Design of the IOT wave suppressor
Bibliographic record
Abstract
The Institute for Ocean Technology specializes in researching and testing models of ships. The Department of National Defence has contracted IOT to develop a stern appendage that will reduce hydrodynamic resistance on the Halifax Class frigate. This appendage will also improve speed and propeller cavitation performance, and reduce the stern wave. The model used for testing contains sensitive electrical equipment which is vulnerable to forces induced by the stern wave impacting the model at the end of high-speed runs. There is also a risk of the stern wave washing over the transom and causing further damage to equipment. Researchers at the Institute have proposed a method to solve these problems through the development of a device called the Wave Suppressor. This device would be used to dissipate the wave energy, and thereby reduce the impact force of the wave on the stern of the model and prevent the stern wave from washing over the transom. This report details the design considerations used in the development of the proposed mechanism and describes its various components. It covers the design criteria that the Wave Suppressor must meet, and how the Wave Suppressor fulfills these requirements. The report first sets the design criteria and then proceeds to describe the Suppressor's connection to the tow tank carriage, the design factors involved in ensuring the wave will be fully suppressed, and the control of the suppressor. It also includes calculations that were used in the design process and provides assembly drawings of the wave suppressor.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".