Acoustic field calibration for noise prediction: the CALCOM'10 data set

Martins, N.; Felisberto, P.; Jesus, S. M.

http://hdl.handle.net/10400.1/1634

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Authors

Martins, N.

Felisberto, P.

Jesus, S. M.

Abstract(s)

Wave energy is one of the marine renewable energies that are becoming increasingly explored. One of the concerns about the respective ocean plants is the noise generated by the mechanical energy converters. This noise may affect the fauna surrounding the energy plant, what induces the idea of planning the location of a prospective plant, optimum in terms of noise minimization. Naturally, in such an approach, the plant noise can be predicted, using information concerning the ocean geometric, water column and bottom properties, if available. This information can be fed to an acoustic propagation code, to solve an acoustic forward problem. Inevitably, this knowledge is often incomplete, and the use of guesses or inferences from nautical charts can lead to erroneous noise predictions. This paper presents a noise prediction tool which can be divided into two steps. The first step consists of characterizing the candidate ocean area, in terms of the environmental properties relevant to acoustic propagation. In the second step, the environmental characteristics are fed to a computational acoustic propagation model, which provides estimates of the plant-noise generated in the candidate area. The first step uses at-sea measured acoustic data, during the CALCOM’10 sea trial (in Portugal), to solve an acoustic inverse problem, which gives environmental estimates. This procedure can be seen as a “field model calibration”, in that the estimated environmental properties are tailored to model the acoustic data. The second step uses the estimates in a forward modeling problem, with the same propagation code. In numerical terms, differences greater than 4.4 dB in the median of the modeled transmission loss difference have been observed, upto 1.6 km from the acoustic source. The results show that the field calibration is important to better model the data at hand, and thus act as a noise prediction tool, as compared to a procedure in which only a partial a priori knowledge of the candidate oceanic area is available. The results are promising, in terms of the application of the present method in the project of ocean power plants.