Optimization design of double gyro-swing reduction device in complex sea state based on nonlinear dynamic equations
Bibliographic record
Abstract
In order to enable ships to operate stably for a long time under complex sea conditions, all kinds of ships have an urgent need for gyroscopic rocking reduction devices.This paper takes the double gyro rocking reduction device with better rocking reduction effect as the research object, establishes its corresponding nonlinear dynamic equations, adopts the energy method to establish the differential equations of motion, and deduces the dynamic model of the rocking reduction double gyro.A parameter optimization model is established with the main objective of improving the shaking reduction effect, and the key components of the shaking reduction double gyro are optimized.The bacterial foraging optimization algorithm is selected to solve the model, and the multi-objective parameter optimization model is established.For one to five wave classes, the middle value of the wave height of the meaningful wave is selected for the dynamic simulation experiment of the double gyro.When the wave level is less than three time level, the rocking reduction performance of the rocking reduction double gyro reaches 87.5%, 78.1% and 77.78%, respectively, and the transverse rocking reduction performance is good.Under the simulation environment of sea state I (wave height 2.5m, average period 7s) and sea state II (righteous wave height 2.5m, average period 12s), the rocking reduction efficiencies of the ship after parameter optimization are improved by 6.44% and 10.09%, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".