A Method for Torsional Damper Tuning Based On Baseline Frequency Response Functions
Bibliographic record
Abstract
Based on Woodbury-Sherman-Morrison formula, a general and efficient method for torsional damper tuning is presented. This method is based on exact calculation of the resulting Frequency Response Functions (FRF's) of the system with the damper by using the original (old) FRF's of the original (baseline) system and the damper's parameters (the mass polar moment of inertia, stiffness and damping coefficient). The only requirement for this method is to have the baseline FRF's at the active points of the structure where the damper is to be attached and those point where the resulting FRF's are of interest. The baseline FRF's can be obtained by either analytical or experimental methods. Once this requirement is met, all possible scenarios of the dampers for their potential and feasibility can be efficiently evaluated before being put into service without the need for costly hardware modification and test cycles on actual structure. As such, a considerable amount of engineering time and effort can be saved.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".