On scaling design and dynamic response prediction of rotorsystems
Bibliographic record
Abstract
Rotor-bearing systems are widely used in wind turbines, gas turbines and other rotating machinery.However, due to economic and realizable factors, it is difficult to directly experiment and test the prototype in the design process of the new rotor system.Therefore, it is very important to design a small-size scaled rotor system that can reflect the dynamic characteristics of the prototype.In this study, a prototype model of the rotor system was established.Based on the similarity theory, six scaled rotor models with different sizes and materials were designed to predict the critical speeds of the prototype rotor system.The results show that when the speed scaling factor is changed, the error of critical speed increases with the decrease of the length scaling factor.The deviation of the first two critical speeds predicted by the model M3, which adjusted the length and speed scaling factor simultaneously, are -0.08% and -0.01%, respectively, this model can accurately predict the first two critical speeds of the prototype rotor system.After the equivalent modeling of rotor system with complex structure, the results presented in this paper can be further applied to the scaling design and fault diagnosis of the full-size rotating machinery.
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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 | 0.000 | 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".