Aeroelastic Design of a High Speed Highly Efficient Rotor
Bibliographic record
Abstract
The aeroelastic design of a high speed highly efficient rotor, from conceptual layout to detailed design, is presented. The overall strategy is described along with details and a demonstration case on the conceptual design optimization strategy. In Phase I, rapid design iterations were performed using lower order quick turnaround tools to establish basic design aspects such as rotor head sizing and stiffnesses. In Phase II, higher order tools are implemented along with a more realistic structural representation of the head and blades. The outcome is a robust preliminary design to be analyzed with CFD-CSD and tuned further in detailed design without requiring major rework. Finally, dynamic tailoring performed on the blade during final design is presented. This was done to maintain or improve frequency placements and vibration levels as relatively small, surgical changes were made to the blade detailed design (ply thicknesses, orientations, blade weights, etc.) to satisfy strength, life, and manufacturability requirements. This work highlights the importance of upfront aeromechanics optimization to establish a robust early conceptual design, and high-fidelity analysis through final design to maintain desired characteristics.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".