Identification of self-noise sources of a maritime unmanned system by CFD analysis

In the last decade, a number of new technologies has been developed to investigate the ocean environment by measuring different types of parameters among which ambient noise represents an important piece of information. The need for silent and autonomous vehicles moved the attention of scientist in the employment of underwater gliders as well as wave gliders. The reduced dimensions and the relatively easy layout adaptation of such vehicles make them particularly suited for carrying out different missions. Each new maritime unmanned system (MUS) configuration has to be tested in order to characterize the vehicle self-noise and its interaction with the measured data. The low costs and the small dimensions often give the possibility of facing problems with a trial and error approach slightly changing the layout in order to suppress possible noise sources. In the present paper the low-frequency acoustic data, sampled from the hydrophones positioned on a volumetric array in the aft part of the towfish of a waveglider, were not usable due to significant self-noise. To identify the noise source, a systematic analysis of the initial configuration have been carried out in two different phases. At first, the acoustic as well as the motion data coming from sea trials have been processed in order to highlight the critical operative conditions. In the second part, CFD calculations presented in order to identify the unwanted self-noise sources. Finally, a possible improvement is suggested based on numerical CFD calculations. Copyright © (2018) by International Institute of Acoustics & Vibration. All rights reserved.