@article{(Open Science Index):https://publications.waset.org/pdf/10006383,
	  title     = {Numerical Simulations of Acoustic Imaging in Hydrodynamic Tunnel with Model Adaptation and Boundary Layer Noise Reduction},
	  author    = {Sylvain Amailland and  Jean-Hugh Thomas and  Charles P├ęzerat and  Romuald Boucheron and  Jean-Claude Pascal},
	  country	= {},
	  institution	= {},
	  abstract     = {The noise requirements for naval and research vessels
have seen an increasing demand for quieter ships in order to fulfil
current regulations and to reduce the effects on marine life. Hence,
new methods dedicated to the characterization of propeller noise,
which is the main source of noise in the far-field, are needed. The
study of cavitating propellers in closed-section is interesting for
analyzing hydrodynamic performance but could involve significant
difficulties for hydroacoustic study, especially due to reverberation
and boundary layer noise in the tunnel. The aim of this paper
is to present a numerical methodology for the identification of
hydroacoustic sources on marine propellers using hydrophone arrays
in a large hydrodynamic tunnel. The main difficulties are linked to the
reverberation of the tunnel and the boundary layer noise that strongly
reduce the signal-to-noise ratio. In this paper it is proposed to estimate
the reflection coefficients using an inverse method and some reference
transfer functions measured in the tunnel. This approach allows to
reduce the uncertainties of the propagation model used in the inverse
problem. In order to reduce the boundary layer noise, a cleaning
algorithm taking advantage of the low rank and sparse structure of the
cross-spectrum matrices of the acoustic and the boundary layer noise
is presented. This approach allows to recover the acoustic signal even
well under the boundary layer noise. The improvement brought by
this method is visible on acoustic maps resulting from beamforming
and DAMAS algorithms.},
	    journal   = {International Journal of Mechanical and Mechatronics Engineering},
	  volume    = {11},
	  number    = {2},
	  year      = {2017},
	  pages     = {347 - 353},
	  ee        = {https://publications.waset.org/pdf/10006383},
	  url   	= {https://publications.waset.org/vol/122},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 122, 2017},