Analisis Optimalisasi Nilai Thrust, Torque, Dan Efficiency Propeller B Series Dan Kaplan Series Pada Studi Kasus Kapal Pencalang 15 GT Menggunakan Metode CFD
Bibliographic record
Abstract
Pencalang is a traditional sailing ship used as a merchant ship. Along with the times, Pencalang changed its function to become a patrol boat with the addition of a motor as its driving force. In the traditional wooden ship revitalization project, propeller selection is important because the ship uses a sail and motor system. This research analyzes the use of Kaplan propeller types to optimize thrust, torque, and efficiency where currently the propeller used for Pencalang ships is type B-series. The selection of the Kaplan propeller to be analyzed is based on the diameter that matches the availability of propeller space on the propeller ship or < Dpropeller B-series g. The selection of kaplan propellers resulted in kaplan types K4-55 and K4-70 with diameters of 0.659 and 0.619, respectively. The B-series and kaplan series propellers that have been selected are then analyzed using modeling software and simulation software that has calculated its error using MAPE with the result of an error < 10%. The simulation results in the software show that the Kaplan series propeller can be used as an option for the type of propeller that can be used on Pencalang if a propeller replacement will be carried out later, because with a smaller diameter compared to the B-series propeller it will produce greater thrust and torque. The Kaplan series propeller that produces the highest thrust and torque is K4-70 with a maximum thrust value of 6966.76 N and a maximum torque of 978.977 Nm.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".