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Record W2587138836 · doi:10.1115/imece2016-66277

Effect of Geometric and Material Properties on Thermoelastic Damping (TED) of 3D Hemispherical Inertial Resonator

2016· article· en· W2587138836 on OpenAlexafffund
Jiewen Liu, Joshua Jaekel, Dharamdeo Ramdani, Nabeel Khan, David S.‐K. Ting, Mohammed Jalal Ahamed

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WindsorCMC Microsystems
KeywordsResonatorGyroscopeThermoelastic dampingQ factorRADIUSMaterials scienceHelical resonatorInertial frame of referenceAcousticsMicroelectromechanical systemsQuality (philosophy)OpticsPhysicsOptoelectronicsComputer scienceClassical mechanicsThermal

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.196
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2016
Admission routes2
Has abstractyes

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