Design of Field Experiments for Adaptive Sampling of the Ocean with Autonomous Vehicles
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Author(s)
Zheng, H.
Ooi, Boon Hooi
Cho, Wonjoon
Dao, M. H.
Tkalich, P.
Patrikalakis, Nicholas M.
Date Issued
May 2010
Journal
Proceedings of the 2nd International Symposium on Computational Mechanics and the 12th International Conference on the Enhancement and Promotion of Computational Methods in Engineering and Science (2010) (AIP Conference Proceedings; 1233)
Publisher
American Institute of Physics
Citation
Zheng, H. et al. “Design of Field Experiments for Adaptive Sampling of the Ocean with Autonomous Vehicles.” PROCEEDINGS OF THE 2ND INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL MECHANICS AND THE 12TH INTERNATIONAL CONFERENCE ON THE ENHANCEMENT AND PROMOTION OF COMPUTATIONAL METHODS IN ENGINEERING AND SCIENCE, Hong Kong- Macau (China), 2010. (AIP conference proceedings ; 1233) 905-910.
Version
Author's final manuscript
Abstract
Due to the highly non-linear and dynamical nature of oceanic phenomena, the predictive capability
of various ocean models depends on the availability of operational data. A practical method to improve the
accuracy of the ocean forecast is to use a data assimilation methodology to combine in-situ measured and
remotely acquired data with numerical forecast models of the physical environment. Autonomous surface and
underwater vehicles with various sensors are economic and efficient tools for exploring and sampling the
ocean for data assimilation; however there is an energy limitation to such vehicles, and thus effective resource
allocation for adaptive sampling is required to optimize the efficiency of exploration. In this paper, we use
physical oceanography forecasts of the coastal zone of Singapore for the design of a set of field experiments
to acquire useful data for model calibration and data assimilation. The design process of our experiments
relied on the oceanography forecast including the current speed, its gradient, and vorticity in a given region of
interest for which permits for field experiments could be obtained and for time intervals that correspond to
strong tidal currents. Based on these maps, resources available to our experimental team, including
Autonomous Surface Craft (ASC) are allocated so as to capture the oceanic features that result from jets and
vortices behind bluff bodies (e.g., islands) in the tidal current. Results are summarized from this resource
allocation process and field experiments conducted in January 2009.
MIT Department
Massachusetts Institute of Technology. Center for Ocean Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
Singapore-MIT Alliance in Research and Technology (SMART)
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http://scitation.aip.org/proceedings/confproceed/1233.jsp
