A Resistance Reduction Study of a Trimaran using Waterline Parabolization
Bibliographic record
Abstract
Waterline parabolization or addition of side bulbs about the ship's midbody can significantly reduce the wave-making resistance of a vessel [Calisal, S. M., Goren, O., and Danisman, D. B., 2002 Resistance reduction by increased beam for displacement-type ships, Journal of Ship Research, 46, 3, 208–213]. These side bulbs are designed to create a wave pattern that interacts with the ship wave system of the hull at the desired speed range. This concept was first successfully tested on a coaster tanker and then extended to the UBC series hull, a series typical of Canadian West Coast fishing vessels. Systematic tow tank experiments revealed that while parabolization decreases the total resistance, the form factor suffered an increase. An integral boundary layer solver and a 2D RANS solver both showed that the increase in viscous resistance was mainly caused by an increase in form drag or viscous pressure drag. The parabolization concept was subsequently extended to a high-speed NPL trimaran to determine whether resistance reduction using parabolic side bulbs could be achieved for a very slender multihull vessel. A Rankine source panel method was used to predict the wave-making characteristics of the trimaran, and an integral boundary layer solver and a RANS solver were used to calculate the viscous drag. A parametric study, varying the size and location of bulbs, was first performed on the center hull to design the side bulb. The study was then extended to the trimaran to evaluate the additional wave interactions caused by the outriggers. Experimental model tests validated the numerically predicted wave interactions, as well as the change in viscous drag. Based on the numerical work, a modified NPL trimaran hull form was designed that reduced the total resistance of the vessel by up to 6% in the design speed range and providing critical additional engine or accommodation space.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".