Managed ice loads on a dynamically positioned vessel
Stationkeeping in ice-covered waters has become a large area of interest for research and development in light of heightened interest in Arctic oil and gas exploration. The performance of Dynamic Positioning (DP) control systems for stationkeeping purposes in ice conditions is a difficult challenge...
| Published in: | All Days |
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| Main Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
The Offshore Technology Conference
2012
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| Subjects: | |
| Online Access: | https://doi.org/10.4043/23774-MS https://nrc-publications.canada.ca/eng/view/object/?id=f87d6b0a-116a-401b-9b3c-2d352e4be6d1 https://nrc-publications.canada.ca/fra/voir/objet/?id=f87d6b0a-116a-401b-9b3c-2d352e4be6d1 |
| Summary: | Stationkeeping in ice-covered waters has become a large area of interest for research and development in light of heightened interest in Arctic oil and gas exploration. The performance of Dynamic Positioning (DP) control systems for stationkeeping purposes in ice conditions is a difficult challenge for numerical modeling assessment. Given that full-scale validation data for DP in ice operations is often scarce, physical modeling of stationkeeping in ice offers the best method for assessing the performance of dynamically positioned vessels in these conditions. A series of model tests carried out at the National Research Council of Canada's Ice Tank facility in August and September of 2011 attempted to observe the effects of various managed ice conditions (i.e. ice floes which have been broken into manageable pieces by an ice breaker) on DP performance. Results from these tests are discussed. Of particular interest in this study is the observation of non-linear effects of varying ice conditions on DP performance. The use of machine vision-based data products as potential estimators of ice loading is discussed. It is concluded that simple statistical observations of these conditions will be unable to fully characterize the effects of various ice parameters on performance, and that investigation into more advanced data products available from machine vision systems may be able to aide in characterizing these effects as well as in the development of models capable of predicting ice loads. Peer reviewed: Yes NRC publication: Yes |
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