The Application of Fresnel Intergrals to the Study of Underwater Noise Sources

  • Yang Desen
Part of the Acoustical Imaging book series (ACIM, volume 21)


In this paper a sound imaging system is constucted to investigate the characteristics of underwater noise, especially that of moving objects. Space transformation of the sound field is performed by the Fresnel integral. It is shown how to calculate the Fresnel integral with the Fourier transformation, and the selection of parameters in the Fresnel integral is also presented. Three experiments are described in the paper: the sound image of a cross array with five elements, the sound image of a moving ship model and that of an underwater engine. The method of scanning in experiments is discussed. It is shown that the position of the main sources can be easily recognized from the sound images of measured objects.


Sound Source Measurement Plane Sound Field Source Plane Sound Image 
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  1. 1.
    A.K. Ghatak and K. Thyagarajan 1978, Plenum Press, New York, Contemporary Optics.Google Scholar
  2. 2.
    S. Ueha 1976. Optica ACTA, Vol. 23, No. 2. pp 107–114. Imaging Of Accoustic Radiation Sources With Accoustical Holography.Google Scholar
  3. 3.
    W.A. Veronesi and J.D. Maynard 1989 J. Acoust. Soc. Am. Vol. 85, No. 2, February 588–598. Digital Holographic Reconstruction of Sources with Arbitrarily Shaped Surfaces.Google Scholar
  4. 4.
    Desen. Y 1992. The Study Report of HSEI, Harbin, China. The study of Vibration and Noise For Underwater Engine.Google Scholar

Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • Yang Desen
    • 1
  1. 1.Department of Acoustical EngineeringHarbin Shipbuilding Engineering Institute (HSEI)HarbinPeople’s Republic of China

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