Nonlinear random vibration of planetary gear trains with elastic ring gear
Bibliographic record
Abstract
Planetary gear trains (PGT) are widely used in the field of renewable energy, especially in wind turbines. A wind turbine uses planetary gearboxes to transfer wind torque to a generator, and even though gearboxes are designed according to sound engineering practices, they fail much sooner than their design life estimates. Unexpected PGT failures are costly, and it is vital to detect these failures early. This thesis proposes proper methods and analyses that help the wind turbine industry to prevent future failure by enlightening the nonlinear dynamic behavior of PGT under random force. This thesis will investigate one of the main factors in PGT failure: random vibration caused by wind turbulence. In this thesis, a hybrid dynamic model was proposed to model the stochastic nonlinear dynamics of a PGT with an elastic ring gear, and then the statistical linearization method (SL) was introduced to linearize the model. A new criterion of the SL is introduced to linearize the stochastic nonlinear dynamic model of a gear pair. The stochastic response of a thin-walled ring gear PGT under three equally-spaced random moving loads was also investigated. A series of parametric studies was conducted, and the obtained results revealed that the proposed model for the PGT accurately represented the dynamic behavior of the PGT with an elastic ring gear, and the SL gave acceptable accuracy. Also, the energy-based SL was enough accurate and valid to apply to stochastic nonlinear gear pairs under heavy load conditions, and the accuracy of the SL decreased for light load conditions. Finally, analysis on the effect of random moving loads on the ring gear showed that the mean of displacement was affected by the critical speeds, and random loads' speed does not influence the standard deviation of displacement. Monte Carlo simulations (MCS) were conducted to verify the proposed model and method, and MCS proved the accuracy of the proposed model and process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".