The Proportional-Damping Matrix of Arbitrarily Damped Linear Mechanical Systems
Bibliographic record
Abstract
The vibration of linear mechanical systems with arbitrary damping is known to pose challenging problems to the analyst, for these systems cannot be analyzed with the techniques pertaining to their undamped counterparts. It is also known that a class of damped systems, called proportionally damped, can be analyzed with the same techniques, which mimic faithfully those of single-degree-of-freedom systems. For this reason, in many instances the system at hand is assumed to be proportionally damped. Nevertheless, this assumption is difficult to justify on physical grounds in many practical applications. What this assumption brings about is a damping matrix that admits a simultaneous diagonalization with the stiffness matrix. Proposed in this paper is a decomposition of the damping matrix of an arbitrarily damped system allowing the extraction of the proportionally damped component, which, moreover, approximates optimally the original damping matrix in the least-square sense. Finally, we show with examples that conclusions drawn from the proportionally damped approximation of an arbitrarily damped system can be dangerously misleading.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".