Characterization of air damping mechanism between oscillatingplates
Bibliographic record
Abstract
Fluid damping is used in a variety of applications, but the working fluid is typically a liquid (such as a dashpot using oil). Using a compressible gas is less common, and the literature has primarily been restricted to MEMS and other small-scale applications. These small-scale applications are generally limited to energy dissipation at the order of kilowatts, and gas damping in the megawatt scale has not been explored. The damping mechanism exploits the structural hysteresis mechanisms and surrounding gas as the main sources of damping. As a first step, the investigation is conducted based on a simplified configuration considering two rigid flat plates distanced at a specific air gap (h) and in relative oscillation (h) at atmospheric pressure and ambient temperature. Mounted on a vibration shaker, this test setup allows to control and measure plate oscillations with respect to an air gap height (h) up to 2 mm, an amplitude (h) up to 50% of h, a frequency (f) up to 400 Hz, and measure the force response (F). From the measurements, air damping is evaluated through a dynamic stiffness function. Numerical CFD simulations of the testing conditions are also performed to further characterize the damping mechanisms and to correlate the experimental observations to air pressure distribution. Preliminary results tend to show an increase in damping with the frequency. Combined, these experimental and numerical results provide interesting insights of the gas damping potential in thin structures.
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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 | 0.000 |
| 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) | 0.001 | 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 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".