On a New Nonlinear Reduced-Order Model for Capturing Internal Resonances in Intentionally Mistuned Cyclic Structures
Bibliographic record
Abstract
Abstract Cyclic structures such as turbomachinery present material and geometrical variations between sectors. These discrepancies are called mistuning and break the cyclic symmetry of the structure. Computing the forced response of mistuned cyclic structures is thus a numerical challenge. The Component Nonlinear Complex Mode Synthesis (CNCMS) is one of the few nonlinear reduced-order model formulations that allow to compute the nonlinear response of tuned and mistuned structures. It has been validated successfully for friction problems. However, in the presence of geometric nonlinearities, internal resonances may arise and they cannot be captured correctly with the CNCMS method. The purpose of this work is therefore to present a new methodology for developing a nonlinear reduced-order model that can successfully capture internal resonances for tuned and mistuned structures. This method, called Component Mode Synthesis with Nonlinear Re-evaluation (CMSNR), is based on a variation of the CNCMS approach. The final modal synthesis uses a multi-harmonic procedure and a re-evaluation of the nonlinear forces on each sector independently. The performance and limitations of the proposed approach are assessed using a simplified example of a blisk subject to polynomial nonlinearities. Different internal resonances are exhibited and studied depending on the type of excitation force and on the level of mistuning.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".