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Record W2082427747 · doi:10.1117/12.658829

Air viscous damping effects in vibrating microbeams

2006· article· en· W2082427747 on OpenAlexaff
Patricia Nieva, N.E. McGruer, George G. Adams

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCantileverVibrationOscillation (cell signaling)MechanicsBeam (structure)ResonatorMagnetic dampingMaterials scienceAcousticsPhysicsOpticsComposite material

Abstract

fetched live from OpenAlex

In this paper, the effect of air viscous damping in the out-of-plane motion of vibrating microbeams is modeled analytically and compared with experimental results. This analysis results in a closed-form expression that can be used to accurately predict the dynamic response of microbeams used in MEMS devices such as vibration and pressure sensors, microswitches, and micromechanical resonators. First, the squeeze-film damping for a solid and straight vibrating cantilever beam, caused by the force in the narrow gap, is analyzed using the well-known Reynolds' equation under the conditions of small amplitude oscillations in an incompressible isothermal squeeze-film. The expression is then modified to include the effects of the initial curled-shape of the microbeams due to fabrication and inherent film stress gradients. Next, the airflow damping, caused by the air surrounding the cantilever beam, is determined by solving the modified form of the Navier-Stokes equation using the bead model, which is based on the oscillation of a string of spheres (beads). The closed-form solution is compared against experimental data gathered during the dynamic characterization of micro cantilever beams with different widths and air gaps. By comparing the measured results to those generated by analytical models, we demonstrate that the damping coefficient match to within 10%.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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.

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
Published2006
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicMechanical and Optical ResonatorsFrench-language works237,207