DP in Ice Environment - Improving Safety and Efficiency of Arctic Operations
Bibliographic record
Abstract
Abstract This paper presents an overview of a five year research and development project aiming to develop dynamic positioning (DP) system technologies specifically for ice-rich environments. It has been initiated by the Centre for Marine Simulation (CMS) at the Fisheries and Marine Institute (MI) of Memorial University of Newfoundland, with its technical partner National Research Council’s Ocean Coastal and River Engineering (OCRE-NRC) and commercial partner Kongsberg Maritime Simulation Ltd. (KMS). The primary objective of the project is to develop solutions for some of the critical challenges related to safe Arctic offshore operations by dynamic positioning. More specifically, the objective is to improve the safety and efficiency of oil and gas operations in ice infested environments through the enhancement of existing DP system technologies and training of DP operators in simulated realistic ice environments for ship operations. The project is envisioned to achieve its objective through developing a modularized simulation platform for prototype integration, validation, testing and operational studies/training. Prototypes of a DP control system, a vessel model, an ice force model, and other environmental force models will be developed. The project commenced in 2013 and is set to complete in late 2018. In this first article of the project, a discussion on the contextual aspects and formation of the project, its planning and status to-date is presented. A synopsis of the scientific and engineering research performed to-date within the project scope, with a justification of their relevance to the safe DP operations in ice is given. The high level system design of the validation platform and the deployment strategies of its major components are presented. An introductory discussion on the novel ice force modeling approach is provided. Finally, an overview of the model test program of a fully DP controlled vessel in managed ice conditions, which was completed to provide a database for building and validating the ice force model, is also offered.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".