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Time-Evolving Acoustic Propagation Modeling in a Complex Ocean Environment

Colin, M.E.G.D., T.F. Duda, L.A. te Raa, T. van Zon, P.J. Haley, Jr., P.F.J. Lermusiaux, W.G. Leslie, C. Mirabito, F.P.A. Lam, A.E. Newhall, Y.-T. Lin, J.F. Lynch, 2013. Time-Evolving Acoustic Propagation Modeling in a Complex Ocean Environment, Proceedings of OCEANS - Bergen, 2013 MTS/IEEE , vol., no., pp.1,9, 10-14 June 2013, doi: 10.1109/OCEANS-Bergen.2013.6608051.

During naval operations, sonar performance estimates often need to be computed in-situ with limited environmental information. This calls for the use of fast acoustic propagation models. Many naval operations are carried out in challenging and dynamic environments. This makes acoustic propagation and sonar performance behavior particularly complex and variable, and complicates prediction. Using data from a field experiment, we have investigated the accuracy with which acoustic propagation loss (PL) can be predicted, using only limited modeling capabilities. Environmental input parameters came from various sources that may be available in a typical naval operation.

The outer continental shelf shallow-water experimental area featured internal tides, packets of nonlinear internal waves, and a meandering water mass front. For a moored source/receiver pair separated by 19.6 km, the acoustic propagation loss for 800 Hz pulses was computed using the peak amplitude. The variations in sound speed translated into considerable PL variability of order 15 dB. Acoustic loss modeling was carried out using a data-driven regional ocean model as well as measured sound speed profile data for comparison. The acoustic model used a two-dimensional parabolic approximation (vertical and radial outward wavenumbers only). The variance of modeled propagation loss was less than that measured. The effect of the internal tides and sub-tidal features was reasonably well modeled; these made use of measured sound speed data. The effects of nonlinear waves were not well modeled, consistent with their known three-dimensional effects but also with the lack of measurements to initialize and constrain them.

Spatiotemporal Encoding/Decoding of Nonlinear Dynamics Using Compressive Sensing and Machine Learning

Speaker: J. Nathan Kutz
[Announcement (PDF)]
Speaker Affiliation: Chair Applied Mathematics, Adjunct Professor of Electrical Engineering and Physics, University of Washington
Date: Wednesday 19 June at 11:00AM in 5-314

Pierre Lermusiaux featured on MIT front page

The leader of the MSEAS group, Prof. Pierre Lermusiaux, was featured on the front page of the MIT web site on Wednesday June 12, 2012. The article is entitled “Making waves: Pierre Lermusiaux quantifies uncertainty to develop better ocean simulations”. Prof. Lermusiaux’s family is described, his road to MIT outlined and the research carried on by the MSEAS group is detailed. The full story can be found here.

Predicting Estuarine Transport in Galveston Bay – Challenges with Modelling a Complex Real-World System

Speaker: Matt Rayson
[Announcement (PDF)]
Speaker Affiliation: Environmental Fluid Mechanics Laboratory, Department of Civil and Environmental Engineering, Stanford University
Date: Thursday 6 June at 11:00AM in 5-314

Tapovan Lolla wins MIT award for Academic Excellence

Tapovan Lolla has received the Wunsch Foundation Silent Hoist and Crane Award for Academic Excellence. The honor was presented at the Mechanical Engineering Student Awards Luncheon on Thursday, May 16, 2013. An honor well-deserved. Congratulations Tapovan!