Iceberg Impact Loads on a Concrete Floating Production Facility
Bibliographic record
Abstract
Abstract The majority of exploration, development and production offshore Newfoundland has occurred in shallow water. Currently there are two floating production, storage and offloading (FPSO) vessels and one gravity based structure producing oil on the Grand Banks. In recent years, there has been a move to deeper water offshore Newfoundland. There have been significant discoveries at Bay du Nord and Mizzen, and new licence blocks are opening up towards the north and northwest of the existing producing fields. With the move towards deeper water, new challenges arise. Supply facilities, search and rescue equipment and other required infrastructure is much further away. A GBS structure is no longer an option due to the water depth. A disconnectable concrete Spar is being considered as a concept for deep water locations off Canada’s east coast. The platform is designed to withstand iceberg impacts, or to be disconnected and moved off location to avoid impacts from very large ice features. Global design loads (ice crushing forces on the platform) and mooring systems global loads were estimated using a two-step approach. First the concrete Spar was assumed to be fixed (i.e. not able to move during an iceberg impact) and quasi-static global design loads were estimated using the Iceberg Load Software (ILS). The ILS was developed to model iceberg impacts with a fixed platform such as a gravity based structure (GBS). Assuming the concrete Spar to be fixed is a very conservative assumption for floating platforms which are free to move upon impact, albeit limited by a mooring system. Second, the inertial properties of the platform and the mooring compliance were approximated using a one-dimensional timestep model. Since the time domain model is not as efficient computationally, a subset of the simulated impacts from the quasi-static analysis was used as input into the time domain model, and dynamic design loads were estimated. The resulting design loads were used by designers to ensure that the structure meets the requirements of ISO 19906:2010. The end product is a more effective design for the platform, while not compromising the safety of the personnel onboard or the integrity of the structure, mooring system or risers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".