Reducing Mechanical and Flow-Induced Noise in the Surface Suspended Acoustic Receiver
Abstract
The Surface Suspended Acoustic Receiver (SSAR) is a free-drifting platform intended for use as a receiver in large scale acoustic tomography experiments. Early prototypes of the SSAR exhibited very poor signal-to-noise ratios in the frequency band of the hydrophones. This thesis details efforts to reduce the hydrophone noise level by combining the analysis of experimental data with the results from numerical models. Experiments were conducted to quantify both the frequency content and magnitude of noise generated on the SSAR. Through a program of sea trials and pond testing, two noise sources were identified. The dominant source of noise in the SSAR is velocity dependent flow noise that results from turbulent pressure fluctuations on the hydrophones. A second noise source results from the acceleration sensitivity of the hydrophones in conjunction with high frequency accelerations present in the hydrophone array cable. These high frequency accelerations also show a velocity dependence. The presence of the acceleration-induced noise leads to correlations between the signals from adjacent hydrophones, thus distorting the typical picture that flow noise should be uncorrelated along an array. The primary methods of eliminating the noise are encapsulating the hydrophone in a flow shield, eliminating the array cable, and slowing the system down by replacing the wave following surface buoy with a spar buoy. Using the experimental results, empirical relationships between hydrophone velocity and expected noise level are formed for both shielded and unshielded hydrophones. The numerical models developed as a part of this effort are then used to predict the velocities for a wide range of possible SSAR configurations. The models can also provide information such as system tensions, that is useful in evaluating the longevity and survivability of SSARs. Modeled design fixes
Document Details
- Document Type
- Technical Report
- Publication Date
- May 06, 1997
- Accession Number
- ADA538440
Entities
People
- Jason I. Gobalt
Organizations
- Massachusetts Institute of Technology