Wind turbine noise uncertainty quantification for downwind conditions using metamodeling.

The influence of the ground and atmosphere on sound generation and propagation from wind turbines creates uncertainty in sound level estimations. Realistic simulations of wind turbine noise thus require quantifying the overall uncertainty on sound pressure levels induced by environmental phenomena. This study proposes a method of uncertainty quantification using a quasi-Monte Carlo method of sampling influential input data (i.

Inverse modeling and joint state-parameter estimation with a noise mapping meta-model.

This study aims to produce dynamic noise maps based on a noise model and acoustic measurements. To do so, inverse modeling and joint state-parameter methods are proposed. These methods estimate the input parameters that optimize a given cost function calculated with the resulting noise map and the noise observations.

Emulation of wildland fire spread simulation using deep learning.

Numerical simulation of wildland fire spread is useful to predict the locations that are likely to burn and to support decision in an operational context, notably for crisis situations and long-term planning. For short-term, the computational time of traditional simulators is too high to be tractable over large zones like a country or part of a country, especially for fire danger mapping. This issue is tackled by emulating the area of the burned surface returned after simulation of a fire igniting anywhere in Corsica island and spreading freely during one hour, with a wide range of possible environmental input conditions.

Meta-modeling for urban noise mapping.

Urban noise mapping generally consists of simulating the emission and attenuation of noise in an area by following rules such as common noise assessment methods. The computational cost makes these models unsuitable for applications such as uncertainty quantification, where thousands of simulations may be required. One solution is to replace the model with a meta-model that reproduces the expected noise levels with highly reduced computational costs.

Assimilation of mobile phone measurements for noise mapping of a neighborhood.

Noise maps are a key asset in the elaboration of urban noise mitigation policies. However, simulation-based noise maps are subject to high uncertainties, and the estimation of population exposition to noise pollution generally relies on static averages over an extended period of time. This paper introduces a method to produce hourly noise maps based on temporally averaged simulation maps and mobile phone audio recordings.

Kriging-based spatial interpolation from measurements for sound level mapping in urban areas.

Network-based sound monitoring systems are deployed in various cities over the world and mobile applications allowing participatory sensing are now common. Nevertheless, the sparseness of the collected measurements, either in space or in time, complicates the production of sound maps. This paper describes the results of a measurement campaign that has been conducted in order to test different spatial interpolation strategies for producing sound maps.

Evaluation and calibration of mobile phones for noise monitoring application.

The increasing number and quality of sensors integrated in mobile phones have paved the way for sensing schemes driven by city dwellers. The sensing quality can drastically depend on the mobile phone, and appropriate calibration strategies are needed. This paper evaluates the quality of noise measurements acquired by a variety of Android phones.