Urban noise reduction is a societal priority. In this context, the European Directive 2002/49/EC aims at producing strategic noise maps for large cities. However, nowadays the relevance of such maps is questionable, due to considerable uncertainties, which are rarely quantified. Conversely, the development of noise observatories can provide useful information for a more realistic description of the sound environment, but at the expense of insufficient spatial resolution and high costs. Thus, the CENSE project aims at proposing a new methodology for the production of more realistic noise maps, based on an assimilation of simulated and measured data, collected through a dense network of low-cost sensors that rely on new technologies. In addition, the proposed approach tries to take into account the various sources of uncertainty, either from measurements and modeling. Beyond the production of physical indicators, the project also includes advanced sound environments characterization, through sound recognition and perceptual assessments. CENSE is resolutely a multidisciplinary project, bringing together experts from environmental acoustics, data assimilation, statistics, GIS, sensor networks, signal processing, and noise perception. As the project is in launch state, the present communication will focus on a global overview, emphasizing the innovative and key points of the project.
Modeling a wind turbine sound field involves taking into account the main aeroacoustic sources that are generally dominant for modern wind turbines, as well as environmental phenomena such as atmospheric conditions and ground properties that are variable in both time and space. A crucial step to obtain reliable predictions is to estimate the relative influence of environmental parameters on acoustic emission and propagation, in order to determine the parameters that induce the greatest variability on sound pressure level. Thus, this study proposes a Morris sensitivity analysis of a wind turbine noise emission model combined with a sound propagation model in downwind conditions. The emission model is based on Amiet's theory and propagation effects are modeled by the wide-angle parabolic equation. The whole simulation takes into account ground effects (absorption through acoustic impedance and scattering through surface roughness) and micrometeorological effects (mean refraction through the vertical gradient of effective sound speed). The final results show that the parameters involved in atmospheric refraction and in ground absorption have a significant influence on sound pressure level. On the other hand, in the context of this study the relative air humidity and the ground roughness parameters appear to be negligible on sound pressure level sensitivity.
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