High-Fidelity Finite Element Modeling of Rotorcraft Shafting System for Critical Speed Prediction
Bibliographic record
Abstract
An accurate critical speed prediction of shafting systems in rotorcraft is required during preliminary design, so that dynamic loads do not lead to premature component failure within the full operational speed range, i.e. from stationary to maximum operational speed. If all modes are above the operational speed range, the shafting systems is considered subcritical and when design constraints do not allow for this type of design and so one or more critical speeds must be passed through before the shaft reaches its maximum speed the shafting system is considered supercritical and deflection limiters are required. Analytical simulations of the critical speeds of a shafting system can not only assist designers in making decisions in the earlier phase of design, but also helps mitigate risk during the qualification testing, ground run and flight test of the aircraft. Moreover, there are three factors important for an accurate critical speed prediction, i.e. gyroscopic effect, centrifugal force (CF) stiffening effect, or stress stiffening effect, and boundary conditions. The current paper focuses on the first two factors, especially the stress stiffening effect, but the discussion of the boundary conditions will be in a follow up paper. Ultimately, with the advancements of finite element analysis software, and the parallel processing provided by High Performance Computing, high fidelity finite element modeling of rotorcraft shafting system to assess critical speed is possible and practical.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".