Probabilistic evaluation of tankship damage in collision events

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Date
1998-06
Authors
Crake, Kurtis W.
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DOI
10.1575/1912/5374
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Keywords
Tankers
Collisions at sea
Monte Carlo method
Abstract
An analytical method for precidicting the extent of damage in ship collisions is developed. The method calculates both the longitudinal and transverse extents of damage for the struck ship as a function of collision scenario parameters, such as ship speeds, relative courses, and collision impact point, as well as considering structural details of the ship. This prediction method, or collision model, is used in a Monte Carlo analysis with probability density functions defining the specific collision scenario parameters for each "case". The Monte Carlo analysis generates a statistically significant number of collision events and results which are applied directly to calculate oil outflow, and from which resultant pdfs for longitudinal and transverse extent of damage are calculated to compare structural concepts. The collision model scenario inputs are initially "calibrated" using a MARPOL single-hull model by minimizing the difference between the model result damage pdf's and the pdf's specified by the IMO. A quantitative comparison is made between structural models for an intermediate oil-tight deck (or "mid-deck") tanker, and a series of double-hull tankers based on calculated oil outflow parameters. Of the three ship designs studied, the double-hull series shows the best performance, followed closely by the mid-deck tanker. The single-hull ship results predict both more frequent and larger spills.
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Submitted in partial fulfillment of the requirements for the degree of Master of Engineering at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 1998
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Crake, K. W. (1998). Probabilistic evaluation of tankship damage in collision events [Doctoral thesis, Massachusetts Institute of Technology and Woods Hole Oceanographic Institution]. Woods Hole Open Access Server. https://doi.org/10.1575/1912/5374
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