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Record W4401732039 · doi:10.1088/1361-665x/ad72c1

Stiffness and damping tuning through using a piezoelectric friction damper and a layered structure

2024· article· en· W4401732039 on OpenAlexafffund
Sina Rezvani, Simon S. Park

Bibliographic record

VenueSmart Materials and Structures · 2024
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsDissipationDamperStiffnessActuatorVibrationPiezoelectricityMaterials scienceStructural engineeringFinite element methodDamping torqueVoltageEngineeringAcousticsComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract Vibration suppression is essential for enhancing the performance of mechanical systems, as it prevents structural damage and minimizes noise. Various methods, including passive, semi-active, and active approaches, have been developed to achieve this goal. Among these, friction dampers, primarily categorized as passive, are highly efficient in adjusting system damping and influencing energy dissipation. By modulating the normal force in the friction damper based on external force intensity, performance can be further enhanced. This study employs a piezoelectric actuator to regulate the normal force and introduces an analytical method along with finite element modeling to estimate the normal force in the friction damper. A layered structure is introduced as an additional mean to tune damping and stiffness. The performance of the semi-active piezoelectric friction damper is investigated in free and forced vibrations, including flexural and axial cyclic loads. Furthermore, the advantages of employing layered structures are investigated experimentally. Overall, the piezoelectric friction damper demonstrates effective energy dissipation during macroslip events. Nevertheless, in case of microslip, increasing the actuator voltage results in reduced damping and a marginal rise in stiffness.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.505

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 head0.012
GPT teacher head0.225
Teacher spread0.213 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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