Optimal Craig–Bampton Mode Selection for Nonlinear Flexible Multibody Analysis
Bibliographic record
Abstract
Physics-based simulations are now widely employed in mechanical engineering. Flexible Multibody dynamic Simulations (FMBSs) have proven to be effective in representing the behavior of complex structures with local damping and stiffness nonlinearities. However, due to the broad range of component flexibilities as well as contact behavior between structural elements, time integration analyses can result in high computational burden. The challenge addressed in this article concerns the implementation of an efficient model reduction procedure in order to provide an acceptable tradeoff between calculation time and loss of accuracy in the prediction of system responses and dynamic loads. In most FMBS commercial software, the behavior of linear elastodynamic components is taken into account via imported Craig–Bampton superelements. In this context, dynamic mode selection techniques have been shown to provide a better order reduction than the standard low-frequency truncation. This article provides a review of dynamic mode selection methods that can be found in the literature, followed by a comparison based on simulations of an aircraft engine stator integrated in the full industrial engine model and tested on a speed ramp-up with unbalance.
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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) | 0.001 | 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 | 0.001 |
| 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".