Analysis Characteristics Of Pendulum Oscillation In PLTGL-SB
Bibliographic record
Abstract
Energy is an everlasting demand which sustains humanity and its activity throughout the world. The main problem energy for all country is still using fossil energy. Using fossil energy caused pollution and contaminate environment. Adopting Ocean wave energy we can convert the wave into eco-green energy. Wave energy in Indonesia very abundant, it’s coastal line is 95,181 km number 2 in the world after Canada. The device that used to convert ocean wave energy into electrical energy is called WECs (Wave Energy Converters). Latterly, there are many kinds of WECs that already developed by an engineer. Pendulum system is one sample of WECs, its has uncomplicated working principle. Zamrisyaf is The man who first invented the device in 2010. Recently, the researcher still haven’t found the parameter of pontoon geometry that can afford a good sea keeping for this WECs. In this research, the authors has been performed using experimental approach to test the pendulum system. The purpose of this study is to analyze the novel geometry of pontoon that identical to trimaran that can produce a large amplitude of pendulum oscillation through an experimental approach. The analyzed aspect is the combination of outrigger length, outrigger height, pendulum rod length, pendulum mass, and wave periods which have a maximum amplitude of pendulum oscillation. The analysis results show that the better solution is pontoon thus has outrigger height of 40 mm, pendulum rod length of 106.7 mm, outrigger length of 413 mm, pendulum mass of 20, and wave periods of 0.8 s have a maximum amplitude of pendulum oscillation as big as 60 degrees.
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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 | 0.001 | 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) | 0.006 | 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".