Dynamics Analysis of Planetary Gear Trains in a Wind Turbine Under Mean Wind Speed
Bibliographic record
Abstract
Targeting at planetary gear trains (PGTs) used in wind turbines, this paper investigates their vibration and dynamics under the aerodynamic torque of mean wind speed. Wind shear and tower shadow effects are considered in modeling the torque. A lumped parameter model is then developed to calculate the vibration and dynamics response of the PGT to the aerodynamic torque. In this model, the gear teeth and bearings are modeled as springs and the rotation of the carrier and the planet gears as well as the translation of the sun gear are taken into account. The time varying effect of the stiffness of gear mesh is incorporated into the model. Newmark algorithm is used to solve the vibration model established. In the last, the vibration response and dynamic meshing forces of the PGT are simulated and analyzed for rotors with 2 blades and 3 blades. The simulation result demonstrates that the aerodynamic torque is not a constant even under a constant wind speed. Instead, it changes with a frequency which equals the fundamental rotor frequency multiplied by the number of blades. The torque fluctuation causes corresponding vibration response and dynamic force fluctuation in the PGT.
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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) | 0.000 | 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) | 0.001 | 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".