Road to entire insulation for resonances
Bibliographic record
Abstract
Abstract The effective solution to avoid machinery damage caused by resonance has been perplexing the field of engineering as a core research direction since the resonance phenomenon was discovered by Leonhard Euler in 1750. Numerous attempts have been performed to reduce the influence of resonance since the middle of last century, by introducing a nonlinear structure or a closed-loop control system. However, the existed methodologies cannot eliminate the resonance completely even extra problems were introduced inevitably, which means the technical choke-point of resonance-free remains unsolved. Here we propose an inverse method based upon the requirements of dynamic characteristics of the supporting system to construct a passive archetypal model with zero-stiffness within a designable distance to avoid the resonance. The reliability of the inverse method is demonstrated theoretically and experimentally, which indicate that the constructed system with zero-stiffness can outright eliminate the mechanical resonance by completely isolating the energy transfer between the load and environment. The distinctive geometric characteristics of the zero-stiffness system leads to a new inspiration for the design of resonance-free passive mechanisms or metamaterials, and the inverse method can even adapt the design for a more targeted application based on an arbitrary complex dynamic requirement.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".