Title
Modelling noise from wind, rain and distant shipping in scenarios of varying complexity
Author
Prior, M.K.
Colin, M.
van Riet, M.W.G.
Publication year
2019
Abstract
Passive sonars attempt to detect sound radiated by vessels of interest. This detection takes place against a background of noise, with important sources of noise including distant shipping, rain falling on the sea surface and breaking waves and bursting bubbles associated with sea-surface waves. The estimation of these noises is therefore an important part of the prediction of the likely performance of passive sonars. Mathematical models are available for the prediction of noise and these represent a combination of descriptions of the level and directionality of radiated noise, the propagation of sound between source and receiver and the convolution of receiver beam patterns and the directivity of the noise field at the receiver. These processes vary with acoustic frequency and environmental properties such as water depth, seabed type, sea-water sound-speed profile, precipitation rate and wind speed. The important physical processes are not straightforward to model and all prediction tools are, to some extent, approximations. When assessing model legitimacy, reference solutions are necessary to demonstrate model accuracy. Predictions of rain, wind and distant-shipping noise are presented in a simple environment previously developed for the purposes of verifying sonar-performance models. An existing reference solution for rain noise is used, along with a novel expression for the noise from distant shipping. Calculations are extended to more complicated scenarios including non-uniform shipping distributions and beam-patterns for generic arrays.
Subject
Ambient Noise
Rain Noise
Surface Noise
Shipping Noise
To reference this document use:
http://resolver.tudelft.nl/uuid:c2f81e4e-f12c-486d-93b5-fa8765ebc872
TNO identifier
882182
Source
UACE 2019, Proceedings of Underwater Acoustics Conference, Heraklion Greece, 30 june to 5 july 2019
Document type
conference paper