Speed-Power Performance of 95,000DWT Arctic Tanker Design
Bibliographic record
Abstract
When Arctic offshore development in the 1970s first led to consideration of ice capable tankers, there was a high level of uncertainty over design requirements for both safety and ship performance, and a lack of reliable methods to evaluate design proposals. Since that time, improved understanding of the ice environment has raised the confidence of design specifications. Parallel developments have resulted in a suite of engineering tools for ship performance evaluation at the design stage. Recent development of offshore and near shore oil and gas reserves in several countries, together with economic studies of increased transportation through the Russian Arctic, led to renewed interest in ice capable tanker design. In response, Samsung Heavy Industries (SHI) applied its experience in tanker design and construction to the design of a specialized tanker with ice capability. SHI produced two prototype hull designs for further study. The performance of both hulls and of the propellers was evaluated at the Institute for Ocean Technology (IOT) in St. John’s, Newfoundland. This paper discusses the development of the design, describes the model experiments to determine performance and variations, and presents the results. It shows how physical modeling can provide insight into design features, and points out the areas where further research will have the greatest effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".