Exploring low-cost deep ocean sensing utilizing undersea cable networks
Author(s)
Jung, Kimberly.
Download1191844344-MIT.pdf (514.5Kb)
Other Contributors
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Advisor
Douglas P. Hart.
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The ocean represents more than 99% of the Earth's biosphere, yet we only monitor less than 2% of it. Consequently, we lack a clear and persistent picture of currents, marine life migrations, carbon capture and acidity levels, seismic activity, and many other measurable parameters because we lack the robustness, longevity, and spatial coverage required to monitor the ocean. Most attempts are encompassed by remotely operated vehicles taking point measurements or sets of fixed mooring lines along a continental shelf. Underwater telecommunications cable repeaters offer nodes of power and data transmission across the ocean every ~60 km. From a systems perspective, we are operating in the very first stage of a project life cycle: the concept study. We tackle this problem by examining current resources, the stakeholders, past efforts, and three possible concepts of ocean network sensors attached to telecommunications repeaters. In-depth interviews of stakeholders were conducted. We discuss what parameters are important to measure. We discuss information and sampling theory to flush out the tradeoff of resolution and cost, detecting boundaries, and the method by which we can determine the number of sensors across a spatial area. We construct a general mechanical model of an array of sensors to ensure design for minimum deflection angle from possible transverse currents. Two concepts are proposed: 1) an electro-mechanical vertical array consisting of off-the-shelf sensors and 2) an optical fiber sensor package. This thesis closes with a summary of current status of the ocean sensor networks with an emphasis on sampling strategy while proposing that optical sensor arrays may provide a better long-term solution based on lower power requirements and data transmission scheme than current conventional sensor systems.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020 Cataloged from the official PDF of thesis. Includes bibliographical references.
Date issued
2020Department
Massachusetts Institute of Technology. Department of Mechanical EngineeringPublisher
Massachusetts Institute of Technology
Keywords
Mechanical Engineering.