Wind induced ambient noise modelling and comparison with field measurements in Arabian Sea

Najeem, S.; Sanjana, M. C.; Latha, G.; Durai, P. Edwards

doi:10.1016/j.apacoust.2014.09.014

Cited by 20 publications

(6 citation statements)

References 22 publications

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“…It should be noted that although the variation of the active power generated by the turbines undergoes a modest variation and for a short period, the variation in wind speed was significant. This fact assumes relevance since the wind is itself a significant source of noise [48][49][50]. Table 2 shows the min and max values for active power and wind speed registered in the period under analysis.…”

Section: Acoustic Measurements and Scada Data Analysismentioning

confidence: 99%

Wind Turbine Noise Prediction Using Random Forest Regression

2019

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Wind energy is one of the most widely used renewable energy sources in the world and has grown rapidly in recent years. However, the wind towers generate a noise that is perceived as an annoyance by the population living near the wind farms. It is therefore important to new tools that can help wind farm builders and the administrations. In this study, the measurements of the noise emitted by a wind farm and the data recorded by the supervisory control and data acquisition (SCADA) system were used to construct a prediction model. First, acoustic measurements and control system data have been analyzed to characterize the phenomenon. An appropriate number of observations were then extracted, and these data were pre-processed. Subsequently two models of prediction of sound pressure levels were built at the receiver: a model based on multiple linear regression, and a model based on Random Forest algorithm. As predictors wind speeds measured near the wind turbines and the active power of the turbines were selected. Both data were measured by the SCADA system of wind turbines. The model based on the Random Forest algorithm showed high values of the Pearson correlation coefficient (0.981), indicating a high number of correct predictions. This model can be extremely useful, both for the receiver and for the wind farm manager. Through the results of the model it will be possible to establish for which wind speed values the noise produced by wind turbines become dominant. Furthermore, the predictive model can give an overview of the noise produced by the receiver from the system in different operating conditions. Finally, the prediction model does not require the shutdown of the plant, a very expensive procedure due to the consequent loss of production.

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Section: Acoustic Measurements and Scada Data Analysismentioning

confidence: 99%

Wind Turbine Noise Prediction Using Random Forest Regression

2019

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“…The research methods for wind-induced noise are similar to other studies in the field of wind engineering, and they are classified into three approaches: field measurement, wind tunnel test, and numerical simulation. Usually, winddriven ambient noise in the natural environment is generally obtained by the method of field measurement [18][19][20][21]. Because the noise of a microphone affects the accuracy of the noise measurements in an outdoor environment [22,23], Alamshah et al [24] invented a new approach to distinguish the contribution of the wind-induced noise from an acoustic signal by the incoherent output power analysis of two microphone signals.…”

Section: Introductionmentioning

confidence: 99%

Numerical Study on Wind‐Induced Noise of High‐Rise Building Curtain Wall with Outside Shading Devices

2018

Shock and Vibration

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Perforation metal plates with dense holes are often used as external curtain walls of high-rise buildings. When air flow passes through these holes at a high speed, complex vortex is generated and causes a significant issue of wind-induced noise. In this study, both Reynolds-averaged Navier–Stokes (RANS) simulations and large-eddy simulations (LES) were conducted to study flow around high-rise buildings with an external sunshade curtain wall. First, wind speed distributions at the height of a typical level under 16 wind directions were acquired. Then, the maximum wind speed ratio and its corresponding azimuth were identified. Second, the sound pressure levels in the vicinity of the shading devices with two types of perforation plate schemes were calculated to evaluate the acoustic characteristics by using the FW-H equation to simulate sound generation and propagation. The results indicate that the maximum wind speed around the buildings exists at the building corners, and the maximum wind speed ratio is 2.8 observed at 0-degree wind direction. Under two different wind conditions, the aeroacoustic performance of perforation plate is enhanced with reducing end plate size and increasing aperture size. The overall sound pressure level (OSPL) and A-weighted sound pressure level (ASPL) around the shading devices are 80 dB and 68 dB(A), respectively, for the improved perforation plate scheme under the 1-year return period maximum speed, which are changed to 58dB and 45dB(A) under the annual average speed. Therefore, it is believed that perforation plates with small end plate size and large aperture size are desirable for the noise prevention design of shading devices.

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“…[3][4][5] SST is the most important factor controlling wind and hence surface waves. Various studies have been proposed representing a linear relationship between SST and thermally driven wind speed.…”

Section: Ambient Noise Modellingmentioning

confidence: 99%

“…1 The site-specific spatio-temporal fluctuations of the ambient noise due to wind, wave, and currents, which are driven by the corresponding SST fluctuations in these regions, preclude the possibility of designing generalized mitigation strategies. [2][3][4][5][6][7][8][9][10][11][12] The SST determines the thermally driven wind and thus the ambient noise levels in the region, yet among the sea surface parameters, it is the least explored. [10][11][12] The wind driven ambient noise, manifest dominantly in the spectral band of 3-10 kHz, that has a direct impact on the mid-frequency sonar used for military and non-military applications like anti-submarine warfare (ASW) missions, fisheries, geological exploration, navigation, and surveillance.…”

Section: Introductionmentioning

confidence: 99%

“…[10][11][12] The wind driven ambient noise, manifest dominantly in the spectral band of 3-10 kHz, that has a direct impact on the mid-frequency sonar used for military and non-military applications like anti-submarine warfare (ASW) missions, fisheries, geological exploration, navigation, and surveillance. [3][4][5][6][7] The novelty of this work is an estimation of ambient noise statistical characteristics in the frequency band of 3-10 kHz based on the probability distribution function (pdf) of the SST. A detailed analysis of the relationship between sea surface parameters like SST and wind speed has also been carried out to support the conclusions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tropical littoral ambient noise probability density function model based on sea surface temperature

Asolkar¹,

Das

Gajre³

et al. 2016

The Journal of the Acoustical Society of America

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Ambient noise variability in tropical shallow water presents a critical challenge for sonar designers and operators due to site-specific sea surface fluctuations. Sea surface temperature (SST) is a direct measure of energy balance defining the local climate of the region and hence ambient noise characteristics. In this work, an ambient noise probability density function (pdf) model for a spectral band of 3–10 kHz has been designed based on the statistical distribution of SST and validated using real field data. This will enable early ambient noise prediction compared to existing wind speed based models to facilitate structured mitigation strategies for improving sonar performance.

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Wind induced ambient noise modelling and comparison with field measurements in Arabian Sea

Cited by 20 publications

References 22 publications

Wind Turbine Noise Prediction Using Random Forest Regression

Wind Turbine Noise Prediction Using Random Forest Regression

Numerical Study on Wind‐Induced Noise of High‐Rise Building Curtain Wall with Outside Shading Devices

Tropical littoral ambient noise probability density function model based on sea surface temperature

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