A CONCISE METHODOLOGY FOR THE DESIGN OF STATICALLY-EQUIVALENT DEEP-OFFSHORE MOORING SYSTEMS

被引:0
|
作者
Udoh, Ikpoto E. [1 ]
机构
[1] Houston Offshore Engn, Houston, TX 77094 USA
关键词
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中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Model testing of deepwater offshore structures often requires the use of statically-equivalent deepwater mooring systems. The need for such equivalent systems arises due to the spatial limitations of wave basins in accommodating the dimensions of the direct-scaled mooring system. With the equivalent mooring system in place and connected to the model floater, the static global restoring forces and global stiffness of the prototype floating structure can be matched (to within some tolerance) by those of the model for specified offsets in the required degrees of freedom. A match in relevant static properties of the system provides the basis for comparisons of dynamic responses of the model and prototype floaters. Although some commercial programs are capable of designing equivalent mooring systems, the physics applied in these programs are protected by intellectual property, and their methodologies are generally inflexible. This paper illustrates a concise approach to the design of statically-equivalent deepwater mooring systems. With this approach, either manual or advanced optimization techniques can be applied as needed based on the complexity of the equivalent system to be designed. A simple iterative scheme is applied in solving the elastic catenary equations for the optimal static configuration of each mooring line. Discussions cover the approach as applied in developing a fit-for-purpose tool called STAMOORSYS, its validation, and its application to the design of an equivalent mooring system for a spar platform in deepwater. The spar model parameters are representative of a structure which could be tested in the Offshore Technology Research Center, College Station, Texas, USA. Results show that the method is capable of producing good design solutions using manual optimization and a genetic algorithm.
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页数:11
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