A New Reduced Order Modeling for Stability and Forced Response Analysis of Aero-Coupled Blades Considering Various Mode Families
Bibliographic record
Abstract
This paper presents the description and application of a new method for stability and forced response analyses of aerodynamically coupled blades considering the interaction of various mode families. The method, here referred as MLS (Multimode Least Square), considers the unsteady forces due to the blade motion at different modes shape families and calculates the aerodynamic matrixes by means of a least square (L2) approximations. This approach permits the prediction of mode families’ interaction with capabilities of structural, aerodynamic and force mistuning. A projection technique is implemented in order to reduce the computational domain. Application of the method on tuned and structural mistuned forced response and stability analyses is presented on a highly loaded transonic compressor blade. When considering structural mistuning the forced response amplitude magnification is highly affected by the change in aerodynamic damping due to mistuning. Analyses of structural mistuning without aerodynamic coupling might result in over-estimated or under-estimated response when the source of damping is mainly aerodynamic. The frequency split due to mistuning can cause that mode families’ interact due to reducing their frequencies separation. The advantage of the present method is that the effect of mode family interaction on aerodynamic damping and forced response is captured not being restricted to single mode families.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".