the three-dimensional (consisting of streamwise wavenumber, spanwise wavenumber and frequency) spectrum of wall pressure fluctuations. To develop a predictive model of the wavenumber–frequency spectrum from the wavenumber spectrum, the time decorrelation mechanisms of wall pressure fluctuations are investigated. It is discovered that the energy-containing part of the wavenumber–frequency spectrum of wall pressure fluctuations can be well predicted using a similar random sweeping model for streamwise velocity fluctuations. To refine the investigation, we further decompose the spectrum of the total wall pressure fluctuations into the autospectra of rapid and slow pressure fluctuations, and the cross-spectrum between them. We focus on evaluating the assumption applied in many predictive models, that is, the magnitude of the cross-spectrum is negligibly small. The present DNS shows that neglecting the cross-spectrum causes a maximum error up to 4.7 dB in the subconvective region for all Reynolds numbers under test. Our analyses indicate that the approximation of neglecting the cross-spectrum needs to be applied carefully in the investigations of acoustics at low Mach numbers, in which the subconvective components of wall pressure fluctuations make important contributions to the radiated acoustic power.
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