First-year Ridge Loads on Moored Offshore Structures
Bibliographic record
Abstract
Abstract This paper describes the background, test methodology, experiment program and analytical methods used for a project to predict the peak mooring system loads that occur when a floating offshore structure encounters a first year ice ridge. The project included the construction of simplified physical models of a Spar at 1/30 scale and a Floating Production Unit (FPU) at 1/40 scale. The Spar was a representative structure with a downward breaking cone and a vertical neck designed for operation in ice. A partial spar, consisting of the cone only was also evaluated. The Floating Production Unit (FPU) was a simplified barge. This model was tested with a rigid connection to the towing carriage and also on a representative mooring system that allowed for some compliance. The model FPU was also fitted with two load measuring panels designed to measure local ice loads on the hull. The methods of producing the model ridges and measuring their physical properties are described. Each model was tested in a range of ridge geometries from temperate to Arctic regions. The experiments were carried out in the ice tank of the National Research Council of Canada's Institute for Ocean Technology. This paper describes the first phase of experiments on the spar model completed in December 2009 and the first phase of experiments on the FPU completed in June 2010. The paper also presents the description of a parallel numerical approach to the problem, which looks at the methods needed to model the ridge-structure interaction using finite element methods. The results of the simulations and experiments are discussed in general terms. INTRODUCTION In the global search for new hydrocarbon deposits, the Arctic is once again receiving a high degree of scrutiny as it is expected that as much as 25% of the world's undiscovered reserves may be present in northern regions. World-wide demand for this energy has generated pressure to extract these resources, much of which lie beneath the sea bed. This presents a problem for many petroleum industry operators since exploration and production activities in such a harsh environment will require unique solutions to deal with the extremes of cold temperatures and large ice loads. The ice engineering challenges are great for everything that is used in the Arctic, including production equipment, emergency evacuation systems, supply vessels, floating platforms and fixed structures. In fact, even the task of installing infrastructure is more complicated by the harsh conditions found in the Arctic. The continuous interest of Total E&P and the engineering companies (Saipem, Technip, Doris, Entrepose) for the Arctic and Sub-Arctic regions has lead them to begin a R&D study on the topic "Firstyear Ridge Loads on Moored Offshore Structures" through a French JIP (CITEPH, 2009).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".