Model Tests: LNG-Carriers in Ice
Bibliographic record
Abstract
Approximately on third of the world’s known and not yet exploited reserves of natural gas are in Russia. The overwhelming majority of these reserves are in Artic and Subarctic areas. But not only in Russia, also in other areas like Canada and USA, natural gas reserves are found in harsh and ice covered environments. As a consequence, the LNG ship technology is going towards Arctic LNG-Carriers. New developments in ice navigation, winterization and ship sizes are generating a new exiting challenge for shipping and ship building industries all over the world. Existing ice class regulations should be only considered as a first guide for designing ice-going vessels. Because the future performance in ice covered waters of new developed LNG-Carriers needs to be investigated in much more detail, therefore ice model tests are imperative. It is common practice guiding ships in ice-covered waters by using one or two icebreakers for wider LNG-Carriers. The LNG-Carrier is following in the broken channel of about 1.25 to 2 times the widths of its beam. For the model tests a parental level ice sheet of target ice thickness will be prepared according to HSVA’s standard model ice preparation procedure. In order to obtain a defined friction coefficient between the ice and the model hull, HSVA applies a special paint composition to the models of ice-going vessels. The channel will be broken with the help of two stock icebreakers towed through the level ice generating the most realistic wide ice channel. The prime objectives of such ice model tests are: • Evaluation of the icebreaking performance in a wide ice channel, • Propeller-ice-interactions and • how the ice is transferred aside and below the vessel.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".