A new horizontal planar motion mechanism (PMM) for the NRC-IOT towing tank and ice tank
Bibliographic record
Abstract
A Planar Motion Mechanism (PMM) is an electromechanical device used to conduct manoeuvring studies and experiments on scale models of ships. A PMM moves a vessel model in exact pre-programmed patterns while resulting forces and moments are measured using a force dynamometer attached to the model. From these studies, manoeuvring derivatives can be calculated and then used to estimate or simulate motions of the full-scale vessel. In 1995, the National Research Council’s Institute for Ocean Technology (NRC-IOT) developed a PMM for its Towing Tank to measure hydrodynamic derivatives and then tried to extend the device to measuring ice-induced manoeuvring forces as part of a new research effort for icebreaking vessels. Using this device in the Ice Tank highlighted strength limitations associated with the mechanism. In 2007 Cussons Technology Ltd (CTL), of Manchester was contracted by NRC-IOT to design and fabricate a new PMM, capable of both hydrodynamic manoeuvring studies and ice manoeuvring studies. This paper describes the development and implementation processes of the PMM from initial specification to design, manufacture, test/commissioning, and finally model testing at NRC-IOT. The paper illustrates the improved performance of the new PMM, which paves the way for an expanded NRC-IOT research program related to in-ice manoeuvring.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".