Comparative Evaluation of Design Variations in Prototype Fast Boats: A Hydrodynamic Characteristic-Based Approach
Bibliographic record
Abstract
As one of the world's largest archipelagic nations, Indonesia is tasked with the crucial responsibility of supervising and protecting its territorial waters from threats such as illegal fishing and damage to coral reefs.The effective and efficient execution of this task relies heavily on the use of fast patrol boats.Consequently, the need to investigate the hydrodynamic characteristics of these boats' hulls is paramount.This study is primarily focused on the analysis and design of fast patrol boat hull prototypes.Our objective is to ascertain a practical design methodology that yields the optimal shape and size of the boat's hull.The adopted research methodology involved the design and analysis of eleven hull prototypes, evaluated based on resistance, stability, and seakeeping criteria.Five models were adapted from the reference ship, with a deadweight tonnage (DWT) variation of 2-3.5 tons.Three models employed the regression method with a block coefficient (CB) variation of 0.45-0.46,while the remaining three models utilized the scaling method, derived from the reference ship with the lowest resistance.The models in both the regression and scaling methods applied the primary size derived from the linear regression results of the five reference vessels.From the analysis, it was found that models developed using the regression method demonstrated superior hydrodynamic characteristics, denoted by consistently higher total values.This research provides valuable insights for the development of efficient fast patrol boats, which is crucial for the effective management of Indonesia's expansive maritime territory.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".