Reduction of Wave Resistance of Displacement Vessels by Waterline Parabolization
Bibliographic record
Abstract
Waterline parabolization is a method to reduce the wave resistance of a displacement type hull with a parallel mid-body by adding amidships bulbs to the hull. Tow tank tests of the optimized bulbs at ITU confirmed that significant reductions in total resistance were obtained. This paper describes the use of a nonlinear optimization technique developed at the University of British Columbia (UBC) and tank testing at Istanbul Technical University (ITU) to find the optimum shape and location of midship bulbs as well as midship bulbs and a bow bulb together. Tow-tank tests at UBC and ITU have shown that mid-ship bulbs can provide significant reductions in total resistance. The study validated the use of the technique both constraining the displacement and when only constraining the draft (retrofit). The optimization algorithm considers only wave resistance which is calculated with a Dawson's method type program; TRAWSON. In this study a RO/RO ferry hull is used as the baseline hull form. The tank tests revealed that the bulb location and geometry identified as the optimum, at Froude number Fn = 0.33, by the optimization program achieved a reduction in total resistance of 15 percent at constant displacement and retrofit applications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".