Effect of Geometric and Material Properties on Thermoelastic Damping (TED) of 3D Hemispherical Inertial Resonator
Bibliographic record
Abstract
High quality factor (Q-factor) is a crucial parameter for the development of precision inertial resonators. Q-factor indicates efficiency of a resonator in retaining its energy during oscillations. This paper explores the effects of different design parameters on Q-factor of a 3D hemispherical (wine-glass) inertial resonator. Thermo-elastic damping (TED) loss mechanisms in a 3D non-inverted wine-glass (hemispherical) shell resonator is systematically investigated and presented in this paper. We investigated TED loss resulting from the effects of hemisphere geometric parameters (such as thickness, height, and radius), mass imbalance, thickness non-uniformity, and edge defects. We used glassblowing to fabricate hemispherical 3D shell resonators. The results presented in this paper can facilitate selecting efficient geometric and material properties for achieving desired Q-factor in 3D inertial resonators. Enhancing the Q-factor in MEMS based 3D resonators can further enable the development of high precision resonators and gyroscopes.
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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) | 0.000 | 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".