Predicting the Effect of Hull Roughness on Ship Resistance Using a Fully Turbulent Flow Channel
Bibliographic record
Abstract
The consequences of poor hull surface conditions on fuel consumption and emissions are well-known. However, their rationales are yet to be thoroughly understood. The present study investigates the hydrodynamics of fouling control coatings and mimicked biofouling. Novel experimental roughness function data were developed from the “young” fully turbulent flow channel facility of the University of Strathclyde. Different surfaces, including a novel hard foul-release coating, were tested. Finally, the performance of a benchmark full-scale containership was predicted using Granville’s similarity law scaling calculations. Interestingly, the numerical predictions showed that the novel hard foul-release coating tested had better hydrodynamic performance than the smooth case. A maximum 3.79% decrease in the effective power requirements was observed. Eventually, the results confirmed the practicality of flow channel experiments in combination with numerical-based methods to investigate hull roughness effects on ship resistance and powering. The present study can also serve as a valuable guide for future experimental campaigns using the fully turbulent flow channel facility of the University of Strathclyde.
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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) | 0.000 | 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".