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dc.contributor.advisorNicholas C. Makris.en_US
dc.contributor.authorSrinivasan, Jagannathanen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Mechanical Engineering.en_US
dc.date.accessioned2011-12-09T21:28:23Z
dc.date.available2011-12-09T21:28:23Z
dc.date.copyright2011en_US
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/67587
dc.descriptionThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 267-294).en_US
dc.description.abstractFish populations often comprise the largest biomass in a productive marine ecosystem. They typically play an essential role in inter-trophic energy transport, and serve as a mainstay for human consumption comprising roughly 16% of the animal protein consumed by the world's population. Despite their ecological importance, there is substantial evidence that fish populations are declining worldwide, motivating the need for an ecosystem approach to fisheries management through ecosystem scale sensing of fish populations and behavior. In this Thesis, it is shown how the recently developed Ocean Acoustic Waveguide Remote Sensing (OAWRS) technique can be used to (1) quantify the acoustic scattering response of fish and remotely infer their physiological characteristics to enable species classification, and (2) remotely assess shoaling populations and quantify their group behavior in a variety of oceanic ecosystems. Shoal dynamics is studied by developing a novel Minimum Energy Flow (MEF) method to extract velocity and force fields driving motion from time-varying density images describing compressible or incompressible motion. The MEF method is applied to experimentally obtained density images, spanning spatial scales from micrometers to several kilometers. Using density image sequences describing cell splitting, for example, we show that cell division is driven by gradients in apparent pressure within a cell. By applying MEF to fish population density image sequences collected during the OAWRS 2003 experiment in the New Jersey strataform, we quantify (1) inter-shoal dynamics such as coalescence of fish groups over tens of kilometers, (2) fish mass flow between different parts of a large shoal and (3) the stresses acting on large fish shoals. Observations of fish shoals made during the OAWRS 2006 experiment in the Georges Bank are used to confirm general theoretical predictions on group behavior believed to apply in nature irrespective of animal species. By quantifying the formation processes of vast oceanic fish shoals during spawning, it is shown that (1) a rapid transition from disordered to highly synchronized behavior occurs as population density reaches a critical value; (2) organized group migration occurs after this transition; and (3) small sets of leaders significantly influence the actions of much larger groups. Several species of fish, birds, insects, mammals and other self propelled particles (SPPs) are known to group in large numbers and exhibit orderly migrations. The stability of this orderly state of motion in large SPP-groups is studied by developing a fluid-dynamic theory for flocking behavior based on perturbation analysis. It is shown that an SPP group where individuals assume the average velocity of their neighbours behaves as a fluid over large spatial scales. The existence of a critical population density above which perturbations to the orderly state of motion are damped is also shown. Further, it is shown that disturbances can propagate within mobile groups at speeds much higher than that of the individuals, facilitating rapid information transfer. These findings may explain how large shoals of fish and flocks of birds are able to stay together and migrate over large distances without breaking up. Fish shoals are ubiquitous in continental shelf environments and so are a major cause of acoustic clutter in long-range Navy sonars. It is shown that man-made airfilled cylindrical targets have very different spectral acousic scattering response than fish, so that they can be distinguished using multi-frequency measurements. It is also shown that the use of the Sonar Equation to model scattering from the man-made targets leads to large errors differing by up to an order of magnitude from measurements. A Greens' Theorem-based full-field model that describes scattering from vertically extended cylindrical targets in range-dependent ocean waveguides is shown to accurately describe the statistics of the targets' scattered field measured during OAWRS 2001, 2003 and 2006 experiments. Measurements of infrasound made during the 2004 Indian Ocean Tsunami event that occured on December 26, 2004 have suggested that large-scale tsunamis may produce deep-infrasonic signals that travel thousands of kilometers in the atmosphere. By developing an analytical model to describe air-borne infrasound generation by tsunamis and applying it to the 2004 Indian Ocean Tsunami, it is shown that the mass flow of air caused by changes in sea-level due to a tsunami can generate infrasound of sufficient amplitude to be picked up thousands of kilometers away. The possibility of detecting tsunamis via seismic means is also examined by developing an analytical model for quantifying very low frequency (0.01-0.1 Hz) Rayleigh waves generated by a tsunami.en_US
dc.description.statementofresponsibilityby Srinivasan Jagannathan.en_US
dc.format.extent294 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMechanical Engineering.en_US
dc.titleSensing animal group behavior and bio-clutter in the ocean over continental shelf scalesen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Mechanical Engineering.en_US
dc.identifier.oclc763452951en_US


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