Dissertation/Thesis Abstract

The author has requested that access to this graduate work be delayed until 2019-11-02. After this date, this graduate work will be available on an open access basis.
Development of ICECON: A Ship-Specific Ice Forecasting System for the Great Lakes
by Campbell, Seth Wescott, M.S., University of Alaska Anchorage, 2019, 103; 13858590
Abstract (Summary)

There are two focuses to this thesis. The main focus of this thesis is stripping down the existing Ice Condition Index model proposed by the National Ice Center and re-evaluating the effect of various environmental parameter inputs of this model using a Monte Carlo simulation. The secondary focus is establishing a ship classification system for the Ice Condition Index. In the main focus, the effect of adding an ice divergence factor to the Ice Condition Index model is examined, which would account for bulk ice movement into or out of an area. Modeled nowcast and forcing data as well as observational data are used to test five separate models. A 3-parameter model using nowcast ice concentration, ice thickness, and temperature data was used as a baseline for comparisons. The other four models were a similar 3-parameter model using observational data instead of nowcast data, and three separate 4-parameter models using all three of  data ice concentration, ice thickness, and air temperature data and only one of wind, ice type, or ice divergence data. It was found that the 4-parameter ice type model was the most accurate model, but the 4-parameter wind and ice divergence models also added value over the baseline. Additionally, the accuracy of the nowcast and forcing data source is examined. The second focus, establishing a ship classification system, was accomplished by using linear regression to fit ship characteristics to their ice capability. Engine power and deadweight tonnage were found to be excellent predictors of ice capability, while length and beam are fair predictors of ice capability. Length-to-beam ratio and horsepower-to-length ratio were found to be poor predictors of ice capability.

Indexing (document details)
Advisor: Ravens, Thomas
Commitee: Butler, Shawn, Mahoney, Andy
School: University of Alaska Anchorage
Department: Civil Engineering
School Location: United States -- Alaska
Source: MAI 58/05M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Geophysics, Ocean engineering, Transportation
Keywords: Geophysics, Ice engineering, Monte Carlo, Ship classification, Shipping, Transport
Publication Number: 13858590
ISBN: 9781392136430
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