• The Journal of the Acoustical Society of Korea
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• v.32 no.5
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• pp.369-376
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• 2013

Using Lowson's acoustic analogy, low frequency noise of a wind turbine (WT) is predicted in time domain and the noise sources contributing to the low frequency noise is analyzed. To compute averaged pressure distribution on blades of the WT as noise source, XFOIL is utilized. The blade source domain is divided into several segments along the span direction to compute force exerted on air surrounding the blade segments, which is used as input for noise prediction. The noise sources are decomposed into three terms of force fluctuation, acceleration and velocity terms and are analyzed to investigate each spectral contribution. Finally, predicted spectra are compared with measured low frequency noise spectrum of a wind turbine in operation. It is found that the force fluctuation component contributes strongly in low frequency range with increasing wind speed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.90-95
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• 2014

In order to investigate low frequency swishing noise of wind turbines, singularity in circular motion with large radius is introduced as a noise source model. By employing Lowson's acoustic analogy, simple exact solution is obtained. The solution shows that time histories of acoustic pressure at receiver points varied significantly according to receiver's directional location, even when the retarded time distributions are similar. However, the corresponding spectra of sound pressure for the receiver locations where the retarded time distributions are almost the same are not significantly different. It can be inferred from these results that the time-averaged sound pressure spectra which cannot take into account the detailed difference in the time-variation of wind turbine noise may not represent the sound quality of wind turbines due to its swishing.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.3
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• pp.201-208
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• 2012

In this paper, a series of experiment is performed for a scaled hovering rotor in a semi-anechoic chamber and the results are compared to the noise spectra predicted by using Lowson's loading noise equation and FW-H equation. It was founded that the sound directivity pattern for both experiments and predictions are similar in their trend. Meanwhile the FW-H equation showed better agreement with experiments in the near-field noise spectra, but at the far-field the Lowson's equation performed better. The discrete noise are known to be proportional to the loading on the blades, which can be controlled by collective pitch angle of the blades. It was founded that the predicted spectra with FW-H equation come close to the measured noise spectra in low collective pitch, but in high collective pitch angles the Lowson's equation be more reliable.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.7
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• pp.569-574
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• 2014

In order to investigate low frequency swishing noise of wind turbines, acoustic source model using a singularity in circular motion is introduced to derive analytic solution of Lowson acoustic analogy in time domain. Results in time and frequency domains computed by the solution show apparent modulation of amplitude and frequency. The solution indicates that time histories of acoustic pressure at receiver points varied significantly according to receiver's directional location, even when the retarded time distributions are similar. However, the corresponding time-averaged spectra of sound pressure at the receiver locations where the retarded time distributions are almost same are not significantly different. It can be inferred from these results that the time-averaged sound pressure spectra which cannot take into account the detailed difference in the time-variation of wind turbine noise may not represent the sound quality of wind turbines due to its swishing. Finally, as an introduction of procedure to quantify low frequency swishing noise level, relative variation of overall sound pressure level is obtained using tonal low frequency noise model.