DP in Ice Environment – Improving Safety and Efficiency of Arctic Operations: An Update
Bibliographic record
Abstract
Abstract The objective of this paper is to present an update of various research activities of a multi-year research and development project aiming at developing dynamic positioning (DP) system technologies specifically for ice-rich environments. Since the beginning of the project in 2014, significant progress has been made in various activities that aimed at achieving the primary project objective of improving the safety and efficiency of oil and gas operations in ice infested environments through the enhancement of existing DP system technologies for efficient operations and training of DP operators in simulated realistic ice environments by providing necessary exposure to DP operations in ice. Prototypes of multiple vessel models, ice force models, and other environmental force models have been developed and are being validated. A modularized simulation and validation platform has been developed for the integration, validation, testing of all these prototypes. The research team at the National Research Council's Ocean Coastal and River Engineering (NRC-OCRE) is on the verge of delivering the complete package of the simulation platform to the project lead, the Centre for Marine Simulation (CMS) at the Fisheries and Marine Institute (MI) of Memorial University of Newfoundland, for comprehensive checking and testing of the platform by the project commercial partner Kongsberg Digital Simulation (KDS) Ltd. In this article, an update on various activities regarding the physical model testing, numerical modeling and development of simulation platform is presented. Various modules of the prototype validation platform and their integration are discussed along with their current development status. A brief discussion on various components of the ice force modeling approach, the algorithms and implementation strategies is provided. Finally, the initial results of a number of DP in ice simulation cases and comparison with validation data is presented. A brief outline of the work remaining to be completed for achieving the project objectives, along with the associated limitations are also provided.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".