2026-09-19T04:38:54Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/806642022-01-13T07:54:05Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Nicholas M. Patrikalakis.
Gildner, Matthew Lee
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2013-09-12T19:17:49Z
2013-09-12T19:17:49Z
2013
2013
http://hdl.handle.net/1721.1/80664
857588174
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 125-130).
This thesis presents a framework for the sampling of thermal and effluent jets and plumes using multiple autonomous surface vehicles. The framework was developed with the goal of achieving rapid and accurate in-situ measurement and characterization of these features. The framework is presented as a collection of simulation, estimation and field tools for use within the Mission Oriented Operations Suite (MOOS) and a novel Acoustic Doppler Current Profiling system that is capable of reorientation and real-time feedback. Key features developed within MOOS include a multi-parameter model of thermal and effluent jet and plume fields, online parameter estimation and sensor fusion. Using these tools, a collaborative adaptive sampling strategy is implemented to efficiently sample an industrial jet and plume. The capabilities of this strategy are demonstrated in realistic mission simulations and in field trials using a fleet of autonomous kayaks equipped with environmental sensors.
by Matthew Lee Gildner.
S.M.
130 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
Framework for multi-vehicle adaptive sampling of jets and plumes in coastal zones
Thesis
application/pdf
Publication
unknown
unknown
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<Abstract>This thesis presents a framework for the sampling of thermal and effluent jets and plumes using multiple autonomous surface vehicles. The framework was developed with the goal of achieving rapid and accurate in-situ measurement and characterization of these features. The framework is presented as a collection of simulation, estimation and field tools for use within the Mission Oriented Operations Suite (MOOS) and a novel Acoustic Doppler Current Profiling system that is capable of reorientation and real-time feedback. Key features developed within MOOS include a multi-parameter model of thermal and effluent jet and plume fields, online parameter estimation and sensor fusion. Using these tools, a collaborative adaptive sampling strategy is implemented to efficiently sample an industrial jet and plume. The capabilities of this strategy are demonstrated in realistic mission simulations and in field trials using a fleet of autonomous kayaks equipped with environmental sensors.</Abstract>
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